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检索条件"任意字段=IEEE International Conference on Data Science and Data Intensive Systems"

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Enhancing Energy Efficiency in Wireless Sensor Networks using Deep Learning

Enhancing Energy Efficiency in Wireless Sensor Networks usin...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Raskar, Sandeep Dhasmana, Gulshan Lakshminarayana, M. Patil, Harshal Bhagya Shree, S. Natrayan, L. Terna Engineering College Department of Artificial Intelligence and Data Science Nerul Maharashtra Navi Mumbai400706 India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India M S Ramaiah Institute of Technology Department of Medical Electronics Engineering Karnataka Bengaluru560054 India School of Computer Science & Engineering Iilm University Uttar Pradesh Greater Noida201 306 India Brindavan College of Engineering Department of Physics Karnataka Bengaluru560063 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331509828

Wireless Sensor Networks (WSNs) are essential for many applications, but because of their limited power resources, they confront major energy efficiency difficulties. This research offers a novel approach for improving WSN energy efficiency that makes use of deep learning, namely deep reinforcement learning. The framework uses sophisticated energy data transformation methods to pre-process sensor data, derive important information, and spot energy usage trends. In order to better comprehend the model and represent the distinct dynamics of WSNs, domain-specific features including residual energy, transmission power, and path length are designed. The system learns the best routing and energy-saving strategies by incorporating deep reinforcement learning, which strikes a balance between energy usage and data transmission efficiency. When compared to traditional techniques, evaluation metrics show that the suggested framework can greatly reduce energy depletion and increase network longevity. Deep learning has the ability to address WSN energy concerns by providing a scalable and adaptable solution for energy-efficient network operations. © 2025 ieee.

关键词： Deep reinforcement learning

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Deploying Deep Learning to Improve the Performance of MIMO-OFDM systems Using LDPC Channel Coding in V2X Communications 19

Deploying Deep Learning to Improve the Performance of MIMO-O...

引用

19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Nga, Nguyen Thu Duc, Nguyen Dinh Tien, Trinh Manh WICOM Lab School of Electrical and Electronic Engineering SEEE Hanoi University of Science and Technology HUST Hanoi Viet Nam

ISBN: (纸本)9798331507817

This paper focuses on the application of Deep Learning technology to improve the performance of V2X (Vehicle-to-Everything) communication systems. Of the Multiple-input Multiple-output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system over a traffic environment, supported by Dedicated Short-Range Communication (DSRC) technology, we deploy channel coding algorithm of Low-density parity-check code (LDPC) for correcting error derived from the transmission media. Artificial Neural Network (ANN), an algorithm that can interpret raw data, is used to simulate real-world data. We apply ANN with layers as a mathematical model to train the path gain parameter, which thereafter examined the system performance BER of 5G channel coding MIMO-OFDM system over Rayleigh fading channel. We evaluate the BER and the throughput at different vehicle speeds of 60 km/h or 120 km/h as well as training process of 50 data samples or 250 data samples in scenarios of 2×2 or 4×4 MIMO antennas deployed DSRC technique. From the simulation results, training model for optimizing path gain has yielded positive results in terms of system performance and throughput, even at lower 50 data sample training. It can be said that the moving speeds of vehicles significantly affect the performance of DSRC systems due to the influence of the Doppler effect. After ANN training, if speeding up the velocity, the throughputs of 4×4 MIMO-OFDM system are increasing at lower data sample training. © 2025 ieee.

关键词： Orthogonal frequency division multiplexing

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Optimization of Target Detection and Tracking Algorithm Combined with Multimodal Photoelectric Sensors 1

Optimization of Target Detection and Tracking Algorithm Comb...

引用

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

作者： Liu, Yiwei Wu, Yanshuo Xie, Wenbin School of Integrated Circuit Science and Engineering Tianjin University of Technology Tianjin China

ISBN: (纸本)9798331529246

In complex environments, the accuracy and robustness of single-mode optoelectronic sensors in target detection and tracking are often limited. This article proposes an algorithm optimization method that combines multimodal optoelectronic sensors. Firstly, integrate data from multiple sensors such as visible light, infrared, and radar to obtain more comprehensive target information. Secondly, a target detection model based on YOLO (You Only Look Once) was designed. Then, the improved Kalman filter algorithm is used for target tracking. Finally, optimize the collaborative processing of multisource information through data fusion strategies. The results indicate that the research system exhibits good adaptability, verifying the effectiveness of multimodal fusion and algorithm optimization. Specifically, based on visible light sensors, the detection accuracy was improved by 10.1% after adopting multimodal fusion methods;Compared with the traditional Kalman filter, the improved Kalman filter reduces tracking error by 51.7%;When dealing with complex scenarios, the accuracy and recall of the system reach 92.6% and 90.8%, respectively. © 2025 ieee.

关键词： Kalman filters

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Predicting Customer Purchase Intent Using Logistic Regression: A data-Driven Approach 2

Predicting Customer Purchase Intent Using Logistic Regressio...

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

作者： Rajarajachozhan, C. Karthika, G. Rajakrishnammal, A. Mano, M. Kumar, P. Nithish Jayahari, E. Saveetha Engineering College Dept. of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Engineering College Dept. of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha Engineering College Dept. of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

Predicting customer purchase intent is crucial for modern businesses in order to improve engagement, refine marketing strategies, and increase revenue. Conventional rule-based systems encounter difficulties with dynamic and extensive customer data, limiting their flexibility and precision. This project employs a machine learning methodology utilizing logistic regression to forecast customer intent based on demographic, transactional, and behavioral data. The proposed model analyses input features including age, browsing history, and previous purchases, employing sophisticated data preprocessing methods such as One-Hot Encoding and feature scaling to guarantee data integrity. Through hyperparameter tuning and the assessment of critical metrics including accuracy, precision, recall, and Fl-score, the system provides dependable and actionable insights. Real-time predictive capabilities enable businesses to customize customer interactions, facilitating targeted marketing and enhancing customer experience. This project underscores the capacity of predictive analytics to tackle real-world issues, facilitating data-driven decision-making and sustainable business development. © 2025 ieee.

关键词： Logistic regression

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Role of Machine Learning in Climate Change Prediction and Mitigation 2

Role of Machine Learning in Climate Change Prediction and Mi...

引用

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

作者： Jikar, Nayan Tale, Yash Tale, Abhay Barhate, Aditya Verma, Prateek Jikar, Aman Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Climate change is one of the biggest challenges of the 21st century, with profound environmental, economic, and social impacts. Machine learning (ML) has emerged as a transformative tool to address climate challenges through data-driven solutions, offering predictive capabilities for extreme weather events and enhancing the efficiency of clean-energy systems. This technology benefits the optimization of renewable energy generation, intelligent grid management, and resource utilization towards reducing greenhouse gas emissions. Moreover, ML promotes climate justice through targeted region-specific intervention and inclusive solution-making for the most vulnerable. Several technical and ethical challenges associated with data quality, scalability, and model generalization must still be overcome for ML to realize its potential. This paper reviews the role of ML in climate change prediction and mitigation discusses ongoing real-world applications and outlines the future research agenda necessary to address existing challenges. Integrating ML with emerging technologies like the Internet of Things (IoT) and explainable AI, alongside global collaboration and a focus on climate justice, will be essential in advancing effective climate action. © 2025 ieee.

关键词： Global warming

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Advancing Healthcare Through Internet of Things: A Comprehensive Review of Smart Healthcare systems and Their Applications 2

Advancing Healthcare Through Internet of Things: A Comprehen...

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

作者： Raushan, Rahul Chaudhri, Shiv Nath Suman, Saurabh Kumar, Dhiraj Kumar, Gulshan Kumar, Abhishek Faculty of Engineering and - Technology Department of Computer Science & Design Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

The Internet of Things (IoT) has turned the healthcare domain in a better direction due to more interconnected and smarter systems that enhance care delivery. Certainly, the IoT-accessible applications in these fields are only possible if devices, sensors, and real-time data are interconnected. Among today's marvels are remote patient monitoring, wearable health trackers, telemedicine, and smart hospital management. Medicine gets more personal, effective, and accessible due to these improvements. However, its development remains unmatched being faced with complexities such as security of personalized data, safety risks resolution, and seamless device-to-system integration. This paper examines the influential role of IoT in advanced healthcare systems, with its virtues, major applications, and implementation obstacles as the subject. The Internet of Things as a mode of health and technology bridging can entirely change how medicines are being practiced and even lead to the improvement of the lives of patients by introducing adaptive and efficient hospital systems. Thus, IoT can be a technology that balances tech and medicine for the betterment of patient treatment outcomes and make the system adaptive and efficient. © 2025 ieee.

关键词： Personalized medicine

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Integrating Explainable Machine Learning (XAI) in Stroke Medicine: Opportunities and Challenges for Early Diagnosis and Prevention 2

Integrating Explainable Machine Learning (XAI) in Stroke Med...

引用

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

作者： Shobana, V. Maheshwari, S. Savithri, M. Ramasamy, Siva Shankar Kumar, N. Dr. N.G.P. Arts and Science College Department of Computer Science with Cyber Security Coimbatore India Dr. N.G.P. Arts and Science College Department of Computer Science Coimbatore India Christ University Department of Data Science Bengaluru India International College of Digital Innovation Chiang Mai University Thailand

ISBN: (纸本)9798331505745

Stroke is a leading cause of mortality and disability worldwide, emphasizing the critical need for early diagnosis and prevention. Machine learning (ML) has demonstrated significant potential in improving stroke prediction and management by analysing complex datasets for risk stratification, diagnosis, and treatment planning. However, the adoption of ML in stroke medicine is limited by the opacity of these models, which can hinder clinical trust and decision-making. Explainable Artificial Intelligence (XAI) addresses this challenge by making ML models more interpretable and transparent, enabling healthcare professionals to understand, validate, and trust their outputs. This research work explores the integration of XAI in stroke medicine, highlighting its potential to enhance early diagnosis, personalized prevention strategies, and treatment planning. We discuss the opportunities XAI provides in identifying high-risk patients, uncovering critical predictors, and enabling informed clinical decisions. Furthermore, we examine challenges such as ensuring model reliability, addressing biases in stroke datasets, and navigating ethical considerations related to patient data privacy and algorithmic accountability. © 2025 ieee.

关键词： Diagnosis

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Real-time Air Quality Monitoring and Context-aware Alert System 17

Real-time Air Quality Monitoring and Context-aware Alert Sys...

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17th international conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Kakde, Anuj Pagoti, Yashwant Krishna Nikumbh, Sarthak Karmakar, Prasenjit Chakraborty, Sandip Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India

ISBN: (纸本)9798331531195

Air quality monitoring is gaining increasing importance as awareness about the health impacts of air pollution continues to grow. These monitors typically track air pollutants to give users a clearer picture of their environment. Accurate and real-time monitoring increases our understanding of the surrounding air we breathe and allows us to improve it. The proposed system helps users minimize their exposure to harmful pollutants by providing real-time data on the concentration of various gases. The system triggers alerts directly to the subscribed user's mobile phones in case of any hazardous situations so that the user can take appropriate actions immediately to resolve the issue. Our extensive data collection and evaluation with off-the-shelf machine learning models indicated an overall F1-score of 99.54 (±0.5)% with a simple random forest model in classifying burning, cooking, occupancy in a closed room, using aerosol products, based on the sensor's readings. © 2025 ieee.

关键词： Indoor air pollution

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Expanding the PDE Cache by Exploiting Underutilized TLB Entries for data-intensive Workloads

Expanding the PDE Cache by Exploiting Underutilized TLB Entr...

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2025 international conference on Electronics, Information, and Communication, ICEIC 2025

作者： Kim, Hyeong Jin Kim, Woo Hyun Han, Tae Hee Sungkyunkwan University Department of Semiconductor Convergence Engineering Suwon Korea Republic of Sungkyunkwan University Department of Semiconductor Systems Engineering Suwon Korea Republic of

ISBN: (纸本)9798331510756

data-intensive applications access large address spaces and exhibit low locality when accessing virtual addresses (VA). The level 2 translation lookaside buffer (L2 TLB) misses trigger page table walks (PTWs), and the PTWs become a bottleneck, requiring up to five memory accesses for address translation. Among four page caches used to reduce memory accesses during PTWs, the page directory entry cache (PDE cache) experiences the highest number of miss counts. In addition, the L2 TLB indicates an underutilized characteristic with a low reuse count of L2 TLB entries. The study proposes an architecture that stores PDE cache entries in the L2 TLB, leveraging the underutilized property of the L2 TLB and the strain on the PDE cache. The expansion of the PDE cache storage space results in a diminution of memory accesses during PTWs, leading to enhanced performance in data-intensive applications. Even with the reduced L2 TLB capacity, the impact on performance degradation is minimal since the reduction affects the space occupied by underutilized entries. The proposed architecture demonstrates a 76.77% decrease in average memory access counts during PTWs and a 6.41% improvement in average instructions per cycle (IPC). © 2025 ieee.

关键词： Virtual addresses

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Application of CNN Based Deep Learning Algorithm for Herbal Plant Leaf Detection

Application of CNN Based Deep Learning Algorithm for Herbal ...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Pongiannan, R.K. Das, Sagardeep Purbia, Rudra Prakash Brindha, R. Jame, S. Lourde Shiju Kumar, P.S. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India

ISBN: (纸本)9798331509828

The medicinal plant market faces challenges due to inefficiencies and a lack of transparency. In this paper, a total of 10GB dataset with 10,000 entries, including 15 attributes such as plant name, region, price, and high-resolution images capture seasonal and geographical variations. Designed for precise plant identification and market forecasting, the dataset also contains metadata and historical data on prices and demand trends. Deep learning models, including recurrent neural networks (RNN) and convolutional neural networks (CNNs), are employed for accurate plant identification. Time series analysis is used to forecast market patterns. data optimization techniques are applied to ensure real-time insights, allowing farmers and traders to make informed decisions. This system aims to enhance market transparency and improve the decision-making process in the medicinal plant industry. © 2025 ieee.

关键词： Convolutional neural networks

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