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Cluster Head Selection and Multipath Routing Based Energy Efficient Wireless Sensor Network

Intelligent Automation & Soft Computing 2023年第4期36卷 879-894页

作者： T.Shanmugapriya Dr.K.Kousalya Department of Information Technology SNS College of TechnologyCoimbatore641035India Department of Computer Science and Engineering Kongu Engineering CollegeErode638060India

The Wireless Sensor Network(WSN)is a network of Sensor Nodes(SN)which adopt radio signals for communication amongst *** is an increase in the prominence of WSN adaptability to emerging applications like the Internet of Things(IoT)and Cyber-Physical Systems(CPS).Data secur-ity,detection of faults,management of energy,collection and distribution of data,network protocol,network coverage,mobility of nodes,and network heterogene-ity are some of the issues confronted by *** is not much published information on issues related to node mobility and management of energy at the time of aggregation of *** the goal of boosting the mobility-based WSNs’network performance and energy,data aggregation protocols such as the presently-used Mobility Low-Energy Adaptive Clustering Hierarchy(LEACH-M)and Energy Efﬁcient Heterogeneous Clustered(EEHC)scheme have been exam-ined in this work.A novel Artiﬁcial Bee Colony(ABC)algorithm is proposed in this work for effective election of CHs and multipath routing in WSNs so as to enable effective data transfer to the Base Station(BS)with least energy *** is avoidance of the local optima problem at the time of solution space search in this proposed *** have been conducted on a large WSN network that has issues with mobility of nodes.

关键词： Wireless sensor network routing clustering mobility low-energy adaptive clustering hierarchy energy efﬁcient heterogeneous clustered artiﬁcial bee colony

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Group-PTP: A Pedestrian Trajectory Prediction Method Based on Group Features

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IEEE Transactions on Multimedia 2025年 27卷 3527-3541页

作者： Zhang, Chuanyang Zhang, Guijuan Zheng, Zhuoran Lu, Dianjie Shandong Normal University School of Information Science and Engineering Jinan250014 China State Key Laboratory of High-End Server Storage Technology Organization Jinan250358 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

Group features have significant effects on pedestrian movement and constitute a focal point in pedestrian trajectory prediction research. In reality, pedestrians within a group exhibit notable consistency features due to their compact spatial positions, close destinations, and factors such as coordination within the group. In contrast, owing to the dispersed destinations among groups and the lack of coordination, there are significant differences in velocity and direction between the groups, leading to strong conflicts. However, existing pedestrian trajectory prediction models based on group features lack sufficient quantification of both within-group and between-group features. To address this problem, we propose Group-PTP, a novel pedestrian trajectory prediction model based on group features. Specifically, we first propose a group graph attention network-based group features aggregation method (Group-GAT). By quantifying and aggregating the intra-consistency and inter-conflict features exhibited by the groups, our method can better capture the features and interactions both within and between groups. Second, we propose a group multi-feature information representation model that fuses captured group aggregate features, pedestrian coordinates, surrounding pedestrian features, and obstacle features through fusion concatenation. Finally, we propose a multi-feature temporal convolutional network (MF-TCN) that embeds the impact weights of multi-feature information into pedestrian coordinates to obtain feature outputs and conducts temporal operations on feature outputs to predict future trajectories. The experimental results demonstrate that our proposed Group-PTP achieves state-of-the-art performance on several different trajectory prediction benchmarks. © 1999-2012 IEEE.

关键词： Convolution

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Enabling IoT Service Classification: A Machine Learning-Based Approach for Handling Classification Issues in Heterogeneous IoT Services

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IEEE Access 2023年 11卷 89024-89037页

作者： Abbasi, Mohammad Asad Chen, Yen-Lin Khan, Abdullah Ayub Memon, Zulfiqar A. Durrani, Nouman M. Yang, Jing Ku, Chin Soon Por, Lip Yee Benazir Bhutto Shaheed University Lyari Department of Computer Science and Information Technology Karachi75660 Pakistan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Sindh Madressatul Islam University Department of Computer Science Karachi74000 Pakistan -FAST Department of Computer Science Islamabad44000 Pakistan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia

The Internet of Things (IoT) is a form of Internet-based distributed computing that allows devices and their services to interact and execute tasks for each other. Consequently, the footprint of the IoT is increasing and becoming more complex to the highest degree. This has also given birth to new IoT-enabled applications and services. Efficient service interaction and management also call for understanding and analyzing the nature of IoT services. Further, IoT services must be characterized into various classes, and different service-related attributes must be considered for the classification. This article assesses the requirements of heterogeneous IoT services by examining their interactions. Principally, heterogeneous IoT and their service interactions are targeted. The research work performs classification of IoT services into various classes. Services are classified on the basis of various attributes. The attributes reflect different characteristics of the services. This research enables improved utilization of IoT services through efficient classification of available resources using machine learning methods. To demonstrate service classification applicability, the SVM, voting classifier, and decision tree have been applied in a service-oriented environment along with different types of services. All the services in the data set were analyzed and divided into five classes. Moreover, the decision tree performed well and achieved higher accuracy values in all classes. However, the overall prediction and classification of the decision tree model were observed to be good and satisfactorily high. © 2013 IEEE.

关键词： Decision trees

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Harmony in transcripts: a systematic literature review of transcriptome-wide association studies

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Journal of engineering and Applied science 2024年第1期71卷 1-15页

作者： Mashhour, Mahinaz A. Kandil, Ahmed Hisham AbdElwahed, Manal Mabrouk, Mai S. Biomedical Engineering Department Misr University for Science and Technology Giza Egypt Systems and Biomedical Engineering Department Cairo University Giza Egypt School of Information Technology and Computer Science Nile University Sheikh Zayed City Egypt

Transcriptome-wide association studies (TWAS) goal is to better understand the etiology of diseases and develop preventative and therapeutic approaches by examining the connections between genetic variants and phenotypes while overcoming the limitations of the genome-wide association study (GWAS). It is a valuable complement to GWAS, reducing the negative effects of multiple tests and enabling a more thorough investigation of gene expression patterns in various tissues. A systematic review is presented in this paper to identify articles that utilize TWAS to understand the genetic factors behind complex diseases. A detailed selection process was carried out using standard PRISMA criteria to select relevant articles for the review. Twenty-five articles passed the inclusion criteria and were selected for additional review. The studies cover a diverse range of disorders, including Tourette’s syndrome, Alzheimer’s disease, rheumatoid arthritis, and major depression. Leveraging gene expression data from different tissues and populations, these investigations successfully identified novel genes and pathways associated with the studied conditions. The collective findings highlight the transformative impact of integrative genomics in advancing our understanding of complex diseases, providing insights into potential therapeutic targets, and laying the foundation for precision medicine approaches. © The Author(s) 2024.

关键词： Genome

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Automating Optimal Healthcare Delivery Through Hyper Spectral Imaging in Sustainable Medical Environments

Automating Optimal Healthcare Delivery Through Hyper Spectra...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Seth, Jyoti Kavitha, R. Yadav, Dhyan Chandra Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

Hyperspectral imaging (HSI) is a progressive imaging method being explored, which will aid in the automation of foremost healthcare transport in sustainable scientific environments. HSI requires an enormous variety of spectral bands compared to other spectral imaging methods, including visible mild imaging. This more statistics let in for a greater sure and correct evaluation of the spectral information within an image, presenting more remarkable sturdy outcomes while assessing the health of clinical situations. The information accrued thru HSI can be used to create automatic healthcare shipping models which could autonomously adjust and reply to changes in the clinical situation of an affected person. Similarly, this technology may be used to hit upon ailments, including cancer, offering a treasured perception of a patient's health and considering the selection of timely and appropriate treatments. Moreover, HSI can be used to create green and sustainable clinical surroundings, as it requires reduced personnel, area and fewer patient trips to and from the health facility. This technology can lower prices and improve patient consequences by providing automated selections for healthcare professionals. © 2024 IEEE.

关键词： autonomously Hyperspectral information progressive imaging

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Statistical Detection of Malicious iOS Apps through Ensemble and Active Learning 2023

Statistical Detection of Malicious iOS Apps through Ensemble...

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15th International Conference on Contemporary Computing, IC3 2023

作者： Bhatt, Arpita Jadhav Agarwal, Parul Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology India

ISBN: (纸本)9798400700224

iOS is one of the most broadly used mobile operating systems after Android. In today's era, smartphones are widely used to perform several tasks such as net banking, GPS tracking, ordering products, etc. One of the main features of smartphones is that their capabilities can be expanded by installing third-party apps. However, these apps pose privacy threats to the user as they contain a lot of user's personal information, which may be vulnerable to attackers. Background studies have identified that a lot of user's personal and sensitive data is stored on smartphones and thus, they have become a perfect target for privacy threats in the whole mobile ecosystem. Machine learning techniques to detect malware apps have offered promising results for the Android platform whereas nominal work has been done for the iOS platform. In this paper, we are incorporating various machine learning techniques to detect malicious iOS apps from several categories. Some of the ensemble learning techniques with different classification algorithms are inculcated to enhance the precision of classifiers. A weighted average method on active and ensemble learning has been proposed which proved to be more efficient as compared to other machine learning techniques in most of the categories. Our empirical analysis has demonstrated that the proposed technique is statistically significant than active and ensemble learning. © 2023 ACM.

关键词： Smartphones

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Automating Object Identification from Hyper Spectral Data with Markov Random Fields

Automating Object Identification from Hyper Spectral Data wi...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Chaudhary, Chetan Devrani, Rekha Aadil, Mir Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Hyper Spectral statistics (HSD) is a type of fact acquired from optical sensors, which include satellites, scanners, and microscopes that captures and measures various wavelengths of energy. Object identification from HSD is problematic because of the high spectral variability in pixels of the image and the great variety of product classes usually present. Markov Random Fields (MRF) is a graphical model that may be used to version spatial dependencies among neighboring pixels which can assist in decorating the object identification process. This summary explores the feasibility of automating item identity from HSD through MRF. We introduce the idea of a priori information, which includes labels assigned to distinct spectral signatures within the image and a classification system that uses MRF as a priori information to enhance object identity accuracy. We describe and demonstrate the exact rules for this challenge via simulation. The consequences of the take look advocate that utilizing the MRF-based category gadget to improve item identification accuracy from HSD is possible. © 2024 IEEE.

关键词： classification consequences Hyper Spectral microscopes neighboring

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Building an effective KPI monitoring system for an OTT Platform 4

Building an effective KPI monitoring system for an OTT Platf...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Jena, Biswaranjan Mishra, Debahuti Mishra, Smitaprava University Department of Computer Science and Engineering Bhubaneswar India University Department of Computer Science and Information Technology Bhubaneswar India

ISBN: (纸本)9798350349856

Success of any Over the Top (OTT) platform depends on how the platform is providing best user experience along with content to its customers. Being in media and entertainment space customers need to access content from various devices like mobile, tab, desktop, tv including set top boxes. To measure the effectiveness of the systems its inevitable to define standard Key Performance Indicators (KPIs) and continuously monitor the application along with standard monitoring capabilities at infrastructure level to make the application available with required throughput. This paper describes in detail various application KPIs respective to different channels and systems along with tools used to measure the same. © 2024 IEEE.

关键词： Benchmarking

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BEDiff: denoising diffusion probabilistic models for building extraction

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Optoelectronics Letters 2025年第5期21卷 298-305页

作者： LEI Yanjing WANG Yuan CHAN Sixian HU Jie ZHOU Xiaolong ZHANG Hongkai College of Computer Science and Technology Zhejiang University of Technology Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information China Three Gorges University Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province Wenzhou University College of Electrical and Information Engineering Quzhou University School of Electronic and Information Engineering Anhui Jianzhu University

Accurately identifying building distribution from remote sensing images with complex background information is challenging. The emergence of diffusion models has prompted the innovative idea of employing the reverse denoising process to distill building distribution from these complex backgrounds. Building on this concept, we propose a novel framework, building extraction diffusion model(BEDiff), which meticulously refines the extraction of building footprints from remote sensing images in a stepwise fashion. Our approach begins with the design of booster guidance, a mechanism that extracts structural and semantic features from remote sensing images to serve as priors, thereby providing targeted guidance for the diffusion process. Additionally, we introduce a cross-feature fusion module(CFM) that bridges the semantic gap between different types of features, facilitating the integration of the attributes extracted by booster guidance into the diffusion process more effectively. Our proposed BEDiff marks the first application of diffusion models to the task of building extraction. Empirical evidence from extensive experiments on the Beijing building dataset demonstrates the superior performance of BEDiff, affirming its effectiveness and potential for enhancing the accuracy of building extraction in complex urban landscapes.

关键词： A

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Exploration of the Impact of 5G on Mobile Communication Systems

Exploration of the Impact of 5G on Mobile Communication Syst...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Singh, Akhilendra Pratap Seth, Jyoti Kamalraj, R. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

The fifth era (5G) of cell communication systems is a progressive jump forward in the evolution of wireless networks. It promises to deliver a brand new user enjoyment, characterized by using quicker speeds and greater capability, with improved energy efficiency. The increased speed and efficiency of 5G networks could have a big impact on a wide variety of packages, which includes stepped forward user revel, advanced great of provider, and green use of sources. This paper explores the ability impact of 5G on cellular communique systems by inspecting the important thing demanding situations and case studies that highlight specific advantages that may be realized. We evaluate the primary technological advances that will make contributions to the success of 5G structures, including millimeter waves, large MIMO and beamforming, and community cutting. The paper also considers the capability effect that 5G will have on modern-day network architectures, regulatory and policy frameworks, and new enterprise fashions in order to be taken into consideration. Through a variety of case research, we pick out the potential possibilities and challenges that 5G might also carry and provide concrete suggestions on how first-rate to ensure successful deployment. In the end, we present the concept that 5G can be the most effective engine of digital transformation, furnishing the right mixture of innovation, investment, and partnership. © 2024 IEEE.

关键词： Beamforming Communication Efficiency Enterprise Networks Partnership

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