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Early Detection and Self-Management of Diabetes Using Wearable Devices and Machine Learning applications 1

Early Detection and Self-Management of Diabetes Using Wearab...

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1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Senapati, Madhusmita Dodia, Manish Kamal, Lubna Mahapatra, Hariballav Hudar, Jayant Maruti Khuntia, Monalisa School of Sports and Yogic Sciences KIIT UNIVERSITY Odisha Bhubaneswar India Veer Narmad University Gujarat Surat India State JLN HOMEOPATHIC MEDICAL COLLEGE Uttar Pradesh Lucknow India Sevayan Diabetes Centre Puri Shree Hospitals Department of Diabetology Odisha Bhubaneswar India University Switzerland Navi Mumbai India KIIT School of Rural Management KIIT Deemed to be University Odisha Bhubaneswar India

ISBN: (纸本)9798331518578

In order to lessen the burden on medical systems and improve patient results, the increasing incidence of diabetes has called for creative strategies for early identification and efficient self-management. When combined with machine learning (ML) applications, wearable technology has the potential to revolutionise the treatment of diabetes. These gadgets can continuously check vital physiological indicators including blood sugar levels, heart rate, activity patterns, and sleep quality since they are outfitted with sophisticated biosensors. This rich data stream may be analysed to find patterns, forecast trends, and offer tailored suggestions owing to the incorporation of ML techniques. ML-driven methods that use information from wearable technology and other medical sources to identify early indicators of glucose dysregulation-often before clinical symptoms appear-help predict diabetes upfront. Simultaneously, ML-powered self-management technologies assist people in making the best lifestyle decisions possible by offering real-time feedback and forecast insights. Features involving glycaemic trend projections, automated alerts for potential hypoglycemia or hyperglycemia, and tailored suggestions for food, exercise, and medication compliance enable consumers to maintain better glycaemic control. Additionally, by facilitating data sharing as well as remote surveillance, these devices can enhance patient-provider relations and guarantee prompt interventions. Considering its potential, issues like algorithm bias, data privacy, and availibility need to be resolved to guarantee fair and efficient use. This study examines how wearable technology and machine learning can work together to improve diabetes treatment by boosting proactive management, boosting early detection, and eventually raising the quality of life for those who have the disease. © 2025 ieee.

关键词： Diseases

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real-time, Multistatic RFID Localization with Commodity, Unsynchronized Radios

Real-Time, Multistatic RFID Localization with Commodity, Uns...

引用

ieee international conference on RFID

作者： Dimitrios Angelou Aggelos Bletsas School of Electrical and Computer Engineering Technical University of Crete Chania Greece Dept. of Electrical and Computer Engineering Rutgers WINLAB The State University of New Jersey

ISBN: (数字)9798331509057

ISBN: (纸本)9798331509064

Passive RFID tag localization has been mostly focused on monostatic and/or synchronized setups. This work moves a few steps further and performs real-time localization with distributed, i.e., multistatic radios, which are also unsynchronized at the carrier level. It succeeds by exploiting the elliptical direction of arrival (EllDoA) algorithm with a “playback” carrier frequency offset (CFO) mitigation method, showing localization feasibility at a small error cost, even with very cheap software defined radios. The inherent carrier phase offset (CPO) of such distributed setups is addressed, and it is shown that the calibration step needs to be performed only once, and not for further reruns of the experiment; thus, the proposed method is suitable for many real-world and real-time applications, which has not been shown before, to the best of our knowledge.

关键词： Location awareness Three-dimensional displays Prevention and mitigation Scalability Software algorithms real-time systems Calibration Synchronization Radiofrequency identification Software radio

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Rapid Precise time and Frequency Transfer Using GPS and Galileo Multi-Frequency Precise Point Positioning with Ambiguity Resolution

Rapid Precise Time and Frequency Transfer Using GPS and Gali...

引用

Power Electronics, Computer applications (ICPECA), ieee international conference on

作者： Haoyu Wang Wei Jiang Bingyan Zheng Mingyu Men Beijing University of Civil Engineering Architecture Beijing China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Precise Point Positioning (PPP) has become a versatile and competitive technique for time and frequency transfer across various fields. However, PPP's long convergence time, caused by slow satellite geometry changes and low pseudorange accuracy, limits its use in real-time time and frequency transfer. This study investigates the impact of GPS and Galileo multi-frequency PPP ambiguity resolution (PPPAR) on time and frequency transfer stability and convergence. We established four time links and evaluated six system and frequency combinations. The results show that multiconstellation and multi-frequency approaches significantly improve short-term stability. They reduce the standard deviation of epoch differences by 10% to 25%. The modified Allan deviation (MDEV) results indicate that multiconstellation and multi-frequency approaches enhance frequency stability by 15 % to 30 % over a 90 s averaging period. While long-term stability remains influenced by satellite clock offsets and environmental factors, multi-constellation and multi-frequency PPP-AR proved to be a robust solution for rapid convergence and enhanced short-time stability, making it particularly suitable for real-time applications.

关键词： time-frequency analysis Satellites Accuracy real-time systems Environmental factors Standards Signal resolution Convergence Global Positioning System Clocks

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A Deep Learning-Driven Landslide Detection System Using Optimized AlexNet

A Deep Learning-Driven Landslide Detection System Using Opti...

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ieee Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： B Janani A Praveen B Santhosh Kumar J R Tamilmani Department of Computer Science and Engineering K. S. Rangasamy College of Technology India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Landslip detection is a key duty in disaster management, necessitating quick and precise diagnosis to avoid loss of life and property. This work describes a deep learning-driven landslip detection system based on an optimized AlexNet architecture. The suggested method improves the traditional AlexNet model by employing optimization strategies targeted at increasing detection accuracy while decreasing computing complexity. The method detects landslides from satellite and aerial imagery more effectively after fine-tuning the network’s hyperparameters and including additional layers for feature extraction. Comprehensive trials on benchmark landslide datasets reveal that the optimized AlexNet model achieves an accuracy of 92.1%, significantly outperforming standard machine learning (ML) approaches. The model’s precision, recall, and F1-score for landslide detection are 0.88, 0.85, and 0.86, respectively, while for non-landslides, these metrics are 0.94, 0.96, and 0.95, respectively. The system’s efficient processing capability qualifies it for real-time applications, making it a promising solution for large-scale landslip monitoring and early warning systems. This paper adds to the expanding body of research on deep learning-based disaster management systems by introducing a robust, scalable, and efficient landslip detection framework.

关键词： Landslides Accuracy Satellites Computational modeling Disaster management Alarm systems real-time systems Satellite images Standards Optimization

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Enhancing Carbon Emission Tracking with Gemini: An AI Solution for Sustainable Development

Enhancing Carbon Emission Tracking with Gemini: An AI Soluti...

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AIML-applications for Engineering & Technology (ICAET), international conference on

作者： Annada Dash Bhavini Mathur Piyusha Patil B.Tech in Computer Engineering Honors in Data Science and ML Cummins College of Engineering for Women Pune India B.Tech in Computer Engineering Cummins College of Engineering for Women Pune India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

This research explores the integration of advanced AI technologies, specifically Google's Gemini-1.5-Flash-001 model and Vertex AI, into the development of EcoTrack-a mobile application that helps users monitor and reduce their carbon emissions. At the core of this innovation is EcoBot, an AI-powered chatbot embedded within EcoTrack, designed to deliver personalized, actionable insights on sustainability. By leveraging Gemini's capabilities alongside real-time data through Vertex AI, EcoBot enhances user engagement by offering tailored advice on reducing their carbon footprint. The study illustrates how EcoBot processes various data points, including user input and environmental metrics, to generate precise recommendations for users looking to make more sustainable choices. The focus of this work is on the importance of scalable AI frameworks that can adapt to evolving sustainability goals while efficiently managing real-time data. Additionally, this paper addresses the ethical dimensions of using AI for environmental purposes, discussing its potential to drive meaningful action at both the individual and societal levels, contributing to the global effort to combat climate change.

关键词： Climate change Carbon dioxide Chatbots real-time systems Mobile applications Artificial intelligence Sustainable development Recommender systems Monitoring

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Dense Point Cloud Mapping by Leveraging Neural-Based Monocular Depth Estimation

Dense Point Cloud Mapping by Leveraging Neural-Based Monocul...

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Annual ieee systems conference

作者： Luiz E. S. Araujo Filho Kleber Cabral Sidney Givigi Cairo L. Nascimento Division of Electronic Engineering Instituto Tecnológico de Aeronáutica São José dos Campos SP Brazil School of Computing Queen's University Kingston ON Canada

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

Accurate and efficient 3D mapping is essential for applications such as robotics, autonomous navigation, and augmented reality. Traditional mapping methods often rely on expensive depth sensors or sparse monocular Simultaneous Localization and Mapping (SLAM) techniques, both of which face limitations in certain environments. This paper proposes a novel pipeline that integrates neural network-based monocular depth estimation (MDE) into an RGB-D SLAM framework, enabling dense 3D mapping using only RGB images. The pipeline utilizes a neural network to infer depth maps from monocular RGB input, followed by a filtering module to ensure depth consistency based on visual odometry. The resulting dense depth maps are used alongside RGB data to generate detailed point clouds. Experimental evaluations conducted in indoor environments demonstrate that the proposed approach significantly enhances the volumetric density and geometric fidelity of 3D point cloud maps compared to traditional RGB-D SLAM systems having near real-time inference rate. The method also addresses key challenges such as sparsity and limited sensor range, while being modular and easy to adapt for other models and subsystems, laying the foundations for robust and cost-effective mapping solutions. Future work will explore extending the framework to outdoor environments and improving real-time performance as well robustness to out-of-distribution scenarios.

关键词： Point cloud compression Simultaneous localization and mapping Three-dimensional displays Depth measurement Pipelines Neural networks real-time systems Robustness Sensors Visual odometry

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A Lightweight Image Encoding Framework for High-Speed Acquisition and real-time Detection in embedded systems 25

A Lightweight Image Encoding Framework for High-Speed Acquis...

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Proceedings of the 4th international conference on Computer, Artificial Intelligence and Control Engineering

作者： Fan Wang Jian Zheng Yingwei Xia Jialing Wu Anhui Institute of Optics and Fine Mechanics Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei Anhui China School of Physics and Electronic Information Anhui Normal University Wuhu Anhui China

ISBN: (纸本)9798400712647

With the rapid development of artificial intelligence technology, image processing systems have become an important technical foundation to support real-time surveillance and other tasks in the security and military fields. Addressing the diversified demands of complex environments, the study of lightweight and high-frame-rate image processing systems holds significant value in enhancing system real-time performance and computational efficiency. Moreover, it provides essential support for a wide range of applications under resource-constrained conditions. To this end, this paper proposes a design scheme for a lightweight real-time image processing system based on FPGA and Hisilicon platform. The system achieves high-frame-rate image acquisition through a Cameralink camera and FPGA embedded platform. It leverages the Hisilicon platform's coding and RTSP modules to perform efficient image encoding and transmission. Additionally, improvements in the image coding processing unit enhance the execution efficiency of downstream tasks, such as target detection. At 640×480 resolution, the system achieves an image acquisition frame rate of 333fps and a neural network inference speed of 22-24fps, which is 37.68% faster than the RK3568 platform and 74.91% faster than the V853 platform. Experimental results show that the system performs well in improving real-time image processing performance, providing a lightweight solution for efficient and reliable real-time intelligent image analysis in the security field.

关键词： embedded

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Framework Design of Fault Diagnosis System Based on Improved LSTM Networks

Framework Design of Fault Diagnosis System Based on Improved...

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Power Electronics, Computer applications (ICPECA), ieee international conference on

作者： Yixin Wang University of Geosciences (Wuhan) Wuhan China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

In the current era of high industrialisation, fault diagnosis of complex industrial systems is facing unprecedented challenges. Traditional fault diagnosis methods based on physical models and signal processing gradually show their limitations, especially when facing nonlinear and nonsmooth signals as well as fusion of multi-source information. In this paper, we propose a framework design of a fault diagnosis system based on a long short-term memory network (LSTM) with improved attention mechanism. The system employs high-precision sensors to collect real-time operating signals from key components, and analyses and diagnoses these signals through a deep learning model to quickly and accurately determine whether there is a fault in the equipment and provide detailed fault type and location information. In particular, an attention mechanism is introduced to enhance the model's focus on key features in the fault signals, thereby improving the accuracy and reliability of fault diagnosis. Experimental results show that the proposed LSTM-Attention network performs well on the bearing fault dataset and achieves high classification accuracy.

关键词： Fault diagnosis Deep learning Attention mechanisms Accuracy Signal processing Sensor systems real-time systems Power electronics Sensors Long short term memory

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Marine Mammals Sound Classification Using a Lightweight CNN for Edge computing Devices

Marine Mammals Sound Classification Using a Lightweight CNN ...

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international conference on Communication systems and Network Technologies (CSNT)

作者： Ankit Kumar Vellalacheruvu Venkata Siva Sai Anil Kumar Bethi Pardhasaradhi Sagar Koorapati Linga Reddy Cenkeramaddi ICT Dept. University of Agder Norway Smart Manufacturing Department Indian Institute of Information Technology Design and Manufacturing Kancheepuram ICT Dept. Manipal Academy of Higher Education Manipal Institute of Technology Bengaluru India Qualcomm Technologies CPU Design Group Austin TX

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

This paper presents a lightweight Convolutional Neural Network (CNN) for marine mammals sound classification. Seven classes are considered in this work, namely humpback whale, killer whale, pilot whale, sperm whale, spinner dolphin, striped dolphin, and white whale. The sound signals are transformed from the .wav file to time-frequency images through Continuous Wavelet Transform (CWT). The proposed CNN consumes time-frequency images as input and consumes only a 0.8 MB size model benchmarked against several well-known pre-trained models, including DenseNet, Efficient-Net, InceptionNet, MobileNet, NASNet, ResNet, VGGNet, and Xception. Experimental results demonstrate that the proposed lightweight CNN significantly outperformed pre-trained models, able to achieve a validation accuracy of 80.9% in a ten-fold cross-validation framework. The best performance among the pre-trained models is Mo-bileNetV3Small, which achieved only 78.91% validation accuracy in a ten-fold cross-validation framework with a considerably larger model size of 4 MB. Furthermore, the proposed model was successfully deployed on the Raspberry Pi IOT module and able to run with 30.134 milliseconds per inference. The lightweight CNN’s high accuracy and low latency make it an ideal candidate for applications in real-time audio event detection in underwater with resource-constrained devices.

关键词： time-frequency analysis Accuracy Continuous wavelet transforms Computational modeling Whales real-time systems Convolutional neural networks Internet of Things Edge computing Dolphins

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Anomaly Detection in Meteorological Data Using Machine Learning Techniques

Anomaly Detection in Meteorological Data Using Machine Learn...

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ieee Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Uddhav Rawal Subhas Patel School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Sehore Madhya Pradesh India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Anomaly detection is a very important component in weather forecasting, disaster preparedness and climate modeling in meteorology. We use Density Based Spatial Clustering of applications with Noise, Isolation Forest, Local Outlier Factor, Elliptic Envelope and One Class Support Vector Machine to detect anomalies in Tunisian meteorological data in this study. Key meteorological parameters, such as wind speed, rainfall and temperature, was analyzed as the important indicators of atmospheric conditions. DBSCAN detected local density changes very well and is therefore a good choice for anomaly detection in areas with complex spatial patterns from the models used. Isolation Forest algorithm was able to find global anomalies with robust performance, giving very high true positive rates with minimum false positives. The use of these machine learning techniques is complementary and this dual approach shows how they can be used to detect different kinds of anomalies. The results show how machine learning can be used to make environmental monitoring and weather prediction much more accurate and reliable, with the ability to detect anomalies precisely and reliably. Having such capabilities is critical for taking proactive actions in response to extreme weather events, and to be able to intervene at the right time to minimize the impacts of the events. Additionally, these models could be integrated into real time monitoring systems to provide additional utility in optimizing their use and provide actionable insight for disaster management and climate resilience. Further research could involve the fusion of these algorithms with deep learning methods, larger datasets, and real time deployment to build intelligent systems that can learn and adapt to changing environmental conditions. This would be a large advance in applying machine learning in meteorology.

关键词： Support vector machines Machine learning algorithms Rain Wind speed Atmospheric modeling Weather forecasting Forestry real-time systems Tuning Anomaly detection

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