检索结果-内蒙古大学图书馆

Electronics and Renewable Systems (ICEARS), International Conference on

作者： P. Chinnasamy Patan Feridoz Khan C Sasikala Mavalluru Swathi M Prema Kumar Ajay Kumar Yadav Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati Andhra Pradesh India Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Ananthapuramu Andhra Pradesh India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of ECE Shri Vishnu Engineering College for Women (A) Bhimavaram Andhra Pradesh India Department of Computer Application United Institute of Management Prayagraj India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time.

关键词： Cloud computing Machine learning algorithms Automation Accuracy Linear regression Energy efficiency Virtual machines Time factors Industrial Internet of Things Smart manufacturing

来源：评论

学校读者我要写书评

暂无评论

Structured Tensor Decomposition Based Channel Estimation and Double Refinements for Active RIS Empowered Broadband Systems

引用

IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Yirun Wang, Yongqing Shen, Yuyao Wang, Gongpu Tellambura, Chintha Beijing Institute of Technology School of Information and Electronics Beijing100081 China Beijing Jiaotong University Engineering Research Center of Network Management Technology for High Speed Railway Ministry of Education School of Computer Science and Technology Beijing100044 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Channel parameter recovery is critical for the next-generation reconfigurable intelligent surface (RIS)-empowered communications and sensing. Tensor-based mechanisms are particularly effective, inherently capturing the multi-dimensional nature of wireless channels. However, existing studies assume either a line-of-sight (LOS) scenario or a blocked TX-RX channel. This paper solves a novel problem: tensor-based channel parameter estimation for active RIS-aided multiple-antenna broadband connections in fully multipath environments with the TX-RX link. System settings are customized to construct a fifth-order canonical polyadic (CP) signal tensor that matches the five-dimensional channel. Four tensor factors contain redundant columns, rendering the classical Kruskal's condition for decomposition uniqueness unsatisfied. The fifth-order Vandermonde structured CP decomposition (VSCPD) is developed to address this challenge, making the tensor factorization problem solvable using only linear algebra and offering a relaxed general uniqueness condition. With VSCPD as a perfect decoupling scheme, a sequential triple-stage channel estimation algorithm is proposed based on one-dimensional parameter estimation. The first stage enables multipath identification and algebraic coarse estimation. The following two stages offer optional successive refinements at the cost of increased complexity. The closed-form Cramér-Rao lower bound (CRLB) is derived to assess the estimation performance. Herein, the noise covariance matrix depends on multipath parameters in our active-RIS scenario. Numerical results are provided to verify the effectiveness of proposed algorithms under various evaluation metrics. Our results also show that active RIS can significantly improve channel estimation performance compared to passive RIS. © 2025 IEEE.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Experimental Investigation for Hybrid Energy Harvesting System for Remote Monitoring Applications

Experimental Investigation for Hybrid Energy Harvesting Syst...

引用

Computing, sciences and Communications (ICCSC), International Conference on

作者： Abhishek Tripathi A. C. Deena T. R. Arvinthan G. Vijayaragavan Shubham Anjankar Subhashish Tiwari Gireesh G Soni Department of Computer Science Engineering Kalasalingam Academy of Research and Education Tamil Nadu India Kalasalingam Academy of Research and Education Tamil Nadu India Department of Electronics Engineering Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Electronics Engineering GITAM University Bangalore India Shri G S Institute of Technology and Science Indore India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

This study addresses the demand for sustainable energy systems in remote monitoring scenarios, particularly for Wireless Sensor Networks and IoT apparatuses. It introduces a hybrid approach to energy harvesting that utilizes both solar power and kinetic energy from piezoelectric sources, aiming to develop dependable and efficient power solutions that support extended operations in isolated settings. By integrating the solar and piezoelectric technologies, the proposed system ensures a continuous energy supply, crucial for the uninterrupted functionality of remote sensors. The implementation of ESP8266 modules facilitates WiFi connectivity, allowing data transmission to ThingSpeak platforms for effective data visualization and analysis. This configuration not only enhances the capability for real-time remote monitoring but also maximizes the operational lifespan of the sensor network without the need for frequent maintenance or battery replacements, thereby offering a significant advancement in the sustainability of IoT deployments.

关键词： Wireless sensor networks Data visualization Real-time systems Hybrid power systems Maintenance Energy harvesting Kinetic energy Sustainable development Remote monitoring Wireless fidelity

来源：评论

学校读者我要写书评

暂无评论

Cloud Computing in Industry 4.0: Enabling Real-Time Data Processing for Smart Factories

Cloud Computing in Industry 4.0: Enabling Real-Time Data Pro...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： N. Thangarasu Sunila Choudhary Umi Salma Basha Jana Ibrahim Otiaf Sireesha Nanduri V.S. Gaikwad Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Jazan University Saudi Arabia School of Mangement PG Management JAIN (Deemed to be University) Bangalore Karnataka Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

With Industry 4.0 leading the charge, cloud computing has revolutionized how industries work today. The cloud computing feature added in Industry 4.0 paved the way for real-time data processing that seeped right through intelligent factories and reflected the most significant improvements in efficiency, production, and cost reduction, all playing an important role today. It is a predictive maintenance technology that contributes to the concept of IoT in manufacturing, with proper machine-to-machine and system communication channels for data exchange. Cloud computing provides a scalable and adaptable data storage/analysis environment for large amounts of information to be maintained in predictive maintenance mode with optimization processes based on real-time applications. A data layer standing in between, rendering relevant information when needed to oversee the remote operation of plants can facilitate smart manufacturing over time. The cloud also allows collaboration and visibility at every point in your supply chain, significantly boosting overall SCM to Industry 4.0.

关键词： Industries Cloud computing Supply chains Collaboration Data processing Real-time systems Production facilities Fourth Industrial Revolution Smart manufacturing Predictive maintenance

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Study on the Real-Time Text Categorization of User Generated Content using Artificial Intelligence

A Comprehensive Study on the Real-Time Text Categorization o...

引用

Communications and Information technologies (GCCIT), Global Conference on

作者： G Rajalakshmi Amit Kansal Shaji Thomas Manisha P. Mali D. Anandhasilambarasan Dikshit Sharma Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Quantum University Research Center Quantum University Roorkee Uttarakhand India School of Mangement - PG Management JAIN Deemed to be University Bangalore Karnataka Computer Engg. Vishwakarma Institute of Technology Pune Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

With the rapid increase in social media usage, there has been a corresponding surge in user-generated content (UGC) being generated on a daily basis. This has created a demand for efficient and effective real-time text classification of UGC to monitor and extract valuable insights for businesses and organizations. Our goal in doing this systematic review was to compile all of the most up-to-date information on the best practices for user-generated content (UGC) text categorization in real-time on social media. Various sources, such as academic databases, digital libraries, and online periodicals, are combed through to find relevant material for the review. The focus is primarily on recent research conducted between 2015 and 2023, with an emphasis on identifying emerging trends, challenges, and opportunities in the field. The review critically examines the different approaches employed for text classification, encompassing machine learning, deep learning, and natural language processing techniques. Furthermore, it explores the specific social media platforms where text classification is carried out, such as Twitter, Facebook, and Instagram. The review highlights the key challenges associated with real-time text classification of UGC, such as handling large data volumes, dealing with language ambiguity, and the need for continuous training and updates. Additionally, the study underscores the potential opportunities for future research in this domain, such as the development of hybrid models that combine multiple approaches to enhance accuracy and scalability. The insights gained from this systematic review will be valuable for researchers, practitioners, and businesses engaged in real-time text classification of UGC on social media platforms.

关键词： Machine learning algorithms Social networking (online) Text categorization User-generated content Software algorithms Blogs Real-time systems Classification algorithms Web sites Systematic literature review

来源：评论

学校读者我要写书评

暂无评论

Segmentation of Breast Cancer from Mammogram Images using Fuzzy Clustering Approach

Segmentation of Breast Cancer from Mammogram Images using Fu...

引用

Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Sumit Kushwaha Parvatham Sathish Thamari Thankam K. Rajkumar M Dileep Kumar Sai Sudha Gadde Department of Computer Applications University Institute of Computing Chandigarh University Punjab India Department of Electronics and Communication Engineering SR University Hanumkonda Telangana India Department of Community Health Nursing Cihan University Erbil Iraq Faculty of Management Sciences Hensard University Bayelsa Nigeria Department of Computer Science and Engineering KoneruLakshmaiah Education Foundation Vaddeswaram Telangana India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Within the field of breast cancer segmentation, abnormalities occur when specific breast characteristics behave abnormally, possibly leading to different levels of cell damage. A variety of segmentation techniques, including K-means, Watershed, and Fuzzy Clustering, are used to examine and identify these anomalies. The purpose of this study is to assess how well various segmentation methods detect breast cancer. Using the INbreast dataset, which consists of images from several sources of breast scans, improves the segmentation process overall. After that, a few preprocessing methods are applied to the images. On preprocessed images, the three selected algorithms—K-means, Fuzzy Clustering, and Watershed—are used for testing and training. The findings show that the accuracy of each method in spotting anomalies in breast images varies. The findings show that the accuracy of each method in spotting anomalies in breast images varies. While segmentation algorithms can detect breast abnormalities, fuzzy clustering proves to be the most successful approach. Watershed segmentation shows noteworthy effectiveness in this regard. K-means outperforms other image processing models even if it yields less desirable results than the other two techniques. The research culminates in a comprehensive evaluation of the fuzzy clustering method, demonstrating accuracy in identifying anomalies and providing significant perspectives on its dependability and efficiency. In all cases, the alignment between the observed presence and ground truth is the same.

关键词： Training Image segmentation Accuracy Clustering algorithms Watersheds Breast cancer Mammography

来源：评论

学校读者我要写书评

暂无评论

Automated Seizure Detection and Diagnosis using Cascaded Reflection Equivariant Visual Attention Quantum Neural Network with Dung Beetle Optimization Algorithm Applied to EEG Signals

Automated Seizure Detection and Diagnosis using Cascaded Ref...

引用

Electronics and Renewable Systems (ICEARS), International Conference on

作者： B. Jagadeeswari Ranjitham. G Prasanta Kumar Parida L. Guganathan Megha D Bengalur Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India School of Rural Management KIIT University Bhubaneswar Odisha India Department of Physics Saveetha School of Engineering Saveetha Insitute of Medical and Technical Science (SIMATS) Thandalam Chennai Tamil Nadu India Department of Computer Science and Engineering Presidency University Bengaluru Karnataka India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Automated seizure detection and diagnosis for EEG signals refers to using deep learning algorithms and computational methods to analyze electroencephalogram data, identifying patterns that indicate epileptic seizures. We can assert that these systems speed up the process of correct diagnosis reducing the differentiation between the seizures and normal brain activity, thus improving the overall clinical work and patients’ outcomes. Conventional techniques fail to give precise and rapid results relating to complicated patterns of seizures. To address these challenges; this study introduces the Cascaded Reflection Equivariant Visual Attention Quantum Neural Network with Dung Beetle Optimization Algorithm (CasREVAQNNet+DBOA) framework for automated seizure detection and diagnosis in EEG signals. The proposed method leverages the CHB-MIT dataset, with initial preprocessing performed using Gradient Domain Guided Filtering (GDGF) to enhance critical EEG patterns. Feature extraction is carried out using DenseNet-121, which facilitates the effective representation of significant EEG characteristics. For classification, the Cascaded Reflection Equivariant Visual Attention Quantum Neural Network (CasREVAQNNet) is utilized, with performance further optimized by the Dung Beetle Optimization Algorithm (DBOA) to ensure accurate and reliable seizure detection (SD). The CasREVAQNNet+DBOA framework is implemented in Python and evaluated on the CHB-MIT dataset. The proposed approach yields excellent outcomes: accuracy of 99.8% and specificity of 98.6% which are significantly higher in contrast to the current techniques. The outcomes of the suggested method enhance accuracy, reduce false detection rates, and accelerate diagnosis, making it a potentially useful tool for reliable and efficient seizure detection in clinical practice.

关键词： Visualization Accuracy Filtering Brain modeling Electroencephalography Reflection Classification algorithms Reliability Optimization Biological neural networks

来源：评论

学校读者我要写书评

暂无评论

Age of Information Based User Scheduling and Data Assignment in Multi-User Mobile Edge Computing Networks:An Online Algorithm

引用

China Communications 2024年第5期21卷 153-165页

作者： Ge Yiyang Xiong Ke Dong Rui Lu Yang Fan Pingyi Qu Gang School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China Engineering Research Center of Network Management Technology for High Speed Railway of Ministry of Education Beijing 100044China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing 100044China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China Department of Electrical and Computer Engineering College ParkMarylandUSA

This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading *** weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data *** to the non-analytic expression of the WSA *** optimization variables and the unknowability of future network information,the problem cannot be solved with known solution ***,an online Joint Partial Offloading and User Scheduling Optimization(JPOUSO)algorithm is proposed by transforming the original problem into a single-slot data assignment subproblem and a single-slot user scheduling sub-problem and solving the two sub-problems *** analyze the computational complexity of the presented JPO-USO algorithm,which is of O(N),with N being the number of *** results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline *** is shown that both the user’s data assignment and the user’s AoI should be jointly taken into account to decrease the system WSA when scheduling users.

关键词： age of information(aoi) mobile edge computing(mec) user scheduling

来源：评论

学校读者我要写书评

暂无评论

Virtual Learning Environment - Evaluation of Learner’s Behavior Using Topic Models

Virtual Learning Environment - Evaluation of Learner’s Beha...

引用

Annual Information Technology, Electromechanical Engineering and Microelectronics Conference (IEMECON)

作者： Deepak N A Gyanendra Kumar Aunkrisa Sangchumnong Chaithra R S Sur Singh Rawat Aziz Nanthaamornphong Girish Kumar B C Manoj Gupta Computer Science & Engineering RV Institute of Technology and Management Bengaluru India Manipal University Jaipur School of Toursim and Hospitality Management Suan Dusit University Bangkok Thailand Department of MCA SJB Institute of Technology Bengaluru India Computer Science and Engineering JSS Academy of Technical Education Noida UP College of Computing Prince of Songkla University Phuket Thailand Information Science and Engineering RV Institute of Technology and Management Bengaluru India Department of Electrical Engineering Guru Ghasidas Vishwavidyalaya Bilaspur Chhattisgarh India

ISBN: (数字)9798350387315

ISBN: (纸本)9798350387322

online learning platforms come with a number of difficulties. To identify the student who does not do the given assignment within the allotted time. Researchers have been attempting to solve this issue in the literature of late, however most algorithms are unable to produce linearly separable learner clusters and correctly classify the input documents. In an attempt to overcome these problems, the suggested methodology builds clusters of linearly separable learners by applying topic models such as Latent Dirichlet Allocation (LDA). First, the necessary features are retrieved and converted into an appropriate LDA of words and phrases. The topic-modeling algorithm (LDA) is then fed the words to create clusters of related content or learners. A number of experiments were carried out to assess how well various predictive models performed. The results show the topic-modeling algorithm LDA attains significant clustering of documents over the other state-of-art.

关键词： Sequential analysis Electronic learning education Clustering algorithms Predictive models Prediction algorithms Feature extraction Microwave theory and techniques Classification algorithms Resource management

来源：评论

学校读者我要写书评

暂无评论

Development Of an Intelligent Traffic Navigation System by Wireless Sensor Network System

Development Of an Intelligent Traffic Navigation System by W...

引用

2023 International Conference on New Frontiers in Communication, Automation, management and Security, ICCAMS 2023

作者： Kaushal, Rajanish Kumar Kumar, B.Anjanee Chauhan, Krupesh A. Munagala, Madhu Singh, Pavitar Parkash Hazela, Bramah Chandigarh University Department of Electrical Engineering Punjab Mohali India Dadi Institute of Engineering and Technology Anakapalle Electronics and Communication Engineering Visakhapatnam India Sardar Vallabhbhai National Institute of Technology Department of Civil Engineering Surat India Koneru Lakshmaiah Education Foundation Department of English Vaddeswaram India Lovely Professional University Department of Management Phagwara India Amity University Amity School of Engineering & Technology Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350317060

The use of automobiles is becoming more important worldwide, especially in densely populated cities. Current transportation infrastructure technologies include wired sensors, inductive loops, and satellite navigation. The installation and upkeep of surveillance equipment (cameras, etc.) are quite costly. In addition, environmental factors affect these instruments' precision. Traditional methods typically aim to optimize traffic light management for a given traffic density and layout. One key draws back all these methods, however, it may be challenging to represent the dynamic behaviour of traffic volumes and configurations, which is always changing. Therefore, in an effort to solve the aforementioned problem, this study proposes methods for adaptive traffic light management via the use of a distributed sensor network. The purpose of this study is to examine approaches to developing a smart system that can integrate and support some of the current technologies of traffic management, therefore decreasing the typical wait time for cars at a junction. The suggested algorithms are flexible enough to adjust to the particulars of traffic at every given crossroads. (GLD) is a traffic simulation tool used to model real-world traffic conditions and provide graphs of average waiting time vs cycle counts. The collected findings validate the efficacy of the suggested strategy for traffic regulation at a real-world road junction. © 2023 IEEE.

关键词： Wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：