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

作者： Shabu, S.L. Jany Indhuja, V. Jeevitha, R. Refonaa, J. Dhamodaran, S. Poornima, D. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331505745

Sleep apnea disorder manifests as obstructive sleep apnea, which affects immense portions of society because it causes life-threatening health problems that include cardiovascular disease alongside diabetes and decreased productivity. The diagnosis of OSA through manual methods is both time demanding and expensive and contains a significant potential for human errors while requiring heavy resources. This research presents deep learning automation as the novel approach for automatic OSA detection through polysomnographic assessments. Our proposed system implements Genetics Algorithm optimized machine learning algorithms which include Logistic Regression, Support Vector Machines, Random Forests, Artificial Neural Networks and more. The analysis utilizes data from the Sleep Health and Lifestyle Dataset for preprocessing operations and features extraction that leads to better sleep disorder and stage classification accuracy. Through hyperparameter adjustment the Random Forest predictive model reached 95% F1 score which proved most effective for OSA detection. Through automation the classification process benefits from reliability with scalability and efficiency and produces better patient outcomes. © 2025 IEEE.

关键词： Deep learning

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Diluie: constructing diverse demonstrations of in-context learning with large language model for unified information extraction

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Neural Computing and Applications 2024年第22期36卷 13491-13512页

作者： Guo, Qian Guo, Yi Zhao, Jin Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai200433 China

Large language models (LLMs) have demonstrated promising in-context learning capabilities, especially with instructive prompts. However, recent studies have shown that existing large models still face challenges in specific information extraction (IE) tasks. Moreover, it could have more effectively utilized various prompts such as instruction tuning, diverse demonstrations of in-context learning, and long-range token sequences for assisting language modeling in understanding context. In this study, we propose DILUIE, a unified information extraction framework based on in-context learning with diverse demonstration examples. DILUIE is encoded with an EVA attention mechanism and incremental encoding technology. Based on the constructed diverse demonstrations, we expand the size of instances efficiently in both instruction tuning and in-context learning to gain insights into the potential benefits of utilizing diverse information extraction datasets. To deepen the understanding of context, we further design three auxiliary tasks to assist in aligning contextual semantics. Experimental results demonstrate that DILUIE achieves 2.23 and 2.53% improvements in terms of Micor-/Macor-F1 on average relative to the current state-of-the-art baseline, which also significantly outperforms the GPT-3.5-turbo in zero-shot settings, and the average token length of achieving the best performance over tasks is around 15k. Furthermore, we observe that in-context learning shows enhanced performance when provided with more demonstrations during multiple-shot instruction tuning (8 k). Additionally, increasing the length of instructions (10 k) can result in a more substantial improvement in the upper limits of scaling for in-context learning. Code is available on https://***/Phevos75/DILUIE. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Demonstrations

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Improving Robustness of Time-Aware Shaper in Time-Sensitive Networking

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IEEE Internet of Things Journal 2025年第13期12卷 25211-25223页

作者： Ma, Yanying Zhang, Tong Feng, XiaoQin Liu, Ping Yang, Hao Ren, Fengyuan Lanzhou University School of Information Science and Engineering Lanzhou730000 China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China School of Information Science and Engineering Lanzhou China Tsinghua University Department of Computer Science and Technology Lanzhou Beijing730000 and 100084 China

Deterministic delivery of scheduled traffic (ST) is critical in time-sensitive networking (TSN). The time-aware shaper (TAS) defined by IEEE 802.1Qbv is the enabler to ensure deterministic end-to-end delays of ST flows. However, TAS does not consider the emergency sporadic flows that commonly exist in automotive and industrial control applications. Therefore, TAS cannot resist the interference of emergency sporadic traffic on normal scheduled traffic. In this paper, we improve the TAS’s time slot allocation and queue occupancy rule and propose a combined scheduling strategy composed of dynamic local regulation and static global planning. Dynamic local regulation executes the earliest deadline first (EDF) discipline at switching nodes to adjust ST frames’ transmission dynamically. Static global planning calculates the offset of ST flows at the source and guarantees bounded delay and jitter. Furthermore, we formalize the offset optimizing problem with considering the EDF, delay, and jitter constraints. Finally, we implement our prototype on OMNet++ 6.0.1. Simulation results further demonstrate that the combined strategy has bounded latency and jitter in the presence of emergency sporadic flows. Besides, the comparison experiments show that our design is superior to eTAS in terms of the jitter and end-to-end delay. © 2014 IEEE.

关键词： Timing jitter

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Automated Brain Tumor Detection and Classification in MRI Scans using a Deep Convolutional Neural Network and U-Net Architecture 6

Automated Brain Tumor Detection and Classification in MRI Sc...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Devadas, Prathima Mathivanan, G. Department of Computer science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

Brain tumor identification and categorization serve a precarious role in early diagnosis and treatment planning. This study suggests a new deep learning (DL)-based structure for the automatic detection and categorization of brain cancers using medical imaging technologies. The framework uses a deep convolutional neural network (DCNN) with an exclusive segmentation technique, notably the U-Net architecture, to discrete tumor areas in magnetic resonance imaging (MRI) scans. The DCNN model, which contains multiple convolutional layers, is used for feature extraction, followed by fully connected layers for tumor classification. To increase segmentation accuracy, the U-Net model integrates both encoder-decoder structures and skip connections to capture fine-grained spatial details. The suggested algorithm is trained and evaluated on a large dataset of brain MRI images, achieving high accuracy, sensitivity, and specificity for both tumor detection and classification. Experimental results show that the model outclasses standard methods, providing a dependable and operative solution for the early detection and diagnosis of brain cancers, potentially aiding clinical decision-making processes. © 2025 IEEE.

关键词： Magnetic resonance imaging

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Elevating IoT healthcare security using ProSRN and ICOM methodologies for effective threat management

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International Journal of Information technology (Singapore) 2025年 1-19页

作者： Sowjanya, Yemineni Gopalakrishnan, S. Kumar, R. Dinesh Department of Computer Science and Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai 603103 India Department of Information Technology Hindustan Institute of Technology and Science Tamil Nadu Chennai 603103 India Department of Computer Science and Engineering Bhoj Reddy Engineering College for Women Telangana Hyderabad 500059 India

IoT healthcare security is increasingly important, as the interconnectedness of medical devices in itself introduces a major vulnerability, given the impact on patient safety and data integrity. Past works in this domain have been afflicted by persistent issues of high false-positive rates, resource utilization inefficiency, and failure to adapt to rapidly changing threat spaces. The paper presents a novel framework that incorporates ProSRN, which provides the robustness in threat detection, and ICOM for dynamic weight estimation. ProSRN adopts a residual-connected stacking approach to reach high accuracy of detection while reducing the complexity in training and enhancing responsiveness in real time. It is further complemented by ICOM, which will then dynamically optimize network parameters to yield an adaptive IoT health security model with the capability of addressing evolving threats. Together, ProSRN and ICOM advance both detection and optimization significantly, enhancing the adaptiveness, efficiency, and security in IoT healthcare systems. This creates a concrete basis for developing resilient frameworks that can protect sensitive healthcare data against emerging threats. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： And deep learning Attack detection Classification Healthcare systems Internet of things (IoT) Optimization Security

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Cloud Computing Network in Remote Sensing-Based Climate Detection Using Machine Learning Algorithms

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 400-408页

作者： Srinivas, Jhade Raju, Ch VV Narasimha Sasikala, C. Bhaskar, Parumanchala Panyala, Amarendra Reddy Priya, Divya Department of Computer Science and Engineering - Data Science K G Reddy College of Engineering and Technology Moinabad Telangana Hyderabad India Information Technology Marri Laxman Reddy Institute of Technology and Management Dundigal Telangana Hyderabad 500043 India Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Andhra Pradesh Ananthapuramu India Department of Computer Science and Engineering Santhiram Engineering College Nandyal India Department of Computer Science and Engineering (Data Science) Malla Reddy University Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Effective monitoring of the environment over a large area will require mobilization of a considerable amount of information. Otherwise, the use of traditional methods will prove to be costly and would take up so much time. Also, assessing the land cover changes between two times is one of the other methods which can be adopted to measure the environmental sustainability. Using ML techniques for predictive modeling in remote sensing has emerged as a useful approach in solving some of the environmental and climatic challenges. This research proposes a novel method in remote sensing image-based climate detection utilizing cloud computing and machine learning algorithms. Here, the cloud computing module is used in the collection of remote sensing images for the detection of climate changes. Then, the collected image is processed for noise removal as well as normalization. The processed image is segmented, and features are extracted utilizing a fuzzy K-clustering-based contour Gaussian model with graph cut Boltzmann CNN. Experimental analysis is carried out for various remote sensing-based climate change datasets in terms of training accuracy, random precision, AUC, recall, and F-measure. The proposed technique obtained 97% of training accuracy, 95% of random precision, 96% of recall, 93% of AUC, and 94% of F-measure. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate detection Cloud computing Contour Gaussian model Machine learning algorithms Remote sensing images

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Gestational Diabetes Prediction Using ML Integrated with Web UI

Gestational Diabetes Prediction Using ML Integrated with Web...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Shrija, G. Sibi Prasad, D. Hemalatha, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521394

Promoting maternal health is significant as GDM is a severe health concern for both infants and mothers. As opposed to the conventional systems based primarily on clinical diagnosis, this study attempts to develop a system that utilizes machine learning algorithms and Django web application to predict and manage GDM. The system analyzes key indicators such as clinical data, family members' medical history, and lifestyle trends to make predictions through an easy-to-use web-based interface. With an emphasis on the early detection and timely interventions, this study aims to improve wellbeing and health among mothers and her children. © 2025 IEEE.

关键词： Electrotherapeutics

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Kookaburra Optimization Algorithm and Feature Aggregated Region based Differential network for Colon Cancer Detection from Histopathological Images 8

Kookaburra Optimization Algorithm and Feature Aggregated Reg...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Gowthamy, J. Ramesh, S.S. Subashka Rm Institute of Science and Technology Amapuram Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Ramapuram Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350376425

Colon cancer is a significant health concern, ranking as the third leading cause of cancer-related deaths in men and the second in women. Early detection is crucial in reducing the incidence and mortality rates associated with this malignancy. This paper introduces a novel Feature Aggregated Region-based Differential Network (FA-RDNet) designed for the accurate classification of colon cancer from histopathological images. Our approach leverages Multiple Instance Learning (MIL) to effectively handle large datasets structured as bags of instances, addressing the limitations of conventional MIL techniques. A key innovation of our work is the integration of an adaptive feature enhancement module that bilaterally updates multi-level deep features, allowing for efficient extraction of both low-level details and high-level semantic information. Furthermore, we introduce the Kookaburra Optimization Algorithm (KOA) to fine-tune the parameters of FA-RDNet, utilizing a unique two-phase mathematical modeling process inspired by the hunting behaviors of kookaburras. This enhances the model's optimization and classification accuracy. The effectiveness of our proposed system is validated using the publicly available LC25000 dataset, where it achieved a remarkable accuracy of 94.42%, outperforming existing state-of-the-art methods in colon cancer detection. The high computational efficiency and accuracy of FA-RDNet highlight its potential for practical applications in clinical settings, marking a significant advancement in the field of medical image analysis. © 2024 IEEE.

关键词： Lung cancer

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Multi-Objective Molecular Design Through Learning Latent Pareto Set 39

Multi-Objective Molecular Design Through Learning Latent Par...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Yiping Yang, Jiahao Ren, Xuanbai Zhang, Xinyi Liu, Yuansheng Song, Bosheng Zeng, Xiangxiang Ishibuchi, Hisao College of Computer Science and Electronic Engineering Hunan University China Department of Computer Science and Engineering Southern University of Science and Technology China

ISBN: (纸本)157735897X

Molecular design inherently involves the optimization of multiple conflicting objectives, such as enhancing bio-activity and ensuring synthesizability. Evaluating these objectives often requires resource-intensive computations or physical experiments. Current molecular design methodologies typically approximate the Pareto set using a limited number of molecules. In this paper, we present an innovative approach, called Multi-Objective Molecular design through Learning Latent Pareto Set (MLPS). MLPS initially utilizes an encoder-decoder model to seamlessly transform the discrete chemical space into a continuous latent space. We then employ local Bayesian optimization models to efficiently search for local optimal solutions (i.e., molecules) within predefined trust regions. Using surrogate objective values derived from these local models, we train a global Pareto set learning model to understand the mapping between direction vectors (called "preferences") in the objective space and the entire Pareto set in the continuous latent space. Both the global Pareto set learning model and local Bayesian optimization models collaborate to discover high-quality solutions and adapt the trust regions dynamically. Our work is an effective endeavor towards learning the Pareto set for multi-objective molecular design, providing decision-makers with the capability to fine-tune their preferences and thoroughly explore the Pareto set. Experimental results demonstrate that MLPS achieves state-of-the-art performance across various multi-objective scenarios, encompassing diverse objective types and varying numbers of objectives. The effectiveness of MLPS was further validated through real-world challenges in discovering antifungal peptides with low toxicity and high activity. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Set theory

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TrackNCount - Intelligent Vehicle Tracking, Counting, and Speed Estimation using YOLOv8 and DeepSORT Algorithms 6

TrackNCount - Intelligent Vehicle Tracking, Counting, and Sp...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Sathiyaprasad, B. Reddy, Paluru Ambareesh Ram Venkatesh, Digumalla Manisri Department of Computer Science and Engineering Sathyabama institute of science and technology Chennai India

ISBN: (纸本)9798331522667

For effective city planning and traffic control, it is now crucial to develop some effective monitoring systems for vehicle traffic in order to address this quickly growing tendency within a city. As a result, the TrackNCount project combines DeepSORT with YOLOv8 for object identification to create a smart system for tracking, counting, and speed estimation of vehicles. The idea behind this design is to use sophisticated computer vision techniques in conjunction with efficient algorithms to handle real-world scenarios. Setting up a reliable procedure that recognizes cars, gives them distinct IDs, and records their movements through video frames is our first priority. The system computes vehicle speeds using the Euclidean distance formula, manages item trajectories using double-ended queues, and detects entering and outgoing vehicles based on directional thresholds. Motion trails and bounding boxes are two further visual aids that improve the results' readability. With the added benefit of simple speed estimation feature integration, the model provides exceptional accuracy in real-time vehicle monitoring and counting. Experiments conducted on a variety of traffic scenarios consistently showed that this system can deliver precise vehicle counts in designated directions and speed calculations with a small margin of error. The findings show that TrackNCount is a viable and scalable solution for data-driven city planning and intelligent traffic management. © 2025 IEEE.

关键词： Street traffic control

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