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Integrated Circuits and Communication systems (ICICACs), IEEE International Conference on

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

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

AI is making labour-intensive HRM operations like hiring, performance review, and employee engagement more efficient and effective. The incorporation of AI technology 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 feedback and customized development plans to align individual progress with organizational goals. Predictive analytics improve employee engagement by personalizing experiences and meeting needs. The method focuses on preprocessing and model training. Data are cleansed, ordinal and categorical variables encoded, scaling characteristics and engineering features applied, and dimensional homogeneity assured before processing. For model training, the simple and robust one-class svM technique is used. One-class svM model outperformed others with 95.28%. This method worked best compared to GAN and GRU models. AI could improve HRM by 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.

关键词： support vector machines Training Productivity Accuracy Uncertainty Reviews Artificial intelligence Integrated circuit modeling Predictive analytics Recruitment

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Advancing HRM with AI Powered Recruitment Performance Management and Employee Engagement Using One Class svMs 3

Advancing HRM with AI Powered Recruitment Performance Manage...

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3rd International Conference on Integrated Circuits and Communication systems, ICICACs 2025

作者： banu, 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

关键词： Human resource management

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A Case study of the Implementation Difficulties in the Pilot Low Cost Concentrating solar Power systems Deployment in sub-saharan Africa

A Case Study of the Implementation Difficulties in the Pilot...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： sankar babu Potluri Garima Goswami Annu Yadav P G. Ravivarman P. Gajendran s. A. Tiwaskar Department of EEE S V R Engineering College Nandyal India Department of EE Teerthankar Mahaveer University Moradabad India School of COMPUTER Science and Applications IIMT University Meerut India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore India Computer Engineering Vishwakarma Institute of Information Technology Pune Maharashtra India

Among those most important resources for a particular country's expansion and expansion was electrical power. According to the most recent sustainability the growth paper, the continent continues to have a significant electrical power deficit. sunlight concentration appears to be a viable solution to make up the difference. This is due to the fact the majority of focusing solar energy plant elements are easily accessible and reasonably priced on their African marketplace, and authorized locals can construct the structures. Within Africa south of the sahara, steer 50 nm-concentrating solar energy plants are being put into operation with promising outcomes that might be grew and utilized to generate large amounts of electrical power. One notable challenge was identified during an evaluation of a prototype focusing photovoltaic plant within the channel-region: the monitoring system's inability to lower the daily costs required to operate the monitoring control structure and enhance the numerous special machines focusing conduct. This essay emphasizes the current energy landscape and African study of concentrated sunlight is currently progressing. To demonstrate current study strategy about the development of inexpensive monitoring devices in regards to mathematical difficulty, energy use, and special machine alignment precision, the article also provides an in-depth examination of the most recent solar monitoring structures for essential receiver networks.

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AI-Powered Anomaly Detection to strengthen Internet of Things security and Forestall Cyber Attacks in Networked Device Environments

AI-Powered Anomaly Detection to Strengthen Internet of Thing...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： s. varalakshmi suresh Kottur R sasikumar K saravanan Tanaya Kanungo balamurugan Annamalai Department of ECE Bharath Institute of Higher Education and Research Chennai Tamil Nadu India IT Infrastructure Architect/IT Systems V-Soft Consulting Group Inc Louisville KY United States of America Department of Computer Science & Engineering R.M.D Engineering College Kavaraipettai Tamil Nadu India Department of Information Technology R.M.K Engineering College Kavaraipettai Tamil Nadu India Department of ECE St. Peter's College of Engineering and Technology Avadi Tamil Nadu India Department of EEE Shree Sathyam College of Engineering and Technology Sangagiri Tamil Nadu India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

As millions of devices are linked, the IoT has revolutionized numerous businesses by automating and data-driven decision-making. Due to the proliferation of linked devices, cybercriminals target IoT environments for security breaches. This research examines AI-powered anomaly detection systems for real-time threat discovery and mitigation in IoT security. Advanced machine learning and deep learning models based on artificial intelligence can detect suspicious activity in networked environments and early security breaches. Artificial intelligence systems learn from network traffic and device behaviors to respond to emerging cyber threats, unlike rule-based security solutions. The study found that AI-driven anomaly detection can prevent data breaches, malware outbreaks, and DDos attacks. This study compares artificial intelligence algorithms for real-time anomaly detection across IoT frameworks. supervised, unsupervised, and hybrid learning models are used. It also discusses the challenges of large-scale AI system deployment, including the need for high-quality labelled datasets for training and computational resource constraints. The results suggest that AI-enabled anomaly detection could become a crucial part of IoT security, adapting to the growing number of connected devices.

关键词： Accuracy Computational modeling biological system modeling Real-time systems Malware Internet of Things security Hybrid learning Artificial intelligence Anomaly detection

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Cuckoo search Optimization Algorithm and Its solicitations for Real World Applications

Cuckoo Search Optimization Algorithm and Its Solicitations f...

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作者： Niranjanamurthy, M. Amulya, M.P. Niveditha, N.M. Chahar, Dharmendra Department of Master of Computer Applications M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering B G S Institute of Technology Adhichunchanagiri University B G Nagar Mandya571448 India M D College Pallu Rajasthan Hanumangarh India

Cuckoo search is a streamlining calculation created by Yang and Deb in 2009. It is utilized to take care of advancement issues. It was enlivened by a types of winged animal considered a cuckoo that lays its eggs in the homes of other host fowls. The laying and proliferation of cuckoo eggs is the principal essential inspiration for the improvement of another enhancement calculation. This advancement calculation builds proficiency, precision, and union rate. The cuckoo streamlining calculation (COA) is utilized for persistent non-direct enhancement. The calculation can be reached out to more perplexing situations where each home has a few eggs that speak to a lot of arrangements. This exploration work speaks to a concise survey of the cuckoo search calculation just as streamlining and its issues. Diverse cuckoo search classes and different cuckoo search applications are inspected. © 2021, The Author(s), under exclusive license to springer Nature switzerland AG.

关键词： Optimization

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Detection of Fake Tweets Using sentiment Analysis

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sN computer science 2020年第2期1卷 1-7页

作者： Monica, C. Nagarathna, N. Department of Computer Science B. M. S. College of Engineering Bengaluru 560019 India

social platform is one of the most commonly used sites in the today’s world, and people from different places exchange information, express opinions, etc. Twitter is one of the most convenient platforms where Twitter data are frequently used for research and are one of the largest micro-blogging platforms. As there is an increase in number of spam accounts, there is a need to distinguish between genuine and fake user accounts. In Twitter, there is rise in the number of spam accounts, to spread malicious information among the users. so, to overcome the problem of spam in Twitter, an analysis of the opinions and the emotions of users in the form of tweets have been carried out. In this paper, we present a model which performs sentimental analysis on real-time data that are collected on a particular topic of interest for the users who recently posted on that topic. Using this model, we compute the sentiment score of each user with the content-based features to detect spam in Twitter. The proposed method makes use of a custom rule-based algorithm for detection of bots and compares it with few other algorithms like MLP, decision tree and random forest to see how efficient the model is in detection of spam accounts. © 2020, springer Nature singapore Pte Ltd.

关键词： Deep learning algorithm Machine learning algorithm Rule-based algorithm sentiment analysis spam Twitter

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Information security and Privacy in the Digital World: some selected Topics

arXiv

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arXiv 2024年

作者： sen, Jaydip Mayer, Joceli Dasgupta, subhasis Nandi, subrata Krishnaswamy, srinivasan Mitra, Pinaki singh, Mahendra Pratap Kundeti, Naga Prasanthi Rao, Chandra sekhara M.v.P. Chekuri, sudha sree Pallapothu, seshu babu Nanjundan, Preethi George, Jossy P. Allali, Abdelhadi E.I. Morino, Ilham Oussous, salma A.I.T. beloualid, siham Tamtaoui, Ahmed bajit, Abderrahim Department of Data Science Praxis Business School Kolkata India Department of Computer Science and Engineering IIT Guwahati Assam India Department of Electronics and Electrical Engineering IIT Guwahati Assam India Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Mangaluru India Department of CSE Lakireddy Balireddy College of Engineering Affiliated to JNTU Mylavaram Kakinada India Department of CSE RVR & JC college of Engineering Guntur India Department of Mathematics and statistics K.B.N. College Vijayawada India Department of Electrical Engineering Federal University of Santa Catarina Florianópolis Brazil Department of Data Science Christ University Lavasa Pune India National School of Applied Sciences Kenitra Morocco SC Department Mohammed V University Laboratory of Advanced Systems National Institute of Posts and Telecommunications Rabat Morocco

In the era of generative artificial intelligence and the Internet of Things,while there is explosive growth in the volume of data and the associated needfor processing, analysis, and storage, several new challenges are faced inidentifying spurious and fake information and protecting the privacy ofsensitive data. This has led to an increasing demand for more robust andresilient schemes for authentication, integrity protection, encryption,non-repudiation, and privacy-preservation of data. The chapters in this bookpresent some of the state-of-the-art research works in the field ofcryptography and security in computing and communications. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital storage

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Waste Classification using Edge based Multilayer White shark Dense Net-spiking Neural Network from Urban Areas

Waste Classification using Edge based Multilayer White Shark...

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Automation, Computing and Renewable systems (ICACRs), International Conference on

作者： Chitra Kiran. N Ruchira Rawat T. Madhavi sKM. Pothinathan Dhaval Rabadiya Karthik Kumar Department of Electronics and Communication Engineering Alliance University Bangalore Karnataka India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India R. B. V. R. R. Women's College Hyderabad Telangana India Department of Civil Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Faculty of Management Studies Parul University Vadodara Gujarat India School of Civil Engineering REVA University Bengaluru Karnataka India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Effective waste management is crucial for human health and a clean environment. segregating waste based on types and classification is essential for recycling. Traditional methods, like manual sorting, are labor-intensive and error-prone, leading to environmental pollution and resource depletion and to classify the waste several deep learning algorithms used, but the accuracy is decreased, computational time, cost and complexity is increased. To overcome these issues, this work is proposed. This study proposes a Waste classification using Edge based Multilayer White shark Dense Net-spiking Neural Network (EbMW2sD-Net) from urban Areas. In this, for classifying the waste images urban areas are collected using stanford TrashNet dataset. These images are collected from the various IoT sensors, so that, these images are full of noises, so, that these data are pre-processed and its features are extracted using the Orthonormal s-Transforms with Multiple Discrete (OsTMD). Then these data are classified using the Edge based Multilayer skill Dense Net-spiking Neural Network method with seven categories (cardboard, glass, metal, organic, paper, plastic, and trash). Then, cardboard, organic, and paper class images are considered biodegradable waste, and other classes are considered non-biodegradable waste. The White shark Optimization Algorithm (WsOA) further improves prediction accuracy and reduces error rates, with the entire model implemented on the Python platform. From the analysis the EMHs-ParCNN model attains 99.9% accuracy, 99.8% recall and attains better results compared with the existing methods.

关键词： Waste management Accuracy Image edge detection Urban areas Neural networks sharks Nonhomogeneous media Prediction algorithms Classification algorithms Optimization

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visualizing ordered bivariate data on node-link diagrams

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visual Informatics 2023年第3期7卷 22-36页

作者： Osman Akbulut Lucy McLaughlin Tong Xin Matthew Forshaw Nicolas s.Holliman School of Computing Newcastle University1 Science SquareNewcastle upon TyneNE45TGUnited Kingdom CUSP London Department of InformaticsKing’s College LondonBush House30 AldwychLondonWC2B 4BGUnited Kingdom Department of Computer Engineering Faculty of EngineeringDuzce UniversityDuzceTurkey

Node-link visual representation is a widely used tool that allows decision-makers to see details about a network through the appropriate choice of visual ***,existing visualization methods are not always effective and efficient in representing bivariate graph-based *** study proposes a novel node-link visual model–visual entropy(vizent)graph–to effectively represent both primary and secondary values,such as uncertainty,on the edges *** performed two user studies to demonstrate the efficiency and effectiveness of our approach in the context of static nodelink *** the first experiment,we evaluated the performance of the vizent design to determine if it performed equally well or better than existing alternatives in terms of response time and *** static visual encodings that use two visual cues were selected from the literature for comparison:Width-Lightness,saturation-Transparency,and Numerical *** compared the vizent design to the selected visual encodings on various graphs ranging in complexity from 5 to 25 edges for three different *** participants achieved higher accuracy of their responses using vizent and Numerical values;however,both Width-Lightness and saturation-Transparency did not show equal performance for all *** results suggest that increasing graph size has no impact on vizent in terms of response time and *** performance of the vizent graph was then compared to the Numerical values *** Wilcoxon signed-rank test revealed that mean response time in seconds was significantly less when the vizent graphs were presented,while no significant difference in accuracy was *** results from the experiments are encouraging and we believe justify using the vizent graph as a good alternative to traditional methods for representing bivariate data in the context of node-link diagrams.

关键词： bivariate network visualization Edge visualization Uncertainty visualization Node-link diagram Quantitative evaluation

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Objective diagnosis of ADHD through movement analysis by using a smart chair with piezoelectric material

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Pediatrics and Neonatology 2023年第1期64卷 46-52页

作者： Chang, Tung-Ming Wu, Rong-Ching Yang, Rei-Cheng Chiang, Ching-Tai Chiu, Yi-Hung Ouyang, Chen-sen Wang, Yun-Ming Wu, Ming-Hsu Lin, Guang-Chung Lin, Lung-Chang Department of Pediatric Neurology Changhua Christian Children's Hospital Changhua Taiwan Department of Electrical Engineering I-Shou University Taiwan Departments of Pediatrics Kaohsiung Medical University Hospital Kaohsiung Medical University Taiwan Department of Computer and Communication National Pingtung University Taiwan Department of Information Engineering I-Shou University Taiwan Department of Biological Science and Technology Institute of Molecular Medicine and Bioengineering Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B) National Yang Ming Chiao Tung University Taiwan Department of Pediatrics School of Medicine College of Medicine Kaohsiung Medical University Taiwan Department of Biological Science and Technology College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan

background: Attention-deficit/hyperactivity disorder (ADHD) is the most common neuropsychiatric disorder in schoolchildren. ADHD diagnoses are generally made based on criteria from the Diagnostic and statistical Manual of Mental Disorders, Fifth Edition. The diagnosis is made clinically based on observation and information provided by parents and teachers, which is highly subjective and can lead to disparate results. Considering that hyperactivity is one of the main symptoms of ADHD, the inaccuracy of ADHD diagnosis based on subjective criteria necessitates the identification of a method to objectively diagnose ADHD. Methods: In this study, a medical chair containing a piezoelectric material was applied to objectively analyze movements of patients with ADHD, which were compared with those of patients without ADHD. This study enrolled 62 patients—31 patients with ADHD and 31 patients without ADHD. During the clinical evaluation, participants’ movements were recorded by the piezoelectric material for analysis. The variance, zero-crossing rate, and high energy rate of movements were subsequently analyzed. Results: The results revealed that the variance, zero-crossing rate, and high energy rate were significantly higher in patients with ADHD than in those without ADHD. Classification performance was excellent in both groups, with the area under the curve as high as 98.00%. Conclusion: Our findings suggest that the use of a smart chair equipped with piezoelectric material is an objective and potentially useful method for supporting the diagnosis of ADHD. © 2022 Taiwan Pediatric Association

关键词： Attention-deficit/hyperactivity disorder piezoelectric material smart chair

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