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

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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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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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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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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Dynamic UAV Deployment in Multi-UAV Wireless Networks: A Multi-Modal Feature-Based Deep Reinforcement Learning Approach

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IEEE Internet of Things Journal 2025年第12期12卷 18765-18778页

作者： Bai, Yu Xie, Boxuan Liu, Ying Chang, Zheng Jäntti, Riku Aalto University Department of Information and Communications Engineering Espoo02150 Finland University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China University of Jyväskylä Faculty of Information Technology Jyväskylä40014 Finland

The use of Unmanned Aerial Vehicles (UAVs) as aerial base stations has attracted increasing research interest in recent years. A key challenge in this field is determining how to deploy multiple UAVs in dynamic environments, particularly where mobile user demands fluctuate. To address this challenge, this paper presents an adaptive UAV deployment scheme in a dynamic multi-UAV wireless network, considering the mobility of UAVs and users, state variability, and adjustable UAV transmission power. By jointly optimizing the UAVs’ operational modes, transmission power levels, and movement strategies, our objective is to achieve a trade-off between minimizing power consumption and maximizing ground user coverage. A Deep Reinforcement Learning (DRL) approach is proposed to address these challenges. To capture the dynamic variations of users and UAVs in the environment, a multi-modal feature state space is designed, consisting of both a multi-channel image and vectors. The image component integrates real-time data on user distribution and the UAV coverage area, while the vectors represent UAV operational modes, position data, and system temporal information. These multi-modal features are processed using a combination of Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs) for advanced feature extraction. To enhance training stability and efficiency, the proposed approach updates parameters using the Proximal Policy Optimization (PPO) method. Simulation results demonstrate the effectiveness of the proposed scheme in balancing power consumption and coverage while effectively managing system dynamics. © 2014 IEEE.

关键词： Deep reinforcement learning

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Simulation of Vehicular Bots-Based DDoS Attacks in Connected Vehicles Networks

HighTech and Innovation Journal

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HighTech and Innovation Journal 2023年第4期4卷 854-869页

作者： Abdul Razak, Siti Fatimah Fang, Ku Yee Kamis, Noor Hisham Muhammad Amin, Anang Hudaya Yogarayan, Sumendra Faculty of Information Science and Technology Multimedia University Melaka75450 Malaysia Computer and Information Science Department Higher Colleges of Technology Dubai Men's College United Arab Emirates

Connected vehicles are more vulnerable to attacks than wired networks since they involve rapid mobility, continuous data flow across connected nodes, and dynamic network design in a distributed network environment. Distributed Denial of Service (DDOS) is one of the most common and dangerous security attacks on connected vehicle networks. Attackers can remotely control malicious nodes that are programmed to attack other nodes known. The compromised nodes are known as botnets, which will constantly flood the target nodes with User Datagram Protocol (UDP) packets, disrupting the target nodes data flow and operation. Hence, the goal of this research is to create and simulate a vehicular bot-based Distributed Denial of Service (DDoS) assault in connected vehicle networks. A simulation-based methodology is implemented to observe the impact of the number of bots, DDoS rate, and maximum bulk packet size on network performance. Using the NS-3 network simulator, 73 random mobile vehicle nodes with up to 100 vehicle bots were simulated, and the results are discussed. Regardless of the computational constraints, the findings from this study adds to understanding the risks and problems associated with data transmission by analyzing the impact of vehicular bot-based DDoS attacks on connected vehicle performance. © 2023, Ital Publication. All rights reserved.

关键词： Denial-of-service attack

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Machine Learning-Based Methods for Materials Inverse Design: A Review

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computers, Materials & Continua 2025年第2期82卷 1463-1492页

作者： Yingli Liu Yuting Cui Haihe Zhou Sheng Lei Haibin Yuan Tao Shen Jiancheng Yin Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650093China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and TechnologyKunming650500China Yunnan Tin Company Limited Tin Industry Branch Gejiu661000China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming650093China

Finding materials with specific properties is a hot topic in materials *** materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high *** the development of physics,statistics,computer science,and other fields,machine learning offers opportunities for systematically discovering new *** through machine learning-based inverse design,machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired *** paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse ***,three main inverse design methods—exploration-based,model-based,and optimization-based—are analyzed in the context of different application ***,the applications of inverse design methods in alloys,optical materials,and acoustic materials are elaborated on,and the prospects for materials inverse design are *** authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.

关键词： Materials inverse design machine learning target properties deep learning new materials discovery

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TL-IDS: A Transfer Learning Technique for Botnet Detection in IoT 6

TL-IDS: A Transfer Learning Technique for Botnet Detection i...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Abdelhamid, Salma Hegazy, Islam Aref, Mostafa Roushdy, Mohamed Computer Science Department Faculty of Computers and Information Technology Future University in Egypt Cairo Egypt Computer Science Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9798350373875

With the exponential rise of Internet of Things (IoT) devices in our modern-day lifestyle, the potential danger of botnet intrusions has become inevitable. These botnet attacks cause extensive damage to both individual users and large organizations. In the realm of IoT botnet intrusion detection, learning-based Intrusion Detection Systems (IDS) were proposed as a powerful strategy for identifying malicious behaviors and consolidating the security of IoT systems. Moreover, Transfer Learning (TL) techniques have emerged to overcome the problems of scarce data and the long training time of traditional learning approaches. This paper proposes an IDS for IoT networks based on the TL EfficientNet model. We train the system and validate it using the Bot-IoT network intrusion dataset. Our approach incorporates balancing the dataset, redundancy removal, dimensionality reduction, and transformation of the dataset into RGB images to be fed as input to the detection model. The findings of our experimental work prove that the proposed model is robust and effective, and it surpasses existing models with an accuracy of 99.53%, recall of 99.53%, and F1-score of 99.52%. © 2024 IEEE.

关键词： Intrusion detection

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