Big Data and Artificial Intelligence, 2nd Volume

Topic Information

Dear Colleagues,

The evolution of research in Big Data and artificial intelligence in recent years challenges almost all domains of human activity. The potential of artificial intelligence to act as a catalyst for all given business models, and the capacity of Big Data research to provide sophisticated data and services ecosystems at a global scale, provide a challenging context for scientific contributions and applied research. This Topic section promotes scientific dialogue for the added value of novel methodological approaches and research in the specified areas. Our interest is on the entire end-to-end spectrum of Big Data and artificial intelligence research, from social sciences to computer science including, strategic frameworks, models, and best practices, to sophisticated research related to radical innovation. The topics include, but are not limited to, the following indicative list:

• Enabling Technologies for Big Data and AI research:
• Data warehouses;
• Business intelligence;
• Machine learning;
• Neural networks;
• Natural language processing;
• Image processing;
• Bot technology;
• AI agents;
• Analytics and dashboards;
• Distributed computing;
• Edge computing.
• Methodologies, frameworks, and models for artificial intelligence and Big Data research:
• Towards sustainable development goals;
• As responses to social problems and challenges;
• For innovations in business, research, academia industry, and technology;
• For theoretical foundations and contributions to the bodyf knowledgef AI and Big Data research.
• Best practices and use cases;
• Outcomesf R&D projects;
• Advanced data science analytics;
• Industry-government collaboration;
• Systemsf information systems;
• Interoperability issues;
• Security and privacy issues;
• Ethicsn Big Data and AI;
• Social impactf AI;
• Open data.

Prof. Dr. Miltiadis D. Lytras
Prof. Dr. Andreea Claudia Serban
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Big Data and Cognitive Computing BDCC	3.7	7.1	2017	18 Days	CHF 1800
Economies economies	2.1	4.0	2013	21.7 Days	CHF 1800
Information information	2.4	6.9	2010	14.9 Days	CHF 1600
Remote Sensing remotesensing	4.2	8.3	2009	24.7 Days	CHF 2700
Sustainability sustainability	3.3	6.8	2009	20 Days	CHF 2400

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Published Papers (7 papers)

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All BDCC Economies Information Remote Sensing Sustainability

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24 pages, 1343 KiB  

Open AccessArticle

The Role of IT Governance in the Integration of AI in Accounting and Auditing Operations

by Faozi A. Almaqtari

Economies 2024, 12(8), 199; https://doi.org/10.3390/economies12080199 - 1 Aug 2024

Viewed by 2368

Abstract

IT governance is a framework that manages the efficient use of information technology within an organization, focusing on strategic alignment, risk management, resource management, performance measurement, compliance, and value delivery. This study investigates the role of IT governance in integrating artificial intelligence (AI) [...] Read more.

IT governance is a framework that manages the efficient use of information technology within an organization, focusing on strategic alignment, risk management, resource management, performance measurement, compliance, and value delivery. This study investigates the role of IT governance in integrating artificial intelligence (AI) in accounting and auditing operations. Data were collected from 228 participants from Saudi Arabia using a combination of convenience sampling and snowball sampling methods. The collected data were then analyzed using structural equation modeling. Unexpectedly, the results demonstrate that AI, big data analytics, cloud computing, and deep learning technologies significantly enhance accounting and auditing functions’ efficiency and decision-making capabilities, leading to improved financial reporting and audit processes. The results highlight that IT governance plays a crucial role in managing the complexities of AI integration, aligning business strategies with AI-enabled technologies, and facilitating these advancements. This research fills a gap in previous research and adds significantly to the academic literature by improving the understanding of integrating AI into accounting and auditing processes. It builds on existing theoretical frameworks by investigating the role of IT governance in promoting AI adoption. The findings provide valuable insights for accounting and auditing experts, IT specialists, and organizational leaders. The study provides practical insights on deploying AI-driven technology in organizations to enhance auditing procedures and financial reporting. In a societal context, it highlights the broader implications of AI on transparency, accountability, and trust in financial reporting. Finally, the study offers practitioners, policymakers, and scholars valuable insights on leveraging AI advancements to optimize accounting and auditing operations. It highlights IT governance as an essential tool for effectively integrating AI technologies in accounting and auditing operations. However, successful implementation encounters significant organizational challenges like organizational support, training, data sovereignty, and regulatory compliance. Full article

(This article belongs to the Topic Big Data and Artificial Intelligence, 2nd Volume)

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12 pages, 386 KiB  

Open AccessArticle

A Performance Analysis of Stochastic Processes and Machine Learning Algorithms in Stock Market Prediction

by Mohammed Bouasabah

Economies 2024, 12(8), 194; https://doi.org/10.3390/economies12080194 - 24 Jul 2024

Viewed by 1696

Abstract

In this study, we compare the performance of stochastic processes, namely, the Vasicek, Cox–Ingersoll–Ross (CIR), and geometric Brownian motion (GBM) models, with that of machine learning algorithms, such as Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN), for predicting the trends [...] Read more.

In this study, we compare the performance of stochastic processes, namely, the Vasicek, Cox–Ingersoll–Ross (CIR), and geometric Brownian motion (GBM) models, with that of machine learning algorithms, such as Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN), for predicting the trends of stock indices XLF (financial sector), XLK (technology sector), and XLV (healthcare sector). The results showed that stochastic processes achieved remarkable prediction performance, especially the CIR model. Additionally, this study demonstrated that the metrics of machine learning algorithms are relatively lower. However, it is important to note that stochastic processes use the actual current index value to predict tomorrow’s value, which may overestimate their performance. In contrast, machine learning algorithms offer a more flexible approach and are not as dependent on the current index value. Therefore, optimizing the hyperparameters of machine learning algorithms is crucial for further improving their performance. Full article

(This article belongs to the Topic Big Data and Artificial Intelligence, 2nd Volume)

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20 pages, 6873 KiB  

Open AccessArticle

PD-LL-Transformer: An Hourly PM_2.5 Forecasting Method over the Yangtze River Delta Urban Agglomeration, China

by Rongkun Zou, Heyun Huang, Xiaoman Lu, Fanmei Zeng, Chu Ren, Weiqing Wang, Liguo Zhou and Xiaoyan Dai

Remote Sens. 2024, 16(11), 1915; https://doi.org/10.3390/rs16111915 - 27 May 2024

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Abstract

As the urgency of PM_2.5 prediction becomes increasingly ingrained in public awareness, deep-learning methods have been widely used in forecasting concentration trends of PM_2.5 and other atmospheric pollutants. Traditional time-series forecasting models, like long short-term memory (LSTM) and temporal convolutional network [...] Read more.

As the urgency of PM_2.5 prediction becomes increasingly ingrained in public awareness, deep-learning methods have been widely used in forecasting concentration trends of PM_2.5 and other atmospheric pollutants. Traditional time-series forecasting models, like long short-term memory (LSTM) and temporal convolutional network (TCN), were found to be efficient in atmospheric pollutant estimation, but either the model accuracy was not high enough or the models encountered certain challenges due to their own structure or some specific application scenarios. This study proposed a high-accuracy, hourly PM_2.5 forecasting model, poly-dimensional local-LSTM Transformer, namely PD-LL-Transformer, by deep-learning methods, based on air pollutant data and meteorological data, and aerosol optical depth (AOD) data retrieved from the Himawari-8 satellite. This research was based on the Yangtze River Delta Urban Agglomeration (YRDUA), China for 2020–2022. The PD-LL-Transformer had three parts: a poly-dimensional embedding layer, which integrated the advantages of allocating and embedding multi-variate features in a more refined manner and combined the superiority of different temporal processing methods; a local-LSTM block, which combined the advantages of LSTM and TCN; and a Transformer encoder block. Over the test set (the whole year of 2022), the model’s R² was 0.8929, mean absolute error (MAE) was 4.4523 µg/m³, and root mean squared error (RMSE) was 7.2683 µg/m³, showing great accuracy for PM_2.5 prediction. The model surpassed other existing models upon the same tasks and similar datasets, with the help of which a PM_2.5 forecasting tool with better performance and applicability could be established. Full article

(This article belongs to the Topic Big Data and Artificial Intelligence, 2nd Volume)

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14 pages, 4246 KiB  

Open AccessArticle

Image-Based Leaf Disease Recognition Using Transfer Deep Learning with a Novel Versatile Optimization Module

by Petra Radočaj, Dorijan Radočaj and Goran Martinović

Big Data Cogn. Comput. 2024, 8(6), 52; https://doi.org/10.3390/bdcc8060052 - 23 May 2024

Cited by 1 | Viewed by 1613

Abstract

Due to the projected increase in food production by 70% in 2050, crops should be additionally protected from diseases and pests to ensure a sufficient food supply. Transfer deep learning approaches provide a more efficient solution than traditional methods, which are labor-intensive and [...] Read more.

Due to the projected increase in food production by 70% in 2050, crops should be additionally protected from diseases and pests to ensure a sufficient food supply. Transfer deep learning approaches provide a more efficient solution than traditional methods, which are labor-intensive and struggle to effectively monitor large areas, leading to delayed disease detection. This study proposed a versatile module based on the Inception module, Mish activation function, and Batch normalization (IncMB) as a part of deep neural networks. A convolutional neural network (CNN) with transfer learning was used as the base for evaluated approaches for tomato disease detection: (1) CNNs, (2) CNNs with a support vector machine (SVM), and (3) CNNs with the proposed IncMB module. In the experiment, the public dataset PlantVillage was used, containing images of six different tomato leaf diseases. The best results were achieved by the pre-trained InceptionV3 network, which contains an IncMB module with an accuracy of 97.78%. In three out of four cases, the highest accuracy was achieved by networks containing the proposed IncMB module in comparison to evaluated CNNs. The proposed IncMB module represented an improvement in the early detection of plant diseases, providing a basis for timely leaf disease detection. Full article

(This article belongs to the Topic Big Data and Artificial Intelligence, 2nd Volume)

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16 pages, 3440 KiB  

Open AccessArticle

Leveraging Remotely Sensed and Climatic Data for Improved Crop Yield Prediction in the Chi Basin, Thailand

by Akkarapon Chaiyana, Ratchawatch Hanchoowong, Neti Srihanu, Haris Prasanchum, Anongrit Kangrang, Rattana Hormwichian, Siwa Kaewplang, Werapong Koedsin and Alfredo Huete

Sustainability 2024, 16(6), 2260; https://doi.org/10.3390/su16062260 - 8 Mar 2024

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Abstract

Predictions of crop production in the Chi basin are of major importance for decision support tools in countries such as Thailand, which aims to increase domestic income and global food security by implementing the appropriate policies. This research aims to establish a predictive [...] Read more.

Predictions of crop production in the Chi basin are of major importance for decision support tools in countries such as Thailand, which aims to increase domestic income and global food security by implementing the appropriate policies. This research aims to establish a predictive model for predicting crop production for an internal crop growth season prior to harvest at the province scale for fourteen provinces in Thailand’s Chi basin between 2011 and 2019. We provide approaches for reducing redundant variables and multicollinearity in remotely sensed (RS) and meteorological data to avoid overfitting models using correlation analysis (CA) and the variance inflation factor (VIF). The temperature condition index (TCI), the normalized difference vegetation index (NDVI), land surface temperature (LST_nighttime), and mean temperature (Tmean) were the resulting variables in the prediction model with a p-value < 0.05 and a VIF < 5. The baseline data (2011–2017: June to November) were used to train four regression models, which revealed that eXtreme Gradient Boosting (XGBoost), random forest (RF), and XGBoost achieved R2 values of 0.95, 0.94, and 0.93, respectively. In addition, the testing dataset (2018–2019) displayed a minimum root-mean-square error (RMSE) of 0.18 ton/ha for the optimal solution by integrating variables and applying the XGBoost model. Accordingly, it is estimated that between 2020 and 2022, the total crop production in the Chi basin region will be 7.88, 7.64, and 7.72 million tons, respectively. The results demonstrated that the proposed model is proficient at greatly improving crop yield prediction accuracy when compared to a conventional regression method and that it may be deployed in different regions to assist farmers and policymakers in making more informed decisions about agricultural practices and resource allocation. Full article

(This article belongs to the Topic Big Data and Artificial Intelligence, 2nd Volume)

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22 pages, 2151 KiB  

Open AccessArticle

Enhancing Supervised Model Performance in Credit Risk Classification Using Sampling Strategies and Feature Ranking

by Niwan Wattanakitrungroj, Pimchanok Wijitkajee, Saichon Jaiyen, Sunisa Sathapornvajana and Sasiporn Tongman

Big Data Cogn. Comput. 2024, 8(3), 28; https://doi.org/10.3390/bdcc8030028 - 6 Mar 2024

Viewed by 2072

Abstract

For the financial health of lenders and institutions, one important risk assessment called credit risk is about correctly deciding whether or not a borrower will fail to repay a loan. It not only helps in the approval or denial of loan applications but [...] Read more.

For the financial health of lenders and institutions, one important risk assessment called credit risk is about correctly deciding whether or not a borrower will fail to repay a loan. It not only helps in the approval or denial of loan applications but also aids in managing the non-performing loan (NPL) trend. In this study, a dataset provided by the LendingClub company based in San Francisco, CA, USA, from 2007 to 2020 consisting of 2,925,492 records and 141 attributes was experimented with. The loan status was categorized as “Good” or “Risk”. To yield highly effective results of credit risk prediction, experiments on credit risk prediction were performed using three widely adopted supervised machine learning techniques: logistic regression, random forest, and gradient boosting. In addition, to solve the imbalanced data problem, three sampling algorithms, including under-sampling, over-sampling, and combined sampling, were employed. The results show that the gradient boosting technique achieves nearly perfect $Accuracy$, $Precision$, $Recall$, and $F1score$ values, which are better than 99.92%, but its $MCC$ values are greater than 99.77%. Three imbalanced data handling approaches can enhance the model performance of models trained by three algorithms. Moreover, the experiment of reducing the number of features based on mutual information calculation revealed slightly decreasing performance for 50 data features with $Accuracy$ values greater than 99.86%. For 25 data features, which is the smallest size, the random forest supervised model yielded 99.15% $Accuracy$. Both sampling strategies and feature selection help to improve the supervised model for accurately predicting credit risk, which may be beneficial in the lending business. Full article

(This article belongs to the Topic Big Data and Artificial Intelligence, 2nd Volume)

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23 pages, 4267 KiB  

Open AccessArticle

Nowcasting Unemployment Using Neural Networks and Multi-Dimensional Google Trends Data

by Andrius Grybauskas, Vaida Pilinkienė, Mantas Lukauskas, Alina Stundžienė and Jurgita Bruneckienė

Economies 2023, 11(5), 130; https://doi.org/10.3390/economies11050130 - 25 Apr 2023

Cited by 2 | Viewed by 3517

Abstract

This article forms an attempt to expand the ability of online search queries to predict initial jobless claims in the United States and further explore the intricacies of Google Trends. In contrast to researchers who used only a small number of search queries [...] Read more.

This article forms an attempt to expand the ability of online search queries to predict initial jobless claims in the United States and further explore the intricacies of Google Trends. In contrast to researchers who used only a small number of search queries or limited themselves to job agency explorations, we incorporated keywords from the following six dimensions of Google Trends searches: job search, benefits, and application; mental health; violence and abuse; leisure search; consumption and lifestyle; and disasters. We also propose the use of keyword optimization, dimension reduction techniques, and long-short memory neural networks to predict future initial claims changes. The findings suggest that including Google Trends keywords from other dimensions than job search leads to the improved forecasting of errors; however, the relationship between jobless claims and specific Google keywords is unstable in relation to time. Full article

(This article belongs to the Topic Big Data and Artificial Intelligence, 2nd Volume)

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