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

作者： Jaswanth, Ullamparthy Muthukumar, D. Surendiran Abitha, R. Ganesh, Pamu Naveen, Nandyala Kavitha, G. Kalasalingam Academy of Research and Education Department Computer Science and Engineering Tamil Nadu Virudhunagar India Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Department of Artificial Intelligence and Data science Tamil Nadu Chennai India Dr.M.G.R Educational andResarch Institute Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509828

The proposed system aims to create a safe online voting system. Nowadays voting remains a serious safety and security issue so this system provides high performance and security. The System seeks to develop a face recognition-based voting system that authenticates voters using machine learning, especially K-Nearest Neighbors (KNN). Entering the voter's unique username and Password is the first step in the online voting system. If the voter's Username and password are valid, the system proceeds to the next step, requesting permission to turn on the webcam and take a picture of them. Once the photo is taken, it verifies that the voter has only taken it once. The website allows the voter to cast his vote only once. Voter access is denied if the same voter picture is entered again. By logging into the website voter can cast their vote from any place. Every minute the website updates the results of the election. So, voters do not need to wait in long queues to cast their votes. To give voters real-time information on their voting progress. The system also features dynamic voice feedback to guide users through voting, ensuring clarity and engagement. This initiative uses facial recognition technology to enhance the security and efficiency of voting systems. © 2025 IEEE.

关键词： Face recognition

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Differentially Private Spiking Variational Autoencoder 27th

Differentially Private Spiking Variational Autoencoder

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Yadav, Srishti Pundhir, Anshul Goyal, Tanish Raman, Balasubramanian Kumar, Sanjeev Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Roorkee India Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India Department of Mathematics Indian Institute of Technology Roorkee Uttarakhand Roorkee India

ISBN: (纸本)9783031783531

Spiking Neural Networks (SNNs) are poised to lead the next generation of artificial intelligence, offering energy efficiency and performance on par with traditional neural networks. With these advantages, SNNs are finding widespread applications across various domains. One significant area of interest is image generation using deep learning models like Variational Autoencoders (VAE). However, like other deep learning models, SNNs demand substantial training data to achieve desired outcomes, raising concerns about data privacy. Our pioneering contribution is the introduction of a Differentially Private Spiking Variational Autoencoder (DP-SVAE) for image generation and reconstruction. DP-SVAE employs standard Differentially Private Stochastic Gradient Descent (DP-SGD) to ensure privacy preservation. Additionally, we have evaluated the models against various adversarial attacks to highlight the importance of differential privacy. We comprehensively analyze the proposed model through extensive experimentation across publicly available benchmark datasets. This pioneering study marks the first exploration of privacy considerations in SNN-based VAEs and will catalyze further research in this domain. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differential privacy

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Wearable Biosensors: Transformative Innovations and Future Pathways for Continuous Health Monitoring 2

Wearable Biosensors: Transformative Innovations and Future P...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Thute, Trupti Gudadhe, Amit Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Department of Artificial Intelligence and Data Science Maharashtra Wardha India Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Department of Computer Science and Design Maharashtra Wardha India

ISBN: (纸本)9798331505745

Wearable biosensors are revolutionizing healthcare by enabling continuous monitoring of health status, transforming it from an event-based process to an ongoing one. When integrated with AI, nanotechnology, and IoMT (Internet of Medical Things), these devices enhance precision care, telediagnosis, and chronic disease management. Advancements in electrochemical, optical, and biochemical sensors, along with innovations in energy harvesting and miniaturized flexible materials, improve both the performance and comfort of these devices. Wearable biosensors are now crucial for everyday fitness, disease prediction, and the seamless exchange of data between m-health apps and EHRs. Despite these advancements, challenges remain, such as data security, system integration, and user incentives. Addressing these concerns through enhanced security features, standardized protocols, and user-focused designs is critical. The future development of wearable biosensors is aimed at leveraging cost-effective energy sources, utilizing AI for data analysis, and improving user convenience, making significant strides in preventive and personalized healthcare. © 2025 IEEE.

关键词： mHealth

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A Comparative Performance Analysis of Classification and Segmentation Models on Bangladeshi Pothole dataset

arXiv

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arXiv 2025年

作者： Parsa, Antara Firoz Abdullah, S.M. Talukder, Anika Hasan Kabbya, Md Asif Shahidullah Hasan, Shakib Al Islam, Md Farhadul Noor, Jannatun Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Research Group Department of Computer Science and Engineering United International University Dhaka Bangladesh

The study involves a comprehensive performance analysis of popular classification and segmentation models, applied over a Bangladeshi pothole dataset, being developed by the authors of this research. This custom dataset of 824 samples, collected from the streets of Dhaka and Bogura performs competitively against the existing industrial and custom datasets utilized in the present literature. The dataset was further augmented four-fold for segmentation and ten-fold for classification evaluation. We tested nine classification models (CCT, CNN, INN, Swin Transformer, ConvMixer, VGG16, ResNet50, DenseNet201, and Xception) and four segmentation models (U-Net, ResU-Net, U-Net++, and Attention-Unet) over both the datasets. Among the classification models, lightweight models namely CCT, CNN, INN, Swin Transformer, and ConvMixer were emphasized due to their low computational requirements and faster prediction times. The lightweight models performed respectfully, oftentimes equating to the performance of heavyweight models. In addition, augmentation was found to enhance the performance of all the tested models. The experimental results exhibit that, our dataset performs on par or outperforms the similar classification models utilized in the existing literature, reaching accuracy and f1-scores over 99%. The dataset also performed on par with the existing datasets for segmentation, achieving model Dice Similarity Coefficient up to 67.54% and IoU scores up to 59.39%. © 2025, CC BY-NC-SA.

关键词： Classification (of information)

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Motion Planning in Automated Vehicles Based on Hierarchical MPC 1

Motion Planning in Automated Vehicles Based on Hierarchical ...

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kapila, Pooja Kumar, Pankaj Negi, Rahul Bharti, Bhupender Kumar Punhani, Akash Bisht, Suraj Singh IIMT College of Engineering Department of Artifical Intelligence & Data Science India Amity University Department of Statistics Kolkata India Tula's Institute Department of ECE Dehradun248197 India Department of Mechanical Engineering VMSB UTU Campus Pithoragarh India KIET Group of Institutions Department of Computer Science Noida India Bipin Tripathi Kumaon Institute of Technology Department of ECE Dwarahat Almora India

ISBN: (纸本)9798331518578

In this study, a thorough hierarchical control structure that supports autonomous decision-making that arises in autonomous systems and robots is proposed. Distinct state and decision/control sets are frequently used to describe high-level decision making in a conventional hierarchical control architecture. To keep up with proficiency, wellbeing, and solace, robotized vehicles generally depend serious areas of strength for on powerful movement arranging calculations. The capacity of Model Prescient Control (MPC) to oversee imperatives and upgrade directions progressively has made it a strong system for movement arranging. To approve the proposed strategy, this exploration utilizes MATLAB recreations to look at various levelled MPC for movement arranging in robotized vehicles. With the low-level regulator guaranteeing exact direction observing and the undeniable level organizer computing a worldwide direction, a two-layer progressive MPC structure is fabricated. The discoveries show how well the progressive MPC handles street ebb and flow, dynamic hindrances, and continuous computational impediments. It is demonstrated that the suggested autonomous decision-making scheme's recursive practicality and stability are ensured by carefully crafting its essential components. An intelligent vehicle's autonomous lane-changing system is developed using the suggested framework. Simulation demonstrates that it has a potential capacity to manage a variety of behaviours in complex and dynamic contexts. It is anticipated that the suggested framework will be useful for autonomous decision-making in a variety of robotics along with autonomous technologies where safety as well as optimality are important factors. © 2025 IEEE.

关键词： Motion planning

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Monkeypox Disease Classification from Skin Lesion Images using Deep Convolution Neural Network

Monkeypox Disease Classification from Skin Lesion Images usi...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Rajasekaran Thangaraj Balasubramaniam Vadivel S. Manoj Kumar S. Sadesh P Karunakaran Prakash P Department of Computer Science and Engineering Nandha Engineering College Erode India Department of Computer Science and Engineering Sri Ramakrishna Institute of Technology Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Artificial Intelligence and Data Science Velalar College of Engineering and Technology Erode India Department of Artificial Intelligence and Data Science Nandha Engineering College Erode India Department of Information Technology Velalar College of Engineering and Technology Erode India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Recently monkeypox outbreak has raised concerns due to its increasing number of cases and diverse dermatological symptoms in 2024, which can complicate early diagnosis due to similarities with other viral infections such as measles and chickenpox. To enhance diagnostic accuracy, transfer learning models and artificial intelligence (AI) have been explored as effective tools. Pre-trained models such as VGG16, VGG19, ResNet50V2, and MobileNetV2 have been employed for monkeypox detection, each with distinct trade-offs in terms of computational efficiency and diagnostic precision. MobileNetV2 offers superior efficiency, but this comes at the cost of reduced accuracy. VGG models provide higher accuracy but require much more computational *** these models, DenseNet121 stands out by achieving 99% accuracy while requiring 37% less computational power, making it the model optimized choice for efficient and accurate monkeypox classification.

关键词： Deep learning Accuracy Computational modeling Surveillance Transfer learning Neural networks Measles Skin Computational efficiency Reliability

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FedNorm: Optimised Federated Learning Strategy Using Hybrid Regularisation Norm 12th

FedNorm: Optimised Federated Learning Strategy Using Hybrid ...

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12th International Conference on Big data and Artificial Intelligence, BDA 2024

作者： John, Binu Menon, Varun Adhikari, Mainak Department of Computer Science and Engineering SCMS School of Engineering and Technology Kerala Ernakulam683576 India APJ Abdul Kalam Technological University Kerala Thiruvananthapuram695016 India School of Data Science Indian Institute of Science Education and Research Vithura Kerala Thiruvananthapuram695551 India

ISBN: (纸本)9783031818202

Federated Learning (FL) has emerged as a game-changing method for training machine learning (ML) models on decentralized data sources without centralizing the data itself. Conventional FL algorithms frequently exhibit performance instability and inefficiency, especially when working with non-Independent and Identically Distributed (non-IID) data distributions. To overcome these difficulties, we present FedNorm, a new FL method that uses hybrid regularisation approach to improve model stability and accuracy by balancing sparsity and smoothness, to increase generalization across varied data distributions. Our experimental setup uses the MNIST dataset to train models in both Independent and Identically Distributed (IID) and non-IID situations. The findings show that FedNorm routinely outperforms classic algorithms like FedAvg, FedSGD, FedNova and FedSAM, with minimal test loss and high accuracy. FedNorm has an exceptional accuracy rating of 0.98 on both IID and non-IID data. FedNorm addresses the constraints of conventional FL algorithms, resulting in a strong and dependable solution for FL in diverse situations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Fuzzy Logic System-Based Decision Systems for Proactive Cybersecurity Risk Management 6th

Fuzzy Logic System-Based Decision Systems for Proactive Cybe...

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6th International Conference on Smart Computing and Informatics, SCI 2024

作者： Mahadik, Ramchandra Vasant Khetani, Vinit Afaq, Ahmar Pradhan, Sagar Kolekar, V.K. Pawar, Rohit Institute of Management and Entrepreneurship Development Bharati Vidyapeeth (Deemed to Be University) Maharashtra Pune India Cybrix Technologies Maharashtra Nagpur India Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Computer Science and Engineering Arya College of Engineering Jaipur India Department of Computer Engineering VIIT Pune India Department of Computer Science and Engineering (Data Science) Shri Ramdeobaba College of Engineering and Management Maharashtra Nagpur India

ISBN: (纸本)9789819619801

Proactive risk management is crucial in the ever-changing field of cybersecurity to prevent advanced threats. This study presents an innovative approach to improve cybersecurity decision-making systems by combining fuzzy logic and Random Forest, resulting in an exceptional accuracy of 98.33%. The hybrid approach aims to tackle the challenges of uncertainty and imprecision in cybersecurity data by offering a clear and strong decision-making framework. The beginning of this paper highlights the increasing cybersecurity threats and the shortcomings of conventional risk management methods. The integration of advanced decision systems is crucial as the paradigm moves toward proactive strategies. Fuzzy logic, known for its capability to manage imprecise data, is combined with the ensemble learning strength of Random Forest to form a thorough and efficient solution. The novel hybrid approach starts by converting input features into fuzzy values to represent the inherent uncertainty in cybersecurity data. A fuzzy inference system is used to convert fuzzy inputs into precise decisions, which serve as the basis for training the Random Forest model. The hybrid system effectively utilizes the ensemble features of Random Forest to identify intricate patterns in the data, resulting in an impressive accuracy of 98.33%. This research enhances cybersecurity decision systems and highlights the interpretability of the hybrid model. The system combines the clarity of fuzzy logic with the strength of Random Forest to provide a transparent view of decision-making processes and maintain a high level of predictive precision. The results highlight the efficacy of the hybrid approach in proactive cybersecurity risk management, showing its ability to greatly enhance organizational defenses against changing cyber threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cybersecurity Decision systems Fuzzy logic Proactive risk management Random Forest

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Deep Learning Strategies for Multiclass Skin Disease Classification 5th

Deep Learning Strategies for Multiclass Skin Disease Classi...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Venkat, Nakka Lokeswara Satya Tejasri, Masina Naga Vijaya Uppalapati, Padma Jyothi Rao, V. V. R. Maheswara Sripada, V. S. S. Lakshmi Murty, P. Sita Rama Department of Artificial Intelligence and Data Science Vishnu Institute of Technology Andhra Pradesh Bhimavaram534201 India Department of Computer Science and Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram534201 India Department of CSE Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram534201 India Andhra Pradesh Bhimavaram534201 India

ISBN: (纸本)9783031770746

Skin diseases represent a significant global health challenge, necessitating accurate and timely diagnosis for effective treatment. In this paper, we delve into the intricate realm of skin disease classification, leveraging advanced deep learning techniques. Skin diseases, ranging from common conditions like eczema and atopic dermatitis to potentially life-threatening melanoma, pose complex diagnostic challenges due to their varied visual characteristics. Accurate classification demands the ability to discern subtle patterns, textures, and features within medical images. Our paper introduces a novel methodology that includes the integration of our proposed model into the existing deep learning frameworks. Unlike conventional approaches involving the replacement of output layers, our model maintains the integrity of pre-trained structures. It achieves this by utilizing a blend of VGG16 and EfficientNet B2, incorporating dense activations for improved regularization. This novel approach seeks to improve the model’s flexibility to various skin disease patterns while maintaining accurate classification without changing the pre-trained models’ fundamental design. The model’s performance is evaluated in-depth using a variety of criteria, including accuracy, precision, recall, and F1 score. The outcomes of this analysis unveil distinct F1 scores for each disease class across different models, thereby illuminating the models’ strengths and limitations in the context of disease classification. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Dermatitis

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GeoAgriGuard: AI-Driven Pest and Disease Management with Remote Sensing for Global Food Security

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 409-422页

作者： Sharada, K. Choudhary, Shailee Lohmor Harikrishna, T. Dixit, Rinku Sharma Suman, Sanjay Kumar Ayyappa Chakravarthi, M. Bhagyalakshmi, L. Department of Computer Science and Engineering GITAM (Deemed to Be University) AP Visakhapatnam India Department of Artificial Intelligence and Machine Learning New Delhi Institute of Management Delhi India Department of AIML Annamacharya University Andhra Pradesh Rajampet India Department of ECE St. Martin’s Engineering College Telangana Secunderabad India Departmentt of CSE-Data Science KSR Institute of Technology and Science KKR & AP Guntur India Department of ECE Rajalakshmi Engineering College TN Chennai India

Agricultural productivity is increasingly threatened by the growing frequency and severity of pest and disease outbreaks, exacerbated by climate change and evolving environmental conditions. The GeoAgriGuard AI-Driven Pest and Disease Management System integrates remote sensing technologies with advanced AI models to address pest and disease outbreaks in agriculture. Utilizing multi-spectral and hyper-spectral satellite and drone imagery, the system processes crop health data through comprehensive preprocessing steps, including radiometric, geometric, and atmospheric corrections. Vegetation indices such as NDVI and NDWI further enhance the data quality, ensuring accurate detection of crop stress. A hybrid deep learning ensemble, combining models like ResNet, Transformer, DenseNet, and AutoEncoders, processes this data to predict and classify pest and disease occurrences. The proposed model achieved a remarkable accuracy of 97.81%, significantly outperforming existing models such as SVM (82.45%) and CNN (90.12%). By integrating these AI predictions with GIS-based spatial analysis, the system generates real-time risk maps, enabling farmers to take timely and localized actions. Performance metrics, including precision (96.72%), recall (95.83%), and F1-score (96.27%), demonstrate the system’s reliability in real-world applications. GeoAgriGuard provides a scalable, data-driven solution for enhancing crop protection and improving global food security. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： AI-driven agriculture Crop disease Deep learning Food security GIS NDVI Pest detection Precision agriculture Remote sensing Satellite imagery

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