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Improved FGMRP Routing Protocol to Solve Energy Hole Problem in Wireless Sensor Networks 3

Improved FGMRP Routing Protocol to Solve Energy Hole Problem...

引用

3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Signh, Pritpal Signh, Amanpreet Puneet Chandigarh University Computer Science Engineering Mohali India

ISBN: (纸本)9781665438117

In wireless sensor network sensor nodes can sense the information which is based upon the decentralized technique and self-configuring type of network and pass it to the base station. Energy consumption is the major problem related to the wireless sensor and far deployment which is due to decentralized network. In this research paper we work on FGMRP protocol which is performed and upgraded to increase the existence in wireless sensor networks. The basic working of FGMRP protocol is that total network is prorated into assemblage and assemblage heads are deployed in each cluster of the wireless sensor network. In the network the leader nodes are also chosen which collect the information from cluster heads and pass to the base station. In the enhancement of FGMRP protocol to increase the existence of wireless sensor network, more gateway nodes are stationed. In the proposed system the leader nodes send the date to gateway nodes which are deployed near to the base station. The cluster head gives the information to the leader nodes. The proposed FGMRP protocol and FGMRP protocol are performed through the simulator NS2. © 2021 IEEE.

关键词： Base stations

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Blockchain Integrated Cloud Architecture for Surveillance Systems

Blockchain Integrated Cloud Architecture for Surveillance Sy...

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Disruptive Technologies (ICDT), international conference on

作者： Vinay Kumar Varun Singh Raghuvanshi Shashwat Sharma Yash Yadav Arvind Goutam Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

The speedy growth of data-pushed programs, especially in surveillance and IoT systems, has underscored the need for steady, green, and privateness-retaining records control answers. While cloud computing gives scalability, it poses sizeable safety risks, making it liable to records breaches and unauthorized access. Blockchain technology, with its decentralized and immutable ledger, offers promising answers to these challenges, yet it struggles with scalability and computational demands while implemented to high-frequency, actual-time applications. This research paper proposes an technique to address those limitations through an integration of blockchain generation with cloudprimarily based surveillance systems. Through a hybrid version of selective storage and optimized consensus mechanisms, the proposed solution addresses the demanding situations of latency, scalability, and privacy maintenance at the same time while enhancing real-time access control.

关键词： Cloud computing Privacy Surveillance Scalability Smart contracts Real-time systems Safety Maintenance Consensus protocol Disruptive technologies

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Study on HGR by Using Machine Learning

Study on HGR by Using Machine Learning

引用

Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Anand Kumar Sharma A Suresh Kumar Amandeep Nagpal Rajesh Singh Dasari Veera Reddy Anupama Chadha Department of Information Technology Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology Hyderabad Department of CSE Graphic Era Deemed to be University Lovely Professional University Phagwara Uttaranchal Institute of Technology Uttaranchal University India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of Computer Applications Manav Rachna International Institute of Research and Studies Faridabad Haryana

The medical field and other research areas fields heavily rely on artificial intelligence (AI) and machine learning (ML). Hand gesture recognition (HGR), which is a straightforward approach for interacting with machines, has drawn the attention of numerous researchers in the context of AI. Utilizing ML technique, HGR is carried out. Both the image technique and the design approach are parts of it. In the image technique, the hand picture is rebuilt utilizing the attributes of the images. However, when using the model-based approach, the image is recreated using many models, including volumetric, geometric, and others. The key difficulty in gesture control continues to be the ML individual's complexity and preparation time. Its accuracy is used to assess the HGR accuracy. Its precision is used to assess the HGR overall system performance. The many techniques and algorithms utilized in gesture control are covered in detail inside this work.

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Forecasting Stock Price Movement using Long Short Term Memory(FSPMLSTM) 3

Forecasting Stock Price Movement using Long Short Term Memor...

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3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Kumari, Julee Sharma, Vivek Dwivedi, Dupendra G.L Bajaj Institute of Engineering and Greater Noida Computer Science and Engineering UP India

ISBN: (纸本)9781665438117

the stock market forecasts are fraught with risk, they are crucial in both financial and commercial terms. Stock forecasting is influenced by a variety of factors, including rational and irrational behavior, investor emotion, market rumors, and so on. All of these variables contribute to stock price volatility and make accurate forecasting extremely difficult. Every second, a vast quantity of data is created from many sources, which has an influence on the stock market. The idea is to create a stock market forecast model utilizing publicly available financial data. Because of its great reading capacity on a vast data collection, we apply the machine learning process (ML). The LSTM model was used to assess and forecast stock prices in this investigation. The primary goal of this thesis is to compare the performance of India's stock market (Nify50) from January 2, 2017 to January 1, 2021. Based on the study's findings, it's clear that adopting the LSTM model improves performance accuracy. © 2021 IEEE.

关键词： Forecasting

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Arabic Question Classification using Deep Learning 22

Arabic Question Classification using Deep Learning

引用

3rd international conference on control, Robotics and Intelligent System, CCRIS 2022

作者： Balla, Husamelddin Salvador, Marisa Llorens Delany, Sarah Jane School of Computer Science Technological University Dublin Dublin Ireland

ISBN: (纸本)9781450396851

Question classification is one of the essential tasks in Natural Language Processing (NLP). Not many studies have addressed the question classification task where the language is Arabic. This study proposes a language-specific model structure for assessment question classification in Arabic. Approaches proposed in the literature that address Arabic assessment question classification typically have used traditional machine learning techniques. Therefore, the model performance is constrained by the inherent limitations of these traditional approaches. This study addresses this gap and proposes a language-specific feature representation combined with a deep learning model structure. The proposed model uses a modified term frequency-inverse document frequency (M-TF-IDF) term weighting combined with Arabic word embeddings using ArELMo for the question feature representations. The proposed model combines Bi-Directional Gated Recurrent Units (BiGRU) with an attention mechanism and a convolution neural network. The obtained experimental results show the efficacy of the proposed approach which outperforms previous studies on Arabic assessment question classification. © 2022 Owner/Author.

关键词： Text processing

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Recommendation System with Exploratory Data Analytics using Machine Learning 3

Recommendation System with Exploratory Data Analytics using ...

引用

3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Panwar, Manu Wadhwa, Amit Pippal, Sanjeev GL.Bajaj Institute of Technology Computer Science and Engineering Gr.Noida India

ISBN: (纸本)9781665438117

Digital data is generated on a vast scale nowadays and has been for the past two decades. This phenomenon implies shift in how data is managed, and conclusions drawn from it. Furthermore, artificial intelligence approaches and procedures for new ideas of analyzing Big Data, Sentiment Analysis (SA), also known as Opinion Mining (OM), has been a hot topic in recent, because of its ability to extract value from data, it has been around for a long time. It is, however, a topic that has gotten more attention in the fields of engineering and linguistics. As a result, the goal of this research is to provide insights into the field of exploratory data analysis, as well as an orientation toward the study of a recommendation system employing big data in the context of an e-commerce system, through the use of machine learning. probable machine learning based model. Initially the research contribution briefly summarizes discussion of the strategies and processes currently used in Sentiment Analysis and further we work upon the analysis of a recommendation system with e- commerce data using LSTM Model. © 2021 IEEE.

关键词： Sentiment analysis

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Deep Learning-based Techniques for the Automatic Classification of Fundus Images: A Comparative Study 3

Deep Learning-based Techniques for the Automatic Classificat...

引用

3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Bali, Akanksha Mansotra, Vibhakar University of Jammu Department of Computer Science and IT Jammu and Kashmir India

ISBN: (纸本)9781665438117

In fundus pictures, convolutional neural networks (CNN) have excelled. The majority of prior research has concentrated on recognizing a specific fundus illness. A feature extraction network is the first portion, and a multi- label classification neural network is the second. This work reviews various papers on the transfer learning technique for fundus classification. Transfer learning is used in this algorithm. Labeled picture samples are used to train the deep learning (DL) algorithm. The pre-trained DL model uses a transfer learning approach to add fresh pictures to the modal. The output probabilities of several models are combined to provide the final recognition result. As a result, the DL model can be trained on a smaller number of data sets while still producing good outcomes. Several classification techniques had been addressed in the literature for the classification of fundus images automatically in which deep learning techniques outperformed. In this paper different deep learning (DL) techniques for automatic classification of fundus images had been discussed and results are compared on the basis of accuracy, f1-score and AUC. © 2021 IEEE.

关键词： Convolutional neural networks

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Based on Low-level Feature Enhancement for Mining Conveyor Belt foreign object detection algorithm 3

Based on Low-level Feature Enhancement for Mining Conveyor B...

引用

3rd international conference on Image Processing, computer vision and Machine Learning, ICICML 2024

作者： Sun, Yongxin Gao, Han Shi, Shuoyang Zhao, Shihan Beijing Wenhua Network Information Technology Co. Ltd. Business Division Beijing China China University of Mining and Technology School of Information and Control Engineering Xuzhou China

ISBN: (纸本)9798350355413

The mining conveyor belt is an important equipment for coal transportation. During the transmission process, foreign objects (such as stones, anchor rods, ironware, etc.) are mixed in the coal transported by the conveyor, resulting in safety accidents such as belt derailment and fire at the coal drop opening. Aiming at the problem of low accuracy of foreign object detection algorithm due to weak characterization of elongated objects and poor weak semantic feature extraction, a low-level feature enhancement foreign object detection algorithm based on YOLOv7-tiny is proposed, designated as FE-YOLO. Firstly, to prevent the decline of the accuracy of special foreign objects such as anchor rods and steel bars, deformable convolution is introduced to enhance the network's ability to extract long target objects. Secondly, the Low-Level Feature Enhancement Module (LFEM) is constructed to utilize multi-scale feature fusion to improve the weak semantic features of foreign objects and reduce the probability of missed and wrong detection. Finally, Ghost Shuffle Convolution(GSConv) is introduced in the feature fusion stage to minimize the channel information loss caused by the change of feature map size and dimension scaling, ensure the feature fusion efficiency and reduce the number of parameters. To verify the performance of the method, the test is carried out on the foreign object dataset of the self-built underground conveyor (a total of 6332 images). The results demonstrate that the mAP@0.5 of this algorithm can reach 0.871, the mAP@0.5:0.95 can reach 0.535, and the detection speed can reach 77.12fps, verifying the validity and superiority of the proposed algorithm. © 2024 IEEE.

关键词： Coal transportation

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Analyzing Blockchain Technology for Enhancing Security in Smart Home Systems: Current Developments and Future Directions 3

Analyzing Blockchain Technology for Enhancing Security in Sm...

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3rd international conference on Disruptive Technologies, ICDT 2025

作者： Soni, Shiva Singh, Abhilasha Srm Institute of Science and Technology Department of Computer Science and Engineering Delhi-NCR Campus Modinagar Ghaziabad India

ISBN: (纸本)9798331519582

The Internet of Things (IoT) holds a great potential in revolutionizing the development of intelligent home environments. The Internet of Things is seamlessly integrated into our everyday routines, especially in modern smart homes, where it aids in streamlining resource management, enabling convenient remote control, and facilitating pervasive sensing. The use of smart home technology presents a significant challenge due to the range of issues it can cause, including concerns about transparency, security and privacy. The main reason for this is that devices are often used in open networks that may be susceptible to security breaches, due to inadequate device authentication methods, inefficient secure data storage and access procedures, and exposure to various threats. Blockchain shows great potential as a technology that can effectively tackle these challenges and fulfill the necessary criteria. This research study introduces a hybrid blockchain architecture, dynamic key management techniques, cross-domain authentication and lightweight cryptographic solutions to address critical IoT constraints, including security, scalability, latency, interoperability and energy efficiency, providing a unique contribution to advancing smart home security frameworks. In this paper, we studied and analyzed the various research works already done and provided a comprehensive review of current advancements, highlighted the major progress and discussed open challenges for future research directions. © 2025 IEEE.

关键词： Smart homes

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A Cloud Computing Model for Libyan Higher Education Institutions

A Cloud Computing Model for Libyan Higher Education Institut...

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Sciences and Techniques of Automatic control and computer Engineering MI-STA, IEEE international Maghreb Meeting of the conference on

作者： Najwa A. Imragaa Entesar H. Alfsai Bothaina F. Gargoum Abdelsalam M. Maatuk Department of Computer Science University of Benghazi Benghazi Libya Department of Software Engineering University of Benghazi Benghazi Libya

Cloud computing is now changing the IT landscape and playing a significant economic role for companies that develop software. Users of educational resources now have more flexibility and access because of cloud computing. Students, faculty members, and support staff all use cloud computing in academic institutions. Internet-based computing, known as "Cloud Computing," allows users to share IT resources securely and efficiently. The use of cloud computing in universities has many benefits, such as access to files, e-mail, databases, educational resources, and search applications as requested by faculty, administrators, staff, and students at a university alike. Moreover, cloud computing significantly reduces the complexity and cost of IT in universities. This paper discusses cloud computing, its structures, types, and services it provides, and then examines the possibility of implementing cloud services in universities and discovers the various opportunities and benefits of cloud services. The paper presents a proposed model for cloud computing for Libyan universities, which was applied to the University of Benghazi and produced satisfactory results and facilitated a lot of university work. Through the application of the proposed model, effective management of the technological needs of the Libyan university has been achieved, such as connecting programs and providing a platform for development, data storage and computing.

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