检索结果-南通市图书馆

Traffic-Aware Fuzzy Classification Model to Perform IoT Data Traffic Sourcing with the Edge Computing

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第2期78卷 2309-2335页

作者： Huixiang Xu College of information Engineering Zhengzhou University of TechnologyZhengzhou450044China

The Internet of Things(IoT)has revolutionized how we interact with and gather data from our surrounding *** devices with various sensors and actuators generate vast amounts of data that can be harnessed to derive valuable *** rapid proliferation of Internet of Things(IoT)devices has ushered in an era of unprecedented data generation and *** IoT devices,equipped with many sensors and actuators,continuously produce vast volumes of ***,the conventional approach of transmitting all this data to centralized cloud infrastructures for processing and analysis poses significant ***,transmitting all this data to a centralized cloud infrastructure for processing and analysis can be inefficient and impractical due to bandwidth limitations,network latency,and scalability *** paper proposed a Self-Learning Internet Traffic Fuzzy Classifier(SLItFC)for traffic data *** proposed techniques effectively utilize clustering and classification procedures to improve classification accuracy in analyzing network traffic *** addresses the intricate task of efficiently managing and analyzing IoT data traffic at the *** employs a sophisticated combination of fuzzy clustering and self-learning techniques,allowing it to adapt and improve its classification accuracy over *** adaptability is a crucial feature,given the dynamic nature of IoT environments where data patterns and traffic characteristics can evolve *** the implementation of the fuzzy classifier,the accuracy of the clustering process is improvised with the reduction of the computational *** can reduce computational time while maintaining high classification *** efficiency is paramount in edge computing,where resource constraints demand streamlined data ***,SLItFC’s performance advantages make it a compelling choice for organizations seeking to harness the potential of IoT data for real-time insig

关键词： Internet of Things(IoT) edge computing traffic data self-learning fuzzy-learning

Enhancing Secure Development in Globally Distributed Software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第6期79卷 5031-5049页

作者： Marya Iqbal Yaser Hafeez Nabil Almashfi Amjad Alsirhani Faeiz Alserhani Sadia Ali Mamoona Humayun Muhammad Jamal University Institute of information Technology PMAS-Arid Agriculture UniversityRawalpindiPakistan Department of Software Engineering College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of information Systems College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72388Saudi Arabia Department of Mathematics Pir Mehr Ali Shah Arid Agriculture UniversityRawalpindiPakistan

Embracing software product lines(SPLs)is pivotal in the dynamic landscape of contemporary software ***,the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability,underscoring the critical importance of robust cybersecurity *** paper advocates for leveraging machine learning(ML)to address variability management issues and fortify the security of *** the context of the broader special issue theme on innovative cybersecurity approaches,our proposed ML-based framework offers an interdisciplinary perspective,blending insights from computing,social sciences,and ***,it employs ML for demand analysis,dynamic feature extraction,and enhanced feature selection in distributed settings,contributing to cyber-resilient *** experiments demonstrate the framework’s superiority,emphasizing its potential to boost productivity and security in *** digital threats evolve,this research catalyzes interdisciplinary collaborations,aligning with the special issue’s goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics,privacy,and human values.

关键词： Machine Learning variability management cybersecurity digital ecosystems cyber-resilience

Turnip mosaic virus pathogenesis and host resistance mechanisms in Brassica

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Horticultural Plant Journal 2024年第4期10卷 947-960页

作者： Guanwei Wu Xinxin Fang Tianqi Yu Jianping Chen Fei Yan State Key Laboratory for managing Biotic and Chemical Threats to the Quality and Safety of Agro-products Institute of Plant VirologyNingbo UniversityNingboZhejiang 315211China Key Laboratory of Biotechnology in Plant Protection of MARA and Zhejiang Province Institute of Plant VirologyNingbo UniversityNingboZhejiang 315211China

Turnip mosaic virus(TuMV)is a devastating potyvirus pathogen that infects a wide variety of both cultivated and wild Brassicaceae *** urgently need more information and understanding of TuMV pathogenesis and the host responses involved in disease development in cruciferous *** displays great versatility in viral pathogenesis,especially in its replication and intercellular ***,in the coevolutionary arms races between TuMV and its hosts,the virus has evolved to co-opt host factors to facilitate its infection and counter host defense *** review mainly focuses on recent advances in understanding the viral factors that contribute to the TuMV infection cycle and the host resistance mechanism in ***,we propose some future research directions on TuMV pathogenesis and control strategies to design durable TuMV-resistant Brassica crops.

关键词： Turnip mosaic virus Brassica Resistance genes Host factors Infection biology

Optical multipath self-interference cancellation for a wideband in-band full-duplex system using a silicon photonic modulator chip

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Chinese Optics Letters 2024年第7期 131-136页

作者：余骁叶佳闫连山周涛朱越李鹏邹喜华潘炜 Center for information Photonics and Communications School of Information Science and Technology Southwest Jiaotong University Key Laboratory of Electronic information Control Southwest China Research Institute of Electronic Equipment Microwave Photonics Research Laboratory School of Electrical Engineering and Computer Science University of Ottawa

We propose and experimentally demonstrate a photonic method for wideband multipath self-interference cancellation using a silicon photonic modulator chip. The chip generates phase-inverted reference signals by leveraging the opposite phase between optical sidebands. Effectively managing amplitude and phase imbalances between self-interference and reference signals, the approach rectifies discrepancies through consistent chip manufacturing and packaging *** photonic multi-dimensional multiplexing, including wavelength and polarization, enables the acquisition of multiple reference signals. Experimental results show multipath cancellation depths of 25.53 dB and 23.81 dB for bandwidths of500 MHz and 1 GHz, achieved by superimposing 2-path reference signals.

关键词：

Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2024年第6期139卷 3387-3415页

作者： Nazik Alturki Raed Alharthi Muhammad Umer Oumaima Saidani Amal Alshardan Reemah M.Alhebshi Shtwai Alsubai Ali Kashif Bashir Department of information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Engineering University of Hafr Al-BatinHafar Al-BatinSaudi Arabia Department of Computer Science&information Technology The Islamia University of BahawalpurBahawalpurPakistan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Woxsen School of Business Woxsen UniversityHyderabadIndia Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon

The concept of smart houses has grown in prominence in recent *** challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device *** home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical *** paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in *** have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT *** system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing *** have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache *** feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time *** is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation ***,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber *** trial results support the proposed system and demonstrate its potential for use in everyday life.

关键词： Blockchain Internet of Things(IoT) smart home automation cybersecurity

Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第5期79卷 3067-3087页

作者： Arnab Dey Samit Biswas Dac-Nhuong Le Department of Computer Science and Technology Indian Institute of Engineering Science and TechnologyShibpurHowrah711103India Faculty of information Technology Haiphong UniversityHaiphong180000Vietnam

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd datas

关键词： Workout action recognition video stream action recognition residual network GRU attention

Study on Image Recognition Algorithm for Residual Snow and Ice on Photovoltaic Modules

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Energy Engineering 2024年第4期121卷 895-911页

作者： Yongcan Zhu JiawenWang Ye Zhang Long Zhao Botao Jiang Xinbo Huang College of Electronics and information Xi’an Polytechnic UniversityXi’an710048China Xi’an Key Laboratory of Interconnected Sensing and Intelligent Diagnosis for Electrical Equipment Xi’an710048China

The accumulation of snow and ice on PV modules can have a detrimental impact on power generation,leading to reduced efficiency for prolonged ***,it becomes imperative to develop an intelligent system capable of accurately assessing the extent of snow and ice coverage on PV *** address this issue,the article proposes an innovative ice and snow recognition algorithm that effectively segments the ice and snow areas within the collected ***,the algorithm incorporates an analysis of the morphological characteristics of ice and snow coverage on PV modules,allowing for the establishment of a residual ice and snow recognition *** process utilizes both the external ellipse method and the pixel statistical method to refine the identification *** effectiveness of the proposed algorithm is validated through extensive testing with isolated and continuous snow area *** results demonstrate the algorithm’s accuracy and reliability in identifying and quantifying residual snow and ice on PV *** conclusion,this research presents a valuable method for accurately detecting and quantifying snow and ice coverage on PV *** breakthrough is of utmost significance for PV power plants,as it enables predictions of power generation efficiency and facilitates efficient PV maintenance during the challenging winter conditions characterized by snow and *** proactively managing snow and ice coverage,PV power plants can optimize energy production and minimize downtime,ensuring a sustainable and reliable renewable energy supply.

关键词： Photovoltaic(PV)module residual snow and ice snow detection feature extraction image processing

Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第1期78卷 1135-1156页

作者： Shasha Zhao Huanwen Yan Qifeng Lin Xiangnan Feng He Chen Dengyin Zhang College of Internet of Things Nanjing University of Posts and TelecommunicationsNanjing210003China Jiangsu Key Laboratory of Broadband Wireless Communication and Internet of Things Nanjing University of Posts and TelecommunicationsNanjing210003China Tongding Interconnection information Co. Ltd.Suzhou215000China

Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing *** execution time and low load imbalance may be the challenges for some algorithms in resource scheduling *** this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)*** HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC *** testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been *** results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony *** with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local *** a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence ***,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.

关键词： Cloud computing distributed processing evolutionary artificial bee colony algorithm hierarchical particle swarm optimization load balancing

Impact of Artificial Intelligence on Corporate Leadership

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Computer and Communications 2024年第4期12卷 40-48页

作者： Daniel Schilling Weiss Nguyen Mudassir Mohiddin Shaik School of Business and Technology (SBT) Aspen University Phoenix Arizona USA information Technology Indiana Wesleyan University Indianapolis Indiana USA

Artificial Intelligence (AI) is transforming organizational dynamics, and revolutionizing corporate leadership practices. This research paper delves into the question of how AI influences corporate leadership, examining both its advantages and disadvantages. Positive impacts of AI are evident in communication, feedback systems, tracking mechanisms, and decision-making processes within organizations. AI-powered communication tools, as exemplified by Slack, facilitate seamless collaboration, transcending geographical barriers. Feedback systems, like Adobe’s Performance Management System, employ AI algorithms to provide personalized development opportunities, enhancing employee growth. AI-based tracking systems optimize resource allocation, as exemplified by studies like “AI-Based Tracking Systems: Enhancing Efficiency and Accountability.” Additionally, AI-powered decision support, demonstrated during the COVID-19 pandemic, showcases the capability to navigate complex challenges and maintain resilience. However, AI adoption poses challenges in human resources, potentially leading to job displacement and necessitating upskilling efforts. managing AI errors becomes crucial, as illustrated by instances like Amazon’s biased recruiting tool. Data privacy concerns also arise, emphasizing the need for robust security measures. The proposed solution suggests leveraging Local Machine Learning Models (LLMs) to address data privacy issues. Approaches such as federated learning, on-device learning, differential privacy, and homomorphic encryption offer promising strategies. By exploring the evolving dynamics of AI and leadership, this research advocates for responsible AI adoption and proposes LLMs as a potential solution, fostering a balanced integration of AI benefits while mitigating associated risks in corporate settings.

关键词： Artificial Intelligence (AI) Corporate Leadership Communication Feedback Systems Tracking Mechanisms Decision-Making Local Machine Learning Models (LLMs) Federated Learning On-Device Learning Differential Privacy Homomorphic Encryption