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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Govinda rajulu, G. Sharmila, L. Venkatesan, d. Kumar, Nathi Kranthi Venkateswaramma, Jarabhala Nirosha, Chelumala Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Design Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India St. Martin's Engineering College Dept. of Artificial Intelligence and Machine Learning Hyderabad India Hyderabad India Secunderabad India St. Martin's Engineering College Department of EEE Telangana Secunderabad500 100 India

ISBN: (纸本)9798331505745

As technology advances, encryption methods must adapt to protect sensitive information. Organizations must stay informed about emerging threats and regularly update their protocols to stay ahead. Investing in advanced encryption technologies can mitigate potential breaches. Encryption protects intellectual property, multimedia data, and personal data from cyber threats, a crucial consideration for businesses and individuals in the digital age. This work introduces four AES-based threads for encrypting and decoding multimedia data in 5G. Both sequential and parallel methods have been used for data security. Furthermore, time analyses for sequential and parallel processing algorithms have been conducted using images of various sizes and formats. The results indicate that the encryption process will be completed faster and will provide network-wide data safety in parallel method. © 2025 IEEE.

关键词： Sensitive data

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Scalable Safety-Critical Policy Evaluation with Accelerated rare Event Sampling

Scalable Safety-Critical Policy Evaluation with Accelerated ...

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IEEE/rSJ International Conference on Intelligent robots and Systems (IrOS)

作者： Xu, Mengdi Huang, Peide Li, Fengpei Zhu, Jiacheng Qi, Xuewei Oguchi, Kentaro Huang, Zhiyuan Lam, Henry Zhao, ding Carnegie Mellon Univ Pittsburgh PA 15213 USA Columbia Univ New York NY 10027 USA Morgan Stanley Machine Learning Res New York NY USA Toyota Motor North Amer R&D Saline MI USA Tongji Univ Shanghai Peoples R China

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Evaluating rare but high-stakes events is one of the main challenges in obtaining reliable reinforcement learning policies, especially in large or infinite state/action spaces where limited scalability dictates a prohibitively large number of testing iterations. On the other hand, a biased or inaccurate policy evaluation in a safety-critical system could potentially cause unexpected catastrophic failures during deployment. This paper proposes the Accelerated Policy Evaluation (APE) method, which simultaneously uncovers rare events and estimates the rare event probability in Markov decision processes. The APE method treats the environment nature as an adversarial agent and learns towards, through adaptive importance sampling, the zero-variance sampling distribution for the policy evaluation. Moreover, APE is scalable to large discrete or continuous spaces by incorporating function approximators. We investigate the convergence property of APE in the tabular setting. Our empirical studies show that APE can estimate the rare event probability with a smaller bias while only using orders of magnitude fewer samples than baselines in multi-agent and single-agent environments.

关键词： Training Monte Carlo methods Uncertainty Scalability reinforcement learning Intelligent agents reliability

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Unsegmented Heart Sound Classification Using Hybrid CNN-LSTM Neural Networks 43

Unsegmented Heart Sound Classification Using Hybrid CNN-LSTM...

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43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Megalmani, drishti ramesh Shailesh, B. G. rao, Achuth M., V Jeevannavar, Satish S. Ghosh, Prasanta Kumar Ai Hlth Highway India Private Ltd R&D Machine Learning Bangalore Karnataka India Indian Inst Sci Elect Engn Bangalore 560012 Karnataka India

ISBN: (纸本)9781728111797

Cardiac Auscultation, an integral part of the physical examination of a patient, is essential for early diagnosis of cardiovascular diseases (CVds). The ability to accurately diagnose the heart sounds requires experience and expertise, which is lacking in doctors in the early years of clinical practice. Thus, there is a need for an automatic diagnostic tool that would aid medical practitioners with their diagnosis. We propose novel hybrid architectures for classification of unsegmented heart sounds to normal and abnormal classes. We propose two methods, with and without the conventional feature extraction step in the classification pipeline. We demonstrate that the F score using the approach with conventional feature extraction is 1.25 (absolute) more than using a baseline implementation on the Physionet dataset. We also introduce a mechanism to tag predictions as unsure and compare results with a varying threshold.

关键词： Heart Knowledge engineering Sensitivity Pipelines Neural networks Medical services Tools

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Noise robust detection of Fundamental Heart Sound using Parametric Mixture Gaussian and dynamic Programming 43

Noise Robust Detection of Fundamental Heart Sound using Para...

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43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： rao, Achuth M., V Shailesh, B. G. Megalmani, drishti ramesh Jeevannavar, Satish S. Ghosh, Prasanta Kumar Indian Inst Sci Elect Engn Bangalore 560012 Karnataka India Ai Hlth Highway India Private Ltd R&D Machine Learning Bangalore Karnataka India

ISBN: (纸本)9781728111797

In this work, we propose an unsupervised algorithm for fundamental heart sound detection. We propose to detect the heart sound candidates using the stationary wavelet transforms and group delay. We further propose an objective function to select the candidates. The objective function has two parts. We model the energy contour of S1/S2 sound using the Gaussian mixture function (GMF). The goodness of fit for the GMF is used as the first part of the objective function. The second part of the objective function captures the consistency of the heart sounds' relative location. We solve the objective function efficiently using dynamic programming. We evaluate the algorithm on Michigan HeartSound and Murmur database. We also assess the algorithm's performance using the three different additive noises- white Gaussian noise (AWGN), Student-t noise, and impulsive noise. The experiments demonstrate that the proposed method performs better than baseline in both clean and noisy conditions. We found that the proposed method is robust in the case of AWGN noise and student-t distribution noise. But its performance reduces in case of impulsive noise.

关键词： Heart Wavelet transforms Costs databases Heuristic algorithms Hidden Markov models Linear programming

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real-Time GPS Tracking and Health Monitoring for Animals Using IoT and Mobile Application 2

Real-Time GPS Tracking and Health Monitoring for Animals Usi...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Usha, V. Karagatla, Sandeep Gowr, P. Sheela Jegathambal, P.M.G. Lizy, A. Sathya, V. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept. of CSE Chennai India Chennai India Vels Institute of Science Technology & Advanced Studies Chennai India Institute of Science and Technology Dept. of Artificial Intelligence and Machine Learning Chennai India Velammal Engineering College Dept of Information Technology Chennai India

ISBN: (纸本)9798331505745

The Internet of Things (IoT) grounded solution of real Time Global Positioning System (GPS) tracking with Health Monitoring of Animals for Owners to track their animals like cows, buffaloes, dogs etc. This GPS module and a heartbeat sensor are attached to the animals in order to be able to follow them closely. This data is sent to a mobile app, and the owner can see their heart on the map as well as monitor its pulse. Based on analysis of such heart rate variations, the system determines when it is seeing an abnormal activity related to e.g. stress (increased heart rate) or potential health risks (no sign of a heartbeat), which could be interpreted as fear or death. Each animal has an initial heart rate level that is set, and when this rate exceeds it, the system sounds an alert to indicate danger. This method is especially helpful for keeping an eye on animals in urban areas, areas close to forests, and areas with high rates of theft and loss. The project demonstrates the successful execution of a reliable, real-time animal monitoring system with practical applications in both urban and rural environments. © 2025 IEEE.

关键词： Animal Safety GPS Tracking Heartbeat Sensor Lora device Mobile Application real-time Monitoring

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Enhanced diabetic retinopathy detection using U-shaped network and capsule network-driven deep learning

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METHOdSX 2025年 14卷 103052页

作者： Govindharaj, I. Poongodai, A. rajaram, Gnanajeyaraman Santhakumar, d. ravichandran, S. Prabhu, Vijaya Udayakumar, K. Yazhinian, S. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept Comp Sci & Engn Chennai 600062 Tamil Nadu India Madanapalle Inst Technol & Sci Dept Comp Sci & Engn Artificial Intelligence Madanapalle 517325 Andhra Pradesh India Saveetha Inst Medical&Techn Sci Saveetha Sch Engn Dept Comp Sci & Engn Chennai 602105 Tamil Nadu India Kings Engn Coll Dept Artificial intelligence & Machine Learning Kavaraipettai 602117 Tamil Nadu India Sri Manakula Vinayagar Engn Coll Dept Informat Technol Pondicherry 605107 India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci Tec Dept Comp Sci & Engn Chennai 600062 Tamil Nadu India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci Tec Dept Elect & Commun Engn Chennai 600062 Tamil Nadu India

Glaucoma, a severe eye disease leading to irreversible vision loss if untreated, remains a significant challenge in healthcare due to the complexity of its detection. Traditional methods rely on clinical examinations of fundus images, assessing features like optic cup and disc sizes, rim thickness, and other ocular deformities. recent advancements in artificial intelligence have introduced new opportunities for enhancing glaucoma detection. This research explores a hybrid approach combining UNet ++ and Capsule Network (CapsNet) architectures for accurate glaucoma diagnosis. UNet ++ is employed for semantic segmentation, focusing on defining optic discs and cups, which are crucial for detecting the disease. CapsNet leverages its ability to recognize hierarchical patterns, providing more sensitive detection of glaucomatous changes than conventional Convolutional Neural Networks. Pre-processing of retinal images involves advanced techniques like Histogram Equalization and Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance image quality. The model is trained and tested on benchmark datasets, showing superior performance in optic cup/disc segmentation and glaucoma detection accuracy compared to existing state-of-the-art models. center dot Hybrid Model Efficiency: The combined use of UNet ++ and CapsNet offers improved accuracy in optic cup and disc segmentation. center dot Enhanced Image Quality: Application of Histogram Equalization and CLAHE techniques significantly boosts the quality of retinal images. center dot Superior Performance: The hybrid approach outperforms traditional and contemporary models in glaucoma detection accuracy.

关键词： Capsule network Convolutional neural network diabetic retinopathy detection Optic cup Optic disc U-shaped network Vision disorder

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Exploring deep Neural Networks for Accurate diabetic retinopathy Prediction

Exploring Deep Neural Networks for Accurate Diabetic Retinop...

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International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： T. Kujani P. Arivubrakan K. raju P. Mani Sankar B. Surekha reddy E. Bhavana Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Artificial Intelligence and Data Science Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

diabetic retinopathy (dr) emerges as a serious complication associated with diabetes mellitus, causing damage to retinal blood vessels, and resulting in vision loss. The critical need for early detection and effective management to curb the progression towards vision impairment and potential blindness underscores the significance of this issue. To address these challenges, have propose a novel deep learning system utilizing a densely connected Convolutional Network (denseNet-201) for the efficient detection of dr. This model is specifically trained to classify fundus images into six distinct categories: no dr, mild, moderate, and severe, proliferative dr, and hard exudates. The datasets utilized in this study include the publicly available diabetic retinopathy detection 2015 and Aptos 2019 blindness detection datasets from Kaggle. The proposed system comprises multiple stages, including data Collection, Preprocessing, Augmentation, and Modeling. during the preprocessing phase, normalization techniques are to eliminate biases or distortions present in the fundus images. Furthermore, the augmentation of data is employed to enhance the diversity and quantity of the training dataset. In the modeling stage, have designed and implemented the deep learning model, with the robust and high-performing denseNet-201 architecture selected as the model’s backbone. The model exhibits a significant accuracy of 87.7% in its classification task. Additionally, have introduced a deep Neural Network (dNN) model, achieving an impressive accuracy rate of 95.8%. These results underscore the effectiveness of our proposed system in accurately and efficiently detecting dr. The primary objective of this research is to develop a reliable and automated system for diabetic retinopathy detection, with the overarching goal of improving the quality of life for individuals affected by diabetes.

关键词： deep learning Training diabetic retinopathy Transfer learning Visual impairment Blindness robustness

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A Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Lifting

arXiv

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arXiv 2024年

作者： Adachi, Masaki Hayakawa, Satoshi Jørgensen, Martin Hamid, Saad Oberhauser, Harald Osborne, Michael A. Machine Learning Research Group University of Oxford United Kingdom Mathematical Institute University of Oxford United Kingdom Department of Computer Science University of Helsinki Finland Aioi R&D Lab Oxford United Kingdom Mind Foundry Oxford United Kingdom

Parallelisation in Bayesian optimisation is a common strategy but faces several challenges: the need for flexibility in acquisition functions and kernel choices, flexibility dealing with discrete and continuous variables simultaneously, model misspecification, and lastly fast massive parallelisation. To address these challenges, we introduce a versatile and modular framework for batch Bayesian optimisation via probabilistic lifting with kernel quadrature, called SOBEr, which we present as a Python library based on GPyTorch/BoTorch. Our framework offers the following unique benefits: (1) Versatility in downstream tasks under a unified approach. (2) A gradient-free sampler, which does not require the gradient of acquisition functions, offering domain-agnostic sampling (e.g., discrete and mixed variables, non-Euclidean space). (3) Flexibility in domain prior distribution. (4) Adaptive batch size (autonomous determination of the optimal batch size). (5) robustness against a misspecified reproducing kernel Hilbert space. (6) Natural stopping *** Codes 62C10, 62F15 © 2024, CC BY-NC-SA.

关键词： Python

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Visual object tracking by using ranking loss and spatial-temporal features

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machine VISION ANd APPLICATIONS 2023年第2期34卷 30-30页

作者： Saribas, Hasan Cevikalp, Hakan Kahvecioglu, Sinem Eskisehir Osmagazi Univ Machine Learning & Comp Vis Lab Elect & Elect Engn Eskisehir Turkiye Huawei Turkey R&D Ctr Istanbul Turkiye Eskisehir Tech Univ Fac Aeronaut & Astronaut Dept Avion Eskisehir Turkiye

This paper introduces a novel two-stream deep neural network tracker for robust object tracking. In the proposed network, we use both spatial and temporal features and employ a novel loss function called ranking loss. The class confidence scores coming from the two-stream (spatial and temporal) networks are fused at the end for final decision. Using ranking loss in the proposed tracker enforces the networks to learn giving higher scores to the candidate regions that frame the target object better. As a result, the tracker returns more precise bounding boxes framing the target object, and the risk of tracking error accumulation and drifts are largely mitigated when the proposed network architecture is used with a simple yet effective model update rule. We conducted extensive experiments on six different benchmarks, including OTB-2015, VOT-2017, TC-128, dTB70, NfS and UAV123. Our proposed tracker achieves the state-of-the-art results on the most of the tested challenging tracking datasets. Especially, our results on the OTB-2015, dTB70, NfS and TC-128 datasets are very promising.

关键词： Object tracking ranking loss Two-stream network Temporal features

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Optimized disease prediction in healthcare systems using HdBN and CAEN framework

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METHOdSX 2025年 14卷 103338页

作者： Prabaharan, G. Sankar, S. M. Udhaya Anusuya, V. deepthi, K. Jaya Lotus, rayappan Sugumar, r. Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept Comp Sci & Engn Chennai India RMK Coll Engn & Technol Dept CSE Cyber Secur Puduvoyal India Ramco Inst Technol Dept Informat Technol Rajapalayam India Mohan Babu Univ Sch Comp Dept Artificial Intelligence & Machine Learning Tirupati Andhra Pradesh India Saveetha Inst Med & Tech Sci SIMATS Inst CSE SIMATS Engn Chennai India

Classification and segmentation play a pivotal role in transforming decision-making processes in healthcare, IoT, and edge computing. However, existing methodologies often struggle with accuracy, precision, and specificity when applied to large, heterogeneous datasets, particularly in minimizing false positives and negatives. To address these challenges, we propose a robust hybrid framework comprising three key phases: feature extraction using a Hybrid deep Belief Network (HdBN), dynamic prediction aggregation via a Custom Adaptive Ensemble Network (CAEN), and an optimization mechanism ensuring adaptability and robustness. Extensive evaluations on four diverse datasets demonstrate the framework's superior performance, achieving 93 % accuracy, 87 % precision, 95 % specificity, and 91 % recall. Advanced metrics, including a Matthews Correlation Coefficient of 0.8932, validate its reliability. The proposed framework establishes a new benchmark for scalable, high-performance classification and segmentation, offering robust solutions for real-world applications and paving the way for future integration with explainable AI and real-time systems. center dot designed a novel hybrid framework integrating HdBN and CAEN for adaptive feature extraction and prediction. center dot Proposed dynamic prediction aggregation and optimization strategies enhancing robustness across diverse data scenarios.

关键词： Adaptive ensemble learning disease prediction machine learning Feature extraction dense neural networks Classification Segmentation

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