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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 31642

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Special Issue Editors

Dr. Muhammad Arif

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Guest Editor

Department of Computer Science, Superior University, Lahore, Pakistan
Interests: artificial intelligence; big data; cloud computing; cyberspace security; data mining; image processing; medical image processing; privacy; security; e-learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Guojun Wang

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Guest Editor

School of Computer Science and Educational Software, Guangzhou University, Guangzhou 510006, China
Interests: system security; network security; trusted computing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Muhammad Arfan Jaffar

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Guest Editor

Department of Computer Science, Superior University, Lahore, Pakistan
Interests: image processing; artificial intelligence; medical imaging; machine learning; computational intelligence

Dr. Oana Geman

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Guest Editor

Faculty of Electrical Engineering and Computer Sciences, Stefan cel Mare University of Suceava, 720229 Suceava, Romania
Interests: bio-engineering; bio-signal processing; healthcare informatics; deep learning; medical IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Complex networked systems, with interacting elements characterized by non-linearity, high dimensionality, and heterogeneity in the interconnected universe of today, require a solid understanding and control of their structure and dynamics, which has become a challenge for various fields of science. Complex systems and their conceptual knowledge of related approaches and methodologies are geared toward a viable model regarding how different data entities and streams have an impact on and interact with one another for the generation of features and trends on a multitude of spatiotemporal scales. Computational predictive analytics in highly complex and diverse fields have been and are currently developed for the characterization and quantification of concurrently and mutually interacting facets of different scenarios regarding real-world, universal, and natural phenomena. New computational methods with complex data and the related advancements in computations aim at a more profound and versatile understanding of the substantial masses of data and have enabled an improvement of predictions by transferring the results based on data analytics into the benefits at large, which underpins the utility and interdisciplinary approach of the domain. Accordingly, data-driven and multifarious methods are required for optimal prediction solutions and critical decision-making processes, whereby Artificial Intelligence (AI), fractional calculus, and multifractal methods have the capability of learning and modeling the system’s complex behavior, establishing the governing methods from the experimental data. Science pertaining to complex systems relies on data-driven approaches that obtain rigorous principles that generate accurate predictions and reliable laws, enabling the parametrization of models given the available and viable quantification and optimization.

This Special Issue, considering the fact that driven models bring a novel ingredient in the overall modeling of complex systems, focuses on recent advancements, applications, and contributions in Artificial Intelligence (AI) applications, machine learning methods, data analysis, big data analytics, computational predictive analytics, computational complexity, spatiotemporal scales, fractals and multifractional methods, fractional calculus, and dynamical processes as per fixed, variable, and distributed systems. We aim to contribute to the research areas of diverse fields on nonlinear integrated systems in complex natural phenomena, complex signal and image processing, recurrent neural networks, differential/integral equations, multiresolution analysis, entropy, and wavelets as a reflection of the versatile dimensions of the theoretical and applied areas concerned with mathematics, information science, computer science, engineering, and social sciences, in addition to the extensive line of other applied sciences.

This Special Issue aims to collect high-quality timely contributions at the interface between modern data acquisition systems and new computing frameworks that are used to build and deploy intelligent integrated systems. This requires expert knowledge and large-scale evaluations in various sub-domains that include, but are not limited to:

Advanced data analysis and/or data visualization in complex models;
Big data analysis within multifractal analysis or fractional calculus methods in complex systems;
Advanced topics in fractional calculus and complex systems;
AI approaches in complex systems for real-world, universal, and natural phenomena;
Data-driven stochastic differential equations;
Machine learning applications in complex data;
Optimization by deep neural networks;
Advanced AI and embedded vision for complex surveillance environments;
Advanced computational imaging;
Fractional dynamical models in complex systems;
Discrete, stochastic, and hybrid dynamics;
Multifractal systems in real-world, universal, and natural phenomena.

Dr. Muhammad Arif
Prof. Dr. Guojun Wang
Prof. Dr. Muhammad Arfan Jaffar
Dr. Oana Geman
Guest Editors

Manuscript Submission Information

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Published Papers (7 papers)

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Research

16 pages, 4118 KiB

Open AccessArticle

Mobile Customer Satisfaction Scoring Research Based on Quadratic Dimension Reduction and Machine Learning Integration

by Fei Zeng, Yuqing He, Chengqin Yang, Xinkai Hu and Yining Yuan

Appl. Sci. 2023, 13(17), 9681; https://doi.org/10.3390/app13179681 - 27 Aug 2023

Viewed by 1256

Abstract

Customer satisfaction is a measure of the degree of satisfaction of customer experience. Among the three major operators in China, China Mobile plays an important role in the communication field. A study of customer satisfaction with China Mobile will have a significant positive impact on the sustainable development of the entire communication industry. In order to respond to customer needs accurately, a mobile customer satisfaction research method based on quadratic dimensionality reduction and machine learning integration is proposed. Firstly, the core evaluation system of impact satisfaction is established, through the integration of systematic clustering and exploratory factor analysis for quadratic dimensionality reduction. Then, unreasonable data in the core influencing factors are eliminated. Finally, the gradient-boosted decision tree (GBDT) machine learning algorithm is applied to predict satisfaction, with a prediction accuracy of up to 99%, and the highly accurate satisfaction prediction can quickly respond to customer needs and feedback to improve customer experience and satisfaction. Full article

(This article belongs to the Special Issue Computation and Complex Data Processing Systems)

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16 pages, 4321 KiB

Open AccessArticle

Deep Learning for Sarcasm Identification in News Headlines

by Rasikh Ali, Tayyaba Farhat, Sanya Abdullah, Sheeraz Akram, Mousa Alhajlah, Awais Mahmood and Muhammad Amjad Iqbal

Appl. Sci. 2023, 13(9), 5586; https://doi.org/10.3390/app13095586 - 30 Apr 2023

Cited by 7 | Viewed by 5117

Abstract

Sarcasm is a mode of expression whereby individuals communicate their positive or negative sentiments through words contrary to their intent. This communication style is prevalent in news headlines and social media platforms, making it increasingly challenging for individuals to detect sarcasm accurately. To mitigate this challenge, developing an intelligent system that can detect sarcasm in headlines and news is imperative. This research paper proposes a deep learning architecture-based model for sarcasm identification in news headlines. The proposed model has three main objectives: (1) to comprehend the original meaning of the text or headlines, (2) to learn the nature of sarcasm, and (3) to detect sarcasm in the text or headlines. Previous studies on sarcasm detection have utilized datasets of tweets and employed hashtags to differentiate between ordinary and sarcastic tweets depending on the limited dataset. However, these datasets were prone to noise regarding language and tags. In contrast, using multiple datasets in this study provides a comprehensive understanding of sarcasm detection in online communication. By incorporating different types of sarcasm from the Sarcasm Corpus V2 from Baskin Engineering and sarcastic news headlines from The Onion and HuffPost, the study aims to develop a model that can generalize well across different contexts. The proposed model uses LSTM to capture temporal dependencies, while the proposed model utilizes a GlobalMaxPool1D layer for better feature extraction. The model was evaluated on training and test data with an accuracy score of 0.999 and 0.925, respectively. Full article

(This article belongs to the Special Issue Computation and Complex Data Processing Systems)

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14 pages, 765 KiB

Open AccessArticle

Hybrid Facial Emotion Recognition Using CNN-Based Features

by H. M. Shahzad, Sohail Masood Bhatti, Arfan Jaffar, Sheeraz Akram, Mousa Alhajlah and Awais Mahmood

Appl. Sci. 2023, 13(9), 5572; https://doi.org/10.3390/app13095572 - 30 Apr 2023

Cited by 15 | Viewed by 4430

Abstract

In computer vision, the convolutional neural network (CNN) is a very popular model used for emotion recognition. It has been successfully applied to detect various objects in digital images with remarkable accuracy. In this paper, we extracted learned features from a pre-trained CNN and evaluated different machine learning (ML) algorithms to perform classification. Our research looks at the impact of replacing the standard SoftMax classifier with other ML algorithms by applying them to the FC6, FC7, and FC8 layers of Deep Convolutional Neural Networks (DCNNs). Experiments were conducted on two well-known CNN architectures, AlexNet and VGG-16, using a dataset of masked facial expressions (MLF-W-FER dataset). The results of our experiments demonstrate that Support Vector Machine (SVM) and Ensemble classifiers outperform the SoftMax classifier on both AlexNet and VGG-16 architectures. These algorithms were able to achieve improved accuracy of between 7% and 9% on each layer, suggesting that replacing the classifier in each layer of a DCNN with SVM or ensemble classifiers can be an efficient method for enhancing image classification performance. Overall, our research demonstrates the potential for combining the strengths of CNNs and other machine learning (ML) algorithms to achieve better results in emotion recognition tasks. By extracting learned features from pre-trained CNNs and applying a variety of classifiers, we provide a framework for investigating alternative methods to improve the accuracy of image classification. Full article

(This article belongs to the Special Issue Computation and Complex Data Processing Systems)

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15 pages, 8253 KiB

Open AccessArticle

Adaptive Driver Face Feature Fatigue Detection Algorithm Research

by Han Zheng, Yiding Wang and Xiaoming Liu

Appl. Sci. 2023, 13(8), 5074; https://doi.org/10.3390/app13085074 - 18 Apr 2023

Cited by 8 | Viewed by 2675

Abstract

Fatigued driving is one of the leading causes of traffic accidents, and detecting fatigued driving effectively is critical to improving driving safety. Given the variety and individual variability of the driving surroundings, the drivers’ states of weariness, and the uncertainty of the key characteristic factors, in this paper, we propose a deep-learning-based study of the MAX-MIN driver fatigue detection algorithm. First, the ShuffleNet V2K16 neural network is used for driver face recognition, which eliminates the influence of poor environmental adaptability in fatigue detection; second, ShuffleNet V2K16 is combined with Dlib to obtain the coordinates of driver face feature points; and finally, the values of EAR and MAR are obtained by comparing the first 100 frames of images to EAR-MAX and MAR-MIN. Our proposed method achieves 98.8% precision, 90.2% recall, and 94.3% F-Score in the actual driving scenario application. Full article

(This article belongs to the Special Issue Computation and Complex Data Processing Systems)

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17 pages, 2243 KiB

Open AccessArticle

Prediction of Diabetes Complications Using Computational Intelligence Techniques

by Turki Alghamdi

Appl. Sci. 2023, 13(5), 3030; https://doi.org/10.3390/app13053030 - 27 Feb 2023

Cited by 16 | Viewed by 5733

Abstract

Diabetes is a complex disease that can lead to serious health complications if left unmanaged. Early detection and treatment of diabetes is crucial, and data analysis and predictive techniques can play a significant role. Data mining techniques, such as classification and prediction models, can be used to analyse various aspects of data related to diabetes, and extract useful information for early detection and prediction of the disease. XGBoost classifier is a machine learning algorithm that effectively predicts diabetes with high accuracy. This algorithm uses a gradient-boosting framework and can handle large and complex datasets with high-dimensional features. However, it is important to note that the choice of the best algorithm for predicting diabetes may depend on the specific characteristics of the data and the research question being addressed. In addition to predicting diabetes, data analysis and predictive techniques can also be used to identify risk factors for diabetes and its complications, monitor disease progression, and evaluate the effectiveness of treatments. These techniques can provide valuable insights into the underlying mechanisms of the disease and help healthcare providers make informed decisions about patient care. Data analysis and predictive techniques have the potential to significantly improve the early detection and management of diabetes, a fast-growing chronic disease that notable health hazards. The XGBoost classifier showed the most effectiveness, with an accuracy rate of 89%. Full article

(This article belongs to the Special Issue Computation and Complex Data Processing Systems)

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24 pages, 17788 KiB

Open AccessArticle

Classification of Bugs in Cloud Computing Applications Using Machine Learning Techniques

by Nadia Tabassum, Abdallah Namoun, Tahir Alyas, Ali Tufail, Muhammad Taqi and Ki-Hyung Kim

Appl. Sci. 2023, 13(5), 2880; https://doi.org/10.3390/app13052880 - 23 Feb 2023

Cited by 4 | Viewed by 3412

Abstract

In software development, the main problem is recognizing the security-oriented issues within the reported bugs due to their unacceptable failure rate to provide satisfactory reliability on customer and software datasets. The misclassification of bug reports has a direct impact on the effectiveness of the bug prediction model. The misclassification issue surely compromises the accuracy of the system. Manually reviewing bug reports is necessary to solve this problem, but doing so takes a lot of time and is tiresome for developers and testers. This paper proposes a novel hybrid approach based on natural language processing (NLP) and machine learning. To address these issues, the intended outcomes are multi-class supervised classification and bug prioritization using supervised classifiers. After being collected, the dataset was prepared for vectorization, subjected to exploratory data analysis, and preprocessed. The feature extraction and selection methods used for a bag of words are TF-IDF and word2vec. Machine learning models are created after the dataset has undergone a full transformation. This study proposes, develops, and assesses four classifiers: multinomial Naive Bayes, decision tree, logistic regression, and random forest. The hyper-parameters of the models are tuned, and it is concluded that random forest outperformed with a 91.73% test and 100% training accuracy. The SMOTE technique was used to balance the highly imbalanced dataset, which was initially created for the justified classification. The comparison between balanced and imbalanced dataset models clearly showed the importance of the balanced dataset in classification as it outperformed in all experiments. Full article

(This article belongs to the Special Issue Computation and Complex Data Processing Systems)

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24 pages, 4375 KiB

Open AccessArticle

Prediction of Complex Stock Market Data Using an Improved Hybrid EMD-LSTM Model

by Muhammad Ali, Dost Muhammad Khan, Huda M. Alshanbari and Abd Al-Aziz Hosni El-Bagoury

Appl. Sci. 2023, 13(3), 1429; https://doi.org/10.3390/app13031429 - 21 Jan 2023

Cited by 42 | Viewed by 7615

Abstract

Because of the complexity, nonlinearity, and volatility, stock market forecasting is either highly difficult or yields very unsatisfactory outcomes when utilizing traditional time series or machine learning techniques. To cope with this problem and improve the complex stock market’s prediction accuracy, we propose a new hybrid novel method that is based on a new version of EMD and a deep learning technique known as long-short memory (LSTM) network. The forecasting precision of the proposed hybrid ensemble method is evaluated using the KSE-100 index of the Pakistan Stock Exchange. Using a new version of EMD that uses the Akima spline interpolation technique instead of cubic spline interpolation, the noisy stock data are first divided into multiple components technically known as intrinsic mode functions (IMFs) varying from high to low frequency and a single monotone residue. The highly correlated sub-components are then used to build the LSTM network. By comparing the proposed hybrid model with a single LSTM and other ensemble models such as the support vector machine (SVM), Random Forest, and Decision Tree, its prediction performance is thoroughly evaluated. Three alternative statistical metrics, namely root means square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), are used to compare the aforementioned techniques. The empirical results show that the suggested hybrid Akima-EMD-LSTM model beats all other models taken into consideration for this study and is therefore recommended as an effective model for the prediction of non-stationary and nonlinear complex financial time series data. Full article

(This article belongs to the Special Issue Computation and Complex Data Processing Systems)

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