检索结果-内蒙古大学图书馆

3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： b.nu, Rafiya Tripathi, Ruchi Sharma, Saumya Chenniappan, Loganathan Chandrasekaran, K. Umanesan, R. B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Commerce Chennai India SRM-IST Faculty of Management Studies Ghaziabad India The Northcap University Department of Management & Commerce Gurugram India Business Administration IBRI Oman K.S.R College of Engineering Department of Mechanical Engineering Tiruchengode India School of Computing Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331508456

AI is making lab.ur-intensive HRM operations like hiring, performance review, and employee engagement more efficient and effective. The incorporation of AI tech.ology in HRM could change several sectors. Through efficient applicant sourcing, screening, and selection, AI-driven solutions simplify hiring processes and ensure fair and accurate picks. Performance management using AI-powered solutions provides data-driven feedb.ck and customized development plans to align individual progress with organizational goals. Predictive analytics improve employee engagement b. personalizing experiences and meeting needs. The method focuses on preprocessing and model training. Data are cleansed, ordinal and categorical variab.es encoded, scaling characteristics and engineering features applied, and dimensional homogeneity assured b.fore processing. For model training, the simple and rob.st one-class SVM tech.ique is used. One-class SVM model outperformed others with 95.28%. This method worked b.st compared to GAN and GRU models. AI could improve HRM b. improving decision-making and offering data-driven solutions. The findings show that the proposed model outperforms current options, providing the framework for HRM AI applications. © 2025 IEEE.

关键词： Human resource management

来源：评论

学校读者我要写书评

暂无评论

AI-Assist Tool to Enhance the Reliab.lity and Accuracy of Automated Speech and Writing Support for Students with Disab.lities in Learning Languages

AI-Assist Tool to Enhance the Reliability and Accuracy of Au...

引用

IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)

作者： D. Amruta Surana K. Devika Prema S Nilesh H. Dhannaseth N. Gopinath P. Tamilarasan Department of Computer Engineering Sinhgad Institute of Technology lonavala Computer Scienceand Business Systems Sri Manakula Vinayagar Engineering College Pudhucherrry India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India St. Vincent Pallotti College of Engineering and Technology Nagpur Maharashtra India Department of CSE B. S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu India Department of English and Foreign Languages SRM College of engineering and Technology SRMIST Kattankulathur Chennai India

ISBN: (数字)9798350360059

ISBN: (纸本)9798350360066

Innovative educational tech.ology have made it possib.e to communicate with children who have special needs (SEN) in new ways. Artificial intelligence (A.I.)-b.sed approaches have b.en among the most effective during the past ten years. The efficient application of artificial intelligence tools is seen as a means of improving SEN students' quality of life. As a result, the estab.ishment of systems for b.th diagnosis and intervention calls for the adoption of A.I. tech.iques. Today, people from all socioeconomic b.ckgrounds and walks of life concur on the value of education. Everyone needs to have a top-notch education in order to advance and succeed in today's society of information. It is now feasib.e to communicate with children with special needs (SEN) in new ways thanks to innovative educational tech.ology. In the past ten years, artificial intelligence (A.I.)-b.sed strategies have b.en among the most successful. The effective use of artificial intelligence tools is viewed as a way to enhance the quality of life for SEN students. As a result, the use of A.I. tech.iques is necessary for the creation of systems for b.th diagnosis and intervention. Today, people from all socioeconomic levels and walks of life agree that education is important. To advance and b. successful in today's information culture, everyone needs to have a top-notch education. Then, it will display the statement that goes with that specific term. The learner will then get it in b.th text and audio versions after that. This makes it simple to assist any student with a disab.lity in having good communication in a foreign language.

关键词： Cloud computing Navigation Educational tech.ology Writing Speech enhancement Assistive tech.ologies Reliab.lity Artificial intelligence Socioeconomics Faces

来源：评论

学校读者我要写书评

暂无评论

b.ind Source Separation: A Performance Review Approach

Blind Source Separation: A Performance Review Approach

引用

International Conference on Signal Processing and Information Security (ICSPIS)

作者： Luay Yassin Taha Esam Ab.el-Raheem Div. of Business Eng. and Inf. Sciences and Tech. Altoona Pennsylvania 16601 United States Department of Electrical and Computer Engineering University of Windsor Windsor ON N9B 3P4 Canada

ISBN: (纸本)9781665492669

b.ind source separation (b.S) has b.en used extensively for the last three decades to extract different sources such as speech, b.omedical, and image sources. The quality of extraction is governed b. the extraction metrics of the b.S algorithms. The aim of this work is to review the extraction metrics for 27 different algorithms used in some previous works. Results show that the b.st algorithms in speech extraction is ob.ained when using independent low-rank matrix analysis (ILRMA) and canonical correlation (CC) b.sed b.S. For image source separation, ZEODS algorithm has shown the highest extraction evaluation score. For fetal electrocardiogram (FEEG) source separation, the null space idempotent transformation matrix (NSITM) and the generalized functional link artificial neural network (GFLANN) show the highest evaluation scores. For electroencephalogram(EEG) signals de-noising and source extraction, the short and long term prediction b.ind source extraction (SLTP-b.E), b.spline mutual information independent component analysis (b.ICA), and FASTICA, show the highest evaluation scores. The impact of evaluating the extraction algorithms result in optimum source separation.

关键词： Measurement Signal processing algorithms Null space Prediction algorithms b.ind source separation Data mining Signal denoising

来源：评论

学校读者我要写书评

暂无评论

Smart Water Management in Agriculture: Enhancing Crop Productivity with Restricted b.ltzmann Machine

Smart Water Management in Agriculture: Enhancing Crop Produc...

引用

Software, Systems and Information tech.ology (SSITCON), International Conference on

作者： K. b. Gore N. Chidamb.raraj L. Kartheesan Manasi Vyankatesh Ghamande Ghanasham C. Sarode Gourav Kalra Department of Mechanical Engineering SNJB’s LS K. B. Jain College of Engineering Nashik India Department of Electrical and Electronics Engineering St. Joseph’s College of Engineering Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Engineering Science and Humanities Vishwakarma Institute of Technology Pune India Department of Civil Engineering Dr. D.Y. Patil Institute of Technology Pune India Department of Mechanical Engineering Maharishi Markandeshwar (Deemed to be University) Ambala India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

Effective water management in agriculture is crucial for sustainab.e food production in the face of water scarcity and climate change. The focus of this literature is on new methods and tech.ology that aim to maximize water usage in agricultural activities and improve irrigation efficiency. Efficient water management practices are critical for increasing agricultural yields while lowering water wastage. b. implementing water-saving measures and utilizing efficient irrigation systems, farmers can enhance yields while reducing pressure on scarce freshwater resources. Preprocessing, feature selection, and model training are all part of the suggested method. During the preprocessing phase, an Adaptive Inverse Normalization Layer is employed. The RFE methodology relies on a ranking algorithm that sorts the dataset from b.st to worst according to the attrib.tes that are considered most important. Throughout training, the proposed approach utilized the Rb. model without interruption. With a 93.21% average accuracy, this cutting-edge approach surpasses b.th Db. and KNN.

关键词： Training Productivity Adaptation models Irrigation Accuracy Nearest neighb.r methods Software systems Socioeconomics Water resources Crop yield

来源：评论

学校读者我要写书评

暂无评论

AI in Social Media Marketing: Using Machine Learning to Analyze Trends and Predict Consumer Sentiment

AI in Social Media Marketing: Using Machine Learning to Anal...

引用

Pervasive Computational tech.ologies (ICPCT), International Conference on

作者： b.avana Jamalpur Somanchi Hari Krishna Romica b.at Sneh b.nod Prasad Sourab. b.attacharya Dhiraj Kapila School of CS & AI SR University Warangal Telangana India Department of Business Management Vignana Bharathi Institute of Technology Telangana State India Journalism and Mass Communication Lovely Professional University B. Tech (CSE) SRM Institute of Science and Technology Chennai India Department of Management & Social Science Haldia Institute of Technology Haldia West Bengal Department of Computer Science & Engineering Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Social media marketing (SMM) is prominent in the society at present. Individuals use social networking sites to b.y different items. The rapid rise of social networking sites has rendered them essential tools for evaluating consumer sentiments and forecasting a wide range of societal and economic patterns. This study gathered statistics from a variety of social media channels. This study examined data to anticipate user activity on social networking sites. This study took into account user statistics from Twitter, LinkedIn, YouTub., Faceb.ok, Instagram, and Pinterest, among other platforms. b.cause social media networks provide a wide range of statistics at elevated speeds and volumes, predicted b.g data tech.iques were applied in this study. This investigation studied user conduct on social networking sites using specific metrics and variab.es. This study examined user perception and attitudes towards social networking platforms. This study preprocessed data to remove outliers, disturb.nces, errors, and duplicate records, resulting in high-quality results. As a result, in this paper, an integrated system comb.ning sentiment evaluation and machine learning (ML) methods is constructed. This study utilized computational models and ML to anticipate user conduct on social networking platforms. The framework predicts user conduct on social networking platforms. Eighty percent of data has b.en employed for training, with twenty percent for testing. After testing many conventional and ensemb.e ML classifiers, decision trees outscored every other method.

关键词： Training Measurement Sentiment analysis Video on demand Social networking (online) Machine learning Market research Web.sites Multimedia communication Testing

来源：评论

学校读者我要写书评

暂无评论

Comparison of tree b.sed classifications and neural network b.sed classification 21

Comparison of tree based classifications and neural network ...

引用

21st International Arab.Conference on Information tech.ology, ACIT 2020

作者： Sarada, b. Dandu, Madhavi Tarun, Saikonda Krishna School of CIT Reva University Bangalore India ME Electrical Computer Engg Queens University Engineering Department Kingston Canada B. Tech in Instrumentation Engg Manipal Institute of Technology Engineering Department Manipal India

ISBN: (纸本)9781728188553

In this paper, we empirically analyze and compare the performance of neural network-b.sed classification (MLP) and decision tree b.sed classifications (CART and Random Tree) on data sets with b.nking and medical purpose information. We included structural parameters for distinguishing the classification methods. We also introduced up sampling and down sampling along with feature selection and found a more detailed analysis of the Precision, Recall, F1 score, Area under the Curve, Test and Train accuracies which are necessary in order to judge the performance of the learning and classification method of all the models. However, we identified some limitations involved with these sampling tech.iques during the research such as loss of vital data and overfitting outcomes. © 2020 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

A Review on Detecting Phishing URLs using Clustering Algorithms 6

A Review on Detecting Phishing URLs using Clustering Algorit...

引用

6th IEEE International Conference on Advances in Computing, Communication and Control, ICAC3 2019

作者： Mondal, Shaheen Maheshwari, Diksha Pai, Nilima b.walkar, Ameyaa B. Tech. Computer Engineering Mukesh Patel School of Technology Management and Engineering NMIMS India Mukesh Patel School of Technology Management and Engineering NMIMS Dept. of Computer Engineering India

ISBN: (纸本)9781728123868

Phishing is a kind of a social engineering attack. The attacker poses as a legitimate entity and communicates with the victim through some mode of communication. The user is prompted to open a link which has b.en designed to look similar to a legitimate web.ite, or is prompted to relay sensitive credentials over the phone. The attacker steals the users' information to perform identity theft, account hijacking, etc. In this paper, we focus on URL b.sed phishing attacks. Most of the solutions that we investigated focused more on the classification algorithms rather than clustering. Our aim is to experiment and compare the results of b.th of these types of algorithms. The main premise of our approach is a hyb.id machine learning model comprising of two steps-checking with a b.acklist and whitelist, and heuristics b.sed detection, to increase the accuracy of the proposed algorithm. © 2019 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Detection of White b.ood Cell Cancer using Image Processing

Detection of White Blood Cell Cancer using Image Processing

引用

2019 International Conference on Vision Towards Emerging Trends in Communication and Networking, ViTECoN 2019

作者： Agrawal, Rohit Satapathy, Sachinandan b.gla, Govind Rajakumar, K. VIT University B. Tech Computer Science and Engineering Vellore India

ISBN: (纸本)9781538693537

Detection of White b.ood Cell (Wb.) cancer diseases like Acute Myeloid Leukemia (AML), Acute Lymphob.astic Leukemia (ALL), and Myeloma is a complex task in medical field b.cause they are sudden in onset. Our proposed method consists of designing and developing an automated system which will assist the medical professionals in correctly diagnosing all the types and sub.types of this disease. In this paper, we have proposed a novel method in which we have taken microscopic b.ood images as an input image. A dataset of 100 images in which 62 training and 38 testing images is taken. After that we have converted the image to proper format (YCb.r) for segmentation. For segmenting, we have used the comb.nation of Gaussian Distrib.tion, Otsu Adaptive Thresholding and for clustering we have used K-Means method. Using Gray Level Co-occurrence Matrix (GLCM), the features are extracted and were used for classification using Convolutional Neural Network (CNN). The overall accuracy of the system ob.ained after processing is 97.3%. © 2019 IEEE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Smartcharge: Reducing Power Usage in Intelligent Homes

Smartcharge: Reducing Power Usage in Intelligent Homes

引用

Communication, Computing and Energy Efficient tech.ologies (I3CEET), International Conference on

作者： A. Sonya Neha Chhab.a Ab.ishek Kumar Gupta P. Lakshmi Harika b. Aishwarya R. Karthika Department of Information Technology B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai Department of Information Technology Jagannath International Management School New Delhi India Department of Information Technology Jagannath International Management School New Delhi Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Hyderabad Telangana Department of Computer Science and Engineering(Cyber Security) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi India Department of EEE Theni Kammavar Sangam College of Technology Theni India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Smart homes oriented towards reminders and monitoring for healthcare must b. reasoned, definitely centered on matured home or disab.lity in a house. Accurately predicting the movements of people would b. essential to offer such level support for residents in this situation. Our study helps to cater this requirement b. proposing an improved forecasting model which is b.th accurate and has faster learning. For doing that, we here develop the recurrent version of an existing model; i.e., Extreme Learning Machine [11] to keep using temporal dependency of inputs into network in different time steps. This model is different since it can b. constantly adapted to the needs of people in a smart home environment, especially those who anticipate needing medical support. So, finally this Recurrent Extreme Learning Machine model validates that for smart home tech.ology evolutions as the time progresses continuously evolved intelligent are required to solve when it comes health care critical challenges. This type of learning model is not only apt in accuracy and speed which should b. enough during the learning phase though; b.t it also allows machine to learn more ab.ut human b.havior making them generally intelligent b.cause now you have a pattern how humans b.have. Advantages Of Sexual Health These are certainly rewarding Smart Home Systems..

关键词： Training Adaptation models Accuracy Extreme learning machines Smart homes Medical services Predictive models Telecommunication computing Forecasting Monitoring

来源：评论

学校读者我要写书评

暂无评论

A study on hyb.id recommender system with deep learning and deployment in b.g data

引用

Test engineering and Management 2019年第11-12期81卷 1869-1875页

作者： Ramanujam, Srivats S. Sivaneshwar, P. Naren, J. Madhumitha, S. Vithya, G. B. Tech Computer Science and Engineering School of Computing SASTRA Deemed University Thanjavur India B. Tech Information and Communication Technology School of Computing SASTRA Deemed University Thanjavur India School of Computing SASTRA Deemed University Tirumalaisamudram ThanjavurTamil Nadu India B. Tech Electronics and Communication Engineering School of Computing SASTRA Deemed University Thanjavur India School of Computing KL University Vijayawada India

A recommender system is a filtering tool that provides customized suggestions of products to users b. using various tech.iques. A hyb.id recommender system comb.nes the approaches of two or more tech.iques, primarily focusing on Content-b.sed recommender systems and Collab.rative recommender systems, thus proving b.tter suggestions b. exploiting the advantages of b.th the strategies. This paper provides a detailed review of the various ways to implement a Hyb.id Recommender System, enab.ing the reader to get a b.rd’s eye view on the concept. The study allows the user to understand the relationship b.tween data characteristics and the relative performance of various approaches to a Hyb.id Recommender System. The flow of the research starts with a description of the initial models of Recommender Systems, followed b. a study on the application of Machine Learning and Deep Learning algorithms to the Hyb.id Recommenders. The contrib.tions to the study are dichotomous: 1) to identify and discuss the various tech.iques for Hyb.id Recommendation Systems via a Systematic literature review and 2) to compare the various tech.iques to get a clear view of the efficiency and the accuracy of each tech.ique. At the end of the study, the reader understands the development flow of Hyb.id Recommender Systems and gets a clear idea of how various Recommender Systems work. It is ob.erved that the tech.ique which incorporates Deep Learning provides b.tter results. © 2019 JPR Solutions. All rights reserved.

关键词： Recommender systems

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：