检索结果-内蒙古大学图书馆

International Conference on Machine Vision and Augmented Intelligence, MAI 2022

作者： Madhusudhanan, S. Jyothi, N.M. Vishnukumar, A. Sathya, S. Arun Vignesh, N. Department of Artificial Intelligence and Data Science Prathyusha Engineering College Thiruvallur Tamilnadu Chennai India Department of Information Technology Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Vel Nagar Avadi Tamil Nadu Chennai India Department of Electronics and Communication Engineering Gojan School of Business and Technology Edapalayam Red Hills Tamil Nadu Chennai India Department of Electronics and Communication Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9789819901883

With recent advancement in web 2.0, people are expressed their comments through online platforms like social media and even in e-commerce website also. So, text comments are generated more on this medium. Deep learning algorithms are employed to analyze the sentiment accurately by understanding the language. The prediction of stock, predicting public poll, analysis of e-commerce comments, and so on are used this technique. In this work, we have employed and compared the various feature embeddings with simple neural network algorithm which improves the model to utilize words’ relationship. The feature representations are applied on LSTM model. The distributed memory model (DMM) representation achieves excellent result on the customer review dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

The Smart Optimization and Estimating the Performance of Encapsulating Security Payload in Wireless Network Security 3

The Smart Optimization and Estimating the Performance of Enc...

引用

3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Sripriya, T. Borade, Monali R. Deepaware, Neeta Kumar, Jambi Ratna Raja Khalid, Md. Saifullah Singh, K. Ranjith Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Mangalayatan University Department of Management Jabalpur India Genba Sopanrao Moze College of Engineering Balewadi Department of Computer Engineering Maharashtra Pune India Usha Martin University Faculty of Business Mangagement & Commerce Ranchi India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798350319125

The Encapsulating Security Payload (ESP) is the primary issue of safety structure for wireless networks. It inspires to make specific cozy switches of records across a wireless network by offering encrypted messages, information integrity guarantee, and authentication of communications. ESP can also provide excellent selective filtering of community site visitors and provides levels of authentication required for ease of network access. Due to the advances in the wireless era, the overall performance of ESP in Wi-Fi networks is becoming increasingly essential. Consequently, intelligent optimization and estimating the overall performance of ESP in wireless networks is an important task. Clever optimization of ESP in Wi-Fi networks determines the most reliable parameters for ESP to maximize its performance and reduce the network overhead that may arise while looking to take care of records transmission and information protection. Traditional optimization techniques, including Genetic Algorithms, can be used for this project, as they give more accurate outcomes and might effortlessly be implemented in the community infrastructure. Clever optimization can't only decorate the performance of ESP but can also reduce the variety of false alarms in the machine. Moreover, developing an accurate estimation for the overall performance of ESP in Wi-Fi networks is essential to get the most out of its talents. The usage of mathematical models, including a Markov Chain modeling technique, to symbolize various. © 2023 IEEE.

关键词： authentication encapsulating optimization

来源：评论

学校读者我要写书评

暂无评论

Efficient and Resilient Placement Strategy for Electric Vehicle Charging Stations, Incorporating Renewable Energy and Storage Equipment in Distribution Networks

Efficient and Resilient Placement Strategy for Electric Vehi...

引用

2023 International Conference for Technological engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Kumar, Rakesh Begam, S. Shajathi Singh, Dev Lazizovich, Tuychiev Komiljon Muthubalaji, S. Al-Farouni, Mohammed Varanasi, Srinivas GLA University Department of Computer Engineering & Applications Mathura India E.G.S. Pillay Engineering College Department of Computer Science and Business System Tamilnadu Nagapattinam India Allenhouse Institute of Technology Mathematics Department Kanpur India Tashkent State University of Economics Department of Marketing Uzbekistan CMR College of Engineering & Technology Department of Electrical and Electronics Engineering Telangana Hyderabad501401 India The Islamic University Najaf Iraq GRIET Telangana Hyderabad India

ISBN: (纸本)9798350336474

The placement of Electric Vehicle Charging Stations (EVCS) is a significant obstacle to the widespread adoption of Electric Vehicles (EVs). However, integrating EVCS with distributed Renewable Energy (RE) sources may help mitigate the variability of RE production. This article focuses on studying a Microgrid (MG) that consists of power distribution sources such as Wind Turbines (WT) and photovoltaic (PV), as well as EVCSs and Energy Storage Structures (ESS). The analysis considers the ambiguities associated with the charging needs of electric vehicles and the production of distributed Renewable Energy Sources (RES). A comprehensive optimization model is provided for computing the best Charging Stations (CS) locations equipped with distributed electricity. This model combines the electrical network and the grid to ensure efficiency and reliability. The usage variation ratio is employed to assess the level of compatibility among the uncertain production curve of RE and the charging request curves to ascertain the suitable size of the WT and photovoltaic-producing method and energy storage system. The kernel prediction function is utilized to address the problem of excessive caution in adaptable optimizing methods. A simulation is conducted on an IEEE 33-nodal energy distributing system and a 50-nodal transit network. The findings from simulations provide evidence of the model's resilience and cost-effectiveness. © 2023 IEEE.

关键词： Electric power distribution

来源：评论

学校读者我要写书评

暂无评论

Hybrid Blockchain and IoT Driven Framework for Supply Chain Optimization by Enhancing Transparency, Efficiency and Cost Reduction

Hybrid Blockchain and IoT Driven Framework for Supply Chain ...

引用

Electronics and Renewable systems (ICEARS), International Conference on

作者： Pradeep Kumar S CH Hussaian Basha C. Arun Prasath G. Amirthayogam Syed Ismail Abdul Lathif R. Leelavathi Computer Science Design Department Rajalakshmi Engineering College Thandalam Chennai Department of Electrical and Electronics Engineering SR University Warangal Department of ECE Mahendra Engineering College Namakkal Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai Department of Biomedical Engineering Velalar College of Engineering and Technology Erode

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Blockchain technology represents a modern supply chain management revolution which fixes operational challenges while providing enhanced transparency at reduced costs. The research develops HB-SCOF (Hybrid Blockchain-Enabled Supply Chain Optimization Framework) which combines blockchain self-verification databases with automatic smart contracts to maintain continuous supply chain observation and evaluate data credibility. The framework integrates tamper-proof transaction tracking mechanisms with unified supply chain network connectivity for securing data transmission as a trust delivery method through unbreakable shared databases. The retail-industry case study demonstrated a 25% decrease in expenses stemming from agency elimination alongside enhanced flow management and better stock distribution. The system demonstrates its effectiveness through assessments of key performance indicators including transaction speed and data accuracy together with business cost efficiency. This part looks at implementation methods through training stakeholders in addition to transforming organizational policies which produce successful outcomes. Blockchain technology labs verify its capability to enhance supply chain management efficiency while showing realistic deployment methods.

关键词： Training Costs Supply chain management Supply chains Smart contracts Key performance indicator Blockchains Stakeholders Internet of Things Optimization

来源：评论

学校读者我要写书评

暂无评论

IoT-based trusted wireless communication framework by machine learning approach

引用

Measurement: Sensors 2024年 34卷

作者： Chakaravarthi, S. Saravanan, S. Jagadeesh, M. Nandhini, S. Department of Artificial Intelligence and Data Science Panimalar Engineering College India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India Department of Computing Technologies SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India

The traditional Radio-Frequency systems (RFS) authentication methods, designed to ensure secure data transmission on the web, may not always effectively prevent adversaries from gaining access to concealed IDs or asymmetric cryptography through infiltrative, side-channel, training, and computer attacks. In contrast, Unaccounted Information (UAI) has the potential to exploit irregularities in production systems to automatically identify microchips, offering a highly robust and cost-effective security solution. This approach introduces RFS-UAI, a deep neural network-based system that efficiently manages wireless node identification by leveraging synthetic RFS characteristics of remote controls (Tx) learned through supervised methods in Wireless Sensor Networks (WSN). Unlike traditional methods that require the development of specialized transistors for UAI or semantic segmentation, this approach utilizes the existing asymmetrical RFS communication networks. Similar to the way the human brain processes information, Rx handles the entire device identification process at the gateway. According to test results, which include assessing process capability at a specified 65 nm threshold voltage and characteristics such as Local Oscillator (LO) misalignment and I-Q disparity using a probabilistic model with 52 hidden units, the system can distinguish up to 4800 transmitters with a remarkable 99.9 % accuracy under various channel conditions, all without the need for regular preambles. This recommended method can serve as a standalone security measure or be integrated into a biometric identification system. © 2024 The Authors

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Cyber security-based Multikey Management system in Cloud Environment 8

Cyber security-based Multikey Management System in Cloud Env...

引用

8th International Conference on engineering and Emerging Technologies, ICEET 2022

作者： Jeyaselvi, M. Jayakumar, C. Sathya, M. Ibrahim, S Jafar Ali Kalyan Chakravarthy, N.S. SRM Institute of Science and Technology Kattankulathur603203 India Sri Venkateshwara College of Engineering Department of Computer Science and Engineering Sriperumbudur India AMC Engineering College Dept of Information Science and Engineering Bengaluru Karnataka India Vellore Institute of Technology Dept of Internet of Things TN Vellore632014 India QIS College of Engineering and Technology Director-Data Science and Business Systems AP Ongole523 272 India

ISBN: (纸本)9781665491068

The revolutionary expansion in processing and storing mechanisms through the internet has given rise to affordable and powerful computing features. These qualities have been enhanced as a result of this growth. Computing in the cloud is an emerging technology that provides users with the ability to access data storage facilities as well as application access facilities in an online setting. This approach presents an almost infinite number of chances and difficulties. In light of this, ensuring the safety of one's data and preventing the accumulation of duplicate or otherwise identical information in the cloud are both highly critical concerns that must be handled. As a result, a method known as deduplication was created to cut down on the amount of data in the identical storage system. In this investigation, a unique approach for removing repetitive or duplicate data from cloud servers, which will also help reduce the amount of internet access and storage space needed, has been presented. The results of the experiments show that the suggested system not only offers a higher level of protection for the data that is stored in stored in the cloud but also addresses the primary issues of rent systems. © 2022 IEEE.

关键词： Cloud storage

来源：评论

学校读者我要写书评

暂无评论

Classification of EEG signals using Machine learning algorithms 2

Classification of EEG signals using Machine learning algorit...

引用

2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Suganyadevi, S. Shanmuga Priya, S. Kiruba, B. Gomathi, M. Kalshetty, Jagadevi. N. Kpr Institute of Engineering and Technology Department of ECE Coimbatore India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India Nitte Meenakshi Institute of Technology Department of Computer Science & Engineering Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

An alternative to human expert-performed manual identification is automatic detection of epilepsy using electroencephalogram (EEG) data. Automatic epilepsy detection from EEG data need high classification performance in order to eliminate false positives. A classification strategy for automated epilepsy identification using EEG data is being proposed in this work. The signals generated form the EEG device were transformed using the DWT before feature extraction was carried out. Based on various statistical parameters and crossing frequency features, an algorithm dubbed GBMs fusion was developed to identify EEG data. As an added bonus, the significant traits were first selected using a genetic algorithm. EEG data from the University of Bonn has been used to test the suggested method's ability to distinguish between normal and ictal EEG patterns. Experimentation has shown that the suggested GBMs fusion may increase the EEG classification performance. It is also possible to identify epilepsy from EEG data with a 100% accuracy using the suggested GBMs fusion. © 2022 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Constructing a Blockchain-Based Loyalty Program Model for Secure and Transparent Customer Rewards in Retail Marketing 13

Constructing a Blockchain-Based Loyalty Program Model for Se...

引用

13th IEEE International Conference on Communication systems and Network Technologies, CSNT 2024

作者： Anudeep, Ch. Santosh, Kathari Kumar, Sandeep M. Jahnavi, M. Sabareesh, R. Bala, Kiran Gitam School of Business Gitam University A.P. Visakhapatnam India Cmr Institute of Technology Department of Mba Bengaluru India Rv Institute of Management Bengaluru560041 India Tamil Nadu Coimbatore India K. Ramakrishnan College of Engineering Department of Artificial Intelligence and Data Science TN Trichy India

ISBN: (纸本)9798350305463

As the retail industry continues to evolve in the digital era, businesses are exploring innovative solutions to enhance customer engagement and loyalty. This research proposes a novel approach to loyalty programs by leveraging blockchain technology to create a secure and transparent model for customer rewards. Traditional loyalty programs often face challenges related to security, transparency, and trust, leading to issues such as fraud and customer skepticism. The proposed model outlines the design and implementation of a blockchainbased loyalty program, ensuring a secure and immutable ledger for tracking customer transactions and rewards. Smart contracts, self-executing contracts with predefined rules, are utilized to automate and enforce the terms of the loyalty program, reducing the potential for disputes and enhancing transparency. The decentralized nature of the blockchain ensures that customer data is securely stored and accessible only to authorized parties, addressing privacy concerns prevalent in centralized loyalty systems. This research employs a mix of theoretical analysis and practical implementation to validate the feasibility and effectiveness of the blockchain-based loyalty program model. The proposed Ethereum Blockchain model stands out with a significantly higher throughput of 5300 transactions per day, accompanied by an exceptional transaction transparency score of 99.5. © 2024 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Analysis of Hate Speech Detection and Recognition Utilising Advanced Machine Learning Algorithms and Techniques

A Comprehensive Analysis of Hate Speech Detection and Recogn...

引用

Automation and Computation (AUTOCOM), International Conference on

作者： Praveen Kumar Mannepalli Saurabh Sharma Neha Rajput Sanjay Kalamdhad Department of Computer Science & Engineering Chandigarh University Mohali Department of Computer Science and Engineering Chandigarh University Mohali India Department of Computer Science and Engineering Chandigarh University Punjab Department of Computer Science & Business System St. Vincent Pallotti College of Engineering and Technology Nagpur

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Social networking sites have come into demand as a way of sharing ideas and opinions with other people. But unfortunately, people tend to misuse social media to write nasty comments. Victims' lives might be negatively impacted by behaviours that are abusive and humiliating. Social networks give people a great tool to share information on ideas on multiple topics and opinions. However, these platforms are often abused to announce toxic and offensive comments, which eventually negatively affect victims and demographics. This study offers a thorough method for identifying hate speech (HS) on social media by utilising cutting-edge ML algorithms. A publicly available Twitter dataset containing 159,571 tweets classified into six categories, including toxic and identity hatred, was utilised. The methodology comprises data collection, conditioning, and categorisation that employs BiLSTM, which is particularly appropriate for the tasks demanding contextual sensitivity. These operations include symbol removing, stemming and stop-word eliminating, feature extraction using TF-IDF, and using the Bag-of-Words model. For classification of the data between hate and non-hate, the dataset is then split into training and testing datasets and the BiLSTM model used to provide classifications for the text. The evaluation of the BiLSTM model demonstrates superior performance, achieving an accuracy86%, a precision90%, a recall86%, and an F1-score88% • A comparison between the model and other models such as SVM and CNN reveals the strength of the BiLSTM in contextual classification tasks. The results demonstrate that BiLSTM can tackle problems with hate speech detection, providing a strong answer to the problem of harmful content on social media and helping to make the internet a better place for everyone.

关键词： Training Support vector machines Social networking (online) Text recognition Hate speech Bidirectional long short term memory Symbols Speech recognition Context modeling Testing

来源：评论

学校读者我要写书评

暂无评论

Monochromatic Image Dehazing Using Enhanced Feature Extraction Techniques in Deep Learning

Monochromatic Image Dehazing Using Enhanced Feature Extracti...

引用

Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： Nisarg Doshi Sagar Bhavsar D Rajeswari R Srinivasan Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur

Images photographed in foggy weather usually have poor visibility. To mitigate this problem researchers have come up with various image dehazing techniques. Now, more than ever, high-quality images that can be used to glean maximum information from autonomous systems are in high demand. This research work uses different Deep Learning (DL) architectures to draw out essential details from the picture and localize the information recovered to reduce the haze from the picture. The paper investigates to remove the hazes from the dehazed images using DL techniques. The first task of this proposed work attempts three pre-processing techniques namely, air light estimation, contextual regularization and boundary constraint. The second task of this work is to identify the suitable DL model to extract clear images from dehazed images. Evaluation metrics are PSNR value and SSIM value are used to estimate the values of dehazed images compared with clear images. Experimental results proves that AOD-Net outperforms good result with respect to PSNR value.

关键词： Deep learning Measurement Autonomous systems Atmospheric modeling Estimation Feature extraction Knowledge discovery

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：