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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Halder, Anangsha Guha Neogi, Subhrendu Dutta, Shibdas Khamaru, Koustov Bhattacharya, Arghya Roy, Shovan The Neotia University Department of Computer Science and Engineering Sarisha India Dcg Data-Core System India Department of Information Technology Kolkata India University of Engineering and Management Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350373110

The world population relies on agricultural plants for food, and illnesses reduce yield, but proper plant disease monitoring can help to resolve such issues. computer vision and machine learning methods can detect plant illnesses early, reducing their negative consequences and overcoming the drawbacks of continual human monitoring. After pre-processing of input images, the target region needs to be isolated. The proposed models analyzes images for detection and classification to identify edges, colors, and textures. This research has agricultural applications for early disease identification. This study examined ResNet, MobileNet, DenseNet, InceptionV3 and modified CNN model on the PlantVillage dataset to classify tomato illnesses. The F1 score, accuracy, sensitivity, and specificity can evaluate models. This paper evaluates these models using a diverse collection of tomato leaf pictures, contributing to automated disease detection systems and may be helpful for improving farm management and sustainability. © 2024 IEEE.

关键词： Fruits

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Enhanced MADM Strategy with Heptapartitioned Neutrosophic Distance Metrics

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Neutrosophic Sets and systems 2025年 78卷 74-96页

作者： Eassa, Mohamed Myvizhi, M. Elkholy, Mohamed Abdelhafeez, Ahmed Elbehiery, Hussam Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Mathematics KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Faculty of Computer Science& Engineering Alamein International University Egypt

In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Penta-Partitioned Neutrosophic Distance Measures under particular conditional criteria, the suggested technique ranks the options. Additionally, we explore the fundamental characteristics of HNSDM and provide several illustrative theorems. The Heptapartitioned Neutrosophic Set Distance Measures are formulated based on the Hepta Neutrosophic Set. Lastly, a practical numerical example is provided to illustrate the efficacy of the suggested methodology. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioned Neutrosophic set Heptapartitioned Neutrosophic set and MADM strategy Heptapartitioned set Neutrosophic sine distance measure

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Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

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computer Modeling in engineering & Sciences 2024年第3期138卷 2103-2127页

作者： Othman S.Al-Heety Zahriladha Zakaria Ahmed Abu-Khadrah Mahamod Ismail Sarmad Nozad Mahmood Mohammed Mudhafar Shakir Hussein Alsariera Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka(UTeM)Durian TunggalMalaysia College of Computing&Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of Electrical Electronic and System EngineeringUniversiti Kebangsaan MalaysiaUKM BangiSelangorMalaysia Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityKirkukIraq Faculty of Engineering and Electronic Engineering Universiti Tun Hussein Onn Malaysia(UTHM)Batu PahatMalaysia 6Research Center DepartmentThe University of MashreqBaghdadIraq

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the *** this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things *** framework integrates Kalman filtering and *** smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction *** traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction *** evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art ***,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.

关键词： Q-learning intelligent transportation system(ITS) traffic control vehicular communication kalman filtering smart city Internet of Things

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Impact of Fast Frequency Response on Renewable Energy Source Curtailment and Load Shedding

Impact of Fast Frequency Response on Renewable Energy Source...

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CIRED 2024 Vienna Workshop

作者： Panagi, Savvas Lazari, Antonis Koutsoloukas, Vrahimis Aristidou, Petros Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Transmission System Operator of Cyprus Cyprus

ISBN: (纸本)9781837242672

system operators in low-inertia power systems often have to curtail renewable energy sources (RES) and employ strict under-frequency load shedding (UFLS) schemes to ensure frequency security after an event leading to loss of generation. This approach limits the maximum RES penetration in a system and results to the loss of load. To tackle these problems, fast frequency response (FFR) schemes can be used to limit the frequency Nadir after a disturbance and decrease the need for RES curtailments and UFLS. This article provides insights on the interaction between the Kinetic Energy (KE), Frequency Containment Reserves (FCR), and Nadir after a disturbance, which are the driving mechanisms leading to RES curtailment. Then, it analyses the impact of FFR on the Nadir and its ability to alleviate RES curtailment problems. The low-inertia, islanded, Cyprus dynamic model is used to quantify the results and showcase the impact on a real system. © The Institution of engineering & Technology 2024.

关键词： Electric load shedding

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Production Scheduling Heuristic for Mass Customization with Partial Rescheduling 7

Production Scheduling Heuristic for Mass Customization with ...

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7th International Conference on Mechanical Manufacturing and Industrial engineering, MMIE 2024

作者： Tsui, Zhe-Luen Chou, Ywh-Leh Yuan, Cadmus Feng Chia University Department of Industrial Engineering and System Management Taiwan Feng Chia University Department of Mechanical and Computer-Aided Engineering Taiwan

ISBN: (数字)9781510683297

ISBN: (纸本)9781510683280

This research responds to the complex challenges of mass customization and multi-objective parameter optimization. Leveraging the Call-Auction Matching proposed by Cadmus Yuan, Zhe-Luen Tsui, and Ywh-Leh Chou[1], we improved a Production Date Prediction Call-Auction Matching (PDP-CAM) scheduling algorithm. Our numerical analysis validated the ability of algorithm to implement a partial rescheduling mechanism and generate scheduling results in the face of mass customization scenarios. Importantly, the effectiveness of the PDP-CAM scheduling algorithm in minimizing instance rescheduling was demonstrated, thereby enhancing the computational efficiency of the algorithm. © 2024 SPIE.

关键词： Cleaning

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Performance Analysis of Disruptive Instances in Cloud Environment 16

Performance Analysis of Disruptive Instances in Cloud Enviro...

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16th International Conference on COMmunication systems and NETworkS, COMSNETS 2024

作者： Nandy, Pranab Saha, Rounak Satpathy, Anurag Chakraborty, Sandip Addya, Sourav Kanti National Institute of Technology Karnataka Cloud and Smart System Services Lab Department of Computer Science and Engineering Surathkal India Missouri University of Science and Technology Cloud and Smart System Services Lab Department of Computer Science and Engineering United States Indian Institute of Technology Kharagpur Cloud and Smart System Services Lab Department of Computer Science and Engineering India

ISBN: (纸本)9798350383119

Virtualization enables the service providers (SPs) to logically partition the resources into virtual machines (VM) instances. Real-world SPs such as Amazon, Google, Microsoft Azure, IBM, and Oracle provide different flavors of VM instances, such as on-demand, reserved, and low-cost or spot, depending on the type of application hosted. The on-demand instances are short-term and typically incur a higher cost than reserved instances that are provisioned for a longer duration at a discounted rate. Low-cost or spot instances are cost-effective compared to on-demand but are reclaimable by the SPs. The SPs often claim that the on-demand and spot instances achieve similar performance, but it is far from that. This paper studies the performance of spot instances via rigorous experimentation over commercial SPs such as Amazon AWS and Microsoft Azure. Real-world evaluations affirm that spot instances perform poorly compared to their on-demand counterpart concerning memory, CPU, disk read, and write operations. We identify such instances as disruptive and name them so because it does not fulfill the performance, durability, and flexibility expectations like an on-demand instance having the same configuration. We also perform hypothesis testing over the experimental data obtained to corroborate our claim further. © 2024 IEEE.

关键词： Cloud Computing Disruptive Instance On-demand Spot Instance

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CPL-SLAM: Centralized Collaborative Multi-Robot Visual-Inertial SLAM Using Point-and-Line Features

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IEEE Internet of Things Journal 2025年第12期12卷 21866-21875页

作者： Liu, Xin Wen, Shuhuan Liu, Huaping Richard Yu, F. Yanshan University Engineering Research Center The Ministry of Education for Intelligent Control System and Intelligent Equipment Key Lab of Intelligent Rehabilitation and Neuroregulation in Hebei Province Department of Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Shenzhen University College of Computer Science and Software Engineering China Carleton University Canada

Traditional visual-inertial SLAM (Simultaneous Localization and Mapping) systems predominantly rely on feature point matching from a single robot to realize the robot pose estimation and environment map construction. However, in complex scenarios, these traditional systems struggle with issues such as tracking failures due to illumination changes, rapid movements, and low-texture environments, and they perform poorly in terms of mapping efficiency and global consistency. To address these challenges, we propose a centralized collaborative SLAM system that employs both point and line features for tracking in the robot and map fusion in the cloud. The proposed system leverages the fusion of point and line features across all instances in the process, which allows our method to achieve higher localization accuracy in structured, low-texture scenes. With the aid of classifying gravity-aligned vertical lines and spatial parallel lines, the proposed system can deliver faster and more accurate odometry in complex scenes. Furthermore, we developed intra-robot and inter-robot loop closure detection methods based on point and line features, generating a globally consistent sparse point cloud and structured scene map in the cloud. Our method is able to build richer maps while improving accuracy compared to existing methods. Experimental results on public datasets and in real-world environments show that, compared to existing advanced methods, our approach demonstrates better performance. © 2014 IEEE.

关键词： SLAM robotics

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Use of Euclidean Distance to Improve Fingerprint Matching Accuracy 4th

Use of Euclidean Distance to Improve Fingerprint Matching Ac...

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4th International Conference on Innovations in Computational Intelligence and computer Vision, ICICV 2024

作者： Bhargava, Neeraj Yadav, Rajendra Choudhary, Usha Department of Computer Science School of Engineering System Sciences MDS University Rajasthan Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819769919

Fingerprint authentication is the most sophisticated method of all biometric techniques and has been thoroughly verified through various applications. Fingerprint matching has been done using several fingerprint recognition techniques. The methods for matching fingerprints have changed significantly over time, but Euclidean distance is still widely used since it is efficient and straightforward. Euclidean distance is still used in minutiae-based fingerprint matching to produce accurate results. The computational geometry, image processing, and patter recognition fields all make substantial use of Euclidean distance, a fundamental distance matching approach. The purpose of this work is to highlight the importance of normalization in improving contrast, particularly in noisy fingerprint photographs where accuracy is critical. We analyzed how to make fingerprint matching more robots against a reference template by utilizing Euclidean distance between small spots. We also analyzed algorithm’s effectiveness in producing dependable and accurate fingerprint matching results using MATLAB. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computational geometry

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Design an Optimal Augmented PID Controller for Electric Vehicle Speed Control 23th

Design an Optimal Augmented PID Controller for Electric Vehi...

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23rd International Conference on Intelligent systems Design and Applications, ISDA 2023

作者： Abood, Layla H. Ali, Inaam I. Abood, May H. Control and System Engineering Department University of Technology Baghdad Iraq Electrical Engineering Department University of Technology Baghdad Iraq Computer Engineering Department University of Al Iraqia Baghdad Iraq

ISBN: (纸本)9783031648465

The prominence growth of the Electric Vehicle (EV) industry requests from researcher to adopts effective controlling techniques for marinating different issues. In this paper an augmented PID controller is suggested for controlling speed in EV based using sigmoid function relation and the smart and unique Gray wolf Optimization (GWO) algorithm to achieve a satisfied and robust system desired value, a comparison is utilized with PID controllers (Optimal PID, Normal PID) to evaluate its behavior then a robustness analysis is applied by varying system desired speed in multiple periods, the controller track the desired value perfectly and force the system to an efficient response with robust system behavior. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Electric vehicles

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A Scalable Open-Set ECG Identification system Based on Compressed CNNs

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IEEE Transactions on Neural Networks and Learning systems 2023年第8期34卷 4966-4980页

作者： Wu, Shun-Chi Wei, Shih-Ying Chang, Chun-Shun Swindlehurst, A. Lee Chiu, Jui-Kun National Tsing Hua University Department of Engineering and System Science Hsinchu30013 Taiwan University of California at Irvine Department of Electrical Engineering and Computer Science IrvineCA92697 United States

Deep learning (DL) is known for its excellence in feature learning and its ability to deliver high-accuracy results. Its application to ECG biometric recognition has received increasing interest but is also accompanied by several deficiencies. In this study, we focus on applying DL, especially convolutional neural networks (CNNs), to ECG biometric identification to address these deficiencies. Using prestored user-specific feature vectors, the proposed scheme can exclude unregistered subjects to realize 'open-set' identification. With the help of its scalable structure and 'transfer learning,' new subjects can be enrolled in an existing system without the need for storing the ECGs of those previously enrolled. Finally, schemes based on the quantum evolutionary algorithm (QEA) are presented to prune unnecessary filters in the proposed CNN model. The performance of the proposed scheme was evaluated using the ECGs of 285 subjects from the PTB dataset. The experimental results demonstrate an identification rate of more than 99% in closed-set identification. Although incorporating the proposed method for unregistered subject exclusion degraded the identification performance slightly, the ability of the approach to resist a dictionary attack was evident. Finally, using the QEA-based filter pruning method and its two-stage extension reduced the number of floating-point operations required to complete one identity recognition to 1.20% and 0.22% of the original value without significantly impacting the identification accuracy. © 2012 IEEE.

关键词： Biometrics

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