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共 89770 条记录，以下是261-270 订阅

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Performance Evaluation of Machine Learning Approaches for Wearable Sensor-Driven Human Activity Recognition for Elderly People 2

Performance Evaluation of Machine Learning Approaches for We...

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2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Shafi, Shaik Jeeharshitha, K. Sony, G. Reddy, Varshitha Kumar, K Charan Kiran Kumar, G. Electronics and Communication Engineering B V Raju Institute of Technology Medak Narsapur India ECE BVRIT Medak Narsapur India ECE VNR VJIET Hyderabad India

ISBN: (纸本)9798331505745

In the recent past, due to the availability of data from Wearable Sensors and Smart Phones at lower cost with energy efficiency, recognizing and tracking uncharacteristic behavior particularly in elderly people has become focal point in research. In addition, there exists more than 962 million aged people globally and 1 in 6 people will be aged 60 years or over by 2030. Thus, in this paper, we aim to analyze and assist elderly people in performing daily activities using wearable sensor data, considering the decline in both physical activity and mental abilities due to aging factor. To achieve this, monitoring elderly people's daily activities in indoor environments via wearable sensors data is essential. Machine Learning algorithms like Support Vector Machine (SVM), Decision Tree, and Random Forest are utilized for activity recognition using the data collected from wearable sensors. These algorithms are chosen to handle timestamped data and for their ability to deliver high accuracy, and optimized memory usage, making them well-suited for this application. Furthermore, performance of three machine learning algorithms has been evaluated using accuracy, precision, F1 score, recall and support parameters. Among all the techniques tested, the SVM accomplished superior accuracy, achieving 96.34 %, followed by Random Forest at 92.67%, and Decision Tree with 85.57%. © 2025 IEEE.

关键词： Random forests

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Deciphering Diabetes: Illuminating Prognostic Insights with an Interpretable Machine Learning Framework 3rd

Deciphering Diabetes: Illuminating Prognostic Insights with ...

引用

3rd international conference on Intelligent systems and Sustainable Computing, ICISSC 2023

作者： Rony, Mohammad Abu Tareq Islam, Mohammad Shariful Saha, Pritom Johora, Fatama Tuz Ahammad, Mejbah Alam, Shah Md Nazmul Rahman, Md Saifur Nag, Anindya Department of Statistics Noakhali Science and Technology University Noakhali3814 Bangladesh Computer Science and Telecommunication Engineering Noakhali Science and Technology University NoakhaliNoakhali-3814 Bangladesh Computer Science and Engineering Daffodil International University Dhaka Bangladesh Computer Science and Engineering University of Chittagong Chittagong Bangladesh Computer Science and Engineering American International University Bangladesh Dhaka Bangladesh Masters of Computer Science University Arizona State University Tempe United States Strategic Management University UniversityoftheCumberlands Williamsburg United States Computer Science and Engineering Khulna University Khulna9208 Bangladesh

ISBN: (纸本)9789819783540

Diabetes is a chronic disease that impacts millions of people globally. Early detection and management of diabetes can help mitigate potential complications and enhance patient outcomes. Machine learning (ML) algorithms have become more prevalent for predicting diabetes in recent years. The research paper proposed a machine learning-based approach for predicting diabetes utilizing the Diabetes Health indicator dataset and compared the comprehensive performance of multiple ML algorithms. The study used the LIME (Local Interpretable Model-Agnostic Explanations) package to provide interpretable predictions and acquire insights into the factors contributing to the model’s predictions. Although the ML models were interpretable, they also delivered the highest accuracy 86% demonstrated by the XGBoost classifier in prediction. The LIME produces local explanations for individual predictions, emphasizing the most significant features contributing to the prediction. The study found that, for the XGB model, BMI has a negative influence, while Genhlth positively impacts the outcome. Moreover, Age and Income have a positive impact on the outcome of diabetes. The findings of this research can potentially improve patient outcomes and alleviate the global burden of diabetes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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A Novel Routing Protocol for UWSN Using Energy Efficient Marine Predator Optimization Technique

A Novel Routing Protocol for UWSN Using Energy Efficient Mar...

引用

international conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Pandey, Anmol Singh, Anujaya Mishra, Anushka Pal, Ankit Pandey, Ashish Galgotias College of Engineering and Technology Electronics and Communication Engineering Greater Noida Noida India

ISBN: (纸本)9789819795062

The novel metaheuristic algorithms Energy Efficient Marine Predator OptimizationInspired Algorithm (EEMPOIA), Energy Efficient Grey Wolf OptimizationInspired Algorithm (EEGWOIA), Energy Efficient Particle swarm OptimizationInspired Algorithm (EEPSOIA), and Least distance Clustering (LDC) are introduced in this study and are specifically designed to address the problems with underwater wireless sensor networks (UWSNs). In this rigorous environment, the objective is to increase energy efficiency and network longevity. EEMPOIA is found to be the most efficient method through extensive simulations utilizing a three-tier UWSN model, considerably increasing network longevity while lowering energy consumption. These results illustrate EEMPOIA's potential as a promising approach to enhancing UWSN performance. The combination of these algorithms creates new opportunities for using long-lasting and energy-efficient underwater sensing systems in various industrial and environmental settings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Particle swarm optimization (PSO)

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Enhancing GPS/IMU Localization Accuracy in Autonomous Vehicles through Deep Learning-Based Error Correction Models 9

Enhancing GPS/IMU Localization Accuracy in Autonomous Vehicl...

引用

9th international conference on Mechanical engineering and Robotics Research, ICMERR 2025

作者： Onyema, Uchenna Charles Mahmoud, Shafik College of Science and Engineering University of Derby Derby United Kingdom

ISBN: (纸本)9798331509262

Improving the precision of GPS/IMU localisation in autonomous cars is crucial for ensuring safe and efficient navigation. Several research studies have concentrated on enhancing the precision of localisation systems by employing sensor fusion and sophisticated algorithms. This study proposes a novel approach to enhance GPS/IMU localization accuracy in autonomous vehicles using deep learning-based error correction models. Leveraging Long Short-Term Memory (LSTM) networks, the method captures and corrects inherent errors in GPS/IMU data, leading to significantly improved positional accuracy. The LSTM model was meticulously designed to process sequential data, incorporating an LSTM layer with 50 units to capture temporal relationships and a Dense output layer to predict corrected longitude and latitude values. The model was trained using the Adam optimizer and mean squared error (MSE) loss function, achieving notable reductions in prediction error across 50 epochs. Comparative analyses between actual and predicted coordinates demonstrated the model's high precision. The model was further validated through deployment in a Flask application for continuous testing and a web application for real-time tracking of autonomous vehicles. Results underscore the potential of deep learning models to substantially improve localization accuracy, thereby enhancing the reliability and performance of autonomous navigation systems. © 2025 IEEE.

关键词： Deep learning

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Evaluation of UAV Path Planning Approaches for Urban Air Mobility 3

Evaluation of UAV Path Planning Approaches for Urban Air Mob...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Pasha, Tanzeem Shri Hari, M. Shreeram, K.R. Bhuvana Suganthi, D. Munavalli, Jyoti Raghavendra Bnm Institute of Technology Dept. of Electronics & Communication Engineering Bengaluru India

ISBN: (纸本)9798331515911

In contemporary society, it is pertinent to address the feature of the Urban Air Mobility (UAM), a new mode of urban transport, whereby unmanned aerial vehicles transport people and goods with the view to rendering relief to ground traffic congestion. However, challenging as it may be, the deployment of the UAVs within the confines of the urban built environment is complex. In this systematic review, we provide state-of-the-art developments found in multi-UAV path planning for UAM. We highlight the different aspects of navigation strategies, cooperative algorithms, optimization methods, as well as collision avoidance techniques over the years. In total, we conducted analyses on thirty recent studies, such as those that deal with the combination of machine learning, bio-inspired algorithms, and other environmental-induced factors involved in the path planning process. The review results exhibit significant advances in navigation-aware planning, cooperative systems, advanced optimization techniques, and real-alive applications. However, while all these have been achieved, adaptations in real-time, dynamic environment management, and the integration of learning-based methods with conventional control methods are challenges that have continued to be faced. Future research will propose directions to fill in these gaps to further the progress towards developing efficacious, safe, and sustainable urban air transportation systems. © 2025 IEEE.

关键词： Traffic congestion

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Application of Machine Learning algorithms in Artificial Intelligence Image Recognition Technology 1

Application of Machine Learning Algorithms in Artificial Int...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Ouyang, Qi Dai, Siyang Xie, Kexin Sun, Zeyu Wang, Yao Polytechnic of Milan Milan Italy Faculty of Engineering and Information Technology The University of Melbourne VIC Australia College of Business and Public Management Wenzhou-Kean University Wenzhou China School of Environment and Science Griffith University Brisbane Australia Nanjing Stomatological Hospital Affiliated Hospital of Medical School Institute of Stomatology Nanjing University Nanjing China

ISBN: (纸本)9798331529246

In smart city applications such as medical image analysis, autonomous driving, and security monitoring, image recognition faces challenges like complex backgrounds, low-quality images, and diverse targets, affecting accuracy and robustness. This study explores machine learning algorithms to enhance image recognition performance by addressing noise interference, intricate backgrounds, and feature extraction difficulties. It combines Convolutional Neural Networks (CNN) with transfer learning, starting with data preprocessing to reduce noise, using pre-trained CNN models to extract high-level features, and fine-tuning with a ResNet transfer learning strategy for specific tasks. Additionally, ensemble learning methods are employed to further improve model robustness and accuracy. Experimental results show that the ensemble model maintains around 85% accuracy even with high background complexity, and transfer learning achieves 90% accuracy when the sample size reaches 1000. These findings demonstrate that transfer and ensemble learning effectively enhance image recognition accuracy and resilience in complex environments. © 2025 IEEE.

关键词： Adversarial machine learning

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A Survey of Advanced algorithms and Experimental Approaches for Kidney Abnormality Detection in Ultrasound Imaging 3

A Survey of Advanced Algorithms and Experimental Approaches ...

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3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Chitra, N. Thulasi Gogula, Sreenivasulu Satapathy, Sukla Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad500075 India Telangana Hyderabad501218 India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Machine Learning Telangana Hyderabad500075 India

ISBN: (纸本)9798331509675

Kidney abnormalities, such as cysts, tumors, and stones, are critical health concerns requiring early and precise detection. This study reviews advanced algorithms and experimental approaches for kidney abnormality detection in ultrasound imaging, emphasizing deep learning (DL) methodologies and explainable AI (XAI) techniques. Contributions include exploring CNN-based segmentation frameworks like SDFNet and U-Net, multi-scale feature fusion, and transfer learning to enhance diagnostic accuracy. Data augmentation and adversarial training are employed to address dataset bias, while Grad-CAM and CAManim provide interpretability, fostering clinician trust. Results demonstrate significant improvements in diagnostic performance, with sensitivity and specificity exceeding 95% and Dice coefficients surpassing 95% on diverse clinical datasets. The findings highlight the transformative potential of DL to automate kidney abnormality detection, reducing dependency on operator expertise while improving accuracy and efficiency. Challenges such as dataset scarcity and computational complexity persist, but future research on multimodal imaging, CNN optimization, and emerging technologies like quantum machine learning presents promising opportunities. This study serves as a guide for advancing diagnostic tools, paving the way for next-generation solutions in clinical applications. © 2025 IEEE.

关键词： Ultrasonic imaging

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Securing Wireless and Optical Networks: Advanced Strategies for Network and Information Security in Modern Communication systems

Securing Wireless and Optical Networks: Advanced Strategies ...

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2025 IEEE international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Dhasmana, Gulshan Sharma, Vishal Boob, Nandini Shirish Aravind, K. Reddy, R. Akhilesh Yadav, Kanchan Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India

ISBN: (纸本)9798331512965

In the era of rapidly changing communication systems, securing wireless and optical networks has become of essence to protect sensitive data and ensure operation. This study addresses network and information security in current communication systems by studying cutting edge techniques to enhance network and information security in wireless and optical networks. The wireless networks are convenient and may be moved around, but they are also prone to hacking, illegal access or some other cyberattacks. As optical networks possess both a large capacity and relatively low latency, it is not without special security issues such as fibre tapping and signal jamming. This study presents detailed analyses of state of the art authentication techniques, intrusion detection systems (IDS), and encryption algorithms specifically designed for these networks. Then there are, such as Quantum Key Distribution (QKD) which almost assuarly unbreakable techniques are examined alongside conventional cryptographic methods. Additionally, anomaly detection based on machine learning is explored for real time threat identification in optical and wireless channels. It stresses the need of the cross domain solutions and multi layered security frameworks covering the network layer and the physical security measures. Through case studies and recent developments in cyber threats and network resilience, this article provides a thorough understanding of how these tactics could be used to make networks such as wireless network and optical network more resilient against increasing cyber risks. © 2025 IEEE.

关键词： Network intrusion

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Machine Learning-Based Crop Recommendation System for Sustainable Agriculture 4

Machine Learning-Based Crop Recommendation System for Sustai...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sriharsha, Kantheti Chandan Joshi, Kotapuri Rahul Reddy, Vummadi Ravi Sankar Chowdary, Vadlapudi Kalyan Hanuman Hajarathaiah, Koduru Rajak, Shaik School of Computer Science and Engineering VIT-AP University Amaravati India Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Amaravati India

ISBN: (纸本)9798331523923

This research paper addresses the critical challenges in agricultural crop recommendation systems through machine learning approaches. Current systems face significant limitations including limited integration of real-time environmental data, inconsistent prediction accuracy across diverse geographical regions, difficulty in handling multiple conflicting parameters simultaneously, and lack of interpretability in recommendation decisions. Recent techniques like Random Forest, Support Vector Machines, and Deep Neural Networks have attempted to address these issues but with varying degrees of success. This paper proposes an enhanced crop recommendation system that leverages multiple machine learning algorithms to overcome these limitations. The system implements real-time soil and weather parameter integration, while providing interpretable recommendations to farmers. Our approach achieves 99.54% accuracy in crop recommendations, significantly improving upon existing systems. The proposed model serves as a comprehensive solution for sustainable agriculture, contributing to improved crop yield and farmer decision-making processes. The system's effectiveness is validated across different agricultural zones, demonstrating its practical applicability and scalability in diverse farming conditions. © 2025 IEEE.

关键词： Adversarial machine learning

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Comprehensive Analysis on Optimal Routing in Wireless Mesh Network Using Optimization and Machine/Deep Learning algorithms 3

Comprehensive Analysis on Optimal Routing in Wireless Mesh N...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Kumar, Kaata Ranjith Bala, K. Srinivas, Kalyanapu Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Vaagdevi Engineering College Department of Computer Science and Engineering Warangal India

ISBN: (纸本)9798331508456

Wireless Mesh Networks (WMNs) is an important component of wireless communications by enhancing the network coverage. The mesh network topology is interconnecting all nodes in the cluster. However, the existing approaches utilized for optimal routing in WMN face challenges such as increased delays, packet loss, network congestion that lead to inefficient data transmission. WMN has an ability to deliver efficient performance even with a large number of devices. Additionally, WMNs support a wide range of applications, by using its effectiveness in various scenarios. The fundamental architecture of a mesh network integrates multiple wireless nodes interconnected through base stations for efficient routing communication. Mesh networks able to adopt different client-based structures, including infrastructure and hybrid configurations. Optimization, learning techniques and Software defined network methods are utilized for efficient optimal routing to enhance the efficiency of data transmission, while reducing delay. In this study, routing-based optimization and Machine (ML), Deep Learning (DL) algorithms are reviewed along with its advantages and limitations. The performance ML or DL model is utilized for optimal routing in WMN is evaluated based on performance metrics such as Delay, Throughput, Packet Delivery Ratio (PDR) and Connected Client Ration (CCR). © 2025 IEEE.

关键词： Wireless mesh networks (WMN)

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