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3rd International Conference on Digital Transformation and Applications, ICDXA 2024

作者： Lee, Nicholas Mahmud, Rohana Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350373424

The role of automatic generated playlists and recommendations in music streaming services has played a significant role in driving the steady growth of the music industry since 2015. With the evolving landscape of music, Music Emotion Recognition (MER) has gained importance in this era. Contemporary solutions today focus solely on four emotions and popular music genres, neglecting emerging styles and artistes where their music evokes a danceable rhythm, but lyrics convey negativity. This study considers both lyrics and audio features for the classification of eight emotions with the aim to identify key features influencing music emotion, develop a classification model and evaluate its performance. A dataset was created by expanding a base dataset with additional lyrics and audio features from Genius and Spotify API respectively. This study employs established algorithms like K-Nearest Neighbours (KNN), Decision Trees (DT), and Random Forests (RF), with Random Forests emerging with a notable 73% accuracy. A web application named Emotion was developed, allowing data scientists and music researchers to explore the dataset and access details of this study. This study's impact extends to various applications, including mental health and media, enabling tailored music-based interventions for emotional well-being, and improving music selection in media and entertainment. © 2024 IEEE.

关键词： Music

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Exploring the frontiers of image super-resolution: a review of modern techniques and emerging applications

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Neural Computing and Applications 2025年 1-49页

作者： Hassan, Esraa El-Rashidy, Nora Elbedwehy, Samar Abd El-Hafeez, Tarek Saber, Abeer Shams, Mahmoud Y. Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Computer Science Unit Deraya University Minia61765 Egypt Department of Computer Science Faculty of Science Minia University Minia61519 Egypt Department of Information Technology Faculty of Computers and Artificial Intelligence Damietta University New Damietta Egypt

Super-resolution (SR) aims to reconstruct high-resolution images from low-resolution inputs, with deep learning advancements driving substantial improvements in SR performance. This paper presents a comprehensive review of single- and multi-image SR techniques, analyzing findings from 12,873 research papers published between 2015 and 2025 in the computer science field. Key insights are derived from fifteen summary tables covering various SR tasks, including natural, medical, video, burst, depth map, and underwater image SR. The analysis highlights several major findings: (1) the integration of specialized modules, such as attention mechanisms, has led to consistent yearly improvements in performance metrics like PSNR and SSIM;(2) domain-specific architectures often outperform general models, particularly in medical and underwater SR applications;(3) while benchmark datasets enable objective comparisons, real-world validation remains limited, reducing the generalizability of current approaches;(4) inconsistent metric reporting across studies hampers reproducibility and fair evaluation;and (5) practical deployment considerations, including computational efficiency and real-time processing, are rarely addressed. Despite significant progress, challenges such as the need for more diverse training datasets, robust validation, and better interpretability persist. This review synthesizes these critical findings, offering an updated perspective on SR advancements, emerging trends, and future research directions. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Medical computing

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Improving Facial Expression Recognition in HCI: Traditional Techniques and Comparative Analysis 4

Improving Facial Expression Recognition in HCI: Traditional ...

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4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Alshwihde, Rayhan S Eltarhouni, Wafa I University of Benghazi Faculty of Information Technology Department of computer science Benghazi Libya

ISBN: (纸本)9798350372632

Facial expressions play a vital role in human communication, enabling us to convey a wide range of emotions such as happiness, anger, and sadness. Human-computer Interaction (HCI) is a rapidly growing and highly appealing field. By recognizing facial expressions, HCI can enhance communication by making it more interactive and natural. The proposed methodology is based on traditional techniques for extracting and classifying the features from images. To evaluate its effectiveness, we utilize two datasets: CK+ and JAFFE. First, the facial part of the images is cropped and enhanced using a CLAHE filter. Then, the Histogram of Oriented Gradients (HOG) descriptor extracts features from the images, while Principal Component Analysis (PCA) reduces their dimensionality. Finally, three classifiers Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest are used to classify facial expressions. Our methodology achieved an accuracy of 95.94% for CK+ and 93.75% for JAFFE datasets. © 2024 IEEE.

关键词： Support vector machines

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Efficient and Secure Healthcare Data sharing via Blockchain for Edge-Based Internet of Medical Things

Efficient and Secure Healthcare Data sharing via Blockchain ...

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World Conference on information Systems for Business Management, ISBM 2023

作者： Basudan, Sultan Faculty of computer science and information technology Jazan University Jazan Saudi Arabia

ISBN: (纸本)9789819983452

The current study aims to implement a data sharing system that uses the blockchain to ensure data privacy and security-preserving during profile corresponding. The system also utilized a bloom filter to authenticate keyword ciphertext. It also uses a key policy attribute-based encryption (KP-ABE) procedure and smart agreements for profile matching. A two-phase Stackelberg game incentive mechanism is recommended to incentivize users to engage in the system and maximize users’ profit. The security analysis section illustrates that this system model can address various security goals. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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Incorporating syntax information into attention mechanism vector for improved aspect-based opinion mining

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Neural Computing and Applications 2024年第22期36卷 13957-13974页

作者： Aziz, Makera Moayad Yaakub, Mohd Ridzwan Bakar, Azuraliza Abu Computer Science Department College of Computer Science and Mathematics University of Mosul Nineveh Mosul41002 Iraq Center for Artificial Intelligence Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi43600 Malaysia

In Aspect-based Sentiment Analysis (ABSA), accurately determining the sentiment polarity of specific aspects within text requires a nuanced understanding of linguistic elements, including syntax. Traditional ABSA approaches, particularly those leveraging attention mechanisms, have shown effectiveness but often fall short in integrating crucial syntax information. Moreover, while some methods employ Graph Neural Networks (GNNs) to extract syntax information, they face significant limitations, such as information loss due to pooling operations. Addressing these challenges, our study proposes a novel ABSA framework that bypasses the constraints of GNNs by directly incorporating syntax-aware insights into the analysis process. Our approach, the Syntax-Informed Attention Mechanism Vector (SIAMV), integrates syntactic distances obtained from dependency trees and part-of-speech (POS) tags into the attention vectors, ensuring a deeper focus on linguistically relevant elements. This not only substantially enhances ABSA accuracy by enriching the attention mechanism but also maintains the integrity of sequential information, a task managed by adopting Long Short-Term Memory (LSTM) networks. The LSTM’s inputs, consisting of syntactic distance, POS tags, and the sentence itself, are processed to generate a syntax vector. This vector is then combined with the attention vector, offering a robust model that adeptly captures the nuances of language. Moreover, the sequential processing capability of LSTM ensures minimal information loss across the text by preserving the context and dependencies inherent in the sentence structure, unlike traditional pooling methods. Our experimental findings demonstrate that this innovative combination of SIAMV and LSTM significantly outperforms existing GNN-based ABSA models in accuracy, thereby setting a new standard for sentiment analysis research. By overcoming the traditional reliance on GNNs and their pooling-induced information loss, our method

关键词： Sentiment analysis

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Harmful Brain Activity Classification Using Ensemble Deep Learning 66

Harmful Brain Activity Classification Using Ensemble Deep Le...

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66th International Symposium ELMAR, ELMAR 2024

作者： Habijan, Marija Sojo, Robert Tolic, Ivana Hartmann Galic, Irena Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9798350375411

Electroencephalography (EEG) is a crucial tool for monitoring electrical brain activity and diagnosing neurological conditions. Manual analysis of EEG signals is time-consuming and prone to variability, necessitating automated methods for accurate and efficient interpretation. This paper presents an ensemble learning approach for the automated classification of harmful brain activity from EEG data. The model combines the strengths of ResNet1d GRU, EfficientNetB0, and EfficientNetB1 to predict brain activity from spectrograms and raw EEG signals. By leveraging the unique capabilities of each model and employing a weighted averaging scheme, the ensemble achieves a competitive Kullback-Liebler divergence score of 0.3 on the testing dataset. This approach demonstrates the potential of ensemble learning to improve the accuracy and robustness of automated EEG signal classification, offering a valuable tool for early detection and diagnosis of neurological conditions. © 2024 IEEE.

关键词： Contrastive Learning

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Expert Review on Big Data Analytics Quality Model in Enhancing Healthcare Organizational Performance 9

Expert Review on Big Data Analytics Quality Model in Enhanci...

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9th International Conference on information Management, ICIM 2023

作者： Nasir, Wan Mohd Haffiz Mohd Jusoh, Yusmadi Yah Binti Abdullah, Rusli Bin Abdullah, Salfarina Binti Faculty of Computer Science and Information Technology Selangor Malaysia

ISBN: (纸本)9798350336382

Big Data Analytics implementation in healthcare can provide an end-to-end solution with better information value insights. This paper will highlight expert opinion in verifying the quality factors in big data analytics (BDA) that affect the performance of healthcare organizations and present the findings in developing the BDA quality model. This expert verification study was carried out through hybrid approach sessions either face-to-face or by virtual online meetings with two academicians, two specialists, one data scientist, two subject matter experts, and also as BDA expert, one industry expert, one senior ICT Director involve in healthcare, and one content validity expert. All the experts have a vast experience and implementation of BDA in healthcare. The results of these exercises have confirmed and verified ten BDA quality factors which consist of reliability, accuracy, completeness, timeliness, format, accessibility, usability, maintainability, and portability were commensurate with the research model. The analysis of this expert verification study has been done through descriptive analysis such as frequency, mean, and standard deviation. The pilot study will be commenced once the verification process and analysis are completed and the BDA quality model will be validated later through actual study. © 2023 IEEE.

关键词： Data Analytics

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Visualizing Adidas and Nike Customer Segments Using K-Means Clustering 8

Visualizing Adidas and Nike Customer Segments Using K-Means ...

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8th International Conference on Electrical, Telecommunication and computer Engineering, ELTICOM 2024

作者： Br Ginting, Dewi Sartika Maulana Manru, Muhammad Azi Sachio, Wilbert Neilson Tarigan, Abiyan Agitia Marianto, Ivanny Putri Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9798331531249

This study explores the application of the K-Means Clustering algorithm to categorize Adidas and Nike customers based on their behavior and preferences. The analysis was conducted on a dataset of 3,268 products from Adidas and Nike, focusing on grouping customer behavior into actionable segments. The K-Means algorithm identified key features, with Listing Price and Sale Price as significant variables. The Elbow Method determined the optimal number of clusters as three (k=3), and the Silhouette Score confirmed the model's effectiveness. The results show that the K-Means algorithm effectively distinguishes customer segments within the Adidas vs. Nike shoe dataset, represented through scatter plots and silhouette diagrams that reveal well-separated clusters. These findings are valuable for refining marketing and pricing strategies, enabling Adidas and Nike to better target specific customer groups. Future research could enhance these findings by experimenting with additional features, exploring alternative clustering algorithms like hierarchical clustering or DBSCAN, and fine-tuning parameters such as the number of clusters or preprocessing techniques to further optimize results. © 2024 IEEE.

关键词： Costs

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Systematic Literature Review for Optimization System with Advection-Diffusion-Reaction Non-Linear Equation in Water Quality

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2541-2554页

作者： Suryana, Ino Chaerani, Diah Abdullah, Atje Setiawan Prabuwono, Anton Satria Muslihin, Khoirunnisa Rohadatul Aisy Irmansyah, Athaya Zahrani Department of Computer Sciences Faculty of Mathematics and Natural Science Universitas Padjadjaran Sumedang45363 Indonesia Department of Mathematics Faculty of Mathematics and Natural Science Universitas Padjadjaran Sumedang45363 Indonesia Department of Computer Sciences Faculty of Mathematics and Natural Science Universitas Padjadjaran Sumedang45363 Indonesia Department of Information Technology Faculty of Computing and Information Technology in Rabigh King Abdul Aziz University Saudi Arabia Faculty of Mathematics and Natural Science Universitas Padjadjaran Indonesia

Water quality is an essential component of environmental health and sustainability, including various parameters such as chemical, physical, and biological attributes. These parameters determine the suitability of water for consumption, recreation, and ecosystem health. Clean and safe water availability is a crucial foundation for human well-being, ecological balance, and sustainable development, indicating the need to monitor and control water quality to meet human demands. Based on the importance of water quality, this research aimed to provide a comprehensive overview of previous investigations related to the optimization system of the advection-diffusionreaction (ADR) non-linear equation in water quality. Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines were conducted to ensure a structured article selection, data extraction, and analysis approach. This was followed by presenting a bibliometric analysis of selected data to support the study of research trends. The results showed that using ADR non-linear equation systems in modeling water quality optimization was a potential area requiring further exploration. Moreover, this research provided valuable information that could be used as a reference for further investigation of water quality optimization. A future research design is also presented. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Advection-diffusion-reaction Optimization PRISMA Sustainable Development Goals (SDGs) Systematic literature review Water quality

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MoBShield:A Novel XML Approach for Securing Mobile Banking

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computers, Materials & Continua 2024年第5期79卷 2123-2149页

作者： Saeed Seraj Ali Safaa Sadiq OmprakashKaiwartya Mohammad Aljaidi Alexandros Konios Mohammed Ali Mohammed Abazeed Department of Computer Science Nottingham Trent UniversityClifton LaneNottinghamNG118NSUK Department of Computer Science Faculty of Information TechnologyZarqa UniversityZarqa13110Jordan Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Mobile banking security has witnessed significant R&D attention from both financial institutions and *** is due to the growing number of mobile baking applications and their reachability and usefulness to ***,these applications are also attractive prey for cybercriminals,who use a variety of malware to steal personal banking *** literature in mobile banking security requiresmany permissions that are not necessary for the application’s intended security *** this context,this paper presents a novel efficient permission identification approach for securing mobile banking(MoBShield)to detect and prevent malware.A permission-based dataset is generated for mobile banking malware detection that consists large number of malicious adware apps and benign apps to use as training *** dataset is generated from 1650 malicious banking apps of the Canadian Institute of Cybersecurity,University of New Brunswick and benign apps from Google Play.A machine learning algorithm is used to determine whether amobile banking application ismalicious based on its permission ***,an eXplainable machine learning(XML)approach is developed to improve trust by explaining the reasoning behind the algorithm’s *** evaluation tests that the approach can effectively and practically identify mobile banking malware with high precision and reduced false ***,the adapted artificial neural networks(ANN),convolutional neural networks(CNN)and XML approaches achieve a higher accuracy of 99.7%and the adapted deep neural networks(DNN)approach achieves 99.6%accuracy in comparison with the state-of-the-art *** promising results position the proposed approach as a potential tool for real-world scenarios,offering a robustmeans of identifying and thwarting malware inmobile-based banking ***,MoBShield has the potential to significantly enhance the security and trustworthiness of

关键词： Security malware detection deep learning convolutional neural networks deep neural networks

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