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Advancements in Computing (ICAC), International Conference on

作者： Kalna Peiris Ruchira Nelaka Kalpani Manathunga Department of Computer Systems Engineering Department of Information Technology Department of Computer Science and Software Engineering

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Autism Spectrum Disorder (ASD) significantly impacts a child's ability to navigate social interactions, regulate emotions, and develop adaptive skills crucial for daily functioning. While various interventions exist to address cognitive and academic skills, the development of soft skills such as communication, emotional regulation, social interaction, and creativity remains an under explored area. This paper builds upon the foundational work of the Mind Champ platform, which originally targeted both learning and soft skills, by focusing exclusively on enhancing the soft skills development of autistic children. This enhanced version of the Mind Champ platform leverages advanced behavioral and emotional analysis techniques to offer a comprehensive technological solution. Through interactive activities centered on painting and music, the platform creates a structured, engaging, and supportive environment tailored to the unique needs of children with ASD. By prioritizing emotional engagement and creative expression, the platform empowers children to improve their social abilities, emotional regulation, and adaptability. The results from our continued research indicate that these technology-driven interventions contribute significantly to the holistic development of soft skills in autistic children, providing them with valuable tools to navigate social environments more effectively.

关键词： Autism Navigation Variable speed drives Motors Regulation Real-time systems Creativity Next generation networking Painting Resilience

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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SecureChainAI: Integrating Blockchain and Artificial Intelligence for Enhanced Security in IoT Environments 5

SecureChainAI: Integrating Blockchain and Artificial Intelli...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Thamaraiselvi, K. Pushpalatha, A. Chidambarathanu, K. Wankhede, Jaishri P. Alagumuthukrishnan, S. Sarveshwaran, Velliangiri New Horizon College of Engineering Department of Information Science and Engineering Bangalore India Karpagam College of Engineering Department of Computer Science and Design Coimbatore India R.M.K. Engineering College Department of Computer Science and Business Systems Kavaraipettai India Cmr Institute of Technology Department of Computer Science and Engineering Hyderabad India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India

ISBN: (纸本)9798331504403

Internet of Things (IoT) systems develop with paramount importance for security in view of the increased deployment of IoT devices over critical sectors like healthcare, industry automation, and smart cities. The risk exposure of such systems is extremely high to potent cyber threats, including those that may result in data integrity and privacy issues and operational unreliability. Security in IoT systems has thus emerged through heterogeneous integration with blockchain and artificial intelligence models. This blockchain technology provides a decentralized, immutable ledger for secure data transactions. AI models, especially those designed by anomaly detection and prediction algorithms, can identify and mitigate cyber threats. In this paper, the authors propose the SecureChainAI model that synergistically integrates artificial intelligence algorithms with blockchain to safeguard IoT environments. The two core building blocks that will make this proposed model real are CaSNet and the EchoChain algorithm. The CaSNet model integrates LSTM, RNN, and GRU techniques with novelty detection for proficient attack prediction and detection using their peculiar capabilities in sequential data handling and the identification of novel attack patterns. The EchoChain algorithm puts blockchain technology into the IoT infrastructure to provide security in communication and integrity of the values. This makes the architecture lightweight, shifting computational loads to the collector node, and hence suitable for IoT devices with low computational resources. Evaluations of the performance necessary to detect different classes of cyberattacks using SecureChainAI have improved on the detection accuracy, precision, recall, f1-score, and reduced error rates. © 2024 IEEE.

关键词： Network intrusion

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IoT-Aided Charity-Redistribution of Excess Food 2

IoT-Aided Charity-Redistribution of Excess Food

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2nd International Conference on Emerging Trends in information Technology and engineering, ic-ETITE 2024

作者： Yogananth, V.S. Balasubramani, M. Vellore Institute of Technology Department of Computer Applications Tamil Nadu Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350328202

The utilization of the Internet of Things (IOT) has shown significant potential in various aspects of daily life, yet its application in addressing social issues remains underdeveloped. India, with a substantial number of orphanages, faces challenges in efficiently distributing crucial information, hindering donors from obtaining accurate data on food reserves. This project proposes an IOT-based system to tackle this issue, using Node MCU IOT devices connected to sensors that relay data through a network, accessible and monitorable via web applications. The proposed system architecture aims to bridge the gap between those who waste food and those in poverty who desperately need it. We intend to facilitate food donations by implementing smart containers equipped with IOT technology. These containers can detect excess food waste and share this information via a GSM module with trusted charitable organizations. This application is designed to serve three main stakeholders: service providers, donors, and food recipients in need. By creating smart containers, we ensure that donated food reaches the right individuals. We prioritize collaborating with trusted charities to oversee the distribution process. Only authorized charity members can access and distribute the food stored in these containers, ensuring that it reaches those who need it most. © 2024 IEEE.

关键词： Containers

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Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing

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computers, Materials & Continua 2023年第6期75卷 5597-5613页

作者： V.Banupriya S.Anusuya Department of Computer Science and Business Systems M.Kumarasamy College of EngineeringKarur639113TamilnaduIndia Department of Information Technology Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSIMATSChennai602117Tamil NaduIndia

In the modern world,one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy(DR),which will result in retinal damage,and,thus,lead to *** retinopathy(DR)can be well treated with early *** fundus images of humans are used to screen for lesions in the ***,detecting DR in the early stages is challenging due to the minimal ***,the occurrence of diseases linked to vascular anomalies brought on by DR aids in diagnosing the ***,the resources required for manually identifying the lesions are ***,training for Convolutional Neural Networks(CNN)is more *** proposed research aims to improve diabetic retinopathy diagnosis by developing an enhanced deep learning model(EDLM)for timely DR identification that is potentially more accurate than existing CNN-based *** proposed model will detect various lesions from retinal images in the early ***,characteristics are retrieved from the retinal fundus picture and put into the EDLM for *** dimensionality reduction,EDLM is ***,the classification and feature extraction processes are optimized using the stochastic gradient descent(SGD)*** EDLM’s effectiveness is assessed on the KAG-GLE dataset with 3459 retinal images,and results are compared over VGG16,VGG19,RESNET18,RESNET34,and *** results show that the EDLM achieves higher average sensitivity by 8.28%for VGG16,by 7.03%for VGG19,by 5.58%for ResNet18,by 4.26%for ResNet 34,and by 2.04%for ResNet 50,respectively.

关键词： Diabetic retinopathy deep learning classification retinal fundus

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TMRE:Novel Algorithm for Computing Daily Reference Evapotranspiration Using Transformer-Based Models

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computers, Materials & Continua 2025年第5期83卷 2851-2864页

作者： Bushra Tayyaba Muhammad Usman Ghani Khan Talha Waheed Shaha Al-Otaibi Tanzila Saba National Center of Artificial Intelligence(NCAI) Al Khwarizmi Institute of Computer Sciences(KICS)Lahore54890Pakistan Department of Computer Science University of Engineering and Technology LahoreLahore54890Pakistan Artificial Intelligence&Data Analytics Lab Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Reference Evapotranspiration(ETo)iswidely used to assess totalwater loss between land and atmosphere due to its importance in maintaining the atmospheric water balance,especially in agricultural and environmental *** estimation of ETo is challenging due to its dependency onmultiple climatic variables,including temperature,humidity,and solar radiation,making it a complexmultivariate time-series *** machine learning and deep learning models have been applied to forecast ETo,achieving moderate ***,the introduction of transformer-based architectures in time-series forecasting has opened new possibilities formore precise ETo *** this study,a novel algorithm for ETo forecasting is proposed,focusing on four transformer-based models:Vanilla Transformer,Informer,Autoformer,and FEDformer(Frequency Enhanced Decomposed Transformer),applied to an ETo dataset from the Andalusian *** novelty of the proposed algorithm lies in determining optimized window sizes based on seasonal trends and variations,which were then used with each model to enhance prediction *** custom window-sizing method allows the models to capture ETo’s unique seasonal patterns more ***,results demonstrate that the Informer model outperformed other transformer-based models,achievingmean square error(MSE)values of 0.1404 and 0.1445 for forecast windows(15,7)and(30,15),*** Vanilla Transformer also showed strong performance,closely following the *** findings suggest that the proposed optimized window-sizing approach,combined with transformer-based architectures,is highly effective for ETo *** novel strategy has the potential to be adapted in othermultivariate time-series forecasting tasks that require seasonality-sensitive approaches.

关键词： Reference evapotranspiration ETo transformer informer autoformer FEDformer timeseries forecasting self-attention

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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection systems

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computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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Sentiment Analysis of Covid-19 Twitter Data using Deep Learning Algorithm 2

Sentiment Analysis of Covid-19 Twitter Data using Deep Learn...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Valarmathi, B. Gupta, N. Srinivasa Karthick, V. Chellatamilan, T. Santhi, K. Chalicheemala, Dhanush Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Assistant Systems Engineer Tata Consultancy Services Chennai India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

A key method used in the study of Natural Language Processing (NLP) is sentiment analysis, or emotion analysis, plays a pivotal role in text analysis. Its primary function is to discern and categorize the underlying emotions within textual content, classifying them as positive, neutral, or negative sentiments. A variety of textual inputs can be used with this process, including entire documents, individual sentences, and more. Understanding the sentiments expressed by individuals is of paramount importance to organizations in today's digital age. Clients and customers now have the means to express their thoughts and emotions with greater ease and immediacy than ever before. This wealth of sentiment data can offer valuable insights that organizations can leverage to enhance their products, services, and overall customer experience. The present study focuses on the sentiment analysis of Twitter data related to the COVID-19 pandemic, employing the Long Short-Term Memory (LSTM) algorithm. To improve the accuracy of sentiment analysis, a pre-processing procedure is introduced, which involves the use of the Neat Text module in Python to clean the tweets. In this research endeavor, a dataset comprising 1,79,107 COVID-19-related tweets is subjected to sentiment analysis using the proposed pre-processing module and LSTM. The results demonstrate an impressive accuracy rate of 96% in accurately determining the sentiments conveyed in these COVID-19 tweets. In contrast, the existing algorithm, based on Artificial Neural Network (ANN), achieved a significantly lower accuracy rate of 76%. This research not only showcases the effectiveness of LSTM and pre-processing in sentiment analysis but also highlights the significance of sentiment analysis in gaining valuable insights from vast amounts of textual data, especially when important events like the COVID-19 pandemic are involved. © 2024 Elsevier B.V.. All rights reserved.

关键词： COVID-19

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Identification of Diabetic Related Eye Diseases Using Deep Learning 5

Identification of Diabetic Related Eye Diseases Using Deep L...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Wijesinghe, K.H. Dilshan, U.K.T. Dilshan, K.B.G.L. Tharupathi, M.A.U. Kasthuriarachchi, Sanvitha Rajapaksha, Samantha Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

Visual disability seems to be increasing and widespread worldwide. Current diagnostics require manual expertise for diagnosis. The advancement of Artificial Intelligence research for medical applications has been in focus in recent years. Fundus images and oct images are valuable sources of information to help ophthalmologists diagnose vision disorders or eye diseases. Early detection can improve the chances of recovery and prevent blindness. The application of an intelligent computer-based approach to classifying different eye disorders is extremely beneficial for both diagnosis and prevention. The proposed expert system for diagnosing diabetic macular edema, diabetic retinopathy, cataracts, and glaucoma diseases is discussed in this research study. It is based on deep learning image processing techniques. For the detection of eye diseases, software used retinal and OTC images. This study presents Yolo, UNET, customized CNN, and Auto ML model-based system for detecting eye diseases. For the tasks of disease identification and grading, the suggested methods each obtained more than 87% accuracy. The primary objective of this study is to achieve automated identification of eye disease images. This is accomplished through the implementation of a deep learning model alongside advanced image processing techniques, enabling the automatic classification of eyes into either a healthy or diseased category. © 2023 IEEE.

关键词： Diagnosis

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Personalized and Interactive Demented Care and Learning Mate with a Virtual System Using Emotion Recognition 5

Personalized and Interactive Demented Care and Learning Mate...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Kaushan, Nipuna Fernando, Devshan Gayashan, Chamod Udayakantha, Sachitha De Silva, Hansi Perera, Sanduni Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This study investigates the development and application of a personalized, interactive virtual system to enhance dementia care and protect people with dementia. A significant challenge in providing care for individuals with dementia is the propensity to wander and become lost, which can lead to anxiety and distress for both the patient and the carers as well as safety concerns. The system combines cutting-edge emotion recognition technology and healthcare IOT technology to effectively identify and provide real-time solutions in response to users' emotional states. This initiative combines dementia care, educational support, and patient security to establish a comprehensive strategy for maintaining well-being and managing the disease in dementia patients. This project has experimentally designed a security system to enhance the safety of dementia patients, a Health Checker system aimed at maintaining their health stability, and a virtual assistant that offers guidance by identifying real-time emotions and providing activities to improve cognitive function and promote relaxation Given a vast amount of patient data, the AI model has been able to identify 98% of patients' illnesses early. security software successfully detected patients with 99% accuracy using GPS technology and machine learning algorithms. © 2023 IEEE.

关键词： Emotion Recognition

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