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Metaheuristics Based Node Localization Approach for Real-Time Clustered Wireless Networks

computer Systems Science & engineering 2023年第1期44卷 1-17页

作者： R.Bhaskaran P.S.Sujith Kumar G.Shanthi L.Raja Gyanendra Prasad Joshi Woong Cho Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Computer Science and Engineering MEA Engineering CollegeMallapuram679325KeralaIndia Department of Electronics and Communication Engineering Rajalakshmi Institute of TechnologyKuthambakkamChennai600124India Department of Electronics and Communication Engineering Sri Eshwar College of EngineeringCoimbatore641202India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea

In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,*** the same time,proper localization of nodes in real time wireless networks helps to improve the overall functioning of *** study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization(IM-EECNL)approach for real-time wireless *** proposed IM-EECNL technique involves two major processes namely node localization and ***,Chaotic Water Strider Algorithm based Node Localization(CWSANL)technique to determine the unknown position of the ***,an Oppositional Archimedes Optimization Algorithm based Clustering(OAOAC)technique is applied to accomplish energy efficiency in the ***,the OAOAC technique derives afitness function comprising residual energy,distance to cluster heads(CHs),distance to base station(BS),and *** performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency.A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio(PDR)of 0.985.

关键词： Wireless networks real time applications clustering node localization energy efficiency metaheuristics

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Panoptic Region Slicing Segmentation and Optimized Alexnet-Based CNN for Early Melanoma Diagnosis

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SN computer Science 2024年第6期5卷 756页

作者： Ganesh, V.N. Gulappagol, Laxmi Pushparani, M.K. Ganesh, K. Department of Electronics and Communication Engineering Alvas Institute of Engineering and Technology Department of Internet of Things and Cyber Security including Block chain Technology A J Institute of Engineering and Technology Department of Computer Science & amp Design Alvas Institute of Engineering and Technology

Early and accurate diagnosis of melanoma, a potentially life-threatening skin cancer, is crucial for improving patient outcomes. In this study, we propose a novel approach for melanoma detection, termed Panoptic Region Slicing Segmentation (PRS2) using an optimized convolution neural network (PRS2-OCNN) based on AlexNet. The proposed system integrates several advanced methods to enhance the accuracy and efficiency of melanoma identification. The initial step involves preprocessing the dermoscopic image using a 2D Fusion Filter, which enhances the image quality and prepares it for subsequent analysis. Next, the Panoptic Region Slicing Segmentation (PRS2) method is applied to emphasize the boundary regions, allowing for precise localization of melanoma-affected areas. To assess color variations within the segmented regions, we employ Threshold Histogram Evaluation (THE), which effectively characterizes melanoma-specific color patterns. The features extracted using Spread Spectral Menzies’s Feature Selection (SSMFS), reducing the dimensionality and improving the efficiency of the subsequent analysis. The core of our proposed approach lies in the optimized convolution neural network, derived from the influential AlexNet architecture. By fine-tuning the AlexNet-OCNN on the reduced feature set, we maximize its ability to accurately classify melanoma lesions based on their risk level. The PRS2-OCNN identifies melanoma classes according to their risk severity, aiding dermatologists in making informed decisions for timely and appropriate *** evaluations were conducted on a diverse and extensive dataset of dermoscopic images. The proposed system demonstrated superior performance compared to existing methods, exhibiting heightened detection accuracy by deeply analyzing the melanoma-affected regions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： 2D fusion filter AlexNet Early diagnosis Melanoma Panoptic region slicing segmentation PRS2-OCNN

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DGeC: Dynamically and Globally Enhanced Convolution

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 921-933页

作者： Zhang, Zihang Liu, Yuling Zhou, Zhili Yang, Gaobo Liao, Xin Wu, Q. M. Jonathan Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Guangzhou University Institute of Artificial Intelligence and Blockchain Guangzhou510006 China University of Windsor Department of Electrical and Computer Engineering WindsorON250101 Canada

We explore the reasons for the poorer feature extraction ability of vanilla convolution and discover that there mainly exist three key factors that restrict its representation capability, i.e., regular sampling, static aggregation, and limited receptive field. With the cost of extra parameters and computations, existing approaches merely alleviate part of the limitations. It drives us to seek a more lightweight operator to further improve the extracted image features. Through a closer examination of the convolution process, we discover that it is composed of two distinct interactions: spatial-wise interaction and channel-wise interaction. Based on this discovery, we decouple the convolutional blocks into these two interactions which not only reduces the parameters and computations but also enables a richer ensemble of interactions. Then, we propose the dynamically and globally enhanced convolution (DGeC), which includes several components as follows: a dynamic area perceptor block (DAP) that dynamically samples spatial cues, an adaptive global context block (AGC) that introduces the location-aware global image information, and a channel attention perceptor block (CAP) that merges different channel-wise features. The experiments on ImageNet for image classification and on COCO-2017 for object detection validate the effectiveness of DGeC. As a result, our proposed method consistently improves the performance with fewer parameters and computations. In particular, DGeC achieves a 3.1% improvement in top-1 accuracy on ImageNet dataset compared to ResNet50. Moreover, with Faster RCNN and RetinaNet, our DGeC-ResNet50 also consistently outperforms ResNet and ResNeXt. © 2024 IEEE.

关键词： Convolutional neural networks

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Fusion of optimal cross-stage partial net with context-aware segmentation for enhanced damage detection in civil infrastructures using thermal images

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Journal of Supercomputing 2025年第8期81卷 1-23页

作者： Shahzadi, Maryam Khan, Muhammad Zeeshan Khan, Asim Ali, Umar Hassan, Muhammad Arshad Shehzad Department of Computer Science The University of Faisalabad Faisalabad38000 Pakistan German Institute of Engineering Chongqing College of Mobile and Communication P.R. Chongqing401520 China School of Electrical Engineering and Automation Chongqing College of Mobile and Communication Chongqing China

Crack detection plays a critical role in maintaining the structural integrity and longevity of civil infrastructure, yet computer vision-based methods face limitations from inconsistent lighting, environmental noise, and complex backgrounds, which can hinder accurate crack detection. To address these challenges, this work introduces an enhanced crack detection algorithm, CSP-CAS, which incorporates a compact inverted block to optimize the cross-stage partial (CSP) net and integrates it with context-aware segmentation (CAS) in the neck and head of the You Only Look Once (YOLO) v9 architecture. We collected an initial dataset comprising 2000 RGB and 2000 thermal images from multiple historical sites in Pakistan, which was further expanded to 6000 images each, using augmentation techniques. CSP-CAS shows a precision improvement of 8.04%, a recall increase of 17.9%, and an accuracy boost of 9.2% on the RGB dataset as compared to YOLOv9. Further, we conducted a comparative evaluation between thermal and RGB datasets to determine the impact of using multispectral images for crack detection in civil infrastructures. The thermal dataset shows a 13.9% boost in precision, a 20% rise in mAp@0.5, and an 18.4% increase in recall, proving that thermal imaging is much more effective than RGB for finding cracks in civil infrastructure. These results validate the effectiveness of the proposed approach, making it a promising solution for accurate and efficient crack detection in civil infrastructure. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Thermography (imaging)

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Joint Design of Power Allocation and Beamforming for IRS-Assisted Millimeter-Wave communication System with Imperfect CSI

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6566-6582页

作者： Yu, Xiangbin Yang, Chenghong Bai, Jiawei Wang, Kezhi Rui, Yun Dang, Xiaoyu Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering China Brunel University London Department of Computer Science Middlesex UxbridgeUB8 3PH United Kingdom East China Normal University School of Communication and Electronic Engineering China

In this paper, the joint power allocation (PA), passive beamforming (BF) and hybrid BF (HBF) including digital and analogue BFs are designed for an intelligent reflecting surface (IRS)-assisted millimeter-Wave (mmWave) communication system with imperfect channel state information (CSI) and multiple mobile users to optimize the weighted sum rate (WSR) and energy efficiency (EE). The achievable WSR and EE of the IRS-mmWave system are firstly derived based on imperfect cascaded CSI for performance optimization. Then, the non-convex constrained problem is formulated to maximize the WSR, where the PA, HBF, phase and amplitude of IRS elements are jointly optimized. Given PA and passive BF (PBF), closed-form suboptimal HBF is obtained for each iteration. Also, given HBF and PBF, using the block coordinate descent (BCD) methods, closed-form PA is derived. Moreover, the phase and amplitude of IRS elements are derived for PBF design during each iteration. With the obtained HBF, the digital and analogue BFs are also derived. Based on this, joint schemes of PA, HBF and PBF are developed. Besides, an efficient iterative algorithm based upon the alternating optimization (AO), weighted minimum meansquare error (WMMSE) and Dinkelbach methods are presented for EE maximization and the suboptimal solution is obtained. Correspondingly, the energy-efficient design for joint PA, HBF and PBF is provided. Simulation results verify the proposed solutions. © 2002-2012 IEEE.

关键词： Beamforming

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Suppression of over voltage in SiC-based inverter fed induction motor

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Australian Journal of Electrical and electronics engineering 2024年第1期21卷 11-28页

作者： Salau, Ayodeji Olalekan Kanchana, Kadirvel Anoop, Kuttichakku Johny Markus, Elisha Didam Braide, Sepiribo Lucky Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado-Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Electrical Electronics Engineering Saveetha Engineering College Chennai India Department of Electrical and Electronics Engineering VISAT Engineering College Kerala Ernakulam India Department of Electrical/Electronic and Computer Engineering Central University of Technology Free State Bloemfontein South Africa Department of Electrical/Electronic Engineering University of Port Harcourt Rivers State Choba Nigeria

Silicon carbide (SiC) based inverters are widely used due to their fast switching and ability to withstand high temperatures and Voltage. This helps to increase efficiency and reduces switching losses compared to the silicon-based inverter. However, this fast-switching device causes an increase in the Voltage at the junction of the cable and motor due to the reflected wave. The main causes of overvoltage are cable/motor impedance mismatch, fast-switching inverter devices (IGBT), and inverter-motor separation. To extend the induction motor lifespan, overvoltage reduction is necessary. Passive and active filters solve the problem, but passive filters are bulky and have high power loss. This paper presents a shunt active filter approach for an inverter-fed induction motor to reduce overvoltage. To show the effectiveness of the proposed approach, the findings were compared to the topology of the clamp-type active filter already in use. The shunt active filter used Proportional Integral (PI) and fuzzy controllers to generate reference current according to the Synchronous Reference Frame (SRF) theory. Harmonics in the power system were removed using the SRF theory. The unique aspect of this work is how the reference current is produced in the shunt active filter using the SRF theory. The proposed shunt active filter with a fuzzy controller was added to the hardware implementation to achieve better results than previous active filters. ©, Engineers Australia.

关键词： Silicon carbide

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A Novel Routing Protocol over Time-Varying IoT Network Using RTS/CTS Framework 18

A Novel Routing Protocol over Time-Varying IoT Network Using...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Sharma, Neha Pandey, Prajjwal Aryan Rathore, Prateek Pandey, Om Jee Indian Institute of Technology BHU Department of Electronics Engineering Varanasi India National Institute of Technology Department of Electronics and Communication Engineering Silchar India The LNM Institute of Information Technology Department of Communication and Computer Engineering Jaipur India

ISBN: (纸本)9798350391725

In the context of 6G IoT networks, distributed computing has become a critical enabler for seamless communication across diverse sectors, such as smart manufacturing, intelligent transportation, healthcare, and defense. Many of these applications involve mobile IoT devices (IoDs) that change locations dynamically, requiring robust and efficient multi-hop data transmission frameworks. Selecting the optimal relay node in distributed 6G IoT networks is essential, as it directly impacts energy efficiency, latency, data interference, and throughput. Additionally, ensuring the availability of the relay node's queue is crucial to prevent data loss. To address these challenges, this work introduces a novel distributed computing framework based on the Request-To-Send (RTS) and Clear-To-Send (CTS) protocols, specifically designed for time-varying 6G IoT networks. The framework incorporates an RTS/CTS mechanism to manage distributed data transfer and employs the Optimal Relay IoD Selection with Q-Learning (OptRISQL) algorithm to determine the optimal relay path dynamically. By distributing computation across nodes, the proposed approach enhances network flexibility, reduces queue sizes, and minimizes memory costs, leading to improved overall network efficiency. Simulation results demonstrate that this distributed computing method effectively reduces interference and enhances throughput, outperforming other current state-of-the-art techniques. Index Terms-6G Internet of Things (IoT), Distributed Computing, Request-To-Send (RTS), Clear-To-Send (CTS), Quality-of-Service (QoS). © 2024 IEEE.

关键词： Smart manufacturing

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Synergizing Futures: Precision Career Mapping with Llama 2 and AI Fine-Tuning for Personalized Path Prediction and Guided Navigation

Synergizing Futures: Precision Career Mapping with Llama 2 a...

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2024 International Conference on communication, computer Sciences and engineering, IC3SE 2024

作者： Singh, Anuradha Sharma, Himanshu Jindal, Kanika Chaudhary, Ankur Noida Institute Of Engineering And Technology Department Of Computer Science And Engineering Greater Noida India Noida Institute Of Engineering And Technology Department Of Electronics And Communication Engineering Greater Noida India

ISBN: (纸本)9798350366846

These days, a lot of students struggle in how to choose the career. As they progress through their studies, students must recognize their abilities and assess their areas of interest to determine the most appropriate job for them. With the aid of this approach, today's youngsters will be able to determine which career route will yield the best outcomes in the long run if they choose the suggested vocation. This will assist in raising the student's performance and spark their interest to keep them concentrated on their desired job. This system is based on an exam that students must complete;based on their responses, it will produce a summary of the test results. The primary goal of this system is to give a summary of the artificial intelligence methods that we employed to forecast the student's performance. This structure likewise be emphasizing how we are identifying characteristics in student data by employing prediction systems. For educators, educational institutions, and students alike, using this system has proven to be advantageous. In this paper we have used various technologies like Flutter, Llama-2 generative A.I. model, Firebase and UI/UX design tools. Our experimental results of proposed system reduce the career searching effort by 80% when tested for Lyman people. © 2024 IEEE.

关键词： Students

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Less Labeling Burden: A SAR Ship Detection Data Selection Method Based on K-means 2

Less Labeling Burden: A SAR Ship Detection Data Selection Me...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Yang, Yimin Yin, Junjun Lang, Ping Yang, Jian He, Yaomin Tsinghua University Department of Electronic Engineering Beijing China University of Science and Technology School of Computer and Communication Engineering Beijing China Academy of Military Sciences Institute of Systems Engineering Beijing China

ISBN: (纸本)9798331515669

In deep learning-based object detection, labeled data is crucial. However, annotating ship targets in Synthetic Aperture Radar (SAR) images is challenging because of the distinct characteristics of SAR images, especially when employing Oriented Bounding Boxes (OBB). These challenges arise from factors such as varying ship orientations, complex backgrounds, and the presence of noise, making accurate annotation more difficult. To reduce the labeling burden while minimizing its impact on ship detection performance, we propose a SAR data selection method. This method ensures that, despite limited quantities, the data contains a more representative range of scenarios when constructing a new dataset. By selecting 10 features of SAR images and performing K-means clustering based on these features, we choose the samples closest to the cluster centers as the selected data. We have validated the effectiveness of the proposed method by applying two typical object detection models to both randomly selected and clustering-selected datasets, thereby demonstrating the efficacy of proposed approach. © 2024 IEEE.

关键词： Radar imaging

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Identifying Hoaxes in Fake Spotter using XG Boost Machine Learning based Classification Method 4

Identifying Hoaxes in Fake Spotter using XG Boost Machine Le...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Chitti, Sridevi Rinku, Dhruva R. Juluru, Tarun Kumar Rao, P. Ramchandar Gouthami, Shiramshetty Kakatiya Institute of Technology and Science Department of Electronics and Communication Engineering Warangal India Cvr College of Engineering Department of Electronics and Communication Engineering Hyderabad India Jayamukhi Institute of Technological Sciences Department of Computer Science Engineering Warangal India

ISBN: (纸本)9798331529635

Social media has greatly streamlined communication in recent years which uses network, shares information, and keep up with current events. Many posts on social media are questionable and meant to deceive. There is a lot of fake news everywhere and online fake news could cause serious societal issues. It is crucial for us to identify false and misleading internet content. Technically, it is hard for us to differentiate between real and fake information. The development and sharing of content have been made easier by social media platforms, leading to a significant amount that must be examined. Information on the internet is diverse making this process difficult. In order to assess the reliability and purpose of statements, humans and machines must work together. Manually identifying fake news is challenging, and it can only be done when the individual doing the identification has extensive understanding of news. Therefore, there is a need for machine learning classifiers that can identify the false news automatically. The main goal is to measure the accuracy of deceptive detection utilizing various machine learning algorithms. Several machine learning (ML) techniques, such as Decision Trees (DT), Random Forest (RF), Logistic Regression (LR), Passive Aggressive Classifier (PAC), Extra Trees Classifier (ETC) and boosting algorithms were employed on datasets to determine if the content is authentic or untrue in order to identify hoaxes on different platforms for online networking sites. © 2024 IEEE.

关键词： Adversarial machine learning

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