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检索条件"任意字段=International Conference on Engineering of Reconfigurable Systems and Algorithms"

共 89784 条记录，以下是221-230 订阅

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

Deciphering Diabetes: Illuminating Prognostic Insights with ...

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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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Exploring Traffic Signal Control: A Comprehensive Survey on Reinforcement Learning Techniques 3

Exploring Traffic Signal Control: A Comprehensive Survey on ...

引用

3rd IEEE international conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Thamaraiselvi, K. Bohra, Asha Rani Vishal, V. Sunkara, Pawan Sai Sunku, Balaji Nityajignesh, Bhavsar New Horizon College of Engineering Department of Information Science and Engineering Bangalore India New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331533205

Urban traffic congestion at intersections poses a significant challenge, leading to inefficiencies in traffic flow and increased wait times. Traditional fixed-timer traffic signal systems fail to adapt to fluctuating traffic volumes, exacerbating congestion during peak hours and leading to unnecessary delays during off-peak hours. Reinforcement Learning (RL) offers a promising solution by enabling traffic signal control systems to dynamically adjust signal timings based on real-time data. This paper surveys the current state of traffic signal control systems, highlighting the advantages of RL-based methods and presenting future directions for improvement, including the integration of Internet of Things (IoT) devices, advanced RL algorithms, and enhanced pedestrian detection systems. Through a comparative analysis of existing methods, we propose future enhancements that can further optimize urban traffic management. © 2025 IEEE.

关键词： Reinforcement learning

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Minimizing Average Job Completion Time for Multi-resource Queuing Computing systems Using Drift-Plus-Penalty 3rd

Minimizing Average Job Completion Time for Multi-resource Q...

引用

3rd international conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Chen, Chuanxu Li, Ruiting Guan, Quansheng Shi, Zhan Zeng, Ying Zheng, Jianping School of Electronic and Information Engineering South China University of Technology Guangzhou China Electric Power Dispatch and Control Center Guangdong Power Grid Co. Ltd. Guangzhou China

ISBN: (纸本)9789819616978

The Lyapunov drift-based scheduling algorithms control the queue length to stabilize the system and maximize the throughput for multi-resource multi-queue cloud/edge systems. However, these algorithms often ignore the heterogeneity in job length, thus leading to a long delay for job processing. To this end, this paper takes the job completion time (JCT) as a penalty in the drift-plus-penalty expression to minimize the average job competition time, and proposes a Lyapunov drift-plus-time (LDPT) based scheduling algorithm for multi-resource queuing computing systems. The optimality of LDPT with respect to average JCT and queue length is analyzed. By using the Amazon EC2 to simulate, the results show that LDPT can improve average JCT and average queuing time fairness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scheduling algorithms

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From 5G to 6G: The Role of AI, Machine Learning, and Deep Learning in Wireless systems 4

From 5G to 6G: The Role of AI, Machine Learning, and Deep Le...

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

作者： Gote, Pradnyawant M. Kumar, Praveen Verma, Prateek Yesankar, Prajyot Pawar, Adesh Saratkar, Saniya Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

The swift progression of wireless communication technologies-specifically from 5G to 6G is an approach that could be the most significant revolutionary leap towards changing connectivity and data transmission forever. Additionally, algorithms of machine learning (ML) and deep learning (DL) in 5G and 6G can offer promising advancements toward predictive maintenance, dynamic spectrum management, and enhanced security. The review of the potentially transformative nature of these technologies underlines the need for future directions of research efforts called for in circumventing challenges towards making full capability exploitation of 6G possible. © 2025 IEEE.

关键词： Adversarial machine learning

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

Application of Machine Learning Algorithms in Artificial Int...

引用

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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Optimized-Memory and Enhanced-Security Approaches to RSA and ElGamal

Optimized-Memory and Enhanced-Security Approaches to RSA and...

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

作者： Kumar, Maheswer Sunil Balaji, Vinithra Sakkarapani, Sandosh School of computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331509828

This paper demonstrates 2 methods, a reduced memory technique, and a reduced memory along with more security techniques in RSA (Rivest-Shamir-Adleman) and ElGamal which are both asymmetric cryptographic algorithms. Regular RSA and ElGamal are known to be performance efficient, but memory wise, they were not efficient. Also, to enhance security of any algorithm, the key size is increased leading to more memory and computation. To solve these challenges, hashing method is used through which, the memory can be reduced without affecting the security or the performance of the algorithms. Along with these hashmaps, we create an array manipulation method, which enhances the security of data in the algorithm by making the cipher text more complex. The above is performed using pipe-lining and use of python. The objective of this research work is to optimize RSA and ElGamal algorithm with respect to memory load and increasing security, without affecting the overall computational overhead of the system. The results aim to contribute to the advancement of these cryptographic techniques and especially resource constrained environments, where memory is an important factor and for more security required procedures. The hashing method optimizes the performance of RSA and ElGamal and their use in transferring large confidential files and the array method enhances the security using a modulus acting as another key. Furthermore, this optimized technique can be applied to other cryptographic solutions © 2025 IEEE.

关键词： Pipe linings

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Autonomous Vehicle Navigation systems: Machine Learning for Real-Time Traffic Prediction

Autonomous Vehicle Navigation Systems: Machine Learning for ...

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

作者： Praveen, R.V.S. Hundekari, Sheela Parida, Prasanta Mittal, Tanusha Sehgal, Archana Bhavana, Munugapati LTIMindtree Limited Telangana Hyderabad India School of Computer Applications Pimpri Chinchwad University Pune India School of Rural Management KIIT University Odisha Bhubaneswar India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

By making transportation safer and more efficient, autonomous vehicle navigation systems are transforming the transportation industry. Autonomous cars are able to make route selections based on accurate, up-to-theminute traffic predictions, which help them avoid congestion and maximize their trip time. The use of machine learning algorithms to reliably forecast traffic conditions in real time for use in autonomous vehicle navigation is the focus of this research. Deep learning, support vector machines, and random forests are just a few examples of the machine learning models that can accurately forecast traffic conditions by mining massive datasets collected from GPS data, traffic sensors, and past trends. The suggested method incorporates these models into an autonomous vehicle navigation system that operates in real-time, enabling adaptive modifications according to the flow of traffic. We also look at how things like construction, road accidents, and weather affect the precision of traffic predictions. Based on the outcome of decreased trip time, increased safety and efficiency, results demonstrate how navigation systems supplemented with machine learning, far surpass conventional routing approaches. The study suggests that further research needs to be conducted on hybrid algorithms for better traffic prediction, and multi modal data integration to further optimize autonomous vehicle navigation and in general, machine learning presents great promise in this area. © 2025 IEEE.

关键词： Traffic congestion

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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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Empowering Fake News Detection Through Innovative Hybrid Deep Learning-Based Approach

Empowering Fake News Detection Through Innovative Hybrid Dee...

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2025 international conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Suresh, A. Mallika, R.M. Gireesh, S. Singh, G. Hari Kiran Jeevanandham, E.S. Hemalatha, A. Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Puttur India Puttur India

ISBN: (纸本)9798331538538

The essence of getting information to the populace in the shortest time possible through social media and other interactive technologies has made it easier for fake news to thrive hence creating a menace to any society as well as its decision-making systems. This research work presents a sound AI-based approach to classify fake news using DL algorithms embedded within a CNN-BiLSTM model. Using the ISOT dataset, the comparison of the outcomes achieved by the proposed model with the original models shows that the proposed model has the highest accuracy score of 94.07%, a precision of 93.50%, a recall of 94.60%, and an F1-score of 94.00%. The proposed hybrid model architecture of CNN for spatial feature extraction and BiLSTM for capturing contextual dependencies that are essential for text analysis and have relatively high accuracy compared to conventional ML algorithms, including CNN, RNN, LSTM, and other DL methods. The proposed approach is verified by a set of comprehensive measures, including ROC curve analysis and various training-validation metrics that reveal the effectiveness of the presented approach in overcoming the challenges of fake news identification. © 2025 IEEE.

关键词： Deep learning

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Real-Time Testing of Microgrid Control algorithms: Development of Experimental Setup 29

Real-Time Testing of Microgrid Control Algorithms: Developme...

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29th international conference on Information Technology, IT 2025

作者： Forcan, Miodrag Dobrić, Goran Abed, Amir Žarković, Mileta Ikić, Marko Forcan, Jovana Barragán-Villarejo, Manuel Maza-Ortega, José María De Paula Garcia-López, Francisco University of East Sarajevo Faculty of Electrical Engineering East Sarajevo71123 Bosnia and Herzegovina University of Belgrade School of Electrical Engineering Belgrade11120 Serbia University of East Sarajevo Faculty of Philosophy Department of Computer Science and Systems East Sarajevo71420 Bosnia and Herzegovina University of Sevilla Department of Electrical Engineering Seville41004 Spain

ISBN: (纸本)9798331517649

This paper provides an overview of microgrid control strategies, examining differences between centralized and decentralized approaches, and focusing on classical hierarchical control schemes as well as the structure of three-layer microgrid control systems. It also explores Hardware-in-the-Loop modeling, a crucial approach for microgrid simulation, with particular emphasis on devices supporting microgrid modeling. The paper primarily focuses on the design and implementation requirements of an experimental setup for real-time testing of microgrid control algorithms. The setup incorporates Hardware-in-the-Loop device, a power amplifier, a power converter, a battery energy storage system, and a digital controller. It is specifically designed to handle both power and information (control) signals, facilitating a comprehensive approach to evaluating and validating microgrid control strategies in real-world conditions. A detailed analysis of the setup elements and their interactions is also provided. © 2025 IEEE.

关键词： Hardware-in-the-loop simulation

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