Information

33 pages, 2722 KB

Open AccessEditor’s ChoiceArticle

Industry 4.0 Innovation: A Systematic Literature Review on the Role of Blockchain Technology in Creating Smart and Sustainable Manufacturing Facilities

by Moutaz Alazab and Salah Alhyari

Information 2024, 15(2), 78; https://doi.org/10.3390/info15020078 - 31 Jan 2024

Cited by 51 | Viewed by 9663

Abstract

Industry 4.0 has revolutionized manufacturing processes and facilities through the creation of smart and sustainable production facilities. Blockchain technology (BCT) has emerged as an invaluable asset within Industrial Revolution 4.0 (IR4.0), offering increased transparency, security, and traceability across supply chains. This systematic literature [...] Read more.

Industry 4.0 has revolutionized manufacturing processes and facilities through the creation of smart and sustainable production facilities. Blockchain technology (BCT) has emerged as an invaluable asset within Industrial Revolution 4.0 (IR4.0), offering increased transparency, security, and traceability across supply chains. This systematic literature review explores the role of BCT in creating smart and sustainable manufacturing facilities, while exploring its implications for supply chain management (SCM). Through a detailed examination of 82 research articles, this review highlights three areas where BCT can have a dramatic effect on smart and sustainable manufacturing: firstly, BCT can promote green production methods by supporting efficient resource use, waste reduction strategies and eco-friendly production methods; and secondly, it allows companies to implement smart and eco-friendly manufacturing practices through BCT solutions. BCT promotes intelligent manufacturing systems by facilitating real-time data sharing, predictive maintenance, and automated decision-making. Furthermore, BCT strengthens SCM by increasing visibility, traceability, and collaboration between partners of SC operations. The review also highlights the potential limitations of BCT, such as scalability challenges and the need for standardized protocols. Future research should focus on addressing these limitations and further exploring the potential of BCT in IR4.0. Full article

(This article belongs to the Special Issue Advances in Cybersecurity and Reliability)

► Show Figures

Figure 1

29 pages, 4460 KB

Open AccessEditor’s ChoiceArticle

Performance of 4 Pre-Trained Sentence Transformer Models in the Semantic Query of a Systematic Review Dataset on Peri-Implantitis

by Carlo Galli, Nikolaos Donos and Elena Calciolari

Information 2024, 15(2), 68; https://doi.org/10.3390/info15020068 - 23 Jan 2024

Cited by 64 | Viewed by 19157

Abstract

Systematic reviews are cumbersome yet essential to the epistemic process of medical science. Finding significant reports, however, is a daunting task because the sheer volume of published literature makes the manual screening of databases time-consuming. The use of Artificial Intelligence could make literature [...] Read more.

Systematic reviews are cumbersome yet essential to the epistemic process of medical science. Finding significant reports, however, is a daunting task because the sheer volume of published literature makes the manual screening of databases time-consuming. The use of Artificial Intelligence could make literature processing faster and more efficient. Sentence transformers are groundbreaking algorithms that can generate rich semantic representations of text documents and allow for semantic queries. In the present report, we compared four freely available sentence transformer pre-trained models (all-MiniLM-L6-v2, all-MiniLM-L12-v2, all-mpnet-base-v2, and All-distilroberta-v1) on a convenience sample of 6110 articles from a published systematic review. The authors of this review manually screened the dataset and identified 24 target articles that addressed the Focused Questions (FQ) of the review. We applied the four sentence transformers to the dataset and, using the FQ as a query, performed a semantic similarity search on the dataset. The models identified similarities between the FQ and the target articles to a varying degree, and, sorting the dataset by semantic similarities using the best-performing model (all-mpnet-base-v2), the target articles could be found in the top 700 papers out of the 6110 dataset. Our data indicate that the choice of an appropriate pre-trained model could remarkably reduce the number of articles to screen and the time to completion for systematic reviews. Full article

(This article belongs to the Special Issue Biomedical Natural Language Processing and Text Mining)

► Show Figures

Figure 1

28 pages, 2814 KB

Open AccessEditor’s ChoiceArticle

Agroeconomic Indexes and Big Data: Digital Marketing Analytics Implications for Enhanced Decision Making with Artificial Intelligence-Based Modeling

by Nikolaos T. Giannakopoulos, Marina C. Terzi, Damianos P. Sakas, Nikos Kanellos, Kanellos S. Toudas and Stavros P. Migkos

Information 2024, 15(2), 67; https://doi.org/10.3390/info15020067 - 23 Jan 2024

Cited by 31 | Viewed by 6683

Abstract

Agriculture firms face an array of struggles, most of which are financial; thus, the role of decision making is discerned as highly important. The agroeconomic indexes (AEIs) of Agriculture Employment Rate (AER), Chemical Product Price Index (CPPI), Farm Product Price Index (FPPI), and [...] Read more.

Agriculture firms face an array of struggles, most of which are financial; thus, the role of decision making is discerned as highly important. The agroeconomic indexes (AEIs) of Agriculture Employment Rate (AER), Chemical Product Price Index (CPPI), Farm Product Price Index (FPPI), and Machinery Equipment Price Index (MEPI) were selected as the basis of this study. This research aims to examine the connection between digital marketing analytics and the selected agroeconomic indexes while providing valuable insights into their decision-making process, with the utilization of AI (artificial intelligence) models. Thus, a dataset of website analytics was collected from five well-established agriculture firms, apart from the values of the referred indexes. By performing regression and correlation analyses, the index relationships with the agriculture firms’ digital marketing analytics were extracted and used for the deployment of the fuzzy cognitive mapping (FCM) and hybrid modeling (HM) processes, assisted by using artificial neural network (ANN) models. Through the above process, there is a strong connection between the agroeconomic indexes of AER, CPPI, FPPR, and MEPI and the metrics of branded traffic, social and search traffic sources, and paid and organic costs of agriculture firms. It is highlighted that agriculture firms, to better understand their sector’s employment rate and the volatility of farming, chemicals, and machine equipment prices for future investment strategies and better decision-making processes, should try to increase their investment in the preferred digital marketing analytics and AI applications. Full article

(This article belongs to the Special Issue Artificial Intelligence (AI) for Economics and Business Management)

► Show Figures

Figure 1

18 pages, 7127 KB

Open AccessEditor’s ChoiceArticle

Benchmarking Automated Machine Learning (AutoML) Frameworks for Object Detection

by Samuel de Oliveira, Oguzhan Topsakal and Onur Toker

Information 2024, 15(1), 63; https://doi.org/10.3390/info15010063 - 21 Jan 2024

Cited by 15 | Viewed by 7843

Abstract

Automated Machine Learning (AutoML) is a subdomain of machine learning that seeks to expand the usability of traditional machine learning methods to non-expert users by automating various tasks which normally require manual configuration. Prior benchmarking studies on AutoML systems—whose aim is to compare [...] Read more.

Automated Machine Learning (AutoML) is a subdomain of machine learning that seeks to expand the usability of traditional machine learning methods to non-expert users by automating various tasks which normally require manual configuration. Prior benchmarking studies on AutoML systems—whose aim is to compare and evaluate their capabilities—have mostly focused on tabular or structured data. In this study, we evaluate AutoML systems on the task of object detection by curating three commonly used object detection datasets (Open Images V7, Microsoft COCO 2017, and Pascal VOC2012) in order to benchmark three different AutoML frameworks—namely, Google’s Vertex AI, NVIDIA’s TAO, and AutoGluon. We reduced the datasets to only include images with a single object instance in order to understand the effect of class imbalance, as well as dataset and object size. We used the metrics of the average precision (AP) and mean average precision (mAP). Solely in terms of accuracy, our results indicate AutoGluon as the best-performing framework, with a mAP of 0.8901, 0.8972, and 0.8644 for the Pascal VOC2012, COCO 2017, and Open Images V7 datasets, respectively. NVIDIA TAO achieved a mAP of 0.8254, 0.8165, and 0.7754 for those same datasets, while Google’s VertexAI scored 0.855, 0.793, and 0.761. We found the dataset size had an inverse relationship to mAP across all the frameworks, and there was no relationship between class size or imbalance and accuracy. Furthermore, we discuss each framework’s relative benefits and drawbacks from the standpoint of ease of use. This study also points out the issues found as we examined the labels of a subset of each dataset. Labeling errors in the datasets appear to have a substantial negative effect on accuracy that is not resolved by larger datasets. Overall, this study provides a platform for future development and research on this nascent field of machine learning. Full article

(This article belongs to the Special Issue Deep Learning for Image, Video and Signal Processing)

► Show Figures

Figure 1

20 pages, 540 KB

Open AccessEditor’s ChoiceArticle

Unsupervised Learning in NBA Injury Recovery: Advanced Data Mining to Decode Recovery Durations and Economic Impacts

by George Papageorgiou, Vangelis Sarlis and Christos Tjortjis

Information 2024, 15(1), 61; https://doi.org/10.3390/info15010061 - 20 Jan 2024

Cited by 18 | Viewed by 8471

Abstract

This study utilized advanced data mining and machine learning to examine player injuries in the National Basketball Association (NBA) from 2000–01 to 2022–23. By analyzing a dataset of 2296 players, including sociodemographics, injury records, and financial data, this research investigated the relationships between [...] Read more.

This study utilized advanced data mining and machine learning to examine player injuries in the National Basketball Association (NBA) from 2000–01 to 2022–23. By analyzing a dataset of 2296 players, including sociodemographics, injury records, and financial data, this research investigated the relationships between injury types and player recovery durations, and their socioeconomic impacts. Our methodology involved data collection, engineering, and mining; the application of techniques such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN), isolation forest, and the Z score for anomaly detection; and the application of the Apriori algorithm for association rule mining. Anomaly detection revealed 189 anomalies (1.04% of cases), highlighting unusual recovery durations and factors influencing recovery beyond physical healing. Association rule mining indicated shorter recovery times for lower extremity injuries and a 95% confidence level for quick returns from “Rest” injuries, affirming the NBA’s treatment and rest policies. Additionally, economic factors were observed, with players in lower salary brackets experiencing shorter recoveries, pointing to a financial influence on recovery decisions. This study offers critical insights into sports injuries and recovery, providing valuable information for sports professionals and league administrators. This study will impact player health management and team tactics, laying the groundwork for future research on long-term injury effects and technology integration in player health monitoring. Full article

(This article belongs to the Special Issue Real-World Applications of Machine Learning Techniques)

► Show Figures

Figure 1

44 pages, 7889 KB

Open AccessEditor’s ChoiceArticle

Mapping the Landscape of Misinformation Detection: A Bibliometric Approach

by Andra Sandu, Ioana Ioanăș, Camelia Delcea, Laura-Mădălina Geantă and Liviu-Adrian Cotfas

Information 2024, 15(1), 60; https://doi.org/10.3390/info15010060 - 19 Jan 2024

Cited by 23 | Viewed by 9812

Abstract

The proliferation of misinformation presents a significant challenge in today’s information landscape, impacting various aspects of society. While misinformation is often confused with terms like disinformation and fake news, it is crucial to distinguish that misinformation involves, in mostcases, inaccurate information without the [...] Read more.

The proliferation of misinformation presents a significant challenge in today’s information landscape, impacting various aspects of society. While misinformation is often confused with terms like disinformation and fake news, it is crucial to distinguish that misinformation involves, in mostcases, inaccurate information without the intent to cause harm. In some instances, individuals unwittingly share misinformation, driven by a desire to assist others without thorough research. However, there are also situations where misinformation involves negligence, or even intentional manipulation, with the aim of shaping the opinions and decisions of the target audience. Another key factor contributing to misinformation is its alignment with individual beliefs and emotions. This alignment magnifies the impact and influence of misinformation, as people tend to seek information that reinforces their existing beliefs. As a starting point, some 56 papers containing ‘misinformation detection’ in the title, abstract, or keywords, marked as “articles”, written in English, published between 2016 and 2022, were extracted from the Web of Science platform and further analyzed using Biblioshiny. This bibliometric study aims to offer a comprehensive perspective on the field of misinformation detection by examining its evolution and identifying emerging trends, influential authors, collaborative networks, highly cited articles, key terms, institutional affiliations, themes, and other relevant factors. Additionally, the study reviews the most cited papers and provides an overview of all selected papers in the dataset, shedding light on methods employed to counter misinformation and the primary research areas where misinformation detection has been explored, including sources such as online social networks, communities, and news platforms. Recent events related to health issues stemming from the COVID-19 pandemic have heightened interest within the research community regarding misinformation detection, a statistic which is also supported by the fact that half of the papers included in top 10 papers based on number of citations have addressed this subject. The insights derived from this analysis contribute valuable knowledge to address the issue, enhancing our understanding of the field’s dynamics and aiding in the development of effective strategies to detect and mitigate the impact of misinformation. The results spotlight that IEEE Access occupies the first position in the current analysis based on the number of published papers, the King Saud University is listed as the top contributor for the misinformation detection, while in terms of countries, the top-5 list based on the highest contribution to this area is made by the USA, India, China, Spain, and the UK. Moreover, the study supports the promotion of verified and reliable sources of data, fostering a more informed and trustworthy information environment. Full article

(This article belongs to the Special Issue Recent Advances in Social Media Mining and Analysis)

► Show Figures

Figure 1

17 pages, 4757 KB

Open AccessEditor’s ChoiceArticle

Integrated Generative Adversarial Networks and Deep Convolutional Neural Networks for Image Data Classification: A Case Study for COVID-19

by Ku Muhammad Naim Ku Khalif, Woo Chaw Seng, Alexander Gegov, Ahmad Syafadhli Abu Bakar and Nur Adibah Shahrul

Information 2024, 15(1), 58; https://doi.org/10.3390/info15010058 - 18 Jan 2024

Cited by 17 | Viewed by 5730

Abstract

Convolutional Neural Networks (CNNs) have garnered significant utilisation within automated image classification systems. CNNs possess the ability to leverage the spatial and temporal correlations inherent in a dataset. This study delves into the use of cutting-edge deep learning for precise image data classification, [...] Read more.

Convolutional Neural Networks (CNNs) have garnered significant utilisation within automated image classification systems. CNNs possess the ability to leverage the spatial and temporal correlations inherent in a dataset. This study delves into the use of cutting-edge deep learning for precise image data classification, focusing on overcoming the difficulties brought on by the COVID-19 pandemic. In order to improve the accuracy and robustness of COVID-19 image classification, the study introduces a novel methodology that combines the strength of Deep Convolutional Neural Networks (DCNNs) and Generative Adversarial Networks (GANs). This proposed study helps to mitigate the lack of labelled coronavirus (COVID-19) images, which has been a standard limitation in related research, and improves the model’s ability to distinguish between COVID-19-related patterns and healthy lung images. The study uses a thorough case study and uses a sizable dataset of chest X-ray images covering COVID-19 cases, other respiratory conditions, and healthy lung conditions. The integrated model outperforms conventional DCNN-based techniques in terms of classification accuracy after being trained on this dataset. To address the issues of an unbalanced dataset, GAN will produce synthetic pictures and extract deep features from every image. A thorough understanding of the model’s performance in real-world scenarios is also provided by the study’s meticulous evaluation of the model’s performance using a variety of metrics, including accuracy, precision, recall, and F1-score. Full article

(This article belongs to the Special Issue Feature Papers in Information in 2023)

► Show Figures

Figure 1

29 pages, 4963 KB

Open AccessEditor’s ChoiceArticle

A Holistic Approach to Ransomware Classification: Leveraging Static and Dynamic Analysis with Visualization

by Bahaa Yamany, Mahmoud Said Elsayed, Anca D. Jurcut, Nashwa Abdelbaki and Marianne A. Azer

Information 2024, 15(1), 46; https://doi.org/10.3390/info15010046 - 14 Jan 2024

Cited by 26 | Viewed by 8038

Abstract

Ransomware is a type of malicious software that encrypts a victim’s files and demands payment in exchange for the decryption key. It is a rapidly growing and evolving threat that has caused significant damage and disruption to individuals and organizations around the world. [...] Read more.

Ransomware is a type of malicious software that encrypts a victim’s files and demands payment in exchange for the decryption key. It is a rapidly growing and evolving threat that has caused significant damage and disruption to individuals and organizations around the world. In this paper, we propose a comprehensive ransomware classification approach based on the comparison of similarity matrices derived from static, dynamic analysis, and visualization. Our approach involves the use of multiple analysis techniques to extract features from ransomware samples and to generate similarity matrices based on these features. These matrices are then compared using a variety of comparison algorithms to identify similarities and differences between the samples. The resulting similarity scores are then used to classify the samples into different categories, such as families, variants, and versions. We evaluate our approach using a dataset of ransomware samples and demonstrate that it can accurately classify the samples with a high degree of accuracy. One advantage of our approach is the use of visualization, which allows us to classify and cluster large datasets of ransomware in a more intuitive and effective way. In addition, static analysis has the advantage of being fast and accurate, while dynamic analysis allows us to classify and cluster packed ransomware samples. We also compare our approach to other classification approaches based on single analysis techniques and show that our approach outperforms these approaches in terms of classification accuracy. Overall, our study demonstrates the potential of using a comprehensive approach based on the comparison of multiple analysis techniques, including static analysis, dynamic analysis, and visualization, for the accurate and efficient classification of ransomware. It also highlights the importance of considering multiple analysis techniques in the development of effective ransomware classification methods, especially when dealing with large datasets and packed samples. Full article

(This article belongs to the Special Issue Wireless IoT Network Protocols II)

► Show Figures

Figure 1

16 pages, 5487 KB

Open AccessEditor’s ChoiceArticle

Rapid Forecasting of Cyber Events Using Machine Learning-Enabled Features

by Yussuf Ahmed, Muhammad Ajmal Azad and Taufiq Asyhari

Information 2024, 15(1), 36; https://doi.org/10.3390/info15010036 - 11 Jan 2024

Cited by 46 | Viewed by 7470

Abstract

In recent years, there has been a notable surge in both the complexity and volume of targeted cyber attacks, largely due to heightened vulnerabilities in widely adopted technologies. The Prediction and detection of early attacks are vital to mitigating potential risks from cyber [...] Read more.

In recent years, there has been a notable surge in both the complexity and volume of targeted cyber attacks, largely due to heightened vulnerabilities in widely adopted technologies. The Prediction and detection of early attacks are vital to mitigating potential risks from cyber attacks and network resilience. With the rapid increase of digital data and the increasing complexity of cyber attacks, big data has become a crucial tool for intrusion detection and forecasting. By leveraging the capabilities of unstructured big data, intrusion detection and forecasting systems can become more effective in detecting and preventing cyber attacks and anomalies. While some progress has been made on attack prediction, little attention has been given to forecasting cyber events based on time series and unstructured big data. In this research, we used the CSE-CIC-IDS2018 dataset, a comprehensive dataset containing several attacks on a realistic network. Then we used time-series forecasting techniques to construct time-series models with tuned parameters to assess the effectiveness of these techniques, which include Sequential Minimal Optimisation for regression (SMOreg), linear regression and Long Short-Term Memory (LSTM) to forecast the cyber events. We used machine learning algorithms such as Naive Bayes and random forest to evaluate the performance of the models. The best performance results of 90.4% were achieved with Support Vector Machine (SVM) and random forest. Additionally, Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics were used to evaluate forecasted event performance. SMOreg’s forecasted events yielded the lowest MAE, while those from linear regression exhibited the lowest RMSE. This work is anticipated to contribute to effective cyber threat detection, aiming to reduce security breaches within critical infrastructure. Full article

(This article belongs to the Special Issue Emerging Research on Neural Networks and Anomaly Detection)

► Show Figures

Figure 1

23 pages, 2929 KB

Open AccessEditor’s ChoiceReview

Parametric and Nonparametric Machine Learning Techniques for Increasing Power System Reliability: A Review

by Fariha Imam, Petr Musilek and Marek Z. Reformat

Information 2024, 15(1), 37; https://doi.org/10.3390/info15010037 - 11 Jan 2024

Cited by 24 | Viewed by 9554

Abstract

Due to aging infrastructure, technical issues, increased demand, and environmental developments, the reliability of power systems is of paramount importance. Utility companies aim to provide uninterrupted and efficient power supply to their customers. To achieve this, they focus on implementing techniques and methods [...] Read more.

Due to aging infrastructure, technical issues, increased demand, and environmental developments, the reliability of power systems is of paramount importance. Utility companies aim to provide uninterrupted and efficient power supply to their customers. To achieve this, they focus on implementing techniques and methods to minimize downtime in power networks and reduce maintenance costs. In addition to traditional statistical methods, modern technologies such as machine learning have become increasingly common for enhancing system reliability and customer satisfaction. The primary objective of this study is to review parametric and nonparametric machine learning techniques and their applications in relation to maintenance-related aspects of power distribution system assets, including (1) distribution lines, (2) transformers, and (3) insulators. Compared to other reviews, this study offers a unique perspective on machine learning algorithms and their predictive capabilities in relation to the critical components of power distribution systems. Full article

► Show Figures

Figure 1

20 pages, 3183 KB

Open AccessEditor’s ChoiceArticle

Time Series Forecasting Utilizing Automated Machine Learning (AutoML): A Comparative Analysis Study on Diverse Datasets

by George Westergaard, Utku Erden, Omar Abdallah Mateo, Sullaiman Musah Lampo, Tahir Cetin Akinci and Oguzhan Topsakal

Information 2024, 15(1), 39; https://doi.org/10.3390/info15010039 - 11 Jan 2024

Cited by 39 | Viewed by 12976

Abstract

Automated Machine Learning (AutoML) tools are revolutionizing the field of machine learning by significantly reducing the need for deep computer science expertise. Designed to make ML more accessible, they enable users to build high-performing models without extensive technical knowledge. This study delves into [...] Read more.

Automated Machine Learning (AutoML) tools are revolutionizing the field of machine learning by significantly reducing the need for deep computer science expertise. Designed to make ML more accessible, they enable users to build high-performing models without extensive technical knowledge. This study delves into these tools in the context of time series analysis, which is essential for forecasting future trends from historical data. We evaluate three prominent AutoML tools—AutoGluon, Auto-Sklearn, and PyCaret—across various metrics, employing diverse datasets that include Bitcoin and COVID-19 data. The results reveal that the performance of each tool is highly dependent on the specific dataset and its ability to manage the complexities of time series data. This thorough investigation not only demonstrates the strengths and limitations of each AutoML tool but also highlights the criticality of dataset-specific considerations in time series analysis. Offering valuable insights for both practitioners and researchers, this study emphasizes the ongoing need for research and development in this specialized area. It aims to serve as a reference for organizations dealing with time series datasets and a guiding framework for future academic research in enhancing the application of AutoML tools for time series forecasting and analysis. Full article

(This article belongs to the Special Issue New Deep Learning Approach for Time Series Forecasting)

► Show Figures

Figure 1

24 pages, 813 KB

Open AccessEditor’s ChoiceReview

Revolutionizing Vaccine Development for COVID-19: A Review of AI-Based Approaches

by Aritra Ghosh, Maria M. Larrondo-Petrie and Mirjana Pavlovic

Information 2023, 14(12), 665; https://doi.org/10.3390/info14120665 - 18 Dec 2023

Cited by 39 | Viewed by 18266

Abstract

The evolvement of COVID-19 vaccines is rapidly being revolutionized using artificial intelligence-based technologies. Small compounds, peptides, and epitopes are collected to develop new therapeutics. These substances can also guide artificial intelligence-based modeling, screening, or creation. Machine learning techniques are used to leverage pre-existing [...] Read more.

The evolvement of COVID-19 vaccines is rapidly being revolutionized using artificial intelligence-based technologies. Small compounds, peptides, and epitopes are collected to develop new therapeutics. These substances can also guide artificial intelligence-based modeling, screening, or creation. Machine learning techniques are used to leverage pre-existing data for COVID-19 drug detection and vaccine advancement, while artificial intelligence-based models are used for these purposes. Models based on artificial intelligence are used to evaluate and recognize the best candidate targets for future therapeutic development. Artificial intelligence-based strategies can be used to address issues with the safety and efficacy of COVID-19 vaccine candidates, as well as issues with manufacturing, storage, and logistics. Because antigenic peptides are effective at eliciting immune responses, artificial intelligence algorithms can assist in identifying the most promising COVID-19 vaccine candidates. Following COVID-19 vaccination, the first phase of the vaccine-induced immune response occurs when major histocompatibility complex (MHC) class II molecules (typically bind peptides of 12–25 amino acids) recognize antigenic peptides. Therefore, AI-based models are used to identify the best COVID-19 vaccine candidates and ensure the efficacy and safety of vaccine-induced immune responses. This study explores the use of artificial intelligence-based approaches to address logistics, manufacturing, storage, safety, and effectiveness issues associated with several COVID-19 vaccine candidates. Additionally, we will evaluate potential targets for next-generation treatments and examine the role that artificial intelligence-based models can play in identifying the most promising COVID-19 vaccine candidates, while also considering the effectiveness of antigenic peptides in triggering immune responses. The aim of this project is to gain insights into how artificial intelligence-based approaches could revolutionize the development of COVID-19 vaccines and how they can be leveraged to address challenges associated with vaccine development. In this work, we highlight potential barriers and solutions and focus on recent improvements in using artificial intelligence to produce COVID-19 drugs and vaccines, as well as the prospects for intelligent training in COVID-19 treatment discovery. Full article

(This article belongs to the Special Issue Multi-Modal Biomedical Data Science—Modeling and Analysis)

► Show Figures

Figure 1

30 pages, 1795 KB

Open AccessEditor’s ChoiceArticle

Artificial Intelligence in Digital Marketing: Insights from a Comprehensive Review

by Christos Ziakis and Maro Vlachopoulou

Information 2023, 14(12), 664; https://doi.org/10.3390/info14120664 - 17 Dec 2023

Cited by 153 | Viewed by 75750

Abstract

Artificial intelligence (AI) has rapidly emerged as a transformative force in multiple sectors, with digital marketing being a prominent beneficiary. As AI technologies continue to advance, their potential to reshape the digital marketing landscape becomes ever more apparent, leading to profound implications for [...] Read more.

Artificial intelligence (AI) has rapidly emerged as a transformative force in multiple sectors, with digital marketing being a prominent beneficiary. As AI technologies continue to advance, their potential to reshape the digital marketing landscape becomes ever more apparent, leading to profound implications for businesses and their digital outreach strategies. This research seeks to answer the pivotal question: “How could AI applications be leveraged to optimize digital marketing strategies”? Drawing from a systematic literature review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, this study has identified 211 pertinent articles. Through a comprehensive bibliometric analysis, the findings were categorized into distinct clusters, namely: AI/ML (Machine Learning) Algorithms, Social Media, Consumer Behavior, E-Commerce, Digital Advertising, Budget Optimization, and Competitive Strategies. Each cluster offers insights into how AI applications can be harnessed to augment digital marketing efforts. The conclusion synthesizes key findings and suggests avenues for future exploration in this dynamic intersection of AI and digital marketing. This research contributes to the field by providing a comprehensive bibliometric analysis of AI in digital marketing, identifying key trends, challenges, and future directions. Our systematic approach offers valuable insights for businesses and researchers alike, enhancing the understanding of AI’s evolving role in digital marketing strategies. Full article

► Show Figures

Figure 1

35 pages, 1713 KB

Open AccessEditor’s ChoiceReview

Deep Learning for Time Series Forecasting: Advances and Open Problems

by Angelo Casolaro, Vincenzo Capone, Gennaro Iannuzzo and Francesco Camastra

Information 2023, 14(11), 598; https://doi.org/10.3390/info14110598 - 4 Nov 2023

Cited by 177 | Viewed by 63517

Abstract

A time series is a sequence of time-ordered data, and it is generally used to describe how a phenomenon evolves over time. Time series forecasting, estimating future values of time series, allows the implementation of decision-making strategies. Deep learning, the currently leading field [...] Read more.

A time series is a sequence of time-ordered data, and it is generally used to describe how a phenomenon evolves over time. Time series forecasting, estimating future values of time series, allows the implementation of decision-making strategies. Deep learning, the currently leading field of machine learning, applied to time series forecasting can cope with complex and high-dimensional time series that cannot be usually handled by other machine learning techniques. The aim of the work is to provide a review of state-of-the-art deep learning architectures for time series forecasting, underline recent advances and open problems, and also pay attention to benchmark data sets. Moreover, the work presents a clear distinction between deep learning architectures that are suitable for short-term and long-term forecasting. With respect to existing literature, the major advantage of the work consists in describing the most recent architectures for time series forecasting, such as Graph Neural Networks, Deep Gaussian Processes, Generative Adversarial Networks, Diffusion Models, and Transformers. Full article

(This article belongs to the Special Issue New Deep Learning Approach for Time Series Forecasting)

► Show Figures

Figure 1

17 pages, 1420 KB

Open AccessEditor’s ChoiceArticle

Mobility Control Centre and Artificial Intelligence for Sustainable Urban Districts

by Francis Marco Maria Cirianni, Antonio Comi and Agata Quattrone

Information 2023, 14(10), 581; https://doi.org/10.3390/info14100581 - 21 Oct 2023

Cited by 42 | Viewed by 8467

Abstract

The application of artificial intelligence (AI) to dynamic mobility management can support the achievement of efficiency and sustainability goals. AI can help to model alternative mobility system scenarios in real time (by processing big data from heterogeneous sources in a very short time) [...] Read more.

The application of artificial intelligence (AI) to dynamic mobility management can support the achievement of efficiency and sustainability goals. AI can help to model alternative mobility system scenarios in real time (by processing big data from heterogeneous sources in a very short time) and to identify network and service configurations by comparing phenomena in similar contexts, as well as support the implementation of measures for managing demand that achieve sustainable goals. In this paper, an in-depth analysis of scenarios, with an IT (Information Technology) framework based on emerging technologies and AI to support sustainable and cooperative digital mobility, is provided. Therefore, the definition of the functional architecture of an AI-based mobility control centre is defined, and the process that has been implemented in a medium-large city is presented. Full article

(This article belongs to the Special Issue New Generation of Intelligent Transit Systems: Theory and Applications)

► Show Figures

Figure 1

23 pages, 5243 KB

Open AccessEditor’s ChoiceArticle

Generative Adversarial Networks (GANs) for Audio-Visual Speech Recognition in Artificial Intelligence IoT

by Yibo He, Kah Phooi Seng and Li Minn Ang

Information 2023, 14(10), 575; https://doi.org/10.3390/info14100575 - 19 Oct 2023

Cited by 24 | Viewed by 7528

Abstract

This paper proposes a novel multimodal generative adversarial network AVSR (multimodal AVSR GAN) architecture, to improve both the energy efficiency and the AVSR classification accuracy of artificial intelligence Internet of things (IoT) applications. The audio-visual speech recognition (AVSR) modality is a classical multimodal [...] Read more.

This paper proposes a novel multimodal generative adversarial network AVSR (multimodal AVSR GAN) architecture, to improve both the energy efficiency and the AVSR classification accuracy of artificial intelligence Internet of things (IoT) applications. The audio-visual speech recognition (AVSR) modality is a classical multimodal modality, which is commonly used in IoT and embedded systems. Examples of suitable IoT applications include in-cabin speech recognition systems for driving systems, AVSR in augmented reality environments, and interactive applications such as virtual aquariums. The application of multimodal sensor data for IoT applications requires efficient information processing, to meet the hardware constraints of IoT devices. The proposed multimodal AVSR GAN architecture is composed of a discriminator and a generator, each of which is a two-stream network, corresponding to the audio stream information and the visual stream information, respectively. To validate this approach, we used augmented data from well-known datasets (LRS2-Lip Reading Sentences 2 and LRS3) in the training process, and testing was performed using the original data. The research and experimental results showed that the proposed multimodal AVSR GAN architecture improved the AVSR classification accuracy. Furthermore, in this study, we discuss the domain of GANs and provide a concise summary of the proposed GANs. Full article

(This article belongs to the Special Issue Blending Artificial Intelligence and Machine Learning with the Internet of Things: Emerging Trends, Issues and Challenges)

► Show Figures

Figure 1

33 pages, 2449 KB

Open AccessEditor’s ChoiceReview

Exploring Blockchain Research in Supply Chain Management: A Latent Dirichlet Allocation-Driven Systematic Review

by Abderahman Rejeb, Karim Rejeb, Steve Simske and John G. Keogh

Information 2023, 14(10), 557; https://doi.org/10.3390/info14100557 - 12 Oct 2023

Cited by 55 | Viewed by 16637

Abstract

Blockchain technology has emerged as a tool with the potential to enhance transparency, trust, security, and decentralization in supply chain management (SCM). This study presents a comprehensive review of the interplay between blockchain technology and SCM. By analyzing an extensive dataset of 943 [...] Read more.

Blockchain technology has emerged as a tool with the potential to enhance transparency, trust, security, and decentralization in supply chain management (SCM). This study presents a comprehensive review of the interplay between blockchain technology and SCM. By analyzing an extensive dataset of 943 articles, our exploration utilizes the Latent Dirichlet Allocation (LDA) method to delve deep into the thematic structure of the discourse. This investigation revealed ten central topics ranging from blockchain’s transformative role in supply chain finance and e-commerce operations to its application in specialized areas, such as the halal food supply chain and humanitarian contexts. Particularly pronounced were discussions on the challenges and transformations of blockchain integration in supply chains and its impact on pricing strategies and decision-making. Visualization tools, including PyLDAvis, further illuminated the interconnectedness of these themes, highlighting the intertwined nature of blockchain adoption challenges with aspects such as traceability and pricing. Despite the breadth of topics covered, the paper acknowledges its limitations due to the fast-evolving nature of blockchain developments during and after our analysis period. Ultimately, this review provides a holistic academic snapshot, emphasizing both well-developed and nascent research areas and guiding future research in the evolving domain of blockchain in SCM. Full article

(This article belongs to the Special Issue Blockchain, Technology and Its Application)

► Show Figures

Figure 1

26 pages, 1468 KB

Open AccessEditor’s ChoiceArticle

The Impact of Virtual Reality (VR) Tour Experience on Tourists’ Intention to Visit

by Chourouk Ouerghemmi, Myriam Ertz, Néji Bouslama and Urvashi Tandon

Information 2023, 14(10), 546; https://doi.org/10.3390/info14100546 - 5 Oct 2023

Cited by 42 | Viewed by 21973

Abstract

Drawing on media richness theory, this study investigates the effect of rich media, such as virtual reality (VR), on visit intentions for a specific destination. Specifically, this research employs a mixed-method approach, using abductive theorization to explore and confirm the dimensions of the [...] Read more.

Drawing on media richness theory, this study investigates the effect of rich media, such as virtual reality (VR), on visit intentions for a specific destination. Specifically, this research employs a mixed-method approach, using abductive theorization to explore and confirm the dimensions of the VR visit experience, notably those related to telepresence, a key concept in tourism through VR. Furthermore, the study aims to elucidate how telepresence influences mental imagery, attitudes towards tourist destinations, and actual visit intentions. To do this, qualitative data were gathered between February and June 2022 from 34 semi-structured interviews with respondents who viewed a VR video of the destination. A second study collected quantitative data from 400 participants through face-to-face questionnaires after a VR video view between June and August 2022. The findings reveal that telepresence comprises three dimensions: realism of the virtual environment, immersion, and the sense of presence in the virtual environment. Telepresence, in turn, both directly and indirectly affects actual visit intentions, with mental imagery and attitude toward tourist destinations partially mediating those relationships. This study provides methodological, theoretical, and tourism management implications to enhance our comprehension of telepresence’s facets, its measurement, and the process by which VR influences real visit intentions. Full article

(This article belongs to the Special Issue Digital Economy and Management)

► Show Figures

Figure 1

16 pages, 988 KB

Open AccessEditor’s ChoiceArticle

Enriching a Traditional Learning Activity in Preschool through Augmented Reality: Children’s and Teachers’ Views

by Sophia Rapti, Theodosios Sapounidis and Sokratis Tselegkaridis

Information 2023, 14(10), 530; https://doi.org/10.3390/info14100530 - 28 Sep 2023

Cited by 23 | Viewed by 5878

Abstract

Nowadays, Augmented Reality flourishes in educational settings. Yet, little is known about teachers’ and children’s views of Augmented Reality applications in Preschool. This paper explores 71 preschoolers’ opinions of Augmented Reality teaching integrated into a traditional learning activity. Additionally, five educators’ views of [...] Read more.

Nowadays, Augmented Reality flourishes in educational settings. Yet, little is known about teachers’ and children’s views of Augmented Reality applications in Preschool. This paper explores 71 preschoolers’ opinions of Augmented Reality teaching integrated into a traditional learning activity. Additionally, five educators’ views of Augmented Reality applications in Preschool are captured. Mixed methods with questionnaires and semi-structured interviews were used. The questionnaires record children’s preferences regarding their favorite learning activity between traditional and the Augmented Reality one. Additionally, they explore the activity preschoolers would like to repeat and found most enjoyable: playful. Regarding quantitative data analysis, independent/paired samples t-tests and chi-square test along with bootstrapping with 1000 samples were used. As for the qualitative data collection, educators’ semi-structured interviews focused on three axes: (a) children’s motivation and engagement in Augmented Reality activities, (b) Augmented Reality’s potential to promote skills, and (c) Augmented Reality as a teaching tool in preschool. The emerging results are: Preschoolers prefer more Augmented Reality activities than traditional ones. There are no statistically significant gender differences in preferences for Augmented Reality activities. Educators regard Augmented Reality technology as an innovative, beneficial teaching approach in preschool. However, they express concern regarding the promotion of collaboration among preschoolers via Augmented Reality. Full article

(This article belongs to the Collection Augmented Reality Technologies, Systems and Applications)

► Show Figures

Figure 1

18 pages, 741 KB

Open AccessEditor’s ChoiceArticle

Time-Series Neural Network: A High-Accuracy Time-Series Forecasting Method Based on Kernel Filter and Time Attention

by Lexin Zhang, Ruihan Wang, Zhuoyuan Li, Jiaxun Li, Yichen Ge, Shiyun Wa, Sirui Huang and Chunli Lv

Information 2023, 14(9), 500; https://doi.org/10.3390/info14090500 - 13 Sep 2023

Cited by 32 | Viewed by 28936

Abstract

This research introduces a novel high-accuracy time-series forecasting method, namely the Time Neural Network (TNN), which is based on a kernel filter and time attention mechanism. Taking into account the complex characteristics of time-series data, such as non-linearity, high dimensionality, and long-term dependence, [...] Read more.

This research introduces a novel high-accuracy time-series forecasting method, namely the Time Neural Network (TNN), which is based on a kernel filter and time attention mechanism. Taking into account the complex characteristics of time-series data, such as non-linearity, high dimensionality, and long-term dependence, the TNN model is designed and implemented. The key innovations of the TNN model lie in the incorporation of the time attention mechanism and kernel filter, allowing the model to allocate different weights to features at each time point, and extract high-level features from the time-series data, thereby improving the model’s predictive accuracy. Additionally, an adaptive weight generator is integrated into the model, enabling the model to automatically adjust weights based on input features. Mainstream time-series forecasting models such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTM) are employed as baseline models and comprehensive comparative experiments are conducted. The results indicate that the TNN model significantly outperforms the baseline models in both long-term and short-term prediction tasks. Specifically, the RMSE, MAE, and

R^{2}

reach 0.05, 0.23, and 0.95, respectively. Remarkably, even for complex time-series data that contain a large amount of noise, the TNN model still maintains a high prediction accuracy. Full article

(This article belongs to the Special Issue New Deep Learning Approach for Time Series Forecasting)

► Show Figures

Figure 1

35 pages, 9764 KB

Open AccessEditor’s ChoiceArticle

Using ChatGPT and Persuasive Technology for Personalized Recommendation Messages in Hotel Upselling

by Manolis Remountakis, Konstantinos Kotis, Babis Kourtzis and George E. Tsekouras

Information 2023, 14(9), 504; https://doi.org/10.3390/info14090504 - 13 Sep 2023

Cited by 39 | Viewed by 9590

Abstract

Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), such as ChatGPT, and persuasive technologies have opened new avenues for enhancing the effectiveness of those systems. This [...] Read more.

Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), such as ChatGPT, and persuasive technologies have opened new avenues for enhancing the effectiveness of those systems. This paper explores the potential of integrating ChatGPT and persuasive technologies for automating and improving hotel hospitality recommender systems. First, we delve into the capabilities of ChatGPT, which can understand and generate human-like text, enabling more accurate and context-aware recommendations. We discuss the integration of ChatGPT into recommender systems, highlighting the ability to analyze user preferences, extract valuable insights from online reviews, and generate personalized recommendations based on guest profiles. Second, we investigate the role of persuasive technology in influencing user behavior and enhancing the persuasive impact of hotel recommendations. By incorporating persuasive techniques, such as social proof, scarcity, and personalization, recommender systems can effectively influence user decision making and encourage desired actions, such as booking a specific hotel or upgrading their room. To investigate the efficacy of ChatGPT and persuasive technologies, we present pilot experiments with a case study involving a hotel recommender system. Our inhouse commercial hotel marketing platform, eXclusivi, was extended with a new software module working with ChatGPT prompts and persuasive ads created for its recommendations. In particular, we developed an intelligent advertisement (ad) copy generation tool for the hotel marketing platform. The proposed approach allows for the hotel team to target all guests in their language, leveraging the integration with the hotel’s reservation system. Overall, this paper contributes to the field of hotel hospitality by exploring the synergistic relationship between ChatGPT and persuasive technology in recommender systems, ultimately influencing guest satisfaction and hotel revenue. Full article

(This article belongs to the Special Issue Systems Engineering and Knowledge Management)

► Show Figures

Figure 1

26 pages, 2354 KB

Open AccessEditor’s ChoiceArticle

Effects of Generative Chatbots in Higher Education

by Galina Ilieva, Tania Yankova, Stanislava Klisarova-Belcheva, Angel Dimitrov, Marin Bratkov and Delian Angelov

Information 2023, 14(9), 492; https://doi.org/10.3390/info14090492 - 7 Sep 2023

Cited by 174 | Viewed by 24273

Abstract

Learning technologies often do not meet the university requirements for learner engagement via interactivity and real-time feedback. In addition to the challenge of providing personalized learning experiences for students, these technologies can increase the workload of instructors due to the maintenance and updates [...] Read more.

Learning technologies often do not meet the university requirements for learner engagement via interactivity and real-time feedback. In addition to the challenge of providing personalized learning experiences for students, these technologies can increase the workload of instructors due to the maintenance and updates required to keep the courses up-to-date. Intelligent chatbots based on generative artificial intelligence (AI) technology can help overcome these disadvantages by transforming pedagogical activities and guiding both students and instructors interactively. In this study, we explore and compare the main characteristics of existing educational chatbots. Then, we propose a new theoretical framework for blended learning with intelligent chatbots integration enabling students to interact online and instructors to create and manage their courses using generative AI tools. The advantages of the proposed framework are as follows: (1) it provides a comprehensive understanding of the transformative potential of AI chatbots in education and facilitates their effective implementation; (2) it offers a holistic methodology to enhance the overall educational experience; and (3) it unifies the applications of intelligent chatbots in teaching–learning activities within universities. Full article

(This article belongs to the Special Issue Feature Papers in Information in 2023)

► Show Figures

Figure 1

23 pages, 431 KB

Open AccessEditor’s ChoiceReview

A Comprehensive Study of ChatGPT: Advancements, Limitations, and Ethical Considerations in Natural Language Processing and Cybersecurity

by Moatsum Alawida, Sami Mejri, Abid Mehmood, Belkacem Chikhaoui and Oludare Isaac Abiodun

Information 2023, 14(8), 462; https://doi.org/10.3390/info14080462 - 16 Aug 2023

Cited by 212 | Viewed by 78895

Abstract

This paper presents an in-depth study of ChatGPT, a state-of-the-art language model that is revolutionizing generative text. We provide a comprehensive analysis of its architecture, training data, and evaluation metrics and explore its advancements and enhancements over time. Additionally, we examine the capabilities [...] Read more.

This paper presents an in-depth study of ChatGPT, a state-of-the-art language model that is revolutionizing generative text. We provide a comprehensive analysis of its architecture, training data, and evaluation metrics and explore its advancements and enhancements over time. Additionally, we examine the capabilities and limitations of ChatGPT in natural language processing (NLP) tasks, including language translation, text summarization, and dialogue generation. Furthermore, we compare ChatGPT to other language generation models and discuss its applicability in various tasks. Our study also addresses the ethical and privacy considerations associated with ChatGPT and provides insights into mitigation strategies. Moreover, we investigate the role of ChatGPT in cyberattacks, highlighting potential security risks. Lastly, we showcase the diverse applications of ChatGPT in different industries and evaluate its performance across languages and domains. This paper offers a comprehensive exploration of ChatGPT’s impact on the NLP field. Full article

(This article belongs to the Special Issue Advances in Cybersecurity and Reliability)

► Show Figures

Figure 1

16 pages, 2143 KB

Open AccessEditor’s ChoiceArticle

Machinability of Titanium Grade 5 Alloy for Wire Electrical Discharge Machining Using a Hybrid Learning Algorithm

by Manikandan Natarajan, Thejasree Pasupuleti, Jayant Giri, Neeraj Sunheriya, Lakshmi Narasimhamu Katta, Rajkumar Chadge, Chetan Mahatme, Pallavi Giri, Saurav Mallik and Kanad Ray

Information 2023, 14(8), 439; https://doi.org/10.3390/info14080439 - 3 Aug 2023

Cited by 81 | Viewed by 4969

Abstract

Titanium alloys have found widespread use in aviation, automotive, and marine applications, which makes their implementation in mass production more challenging. Conventional methods of removing these alloy materials are unsuitable because of the high wear rate of cutting and slower rate of processing. [...] Read more.

Titanium alloys have found widespread use in aviation, automotive, and marine applications, which makes their implementation in mass production more challenging. Conventional methods of removing these alloy materials are unsuitable because of the high wear rate of cutting and slower rate of processing. The complexities of these materials have prompted the creation of cutting-edge machining methods. Wire Electrical Discharge Machining (WEDM) is a technique that has the potential to be useful for the removal of materials that are harder and electrically conductive. In order to create intricate designs, this method is frequently employed. The input factors, including pulse duration (on/off) and peak current, were taken into account during the experimental design process. The rate of material removal, surface roughness, dimensional deviation, and GD&T errors were opted for as performance indicators. The approach proposed by Taguchi was selected for the investigation of the process factors, and an Analysis of Variance was selected to find out the relative momentousness of each factor. From the analysis it is perceived that the applied current is the predominant factor that influences the chosen output characteristics. The aspiration of this article is to evolve a decision-making model based on a hybrid learning method which can be adopted to predict the selected output measures that affect the WEDM process. According to the findings, the value of the ANFIS-GRG, which was predicted to be 0.7777, was in fact closer to that value than any other value. The proposed model has the ability to help make a variety of different production processes more efficient. The analysis showed that the model’s functionality was enhanced, which helps producers make well-informed decisions. Full article

(This article belongs to the Special Issue Trends in Computational and Cognitive Engineering)

► Show Figures

Figure 1

14 pages, 2220 KB

Open AccessEditor’s ChoiceArticle

Breast Cancer Detection in Mammography Images: A CNN-Based Approach with Feature Selection

by Zahra Jafari and Ebrahim Karami

Information 2023, 14(7), 410; https://doi.org/10.3390/info14070410 - 16 Jul 2023

Cited by 120 | Viewed by 21549

Abstract

The prompt and accurate diagnosis of breast lesions, including the distinction between cancer, non-cancer, and suspicious cancer, plays a crucial role in the prognosis of breast cancer. In this paper, we introduce a novel method based on feature extraction and reduction for the [...] Read more.

The prompt and accurate diagnosis of breast lesions, including the distinction between cancer, non-cancer, and suspicious cancer, plays a crucial role in the prognosis of breast cancer. In this paper, we introduce a novel method based on feature extraction and reduction for the detection of breast cancer in mammography images. First, we extract features from multiple pre-trained convolutional neural network (CNN) models, and then concatenate them. The most informative features are selected based on their mutual information with the target variable. Subsequently, the selected features can be classified using a machine learning algorithm. We evaluate our approach using four different machine learning algorithms: neural network (NN), k-nearest neighbor (kNN), random forest (RF), and support vector machine (SVM). Our results demonstrate that the NN-based classifier achieves an impressive accuracy of 92% on the RSNA dataset. This dataset is newly introduced and includes two views as well as additional features like age, which contributed to the improved performance. We compare our proposed algorithm with state-of-the-art methods and demonstrate its superiority, particularly in terms of accuracy and sensitivity. For the MIAS dataset, we achieve an accuracy as high as 94.5%, and for the DDSM dataset, an accuracy of 96% is attained. These results highlight the effectiveness of our method in accurately diagnosing breast lesions and surpassing existing approaches. Full article

(This article belongs to the Special Issue Advances in Object-Based Image Segmentation and Retrieval)

► Show Figures

Figure 1

38 pages, 585 KB

Open AccessEditor’s ChoiceReview

A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks

by Parvez Faruki, Rati Bhan, Vinesh Jain, Sajal Bhatia, Nour El Madhoun and Rajendra Pamula

Information 2023, 14(7), 374; https://doi.org/10.3390/info14070374 - 30 Jun 2023

Cited by 39 | Viewed by 13536

Abstract

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware [...] Read more.

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection. Full article

(This article belongs to the Special Issue Malware Behavior Analysis Applying Machine Learning)

► Show Figures

Figure 1

20 pages, 7807 KB

Open AccessEditor’s ChoiceArticle

INSUS: Indoor Navigation System Using Unity and Smartphone for User Ambulation Assistance

by Evianita Dewi Fajrianti, Nobuo Funabiki, Sritrusta Sukaridhoto, Yohanes Yohanie Fridelin Panduman, Kong Dezheng, Fang Shihao and Anak Agung Surya Pradhana

Information 2023, 14(7), 359; https://doi.org/10.3390/info14070359 - 24 Jun 2023

Cited by 26 | Viewed by 8880

Abstract

Currently, outdoor navigation systems have widely been used around the world on smartphones. They rely on GPS (Global Positioning System). However, indoor navigation systems are still under development due to the complex structure of indoor environments, including multiple floors, many rooms, steps, and [...] Read more.

Currently, outdoor navigation systems have widely been used around the world on smartphones. They rely on GPS (Global Positioning System). However, indoor navigation systems are still under development due to the complex structure of indoor environments, including multiple floors, many rooms, steps, and elevators. In this paper, we present the design and implementation of the Indoor Navigation System using Unity and Smartphone (INSUS). INSUS shows the arrow of the moving direction on the camera view based on a smartphone’s augmented reality (AR) technology. To trace the user location, it utilizes the Simultaneous Localization and Mapping (SLAM) technique with a gyroscope and a camera in a smartphone to track users’ movements inside a building after initializing the current location by the QR code. Unity is introduced to obtain the 3D information of the target indoor environment for Visual SLAM. The data are stored in the IoT application server called SEMAR for visualizations. We implement a prototype system of INSUS inside buildings in two universities. We found that scanning QR codes with the smartphone perpendicular in angle between

60^{\circ}

and

100^{\circ}

achieves the highest QR code detection accuracy. We also found that the phone’s tilt angles influence the navigation success rate, with

90^{\circ}

to

100^{\circ}

tilt angles giving better navigation success compared to lower tilt angles. INSUS also proved to be a robust navigation system, evidenced by near identical navigation success rate results in navigation scenarios with or without disturbance. Furthermore, based on the questionnaire responses from the respondents, it was generally found that INSUS received positive feedback and there is support to improve the system. Full article

(This article belongs to the Special Issue Feature Papers in Information in 2023)

► Show Figures

Figure 1

17 pages, 3741 KB

Open AccessEditor’s ChoiceArticle

Trend Analysis of Decentralized Autonomous Organization Using Big Data Analytics

by Hyejin Park, Ivan Ureta and Boyoung Kim

Information 2023, 14(6), 326; https://doi.org/10.3390/info14060326 - 9 Jun 2023

Cited by 12 | Viewed by 7038

Abstract

Decentralized Autonomous Organizations (DAOs) have gained widespread attention in academia and industry as potential future models for decentralized governance and organization. In order to understand the trends and future potential of this rapidly growing technology, it is crucial to conduct research in the [...] Read more.

Decentralized Autonomous Organizations (DAOs) have gained widespread attention in academia and industry as potential future models for decentralized governance and organization. In order to understand the trends and future potential of this rapidly growing technology, it is crucial to conduct research in the field. This research aims at a data-driven approach for the objective content analysis of big data related to DAOs, using text mining and Latent Dirichlet Allocation (LDA)-based topic modeling. The study analyzed tweets with the hashtag #DAO and all Reddit data with “DAO”. The results were from the identification of the top 100 frequently appearing keywords, as well as the top 20 keywords with high network centrality, and key topics related to finance, gaming, and fundraising, from both Twitter and Reddit. The analysis revealed twelve topics from Twitter and eight topics from Reddit, with the term “community” frequently appearing across many of these topics. The findings provide valuable insights into the current trend and future potential of DAOs, and should be used by researchers to guide further research in the field and by decision makers to explore innovative ways to govern the organizations. Full article

(This article belongs to the Special Issue Advances in Data and Network Sciences Applied to Computational Social Science)

► Show Figures

Figure 1

19 pages, 624 KB

Open AccessEditor’s ChoiceArticle

Artificially Intelligent Readers: An Adaptive Framework for Original Handwritten Numerical Digits Recognition with OCR Methods

by Parth Hasmukh Jain, Vivek Kumar, Jim Samuel, Sushmita Singh, Abhinay Mannepalli and Richard Anderson

Information 2023, 14(6), 305; https://doi.org/10.3390/info14060305 - 26 May 2023

Cited by 27 | Viewed by 9774

Abstract

Advanced artificial intelligence (AI) techniques have led to significant developments in optical character recognition (OCR) technologies. OCR applications, using AI techniques for transforming images of typed text, handwritten text, or other forms of text into machine-encoded text, provide a fair degree of accuracy [...] Read more.

Advanced artificial intelligence (AI) techniques have led to significant developments in optical character recognition (OCR) technologies. OCR applications, using AI techniques for transforming images of typed text, handwritten text, or other forms of text into machine-encoded text, provide a fair degree of accuracy for general text. However, even after decades of intensive research, creating OCR with human-like abilities has remained evasive. One of the challenges has been that OCR models trained on general text do not perform well on localized or personalized handwritten text due to differences in the writing style of alphabets and digits. This study aims to discuss the steps needed to create an adaptive framework for OCR models, with the intent of exploring a reasonable method to customize an OCR solution for a unique dataset of English language numerical digits were developed for this study. We develop a digit recognizer by training our model on the MNIST dataset with a convolutional neural network and contrast it with multiple models trained on combinations of the MNIST and custom digits. Using our methods, we observed results comparable with the baseline and provided recommendations for improving OCR accuracy for localized or personalized handwritten text. This study also provides an alternative perspective to generating data using conventional methods, which can serve as a gold standard for custom data augmentation to help address the challenges of scarce data and data imbalance. Full article

(This article belongs to the Special Issue Advances in Machine Learning and Intelligent Information Systems)

► Show Figures

Figure 1

21 pages, 3627 KB

Open AccessEditor’s ChoiceArticle

A Study of Machine Learning Regression Techniques for Non-Contact SpO₂ Estimation from Infrared Motion-Magnified Facial Video

by Thomas Stogiannopoulos, Grigorios-Aris Cheimariotis and Nikolaos Mitianoudis

Information 2023, 14(6), 301; https://doi.org/10.3390/info14060301 - 23 May 2023

Cited by 27 | Viewed by 7196

Abstract

This work explores the use of infrared low-cost cameras for monitoring peripheral oxygen saturation (SpO₂), a vital sign that is particularly important for individuals with fragile health, such as the elderly. The development of contactless SpO₂ monitoring utilizing RGB cameras [...] Read more.

This work explores the use of infrared low-cost cameras for monitoring peripheral oxygen saturation (SpO₂), a vital sign that is particularly important for individuals with fragile health, such as the elderly. The development of contactless SpO₂ monitoring utilizing RGB cameras has already proven successful. This study utilizes the Eulerian Video Magnification (EVM) technique to enhance minor variations in skin pixel intensity in particular facial regions. More specifically, the emphasis in this study is in the utilization of infrared cameras, in order to explore the possibility of contactless SpO₂ monitoring under low-light or night-time conditions. Many different methods were employed for regression. A study of machine learning regression methods was performed, including a Generalized Additive Model (GAM) and an Extra Trees Regressor, based on 12 novel features extracted from the extracted amplified photoplethysmography (PPG) signal. Deep learning methods were also explored, including a 3D Convolution Neural Network (CNN) and a Video Vision Transformer (ViViT) architecture on the amplified forehead/cheeks video. The estimated SpO₂ values of the best performing method reach a low root mean squared error of 1.331 and an

R^{2}

score of 0.465 that fall within the acceptable range for these applications. Full article

(This article belongs to the Section Biomedical Information and Health)

► Show Figures

Figure 1

19 pages, 1488 KB

Open AccessEditor’s ChoiceReview

Blockchain and Machine Learning: A Critical Review on Security

by Hamed Taherdoost

Information 2023, 14(5), 295; https://doi.org/10.3390/info14050295 - 17 May 2023

Cited by 39 | Viewed by 15805

Abstract

Blockchain is the foundation of all cryptocurrencies, while machine learning (ML) is one of the most popular technologies with a wide range of possibilities. Blockchain may be improved and made more effective by using ML. Even though blockchain technology uses encryption to safeguard [...] Read more.

Blockchain is the foundation of all cryptocurrencies, while machine learning (ML) is one of the most popular technologies with a wide range of possibilities. Blockchain may be improved and made more effective by using ML. Even though blockchain technology uses encryption to safeguard data, it is not completely reliable. Various elements, including the particular use case, the type of data, and legal constraints can determine whether it is suitable for keeping private and sensitive data. While there may be benefits, it is important to take into account possible hazards and abide by privacy and security laws. The blockchain itself is secure, but additional applications and layers are not. In terms of security, ML can aid in the development of blockchain applications. Therefore, a critical investigation is required to better understand the function of ML and blockchain in enhancing security. This study examines the current situation, evaluates the articles it contains, and presents an overview of the security issues. Despite their existing limitations, the papers included from 2012 to 2022 highlighted the importance of ML’s impact on blockchain security. ML and blockchain can enhance security, but challenges remain; advances such as federated learning and zero-knowledge proofs are important, and future research should focus on privacy and integration with other technologies. Full article

(This article belongs to the Special Issue Machine Learning for the Blockchain)

► Show Figures

Figure 1

18 pages, 6442 KB

Open AccessEditor’s ChoiceArticle

Energy Usage Forecasting Model Based on Long Short-Term Memory (LSTM) and eXplainable Artificial Intelligence (XAI)

by Muhammad Rifqi Maarif, Arif Rahman Saleh, Muhammad Habibi, Norma Latif Fitriyani and Muhammad Syafrudin

Information 2023, 14(5), 265; https://doi.org/10.3390/info14050265 - 29 Apr 2023

Cited by 53 | Viewed by 8806

Abstract

The accurate forecasting of energy consumption is essential for companies, primarily for planning energy procurement. An overestimated or underestimated forecasting value may lead to inefficient energy usage. Inefficient energy usage could also lead to financial consequences for the company, since it will generate [...] Read more.

The accurate forecasting of energy consumption is essential for companies, primarily for planning energy procurement. An overestimated or underestimated forecasting value may lead to inefficient energy usage. Inefficient energy usage could also lead to financial consequences for the company, since it will generate a high cost of energy production. Therefore, in this study, we proposed an energy usage forecasting model and parameter analysis using long short-term memory (LSTM) and explainable artificial intelligence (XAI), respectively. A public energy usage dataset from a steel company was used in this study to evaluate our models and compare them with previous study results. The results showed that our models achieved the lowest root mean squared error (RMSE) scores by up to 0.08, 0.07, and 0.07 for the single-layer LSTM, double-layer LSTM, and bi-directional LSTM, respectively. In addition, the interpretability analysis using XAI revealed that two parameters, namely the leading current reactive power and the number of seconds from midnight, had a strong influence on the model output. Finally, it is expected that our study could be useful for industry practitioners, providing LSTM models for accurate energy forecasting and offering insight for policymakers and industry leaders so that they can make more informed decisions about resource allocation and investment, develop more effective strategies for reducing energy consumption, and support the transition toward sustainable development. Full article

► Show Figures

Figure 1

20 pages, 736 KB

Open AccessEditor’s ChoiceArticle

A Multi-Key with Partially Homomorphic Encryption Scheme for Low-End Devices Ensuring Data Integrity

by Saci Medileh, Abdelkader Laouid, Mohammad Hammoudeh, Mostefa Kara, Tarek Bejaoui, Amna Eleyan and Mohammed Al-Khalidi

Information 2023, 14(5), 263; https://doi.org/10.3390/info14050263 - 28 Apr 2023

Cited by 36 | Viewed by 6742

Abstract

In today’s hyperconnected world, the Internet of Things and Cloud Computing complement each other in several areas. Cloud Computing provides IoT systems with an efficient and flexible environment that supports application requirements such as real-time control/monitoring, scalability, fault tolerance, and numerous security services. [...] Read more.

In today’s hyperconnected world, the Internet of Things and Cloud Computing complement each other in several areas. Cloud Computing provides IoT systems with an efficient and flexible environment that supports application requirements such as real-time control/monitoring, scalability, fault tolerance, and numerous security services. Hardware and software limitations of IoT devices can be mitigated using the massive on-demand cloud resources. However, IoT cloud-based solutions pose some security and privacy concerns, specifically when an untrusted cloud is used. This calls for strong encryption schemes that allow operations on data in an encrypted format without compromising the encryption. This paper presents an asymmetric multi-key and partially homomorphic encryption scheme. The scheme provides the addition operation by encrypting each decimal digit of the given integer number separately using a special key. In addition, data integrity processes are performed when an untrusted third party performs homomorphic operations on encrypted data. The proposed work considers the most widely known issues like the encrypted data size, slow operations at the hardware level, and high computing costs at the provider level. The size of generated ciphertext is almost equal to the size of the plaintext, and order-preserving is ensured using an asymmetrical encryption version. Full article

(This article belongs to the Special Issue Advances in Cybersecurity and Reliability)

► Show Figures

Figure 1

16 pages, 3234 KB

Open AccessEditor’s ChoiceArticle

Generation of Nonlinear Substitutions by Simulated Annealing Algorithm

by Alexandr Kuznetsov, Mikolaj Karpinski, Ruslana Ziubina, Sergey Kandiy, Emanuele Frontoni, Oleksandr Peliukh, Olga Veselska and Ruslan Kozak

Information 2023, 14(5), 259; https://doi.org/10.3390/info14050259 - 26 Apr 2023

Cited by 21 | Viewed by 3637

Abstract

The problem of nonlinear substitution generation (S-boxes) is investigated in many related works in symmetric key cryptography. In particular, the strength of symmetric ciphers to linear cryptanalysis is directly related to the nonlinearity of substitution. In addition to being highly nonlinear, S-boxes must [...] Read more.

The problem of nonlinear substitution generation (S-boxes) is investigated in many related works in symmetric key cryptography. In particular, the strength of symmetric ciphers to linear cryptanalysis is directly related to the nonlinearity of substitution. In addition to being highly nonlinear, S-boxes must be random, i.e., must not contain hidden mathematical constructs that facilitate algebraic cryptanalysis. The generation of such substitutions is a complex combinatorial optimization problem. Probabilistic algorithms are used to solve it, for instance the simulated annealing algorithm, which is well-fitted to a discrete search space. We propose a new cost function based on Walsh–Hadamard spectrum computation, and investigate the search efficiency of S-boxes using a simulated annealing algorithm. For this purpose, we conduct numerous experiments with different input parameters: initial temperature, cooling coefficient, number of internal and external loops. As the results of the research show, applying the new cost function allows for the rapid generation of nonlinear substitutions. To find 8-bit bijective S-boxes with nonlinearity 104, we need about 83,000 iterations. At the same time, the probability of finding the target result is 100%. Full article

► Show Figures

Figure 1

22 pages, 1002 KB

Open AccessEditor’s ChoiceArticle

Digital Audio Tampering Detection Based on Deep Temporal–Spatial Features of Electrical Network Frequency

by Chunyan Zeng, Shuai Kong, Zhifeng Wang, Kun Li and Yuhao Zhao

Information 2023, 14(5), 253; https://doi.org/10.3390/info14050253 - 22 Apr 2023

Cited by 15 | Viewed by 6184

Abstract

In recent years, digital audio tampering detection methods by extracting audio electrical network frequency (ENF) features have been widely applied. However, most digital audio tampering detection methods based on ENF have the problems of focusing on spatial features only, without effective representation of [...] Read more.

In recent years, digital audio tampering detection methods by extracting audio electrical network frequency (ENF) features have been widely applied. However, most digital audio tampering detection methods based on ENF have the problems of focusing on spatial features only, without effective representation of temporal features, and do not fully exploit the effective information in the shallow ENF features, which leads to low accuracy of audio tamper detection. Therefore, this paper proposes a new method for digital audio tampering detection based on the deep temporal–spatial feature of ENF. To extract the temporal and spatial features of the ENF, firstly, a highly accurate ENF phase sequence is extracted using the first-order Discrete Fourier Transform (DFT), and secondly, different frame processing methods are used to extract the ENF shallow temporal and spatial features for the temporal and spatial information contained in the ENF phase. To fully exploit the effective information in the shallow ENF features, we construct a parallel RDTCN-CNN network model to extract the deep temporal and spatial information by using the processing ability of Residual Dense Temporal Convolutional Network (RDTCN) and Convolutional Neural Network (CNN) for temporal and spatial information, and use the branch attention mechanism to adaptively assign weights to the deep temporal and spatial features to obtain the temporal–spatial feature with greater representational capacity, and finally, adjudicate whether the audio is tampered with by the MLP network. The experimental results show that the method in this paper outperforms the four baseline methods in terms of accuracy and F1-score. Full article

(This article belongs to the Special Issue Signal Processing Based on Convolutional Neural Network)

► Show Figures

Figure 1

23 pages, 27370 KB

Open AccessEditor’s ChoiceArticle

Recognizing the Wadi Fluvial Structure and Stream Network in the Qena Bend of the Nile River, Egypt, on Landsat 8-9 OLI Images

by Polina Lemenkova and Olivier Debeir

Information 2023, 14(4), 249; https://doi.org/10.3390/info14040249 - 20 Apr 2023

Cited by 10 | Viewed by 7297

Abstract

With methods for processing remote sensing data becoming widely available, the ability to quantify changes in spatial data and to evaluate the distribution of diverse landforms across target areas in datasets becomes increasingly important. One way to approach this problem is through satellite [...] Read more.

With methods for processing remote sensing data becoming widely available, the ability to quantify changes in spatial data and to evaluate the distribution of diverse landforms across target areas in datasets becomes increasingly important. One way to approach this problem is through satellite image processing. In this paper, we primarily focus on the methods of the unsupervised classification of the Landsat OLI/TIRS images covering the region of the Qena governorate in Upper Egypt. The Qena Bend of the Nile River presents a remarkable morphological feature in Upper Egypt, including a dense drainage network of wadi aquifer systems and plateaus largely dissected by numerous valleys of dry rivers. To identify the fluvial structure and stream network of the Wadi Qena region, this study addresses the problem of interpreting the relevant space-borne data using R, with an aim to visualize the land surface structures corresponding to various land cover types. To this effect, high-resolution 2D and 3D topographic and geologic maps were used for the analysis of the geomorphological setting of the Qena region. The information was extracted from the space-borne data for the comparative analysis of the distribution of wadi streams in the Qena Bend area over several years: 2013, 2015, 2016, 2019, 2022, and 2023. Six images were processed using computer vision methods made available by R libraries. The results of the k-means clustering of each scene retrieved from the multi-temporal images covering the Qena Bend of the Nile River were thus compared to visualize changes in landforms caused by the cumulative effects of geomorphological disasters and climate–environmental processes. The proposed method, tied together through the use of R scripts, runs effectively and performs favorably in computer vision tasks aimed at geospatial image processing and the analysis of remote sensing data. Full article

(This article belongs to the Special Issue Computer Vision for Security Applications)

► Show Figures

Figure 1

17 pages, 286 KB

Open AccessEditor’s ChoiceReview

Transformers in the Real World: A Survey on NLP Applications

by Narendra Patwardhan, Stefano Marrone and Carlo Sansone

Information 2023, 14(4), 242; https://doi.org/10.3390/info14040242 - 17 Apr 2023

Cited by 221 | Viewed by 35189

Abstract

The field of Natural Language Processing (NLP) has undergone a significant transformation with the introduction of Transformers. From the first introduction of this technology in 2017, the use of transformers has become widespread and has had a profound impact on the field of [...] Read more.

The field of Natural Language Processing (NLP) has undergone a significant transformation with the introduction of Transformers. From the first introduction of this technology in 2017, the use of transformers has become widespread and has had a profound impact on the field of NLP. In this survey, we review the open-access and real-world applications of transformers in NLP, specifically focusing on those where text is the primary modality. Our goal is to provide a comprehensive overview of the current state-of-the-art in the use of transformers in NLP, highlight their strengths and limitations, and identify future directions for research. In this way, we aim to provide valuable insights for both researchers and practitioners in the field of NLP. In addition, we provide a detailed analysis of the various challenges faced in the implementation of transformers in real-world applications, including computational efficiency, interpretability, and ethical considerations. Moreover, we highlight the impact of transformers on the NLP community, including their influence on research and the development of new NLP models. Full article

(This article belongs to the Collection Natural Language Processing and Applications: Challenges and Perspectives)

► Show Figures

Figure 1

29 pages, 4153 KB

Open AccessEditor’s ChoiceArticle

Structure Learning and Hyperparameter Optimization Using an Automated Machine Learning (AutoML) Pipeline

by Konstantinos Filippou, George Aifantis, George A. Papakostas and George E. Tsekouras

Information 2023, 14(4), 232; https://doi.org/10.3390/info14040232 - 9 Apr 2023

Cited by 31 | Viewed by 8246

Abstract

In this paper, we built an automated machine learning (AutoML) pipeline for structure-based learning and hyperparameter optimization purposes. The pipeline consists of three main automated stages. The first carries out the collection and preprocessing of the dataset from the Kaggle database through the [...] Read more.

In this paper, we built an automated machine learning (AutoML) pipeline for structure-based learning and hyperparameter optimization purposes. The pipeline consists of three main automated stages. The first carries out the collection and preprocessing of the dataset from the Kaggle database through the Kaggle API. The second utilizes the Keras-Bayesian optimization tuning library to perform hyperparameter optimization. The third focuses on the training process of the machine learning (ML) model using the hyperparameter values estimated in the previous stage, and its evaluation is performed on the testing data by implementing the Neptune AI. The main technologies used to develop a stable and reusable machine learning pipeline are the popular Git version control system, the Google cloud virtual machine, the Jenkins server, the Docker containerization technology, and the Ngrok reverse proxy tool. The latter can securely publish the local Jenkins address as public through the internet. As such, some parts of the proposed pipeline are taken from the thematic area of machine learning operations (MLOps), resulting in a hybrid software scheme. The machine learning model was used to evaluate the pipeline, which is a multilayer perceptron (MLP) that combines typical dense, as well as polynomial, layers. The simulation results show that the proposed pipeline exhibits a reliable and accurate performance while managing to boost the network’s performance in classification tasks. Full article

(This article belongs to the Special Issue Systems Engineering and Knowledge Management)

► Show Figures

Figure 1

16 pages, 8063 KB

Open AccessEditor’s ChoiceArticle

Using a Machine Learning Approach to Evaluate the NOx Emissions in a Spark-Ignition Optical Engine

by Federico Ricci, Luca Petrucci and Francesco Mariani

Information 2023, 14(4), 224; https://doi.org/10.3390/info14040224 - 6 Apr 2023

Cited by 11 | Viewed by 3928

Abstract

Currently, machine learning (ML) technologies are widely employed in the automotive field for determining physical quantities thanks to their ability to ensure lower computational costs and faster operations than traditional methods. Within this context, the present work shows the outcomes of forecasting activities [...] Read more.

Currently, machine learning (ML) technologies are widely employed in the automotive field for determining physical quantities thanks to their ability to ensure lower computational costs and faster operations than traditional methods. Within this context, the present work shows the outcomes of forecasting activities on the prediction of pollutant emissions from engines using an artificial neural network technique. Tests on an optical access engine were conducted under lean mixture conditions, which is the direction in which automotive research is developing to meet the ever-stricter regulations on pollutant emissions. A NARX architecture was utilized to estimate the engine’s nitrogen oxide emissions starting from in-cylinder pressure data and images of the flame front evolution recorded by a high-speed camera and elaborated through a Mask R-CNN technique. Based on the obtained results, the methodology’s applicability to real situations, such as metal engines, was assessed using a sensitivity analysis presented in the second part of the work, which helped identify and quantify the most important input parameters for the nitrogen oxide forecast. Full article

(This article belongs to the Special Issue Computer Vision, Pattern Recognition and Machine Learning in Italy)

► Show Figures

Figure 1

34 pages, 1619 KB

Open AccessEditor’s ChoiceArticle

AutoML with Bayesian Optimizations for Big Data Management

by Aristeidis Karras, Christos Karras, Nikolaos Schizas, Markos Avlonitis and Spyros Sioutas

Information 2023, 14(4), 223; https://doi.org/10.3390/info14040223 - 5 Apr 2023

Cited by 24 | Viewed by 8192

Abstract

The field of automated machine learning (AutoML) has gained significant attention in recent years due to its ability to automate the process of building and optimizing machine learning models. However, the increasing amount of big data being generated has presented new challenges for [...] Read more.

The field of automated machine learning (AutoML) has gained significant attention in recent years due to its ability to automate the process of building and optimizing machine learning models. However, the increasing amount of big data being generated has presented new challenges for AutoML systems in terms of big data management. In this paper, we introduce Fabolas and learning curve extrapolation as two methods for accelerating hyperparameter optimization. Four methods for quickening training were presented including Bag of Little Bootstraps, k-means clustering for Support Vector Machines, subsample size selection for gradient descent, and subsampling for logistic regression. Additionally, we also discuss the use of Markov Chain Monte Carlo (MCMC) methods and other stochastic optimization techniques to improve the efficiency of AutoML systems in managing big data. These methods enhance various facets of the training process, making it feasible to combine them in diverse ways to gain further speedups. We review several combinations that have potential and provide a comprehensive understanding of the current state of AutoML and its potential for managing big data in various industries. Furthermore, we also mention the importance of parallel computing and distributed systems to improve the scalability of the AutoML systems while working with big data. Full article

(This article belongs to the Special Issue Multidimensional Data Structures and Big Data Management)

► Show Figures

Figure 1

23 pages, 1086 KB

Open AccessFeature PaperEditor’s ChoiceArticle

IoT-Enabled Precision Agriculture: Developing an Ecosystem for Optimized Crop Management

by Shadi Atalla, Saed Tarapiah, Amjad Gawanmeh, Mohammad Daradkeh, Husameldin Mukhtar, Yassine Himeur, Wathiq Mansoor, Kamarul Faizal Bin Hashim and Motaz Daadoo

Information 2023, 14(4), 205; https://doi.org/10.3390/info14040205 - 27 Mar 2023

Cited by 102 | Viewed by 18552

Abstract

The Internet of Things (IoT) has the potential to revolutionize agriculture by providing real-time data on crop and livestock conditions. This study aims to evaluate the performance scalability of wireless sensor networks (WSNs) in agriculture, specifically in two scenarios: monitoring olive tree farms [...] Read more.

The Internet of Things (IoT) has the potential to revolutionize agriculture by providing real-time data on crop and livestock conditions. This study aims to evaluate the performance scalability of wireless sensor networks (WSNs) in agriculture, specifically in two scenarios: monitoring olive tree farms and stables for horse training. The study proposes a new classification approach of IoT in agriculture based on several factors and introduces performance assessment metrics for stationary and mobile scenarios in 6LowPAN networks. The study utilizes COOJA, a realistic WSN simulator, to model and simulate the performance of the 6LowPAN and Routing protocol for low-power and lossy networks (RPL) in the two farming scenarios. The simulation settings for both fixed and mobile nodes are shared, with the main difference being node mobility. The study characterizes different aspects of the performance requirements in the two farming scenarios by comparing the average power consumption, radio duty cycle, and sensor network graph connectivity degrees. A new approach is proposed to model and simulate moving animals within the COOJA simulator, adopting the random waypoint model (RWP) to represent horse movements. The results show the advantages of using the RPL protocol for routing in mobile and fixed sensor networks, which supports dynamic topologies and improves the overall network performance. The proposed framework is experimentally validated and tested through simulation, demonstrating the suitability of the proposed framework for both fixed and mobile scenarios, providing efficient communication performance and low latency. The results have several practical implications for precision agriculture by providing an efficient monitoring and management solution for agricultural and livestock farms. Overall, this study provides a comprehensive evaluation of the performance scalability of WSNs in the agriculture sector, offering a new classification approach and performance assessment metrics for stationary and mobile scenarios in 6LowPAN networks. The results demonstrate the suitability of the proposed framework for precision agriculture, providing efficient communication performance and low latency. Full article

(This article belongs to the Special Issue Advances in Computing, Communication & Security)

► Show Figures

Figure 1

10 pages, 407 KB

Open AccessEditor’s ChoiceArticle

Fundamental Research Challenges for Distributed Computing Continuum Systems

by Victor Casamayor Pujol, Andrea Morichetta, Ilir Murturi, Praveen Kumar Donta and Schahram Dustdar

Information 2023, 14(3), 198; https://doi.org/10.3390/info14030198 - 22 Mar 2023

Cited by 37 | Viewed by 7603

Abstract

This article discusses four fundamental topics for future Distributed Computing Continuum Systems: their representation, model, lifelong learning, and business model. Further, it presents techniques and concepts that can be useful to define these four topics specifically for Distributed Computing Continuum Systems. Finally, this [...] Read more.

This article discusses four fundamental topics for future Distributed Computing Continuum Systems: their representation, model, lifelong learning, and business model. Further, it presents techniques and concepts that can be useful to define these four topics specifically for Distributed Computing Continuum Systems. Finally, this article presents a broad view of the synergies among the presented technique that can enable the development of future Distributed Computing Continuum Systems. Full article

(This article belongs to the Special Issue Best IDEAS: International Database Engineered Applications Symposium)

► Show Figures

Figure 1

11 pages, 448 KB

Open AccessEditor’s ChoiceArticle

Feature Selection Engineering for Credit Risk Assessment in Retail Banking

by Jaber Jemai and Anis Zarrad

Information 2023, 14(3), 200; https://doi.org/10.3390/info14030200 - 22 Mar 2023

Cited by 34 | Viewed by 10971

Abstract

In classification, feature selection engineering helps in choosing the most relevant data attributes to learn from. It determines the set of features to be rejected, supposing their low contribution in discriminating the labels. The effectiveness of a classifier passes mainly through the set [...] Read more.

In classification, feature selection engineering helps in choosing the most relevant data attributes to learn from. It determines the set of features to be rejected, supposing their low contribution in discriminating the labels. The effectiveness of a classifier passes mainly through the set of selected features. In this paper, we identify the best features to learn from in the context of credit risk assessment in the financial industry. Financial institutions concur with the risk of approving the loan request of a customer who may default later, or rejecting the request of a customer who can abide by their debt without default. We propose a feature selection engineering approach to identify the main features to refer to in assessing the risk of a loan request. We use different feature selection methods including univariate feature selection (UFS), recursive feature elimination (RFE), feature importance using decision trees (FIDT), and the information value (IV). We implement two variants of the XGBoost classifier on the open data set provided by the Lending Club platform to evaluate and compare the performance of different feature selection methods. The research shows that the most relevant features are found by the four feature selection techniques. Full article

(This article belongs to the Special Issue Machine Learning: From Tech Trends to Business Impact)

► Show Figures

Figure 1

19 pages, 636 KB

Open AccessEditor’s ChoiceReview

A Survey on Feature Selection Techniques Based on Filtering Methods for Cyber Attack Detection

by Yang Lyu, Yaokai Feng and Kouichi Sakurai

Information 2023, 14(3), 191; https://doi.org/10.3390/info14030191 - 17 Mar 2023

Cited by 78 | Viewed by 9474

Abstract

Cyber attack detection technology plays a vital role today, since cyber attacks have been causing great harm and loss to organizations and individuals. Feature selection is a necessary step for many cyber-attack detection systems, because it can reduce training costs, improve detection performance, [...] Read more.

Cyber attack detection technology plays a vital role today, since cyber attacks have been causing great harm and loss to organizations and individuals. Feature selection is a necessary step for many cyber-attack detection systems, because it can reduce training costs, improve detection performance, and make the detection system lightweight. Many techniques related to feature selection for cyber attack detection have been proposed, and each technique has advantages and disadvantages. Determining which technology should be selected is a challenging problem for many researchers and system developers, and although there have been several survey papers on feature selection techniques in the field of cyber security, most of them try to be all-encompassing and are too general, making it difficult for readers to grasp the concrete and comprehensive image of the methods. In this paper, we survey the filter-based feature selection technique in detail and comprehensively for the first time. The filter-based technique is one popular kind of feature selection technique and is widely used in both research and application. In addition to general descriptions of this kind of method, we also explain in detail search algorithms and relevance measures, which are two necessary technical elements commonly used in the filter-based technique. Full article

(This article belongs to the Special Issue Advances in Computing, Communication & Security)

► Show Figures

Figure 1

25 pages, 1760 KB

Open AccessEditor’s ChoiceReview

A Systematic Review of Transformer-Based Pre-Trained Language Models through Self-Supervised Learning

by Evans Kotei and Ramkumar Thirunavukarasu

Information 2023, 14(3), 187; https://doi.org/10.3390/info14030187 - 16 Mar 2023

Cited by 84 | Viewed by 18783

Abstract

Transfer learning is a technique utilized in deep learning applications to transmit learned inference to a different target domain. The approach is mainly to solve the problem of a few training datasets resulting in model overfitting, which affects model performance. The study was [...] Read more.

Transfer learning is a technique utilized in deep learning applications to transmit learned inference to a different target domain. The approach is mainly to solve the problem of a few training datasets resulting in model overfitting, which affects model performance. The study was carried out on publications retrieved from various digital libraries such as SCOPUS, ScienceDirect, IEEE Xplore, ACM Digital Library, and Google Scholar, which formed the Primary studies. Secondary studies were retrieved from Primary articles using the backward and forward snowballing approach. Based on set inclusion and exclusion parameters, relevant publications were selected for review. The study focused on transfer learning pretrained NLP models based on the deep transformer network. BERT and GPT were the two elite pretrained models trained to classify global and local representations based on larger unlabeled text datasets through self-supervised learning. Pretrained transformer models offer numerous advantages to natural language processing models, such as knowledge transfer to downstream tasks that deal with drawbacks associated with training a model from scratch. This review gives a comprehensive view of transformer architecture, self-supervised learning and pretraining concepts in language models, and their adaptation to downstream tasks. Finally, we present future directions to further improvement in pretrained transformer-based language models. Full article

(This article belongs to the Collection Natural Language Processing and Applications: Challenges and Perspectives)

► Show Figures

Figure 1

18 pages, 3214 KB

Open AccessEditor’s ChoiceArticle

QoS-Aware Resource Management in 5G and 6G Cloud-Based Architectures with Priorities

by Spiros (Spyridon) Louvros, Michael Paraskevas and Theofilos Chrysikos

Information 2023, 14(3), 175; https://doi.org/10.3390/info14030175 - 9 Mar 2023

Cited by 25 | Viewed by 6064

Abstract

Fifth-generation and more importantly the forthcoming sixth-generation networks have been given special care for latency and are designed to support low latency applications including a high flexibility New Radio (NR) interface that can be configured to utilize different subcarrier spacings (SCS), slot durations, [...] Read more.

Fifth-generation and more importantly the forthcoming sixth-generation networks have been given special care for latency and are designed to support low latency applications including a high flexibility New Radio (NR) interface that can be configured to utilize different subcarrier spacings (SCS), slot durations, special scheduling optional features (mini-slot scheduling), cloud- and virtual-based transport network infrastructures including slicing, and finally intelligent radio and transport packet retransmissions mechanisms. QoS analysis with emphasis on the determination of the transmitted packets’ average waiting time is therefore crucial for both network performance and user applications. Most preferred implementations to optimize transmission network rely on the cloud architectures with star network topology. In this paper, as part of our original and innovative contribution, a two-stage queue model is proposed and analytically investigated. Firstly, a two-dimension queue is proposed in order to estimate the expected delay on priority scheduling decisions over the IP/Ethernet MAC layer of IP packet transmissions between gNB and the core network. Furthermore, a one-dimension queue is proposed to estimate the average packet waiting time on the RLC radio buffer before being scheduled mainly due to excessive traffic load and designed transmission bandwidth availability. Full article

(This article belongs to the Special Issue 5G Networks and Wireless Communication Systems)

► Show Figures

Figure 1

20 pages, 12585 KB

Open AccessEditor’s ChoiceArticle

Non-Invasive Classification of Blood Glucose Level Based on Photoplethysmography Using Time–Frequency Analysis

by Ernia Susana, Kalamullah Ramli, Prima Dewi Purnamasari and Nursama Heru Apriantoro

Information 2023, 14(3), 145; https://doi.org/10.3390/info14030145 - 23 Feb 2023

Cited by 39 | Viewed by 11427

Abstract

Diabetes monitoring systems are crucial for avoiding potentially significant medical expenses. At this time, the only commercially viable monitoring methods that exist are invasive ones. Since patients are uncomfortable while blood samples are being taken, these techniques have significant disadvantages. The drawbacks of [...] Read more.

Diabetes monitoring systems are crucial for avoiding potentially significant medical expenses. At this time, the only commercially viable monitoring methods that exist are invasive ones. Since patients are uncomfortable while blood samples are being taken, these techniques have significant disadvantages. The drawbacks of invasive treatments might be overcome by a painless, inexpensive, non-invasive approach to blood glucose level (BGL) monitoring. Photoplethysmography (PPG) signals obtained from sensor leads placed on specific organ tissues are collected using photodiodes and nearby infrared LEDs. Cardiovascular disease can be detected via photoplethysmography. These characteristics can be used to directly affect BGL monitoring in diabetic patients if PPG signals are used. The Guilin People’s Hospital’s open database was used to produce the data collection. The dataset was gathered from 219 adult respondents spanning an age range from 21 to 86 of which 48 percent were male. There were 2100 sampling points total for each PPG data segment. The methodology of feature extraction from data may assist in increasing the effectiveness of classifier training and testing. PPG data information is modified in the frequency domain by the instantaneous frequency (IF) and spectral entropy (SE) moments using the time–frequency (TF) analysis. Three different forms of raw data were used as inputs, and we investigated the original PPG signal, the PPG signal with instantaneous frequency, and the PPG signal with spectral entropy. According to the results of the model testing, the PPG signal with spectral entropy generated the best outcomes. Compared to decision trees, subspace k-nearest neighbor, and k-nearest neighbor, our suggested approach with the super vector machine obtains a greater level of accuracy. The super vector machine, with 91.3% accuracy and a training duration of 9 s, was the best classifier. Full article

► Show Figures

Figure 1

33 pages, 3882 KB

Open AccessEditor’s ChoiceSystematic Review

Intelligent Process Automation and Business Continuity: Areas for Future Research

by José Brás, Ruben Pereira and Sérgio Moro

Information 2023, 14(2), 122; https://doi.org/10.3390/info14020122 - 14 Feb 2023

Cited by 23 | Viewed by 18070

Abstract

Robotic process automation and intelligent process automation have gained a foothold in the automation of business processes, using blocks of software (bots). These agents interact with systems through interfaces, replacing human intervention with the aim of improving efficiency, reducing costs and mitigating risks [...] Read more.

Robotic process automation and intelligent process automation have gained a foothold in the automation of business processes, using blocks of software (bots). These agents interact with systems through interfaces, replacing human intervention with the aim of improving efficiency, reducing costs and mitigating risks by ensuring and enforcing compliance measures. However, there are aspects of the incorporation of this new reality within the business continuity lifecycle that are still unclear, and which need to be evaluated. This study provides a multivocal literature review of robotic process automation and intelligent process automation correlated with business continuity, to identify the level of awareness of these two emerging forms of automation within the business continuity management lifecycle. Based on the reviewed literature, the study develops a discussion of the main research areas for investigation, identifying what is attracting the attention of practitioners and researchers and which areas they highlight as promising for future research. Numerous sources from relevant backgrounds reveal an interest in these interrelated topics but there as yet is little or no information available on the direct connection between them. Full article

► Show Figures

Figure 1

19 pages, 750 KB

Open AccessEditor’s ChoiceReview

Smart Contracts in Blockchain Technology: A Critical Review

by Hamed Taherdoost

Information 2023, 14(2), 117; https://doi.org/10.3390/info14020117 - 13 Feb 2023

Cited by 325 | Viewed by 74591

Abstract

By utilizing smart contracts, which are essentially scripts that are anchored in a decentralized manner on blockchains or other similar infrastructures, it is possible to make the execution of predetermined procedures visible to the outside world. The programmability of previously unrealized assets, such [...] Read more.

By utilizing smart contracts, which are essentially scripts that are anchored in a decentralized manner on blockchains or other similar infrastructures, it is possible to make the execution of predetermined procedures visible to the outside world. The programmability of previously unrealized assets, such as money, and the automation of previously manual business logic are both made possible by smart contracts. This revelation inspired us to analyze smart contracts in blockchain technologies written in English between 2012 and 2022. The scope of research is limited to the journal. Reviews, conferences, book chapters, theses, monographs, and interview-based works, and also articles in the press, are eliminated. This review comprises 252 articles over the last ten years with “Blockchain”, “block-chain”, “smart contracts”, and “smart contracts” as keywords. This paper discusses smart contracts’ present status and significance in blockchain technology. The gaps and challenges in the relevant literature have also been discussed, particularly emphasizing the limitations. Based on these findings, several research problems and prospective research routes for future study that will likely be valuable to academics and professionals are identified. Full article

(This article belongs to the Special Issue Emerging Industrial Applications: Orchestration of Machine Learning, the IoT, and Blockchain)

► Show Figures

Figure 1

13 pages, 4182 KB

Open AccessEditor’s ChoiceArticle

Research on Traffic Congestion Forecast Based on Deep Learning

by Yangyang Qi and Zesheng Cheng

Information 2023, 14(2), 108; https://doi.org/10.3390/info14020108 - 9 Feb 2023

Cited by 20 | Viewed by 8102

Abstract

In recent years, the rapid economic development of China, the increase of the urban population, the continuous growth of private car ownership, the uneven distribution of traffic flow, and the local congestion of the road network have caused traffic congestion. Traffic congestion has [...] Read more.

In recent years, the rapid economic development of China, the increase of the urban population, the continuous growth of private car ownership, the uneven distribution of traffic flow, and the local congestion of the road network have caused traffic congestion. Traffic congestion has become an inevitable problem in the process of urban development, bringing hazards and hidden dangers to citizens’ travel and urban development. The management of traffic congestion first lies in the accurate completion of the identification of road traffic status and the need to predict road congestion in the city, so as to improve the use rate of urban infrastructure road facilities and better alleviate road congestion. In this study, a deep spatial and temporal network model (DSGCN) for predicting traffic congestion status is proposed. First, our study divides the traffic network into grids, where each grid represents a different independent region. In this paper, the centroids of the grid regions are abstracted as nodes, and the dynamic correlations between the nodes are expressed in the form of adjacency matrix. Then, Graph Convolutional Neural Network is used to capture the spatial correlation between regions and a two-layer long and short-term feature model (DSTM) is used to capture the temporal correlation between regions. Finally, the DSGCN outperforms other baseline models and has higher accuracy for traffic congestion prediction as demonstrated by experiments on real PeMS datasets. Full article

(This article belongs to the Special Issue Emerging Research in Urban Computing and Intelligent Transport Systems)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Editor’s Choice Articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI