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6th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Thayalan, Eelanila Arumugavel, Ayingaran Ganeshan, Sharujan Dalaxshan, Makenthiran Fernando, Harinda Rajapakse, Kaushalya Department of Information Technology Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350394634

The Disease Prediction System revolutionizes healthcare with advanced machine learning techniques for early detection of skin diseases, notably focusing on skin cancer. Through image processing and Transfer Learning, it excels in identifying potential malignancies, enabling timely interventions. Its capabilities extend to benign tumors and growth disorders, utilizing the ResNet50 Model for precise identification. Additionally, it predicts skin reactions associated with various dermatological conditions, such as Urticaria Hives and Warts, leveraging the efficientnetB0 Model. Integration of the VGG16 Model enhances diagnostic accuracy for inflammatory skin conditions. This holistic approach prioritizes patient-centric care, leveraging diverse datasets and intricate pattern recognition in medical images. The system's proactive nature embodies personalized solutions for early detection, timely intervention, and improved patient outcomes. Its versatility and accuracy underscore itstransformative potential in healthcare delivery. By harnessing diverse datasets and recognizing intricate patterns within medical images, it heralds a new era of personalized healthcare solutions. In essence, the Disease Prediction System exemplifies the transformative potential of machine learning in healthcare, ensuring the highest standards of diagnostic accuracy and efficacy, while prioritizing patient well-being and quality oflife. © 2024 IEEE.

关键词： Deep learning

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Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

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Computers, Materials & Continua 2023年第7期76卷 133-150页

作者： Ali Haider Khan Hassaan Malik Wajeeha Khalil Sayyid Kamran Hussain Tayyaba Anees Muzammil Hussain Department of Software Engineering Faculty of Computer SciencesLahore Garrison UniversityLahorePakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Computer Science&Information Technology University of Engineering and Technology(UET)PeshawarPakistan Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan

To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic *** traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of *** addition,they usually only target one or a few different kinds of eye diseases at the same *** this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs ***_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification *** DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right *** then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel *** the attributes have been analyzed,they are integrated to provide a representation at the patient *** the whole process of ODs categorization,the patient-level representation will be *** efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.

关键词： Ocular disease multi-label spatial correlation CNN eye disease

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Blockchain-Based Decentralized Breast Cancer Patient Monitoring System for Remote Healthcare Security 5

Blockchain-Based Decentralized Breast Cancer Patient Monitor...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Dilshan, K.N. Rathnayake, H.A.C.K. Pathirana, L.P.T.L. Dhanapala, D.H.S.V. Fernando, Harinda Abeygunawardhana, Pradeep K.W Sri Lanka Institute of Information Technology Faculty of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

Breast cancer is a disease that is prevalent all over the world, and that is essential for the efficient treatment and care of breast cancer patients who are monitored remotely. However, traditional conventional centralized systems for patient monitoring face challenges in providing effective and efficient care for breast cancer patients, including limited access to medical records, ineffective communication, and patient participation. Fragmentation of records and difficulty in obtaining information can hinder patient care in terms of both privacy and data security. The proposed system uses machine learning, blockchain, smart contracts, hybrid access control, and hybrid intrusion prevention and detection to diagnose breast cancer decentralized. The technology analyses medical data using advanced machine learning algorithms to diagnose breast cancer accurately and provide healthcare practitioners with relevant insights. The blockchain stores patient information, diagnostic results, and metadata transparently and securely to protect data integrity and privacy. Smart contracts automate and enforce rules, securing data and improving diagnostic workflow. User roles and attributes are used to manage access to sensitive data and improve collaboration. Patients, doctors, and researchers have different permits and limits. Secures granular data and prevents unauthorized access. Scalability and network congestion are addressed via a hybrid network management system. The system optimizes resource allocation, load balancing, and data transfer by integrating centralized and decentralized methodologies. These features reduce delays and assure seamless operation. Experimental results reveal that the suggested system detects breast cancer more accurately than centralized methods. Blockchain, smart contracts, and access control ensure data integrity, privacy, and over-security. Scalability and reactivity are improved by the hybrid network management system, making breast cancer detec

关键词： Blockchain

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AI LipReader-Transcribing Speech from Lip Movements 6

AI LipReader-Transcribing Speech from Lip Movements

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6th IEEE International Conference on Emerging Smart Computing and informatics, ESCI 2024

作者： Ajitha, D. Dutta, Disha Saha, Falguni Giri, Parus Kant, Rohan School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Software Systems Vellore India School of Computer Science and Engineering Vellore Institute of Technology Department of Database Systems Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Information Security Vellore India

ISBN: (纸本)9798350306613

Lip Reading AI is a discipline that is rapidly changing and has numerous applications in security, accessibility and human-computer interaction. This paper proposes a model which combines Convolutional Neural Networks (CNNs) to capture spatial capabilities, Long Short-Term Memory (LSTM) networks to examine temporal dependencies, and an adaptive interest mechanism. Meticulous preprocessing of the MIRACL VC-l dataset addressing challenges including one of a kind lip moves and occlusions accompanied with the aid of transitioning this study effortlessly to LRS2 dataset to complement lexemic versatility is one of its key function. The effects verify its robustness throughout unique datasets with superior overall performance towards cutting-edge techniques. Ablation checks suggest the crucial significance of every element in phrases of improving lip analyzing accuracy. Our proposed model version additionally suggests flexibility in restricted and naturalistic language situations. © 2024 IEEE.

关键词： Attention Mechanism Convolutional Neural Networks (CNNs) Deep Learning Lip Reading Long Short-Term Memory (LSTM) LRS2 Dataset MIRACLVC-J Dataset

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Optimizing beyond boundaries: empowering the salp swarm algorithm for global optimization and defective software module classification

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Neural Computing and Applications 2024年第30期36卷 18727-18759页

作者： Kassaymeh, Sofian Al-Betar, Mohammed Azmi Rjoubd, Gaith Fraihat, Salam Abdullah, Salwani Almasri, Ammar Software Engineering Department Faculty of Information Technology Aqaba University of Technology Aqaba Jordan College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Information Technology Al-Huson University College Al-Balqa Applied University Irbid Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Artificial Intelligence Department Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Data Mining and Optimization Research Group Center for Artificial Intelligence Technology Universiti Kebangsaan Malaysia Bangi Malaysia Management Information Systems Department Al-Balqa Applied University Amman Jordan

This work presents a new version of the salp swarm optimizer (SSA), called "mSSA," that uses complex mathematical expressions to dynamically manipulate the crucial control parameter (c1) during optimization. These expressions are carefully designed to modulate the shift in search strategy from exploratory to exploitative, improving the flexibility and speed of convergence of the algorithm. To evaluate the performance of the developed mSSA variants, a thorough examination is carried out on twenty-three benchmark test functions alongside their application to the complex task of software module classification. The process of classifying defective software modules involves developing a multilayer perceptron (MLP) classifier that is suited to the particular complexity and heterogeneity of the task. Selecting the best optimizer is made easier by systematically evaluating the different mSSA versions as MLP classifier trainers. Based on metrics like classification accuracy, convergence speed, and avoidance of local minima, a comparative analysis in opposition to six previously published metaheuristic optimizers shows that mSSA3, when combined with the developed MLP classifier, outperforms both other mSSA variations and state-of-the-art metaheuristic optimizers in terms of overall performance. The excellent classification accuracy, swift convergence, and ability to avoid local minima of mSSA3 highlight its superiority and establish it as a cutting-edge method in the application of metaheuristic algorithms. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Global optimization

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A Parallel Hybrid Testing Technique for Tri-Programming Model-Based software systems

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Computers, Materials & Continua 2023年第2期74卷 4501-4530页

作者： Huda Basloom Mohamed Dahab Abdullah Saad AL-Ghamdi Fathy Eassa Ahmed Mohammed Alghamdi Seif Haridi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia KTH Royal Institute of Technology StockholmSweden

Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple *** different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve *** the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these *** studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming *** the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been ***,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot *** the first time,this paper presents a classification of operational errors that can result from the integration of the three *** paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and *** hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic *** hybrid technique can detect more errors because it combines two distinct *** proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environme

关键词： software testing hybrid testing technique OpenACC OpenMP MPI tri-programming model exascale computing

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Audio-Assisted Smart Home Security Monitoring with Few Samples

Audio-Assisted Smart Home Security Monitoring with Few Sampl...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Chi, Haotian Ma, Qi Zhang, Yuxuan Fu, Chenglong Wang, Yuwei Geng, Haijun Du, Xiaojiang Shanxi University School of Automation and Software Engineering Taiyuan China University of North Carolina at Charlotte Department of Software and Information Systems CharlotteNC United States Stevens Institute of Technology Department of Electical and Computer Engineering HobokenNJ United States

ISBN: (纸本)9798350351255

Smart home IoT devices have always been the target of various cyber attacks. By leveraging the smart home monitoring infrastructure, event-based anomaly detection is effective to detect anomalies that cause unfavorable working state of IoT devices. However, IoT events are proven to be vulnerable to event-targeted attacks which could be achieved by exploiting the vulnerabilities embedded in IoT devices, protocols and/or platforms. Thus, existing event-based anomaly detection is not robust in the case of unreliable input. To address this issue, our insight is that the embedded microphone components in many off-the-shelf home devices (e.g., smart doorbells, speakers, cameras, tablets, laptops, etc.) could be utilized to gather acoustic information to help increase the reliability and capability of smart home security monitoring systems. To verify this idea, we propose an audio-assisted framework IoTAudMon for detecting event-targeted attacks. Considering the heterogeneity and sparsity nature of smart homes IoT devices and events, we employ transfer learning to design a practical pipeline for extracting semantic information from audio, eliminating the requirement of human labeling and mitigating the cold start issue in existing solutions. Experiments on public datasets and real devices demonstrate the effectiveness of IoTAudMon. © 2024 IEEE.

关键词： Smart manufacturing

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The Teacher of the Future

International Journal of Emerging Technologies in Learning

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International Journal of Emerging Technologies in Learning 2023年第16期18卷 87-114页

作者： Drigas, Athanasios Chaidi, Irene Papoutsi, Chara Institute of Informatics and Telecommunications N.C.S.R. ‘Demokritos’ Athens Greece Department of Special Education University of Thessaly Volos Greece Information & Communication Systems Engineering Department University of the Aegean Samos Greece

Schools are cultural curators, along with libraries and museums. The development and use of technologies are a fact and an important tool in the evolution of the educational process, shaping new attitudes in the functioning of the educational community among parents, students, and teachers. At the same time, cultivating and improving the emotional intelligence of all those who make up the school environment will lead to well-being without stress, which is benefitial for the whole world. The teacher of the future is called and must give a resounding presence as he is the connecting link between the school, students, and parents. This article is a bibliographic review of the research results and articles to date about the school and especially the teacher of the future and deals with the existential technological identity of the educational future, its role in shaping the existential identity of the student in educational and social becoming, and the additional important skills a teacher should have for creative, happy, and well-balanced students. © 2023 by the authors of this article. Published under CC-BY.

关键词： Students

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Improving Fake News Detection with a Mixture of Experts: Leveraging Sentiment Analysis and Sarcasm Detection as Core Expert Models 6

Improving Fake News Detection with a Mixture of Experts: Lev...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Gunasekara, Tharindu Thihan, Disira Kodithuwakku, Sansika Dissanayake, Dinuka Fernando, Harinda Vidhanaarachchi, Samitha Sindhujan, Archchana Sri Lanka Institute of Information Technology Malabe Dept. of Software Engineering Sri Lanka Sri Lanka Institute of Information Technology Malabe Dept. of Computer Computer Systems Engineering Sri Lanka University of Surrey Dept. of Computer Science and Electronic Engineering United Kingdom

ISBN: (纸本)9798331517878

Fake news continues to proliferate, posing an increasing threat to public discourse. The paper proposes a framework of a Mixture of Experts, Sentiment Analysis, and Sarcasm Detection experts for improved fake news detection. This approach captures the emotional cues in the text through a Sentiment Analysis expert, which is based on bidirectional encoder representations from Transformers (BERT) models with sentiment vectors generated using SentiWordNet and Integrated Gradients. It combines a sarcasm detection expert based on BERT, recognizing sarcasm and its type to help classify fake news. By fusing these experts through a Mixture of Experts gateway, subtle linguistic cues often found in fake news are more effectively analyzed, leading to improved accuracy in detecting misinformation. Experimental results are presented as 96% for the Sarcasm expert with the BERT base model and 83% for the Sentiment Analysis expert with the distilled version of the BERT (DistilBERT) base model, proving the effectiveness of the proposed approach in beating traditional methods. © 2024 IEEE.

关键词： fake news detection gateway mixture of experts sarcasm detection sentiment analysis

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CASKA-CRT: Chinese Remainder Theorem Empowered Certificateless Aggregate Signcryption Scheme with Key Agreement in IoVs

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-16页

作者： Hou, Yingzhe Cao, Yue Xiong, Hu Sun, Zhili Mumtaz, Shahid Tian, Daxin School of Cyber Science and Engineering Wuhan University Wuhan China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Institute of Communication Systems University of Surrey Guildford U.K Department of Applied Informatics Silesian University of Technology Akademicka Gliwice Poland State Key Laboratory of Vehicle-Infrastructure Integrated ITS the State Key Laboratory of Intelligent Transportation System and the School of Transportation Science and Engineering Beihang University Beijing China

To enhance the privacy of messages in Internet of Vehicles (IoVs), it is critical to preserve the communication security between Road Side Unit (RSU) and numerous vehicles. The primitive of signcryption is introduced to guarantee the confidentiality, integrity and unforgeability of transmitted messages. Nevertheless, the existing signcryption schemes fail to achieve a balance between security and efficiency. In this paper, we construct a Chinese remainder theorem (CRT) empowered certificateless aggregate signcryption scheme with key agreement (CASKA-CRT). As the vehicle joins and leaves dynamically, the proposed scheme can ensure the dynamic security via one modulo division operation. Besides, the certificateless aggregate signcryption and key agreement mechanisms are embedded. The former idea can both address the certificate management and key escrow problems, while the latter technology can create authentication as a premise of secure communication. Based on this construction, the hash-to-group operation and bilinear pairing are avoided, to realize a faster verification with the increased number of messages. Moreover, the security of proposed scheme is proved under the random oracle model. Finally, the performance analysis demonstrates the advantage of CASKA-CRT in terms of security and reliability over related works. IEEE

关键词： Public key cryptography

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