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25th International Arab Conference on Information Technology, ACIT 2024

作者： Samara, Ghassan Elhilo, Ala'Mazen Aljaidi, Mohammad Alamleh, Amneh Al-Mousa, Mohammad Rasmi Batyha, Radwan Alazaidah, Raed Bani-Bakr, Alaa Elrashidi, Ali Zarqa University Faculty of It Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Department of Cybersecurity Zarqa Jordan Applied Science Private University Faculty of Information Technology Amman Jordan Zarqa University Faculty of It Department of Cyber Security Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

This study evaluates federated learning models' performance compared to centralized models using the MNIST and CIFAR-10 datasets. Four models were tested: two Federated Learning models and two centralized models. The evaluation focused on accuracy, training rounds, and CO2 emissions, to optimize convergence and reducing energy consumption while maintaining strong model performance. The results showed that Federated learning models outperformed centralized models, with the Federated Learning MNIST model achieving 98.79% accuracy compared to 87.93% for the centralized model. Also, the accuracy of the Federated Learning CIFAR-10 model achieved 91.20% accuracy, exceeding the centralized model's 90.14%. The most accuracy improvements happened in the early training rounds. An adjusted hill-climbing technique was used to the centralized models, decreasing the number of iterations and CO2 emissions without significantly reducing accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure 3

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. Ramya, R. Kanimozhi, R. Lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 IEEE.

关键词： data integrity

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching 30

FERI: Feature Enhancement and Relational Interaction for Ima...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

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Can Lexicon-Based Sentiment Analysis Boost Performances of Transformer-Based Models? 7

Can Lexicon-Based Sentiment Analysis Boost Performances of T...

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7th International Conference on New Media Studies, CONMEDIA 2023

作者： Manik, Lindung Parningotan Susianto, Harry Dinakaramani, Arawinda Pramanik, Niken Suhardijanto, Totok National Research and Innovation Agency Research Center for Data and Information Sciences Bandung Indonesia University of Nusa Mandiri Department of Computer Science Jakarta Indonesia University of Indonesia Faculty of Psychology Depok Indonesia University of Indonesia Faculty of Computer Science Depok Indonesia University of Indonesia Faculty of Humanities Department of Linguistics Depok Indonesia

ISBN: (纸本)9798350307504

An essential endeavor in natural language processing, sentiment analysis entails determining the sentiment expressed in a text. Transformer-based models like BERT have attained state-of-the-art performance in sentiment analysis tasks. However, these algorithms may have difficulty distinguishing sentiment-laden words. In response, we proposed combining lexicon-based sentiment analysis and transformer-based models. This study investigates the effect of lexicon-based sentiment analysis, particularly SentiStrength, on BERT's efficacy in sentiment analysis tasks. Experimental evaluations reveal that incorporating sentiment lexicons enhances the accuracy and F1-score of classical sentiment analysis compared to the baseline BERT model. Our findings demonstrate the value of incorporating external knowledge sources into transformer-based sentiment analysis models. © 2023 IEEE.

关键词： Sentiment analysis

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LGDB-Net: Dual-Branch Path for Building Extraction from Remote Sensing Image 30

LGDB-Net: Dual-Branch Path for Building Extraction from Remo...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Ronghuan Zhao, Jing Li, Ming Zou, Qingzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong University of Traditional Chinese Medicine School of Intelligence and Information Engineering Jinan China

ISBN: (纸本)9798331515966

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural Networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch Network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity. © 2024 IEEE.

关键词： Convolutional neural networks

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Shuo Gu, Qiliang Zhang, Jianqiang Song, Gongpeng Lu, Qin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9781665410205

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets. © 2024 IEEE.

关键词： Semantics

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Big data Analytics in Healthcare——A Systematic Literature Review and Roadmap for Practical Implementation

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 1-22页

作者： Sohail Imran Tariq Mahmood Ahsan Morshed Timos Sellis the Faculty of Computer Science Karachi Institute of Economics and TechnologyKarachi 75190Pakistan the Faculty of Computer Science Institute of Business AdministrationKarachi 75270Pakistan the School of Engineering and Technology CQ UniversityMelbourne 3000Australia the Data Science Research Institute Swinburne University of TechnologyHawthorn 3122Australia

The advent of healthcare information management systems(HIMSs)continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global *** of this big data allows for boundless potential outcomes for discovering *** data analytics(BDA)in healthcare can,for instance,help determine causes of diseases,generate effective diagnoses,enhance Qo S guarantees by increasing efficiency of the healthcare delivery and effectiveness and viability of treatments,generate accurate predictions of readmissions,enhance clinical care,and pinpoint opportunities for cost ***,BDA implementations in any domain are generally complicated and resource-intensive with a high failure rate and no roadmap or success strategies to guide the *** this paper,we present a comprehensive roadmap to derive insights from BDA in the healthcare(patient care)domain,based on the results of a systematic literature *** initially determine big data characteristics for healthcare and then review BDA applications to healthcare in academic research focusing particularly on No SQL *** also identify the limitations and challenges of these applications and justify the potential of No SQL databases to address these challenges and further enhance BDA healthcare *** then propose and describe a state-of-the-art BDA architecture called Med-BDA for healthcare domain which solves all current BDA challenges and is based on the latest zeta big data *** also present success strategies to ensure the working of Med-BDA along with outlining the major benefits of BDA applications to ***,we compare our work with other related literature reviews across twelve hallmark features to justify the novelty and importance of our *** aforementioned contributions of our work are collectively unique and clearly present a roadmap for clinical administrators,practitioners and professionals to successf

关键词： Big data analytics(BDA) big data architecture healthcare NoSQL data stores patient care roadmap systematic literature review

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Project oriented teaching approach of decentralised applications for undergraduate students 23

Project oriented teaching approach of decentralised applicat...

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Proceedings of the 15th International Conference on Education Technology and computers

作者： Sandi Gec Petar Kochovski Vlado Stankovski Dejan Lavbic Laboratory for Data Technologies Faculty of Computer and Information Science University of Ljubljana Slovenia

ISBN: (纸本)9798400709111

The emergence of new technologies and software engineering methodologies in the decentralised Web, referred to as Web 3.0, often requires using new approaches to supplement conventional development concepts such as object-oriented programming and design patterns. Therefore, it is essential to understand the blockchain fundamentals and modular development of distributed systems built from multiple components. This paper proposes a project-oriented teaching approach of smart contracts-based distributed applications (dApps) integrated into existing (Web 2.0) Web applications. First, we present the main overview of the Web programming course for a bachelor study program, prior knowledge requirements, and the characteristics of the lectures. Second, we define the dApp functional requirements for student projects that intend to consolidate the dApp theoretical concepts with a practical approach to complement the Web 3.0 concepts. The study of our teaching approach is based on an experiment where we thoroughly analyse performance on the course level, projects and individual students. The results indicate a high correlation between students' performance in Web 2.0 and Web 3.0 development, and a higher degree of iterative process is also associated with better-performing students. Moreover, the student project outcomes also provide a basis for possible future course improvements.

关键词： decentralised application smart contract teaching

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Electronic structure prediction of multimillion atom systems through uncertainty quantification enabled transfer learning

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npj Computational Materials 2024年第1期10卷 1423-1437页

作者： Shashank Pathrudkar Ponkrshnan Thiagarajan Shivang Agarwal Amartya S.Banerjee Susanta Ghosh Department of Mechanical Engineering-Engineering Mechanics Michigan Technological UniversityHoughtonMIUSA Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA90095USA Department of Materials Science and Engineering University of CaliforniaLos AngelesCA90095USA Faculty member of the Center for Data Sciences Michigan Technological UniversityHoughtonMIUSA

The ground state electron density—obtainable using Kohn-Sham Density Functional Theory(KSDFT)simulations—contains a wealth of material information,making its prediction via machine learning(ML)models ***,the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation,making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system ***,we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data,while comprehensively sampling systemconfigurations using *** ML models are less reliant on heuristics,and being based on Bayesian neural networks,enable uncertainty *** show that our models incur significantly lower data generation costs while allowing confident—and when verifiable,accurate—predictions for a wide variety of bulk systems well beyond training,including systems with defects,different alloy compositions,and at multi-million-atom ***,such predictions can be carried out using only modest computational resources.

关键词： alloy prediction transfer

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Ocular Cataract Identification Using Deep Convolutional Neural Networks 6

Ocular Cataract Identification Using Deep Convolutional Neur...

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6th International Conference on Artificial Intelligence, Big data, Computing and data Communication Systems, icABCD 2023

作者： Manuel, Feliciana M. E. Saide, Saide Manuel Ali, Felermino D. M. A. Lotfi, Sanae Lurio University Faculty of Engineering Dept. of Computer Engineering Pemba Mozambique New York University Center for Data Science New York United States

ISBN: (纸本)9798350314809

Ocular cataract is among diseases that result in blindness if not treated in time. It affects people worldwide, primarily in underdeveloped countries. This health problem affects the quality of patients' lives. However, early diagnosis avoids blindness and allows the patient to have appropriate treatment. Developing countries, especially those with low income, have a precarious health system, even in the ophthalmology sector, where equipment is lacking. This research aims to develop a deep learning-based model to detect ocular cataracts based on retinal images. We collect 1000 retinal images from Kaggle, which are then equally divided into two classes: with and without cataracts. We then use several neural architectures to correctly classify these images, including ResNet18, ResNet34, InceptionResNetV2, and InceptionV4. We demonstrate that ResNet18 outperforms the other architectures, reaching 95.5% accuracy score. Our results suggest that deep convolutional neural networks can achieve a significant performance in ocular cataracts classification using retinal images. © 2023 IEEE.

关键词： Convolution

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