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Soft Computing for Security Applications (ICSCSA), International Conference on

作者： Dhivya Rathinasamy S. Logesh S. Shri Guru Varshini P. Senthilkumari Department of Artificial Intelligence and Data Science PSNA College of Engineering and Technology Dindigul India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Proliferative Diabetic Retinopathy (PDR) is a severe complication of diabetes that can lead to vision loss if not detected and managed promptly. This data analysis project aims to develop an advanced diagnostic system for PDR detection using retinal images. Going beyond conventional approaches, proposed a novel methodology that integrates multi-modal data and employs explainable artificial intelligence (AI) techniques to enhance accuracy and interpretability. The project utilizes a diverse dataset, including retinal images, patient histories, genetics, and lifestyle data. A multi-modal fusion framework extracts features capturing subtle Proliferative Diabetic Retinopathy (PDR) variations. Alongside precise diagnosis, the study introduces longitudinal analysis for PDR progression *** model not only detects PDR but also provides transparent insights into its decision-making process, empowering clinicians to validate and understand the predictions. The proposed design involves using a Convolutional Neural Network (CNN) to classify images of the retina into different stages of Diabetic Retinopathy. The model will be evaluated using the Confusion Matrix and True Value and Predicted Value Comparison. The outcomes of this project extend beyond conventional PDR detection by offering an interpretable AI-powered tool for clinicians. The system holds promise for revolutionizing PDR management, promoting early detection, and providing tailored interventions for patients.

关键词： Diabetic retinopathy Decision making Predictive models Retina Feature extraction Genetics Convolutional neural networks Security History artificial intelligence

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Fusion Strategy for Improving Medical Image Segmentation

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Computers, Materials & Continua 2023年第2期74卷 3627-3646页

作者： Fahad Alraddady E.A.Zanaty Aida HAbu bakr Walaa M.Abd-Elhafiez Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Information System Department Faculty of Computers and InformationSohag UniversitySohagEgypt Mathematical and Computer Science Department Faculty of ScienceAswan UniversityAswanEgypt College of Computer Science&Information Technology Jazan UniversityJazanKingdom of Saudi Arabia Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohagEgypt

In this paper,we combine decision fusion methods with four metaheuristic algorithms(Particle Swarm Optimization(PSO)algorithm,Cuckoo search algorithm,modification of Cuckoo Search(CS McCulloch)algorithm and Genetic algorithm)in order to improve the image *** proposed technique based on fusing the data from Particle Swarm Optimization(PSO),Cuckoo search,modification of Cuckoo Search(CS McCulloch)and Genetic algorithms are obtained for improving magnetic resonance images(MRIs)*** algorithms are used to compute the accuracy of each method while the outputs are passed to fusion *** order to obtain parts of the points that determine similar membership values,we apply the different rules of incorporation for these *** proposed approach is applied to challenging applications:MRI images,gray matter/white matter of brain segmentations and original black/white images Behavior of the proposed algorithm is provided by applying to different medical *** is shown that the proposed method gives accurate results;due to the decision fusion produces the greatest improvement in classification accuracy.

关键词： Decision fusion particle swarmoptimization(PSO) cuckoo search algorithm CS McCulloch algorithm genetic algorithm CT and MRI

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Feature Selection for Geomagnetic Storm Forecasting Using Particle Swarm Optimisation 25

Feature Selection for Geomagnetic Storm Forecasting Using Pa...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Algendi, Ziad Attar, Hani Hafez, Mohamed A. Al-Faiz, Hussein Ababneh, Jafar Deif, Mohanad A. Misr University for Science and Technology College of Information Technology Department of Artificial Intelligence Egypt Zarqa University Zarqa Jordan College of Engineering Faculty of Engineering Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia INTI-IU Universi Faculty of Engineering Feqs Nilai Malaysia Kajang43000 Malaysia Zarqa University Faculty of Information Technology Cyber Security Department Zarqa Jordan College of Information Technology Department of Artificial Intelligence 6th of October City12566 Egypt

ISBN: (纸本)9798331540012

Geomagnetic activity is a significant research area due to its impact on navigation, satellites, and other technologies. The geomagnetic index Kp is particularly essential for monitoring such activity. Forecasting geomagnetic storms can provide early warnings, helping to mitigate potential disasters. Various methods, including empirical models, physics-based models, and machine learning models, are employed for forecasting. This study focuses on Machine learning models, emphasising selecting appropriate features to enhance prediction accuracy. The primary aim was to improve the 3-hour Kp predictions compared to prior research. Model performance was primarily evaluated using the R2 metric. Additionally, RMSE was calculated to ensure comparability with studies that did not use R2. The model's ability to detect storms was assessed by classifying results as Kp>5 (storm) or Kp2 of 0.71, an RMSE of 0.6671, and an F1 score of 0.6. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Face Mask Detection Using Support Vector Machine

Face Mask Detection Using Support Vector Machine

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2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： Divya Priya, D. Subbulakshmi, R. Purushotham, P. Kiran, Ajmeera Arulananth, T.S. Kavya, S.P. MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Karpagam Institute of Technology Department of Computer Science and Engineering Coimbatore India MLR Institute of Technology Department of Electroncis and Communication Engineering Hyderabad India KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350348217

According to a report published by the World Health Organization (WHO), the only two primary ways in which the COVID-19 virus can be spread are through breathing contaminated air and through direct physical contact. Wearing a mask is one of the bare minimum preventative measures that can be taken against the transmission of certain viral infections, including COVID-19. Therefore, it is much more important to wear a mask properly in very crowded areas such as public parks, supermarkets, and shopping malls. This is because the risk of spreading an infectious disease is significantly higher in these settings. Face Mask Detection is a technique that we developed so that we could avoid problems like these. It can determine whether or not a person is wearing a mask on their face. There have only been a handful of scant studies done on face mask detection. © 2023 IEEE.

关键词： SARS

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TRNet: Two-Tier Recursion Network for Co-Salient Object Detection

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5844-5857页

作者： Cong, Runmin Yang, Ning Liu, Hongyu Zhang, Dingwen Huang, Qingming Kwong, Sam Zhang, Wei Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Northwestern Polytechnical University Brain and Artificial Intelligence Laboratory School of Automation Xi’an710129 China University of Chinese Academy of Sciences School of Computer Science and Technology Beijing101408 China Lingnan University School of Data Science Hong Kong

Co-salient object detection (CoSOD) is to find the salient and recurring objects from a series of relevant images, where modeling inter-image relationships plays a crucial role. Different from the commonly used direct learning structure that inputs all the intra-image features into some well-designed modules to represent the inter-image relationship, we resort to adopting a recursive structure for inter-image modeling, and propose a two-tier recursion network (TRNet) to achieve CoSOD in this paper. The two-tier recursive structure of the proposed TRNet is embodied in two stages of inter-image extraction and distribution. On the one hand, considering the task adaptability and inter-image correlation, we design an inter-image exploration with recursive reinforcement module to learn the local and global inter-image correspondences, guaranteeing the validity and discriminativeness of the information in the step-by-step propagation. On the other hand, we design a dynamic recursion distribution module to fully exploit the role of inter-image correspondences in a recursive structure, adaptively assigning common attributes to each individual image through an improved semi-dynamic convolution. Experimental results on five prevailing CoSOD benchmarks demonstrate that our TRNet outperforms other competitors in terms of various evaluation metrics. © 1991-2012 IEEE.

关键词： HTTP

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IMGTP: A Unified Framework for Improving and Measuring the Generalizability of Trajectory Prediction Models

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第10期9卷 1-15页

作者： Ye, Luyao Zhou, Zikang Wang, Jianping Li, Yung-Hui Jan, Nien-Yi Lin, Yi-Rong Lin, Yen-Cheng National Engineering Research Center of Educational Big Data and the Faculty of Artificial Intelligence in Education Central China Normal University China Department of Computer Science City University of Hong Kong and City University of Hong Kong Shenzhen Research Institute China AI Research Center of Hon Hai Research Institute China

Accurately predicting nearby agents' future trajectories is fundamental for ensuring the safety and efficiency of autonomous driving. However, existing learning-based trajectory prediction models struggle with poor generalization to out-of-distribution (OOD) scenes, potentially leading to failed predictions in real-world applications. To tackle this challenge, we introduce IMGTP, a unified framework for systematically evaluating, improving, and measuring the generalization performance of trajectory prediction models in OOD scenes. IMGTP consists of three primary components: the Argoverse-Shift dataset, the FrenÃ©t+ strategy, and an approach for OOD measurement. First, the Argoverse-Shift is a cross-domain dataset that highlights the significant differences in data distributions across domains, serving as a testing platform for models' generalization ability. Second, the FrenÃ©t+ strategy leverages map centerlines and the FrenÃ©t Frame to enhance the generalizability of baseline models by mitigating the domain gap resulting from road geometry variations. Third, we provide a comprehensive evaluation methodology for OOD measurement, quantifying the range of data that can be effectively handled by prediction models. This measurement approach employs the accuracy rate of the correctly predicted samples and their corresponding mean distance as well as standard deviation to the training set center as indicators of the model's generalization ability. The mean distance also serves as a valid criterion for OOD detection and uncertainty quantification. Experimental results demonstrate that our IMGTP framework enhances the generalization capability of mainstream trajectory prediction models by substantially improving their prediction performance in unseen domains. This advancement contributes to developing more robust and safer prediction models for autonomous driving in the real world. The code can be found at https://***/XIAOYEJIAYOU/Frenet-Strategy. IEEE

关键词： Autonomous vehicles

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Multi-Disease Prediction and Classifier: A Comprehensive Approach for Healthcare Decision Support

Multi-Disease Prediction and Classifier: A Comprehensive App...

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Emerging technology (INCET), International Conference for

作者： Sudha S V Kamali Priya S Krishnapriya P Prathiksha P N Department of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

The speedy identification and early detection of diseases that are deadly are greatly important when it comes to human life saving. On the other hand, the existence of poor medical system alongside the lack of equal distribution of healthcare presents a barrier towards early disease detection leaving death as the only option. One of the ways to tackle these problems is the utilization of machine learning in detection and analysis of diseases risk is machine learning promising. Utilizing predictive analytics in medicine, practitioners can make accurate and relevant medical insights promptly, relying on the information they have. The work focused on generating prediction models to identify seven most severe illnesses: diabetes, pneumonia, malaria, heart disease, kidney disease, breast cancer, and liver disease, using an algorithm of Random Forest Classifier. Alongside this, the Convolutional Neural Networks were utilized for the detection of malaria and pneumonia. Users can get better knowledge about their health problems and at the same time reduce the access constraints to healthcare by using machine learning. This research is also about designing a web app with the Flask framework and will give its consumers an easy-to-use tool for the simultaneous prediction of all seven diseases. Such a user interface grants better healthcare access and results in the use of proactive measures, as people can take a more active role in their wellness.

关键词： Pneumonia Machine learning algorithms Liver diseases Malaria Medical services User interfaces Predictive models

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A Deep Reinforcement Learning-Based Task Offloading Framework for Edge-Cloud Computing 2

A Deep Reinforcement Learning-Based Task Offloading Framewor...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Kalpana, Ponugoti Almusawi, Muntather Chanti, Yerrolla Sunil Kumar, V. Varaprasad Rao, M. Avn Institute of Engineering & Technology Department of Computer Science and Engineering Telangana India The Islamic University Najaf Iraq Sr University School of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India Cvr College of Engineering Department of Data Science Hyderabad India

ISBN: (纸本)9798350317558

By introducing the processing and resources storage to the network edge, edge computing is a new, promising computing paradigm that drastically decreases network traffic and service latency. Many edge computing applications consist of interdependent tasks, wherein the results of one task are the inputs of another. The important and difficult problem of where to position each running task to optimise Quality-of-Service (QoS) is how to offload these tasks to edge of network. In this work, implemented a novel Deep Reinforcement Learning based Task Offloading (DRLTO) method utilized as the intelligent task offloading that uses a Directed Acyclic Graph (DAG) to represent the dependent tasks and off-policy reinforcement learning powered by a Sequence-to-Sequence (S2S) neural network. This research outcomes show that the DRLTO achieved less cloud processing time, and number of single terminal tasks, and percentage of failed task when compared to Deep reinforcement learning-based cloud-edge collaborative mobile computation offloading (DRL-CCMCO) and DeepEdge. © 2024 IEEE.

关键词： and task offloading Deep reinforcement learning Multi-access edge computing sequence to sequence neural networks

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ML for Sustainable Solutions: Applications in Renewable Energy Optimization and Climate Change Prediction 4

ML for Sustainable Solutions: Applications in Renewable Ener...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Awachat, Arya Dube, Ananya Chaudhri, Shivnath Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

This paper delves into the transformative potential of ML in addressing some of the world's most pressing challenges in optimizing renewable energy and mitigating climate change. How ML algorithms work to improve efficiency within solar and wind energy systems, smart grids, and energy storage technologies is revealed by large data sets and predictive analytics. As ML improves the forecasting of energy, minimizes waste, and allows for better grid integration, its contribution to cutting carbon emissions significantly is immense. In addition, it facilitates climate adaptation by accurately predicting extreme weather occurrences and climate variability. Challenges facing the approach include data paucity, computational cost, and integration with older systems. Interdisciplinary collaboration and newer ML models tackle these challenges well. The study recognizes the significant role of machine learning in creating a sustainable environment and notes its ability to actually reconcile environmental, economic, and social objectives toward a robust and greener future. © 2025 IEEE.

关键词： Clean energy

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Integrating Instruction-based Learning with Bidirectional LSTM for Autonomous Vehicle Pedestrian Detection

Integrating Instruction-based Learning with Bidirectional LS...

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2025 International Conference on Multi-Agent Systems for Collaborative intelligence, ICMSCI 2025

作者： Shoba, B. Raghunathan, T. Peter, S.Sam Karthick, K. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Psn College of Engineering and Technology Department of Electronics and Communication Engineering Tirunelveli India Dr.NG.p.Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sona College of Technology Department of Information Technology Salem India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

Pedestrian detection is a crucial aspect of autonomous vehicle (A V) safety, as it enables accurate identification and tracking of individuals in an A V environment. Effective pedestrian detection supports essential A V functions like braking, steering, and speed control to prevent accidents in urban settings. Traditional computer vision methods, such as Support Vector Machines and Random Forests, rely on pre-designed features like the Histogram of Oriented Gradients. While the existing techniques offer some performance, it face significant challenges in dynamic environments, especially under varying lighting conditions, occlusions, and diverse pedestrian poses. Traditional models also require extensive manual feature engineering and tuning, which limits scalability and adaptability. Recent advances in deep learning, particularly with Convolutional Neural Networks (CNNs), have allowed for automatic feature learning, yielding practical improvements over classical methods. However, CNNs can struggle in low light and when facing adversarial attacks, as they mainly capture spatial features. To address these limitations, this research proposes a new methodology combining CNNs for spatial feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) networks to capture temporal dependencies. This hybrid approach enhances detection capabilities by learning both static and dynamic patterns. Our proposed CNN-BiLSTM model achieves a 92 % detection accuracy and an F1 score of 88.5% across diverse conditions, outperforming existing models by up to 15%. By integrating temporal features, the model reduces missed detections and false positives by up to 20%, providing a more reliable and adaptable solution for pedestrian detection in autonomous vehicles. © 2025 IEEE.

关键词： Convolutional neural networks

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