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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 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 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 technique 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

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Introduction and Development of Cyb.r Physical Systems in Smart Grid with b.ockchain technology to Enhance Sustainab.lity

Introduction and Development of Cyber Physical Systems in Sm...

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Augmented Intelligence and Sustainab.e Systems (ICAISS), International Conference on

作者： P. Valarmathi G. Manoj Sandip Kumar Das Sujo Oommen Mohit Tiwari Department of Computer Science & Engineering B. S Abdur Rahman Crescent Institute Tamil Nadu India Department of Management Studies Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India School of Electrical Engineering Kalinga Institute of Industrial Technology (KiiT) Bhubaneswar Odisha India School of Electrical and Electronics Engineering REVA University Bangalore Karnataka India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi India

The need for increased sustainab.lity and efficiency in the energy industry has led to a major increase in interest in the development of cyb.r-physical systems (CPS) in the smart grid domain in recent years. In the context of the smart grid, the term "CPS" refers to a network of physical ob.ects, sensors, and communication systems that are interconnected and allow for the smooth exchange of data and control signals to optimize the production, distrib.tion, and use of electricity. Demand response, energy management, and grid optimization are just a few of the smart grid applications that can b. successfully implemented b. utilizing CPS's capab.lities. On the other side, b.ockchain technology provides a decentralized and secure framework for logging and validating transactions among numerous organizations. Since it is distrib.ted, there is no need for middlemen, transparency is improved, and data integrity and immutab.lity are all guaranteed. These qualities make b.ockchain an excellent choice for tackling the issues that the smart grid is facing, such as data privacy, security, and stakeholder confidence. There are many advantages to integrating b.ockchain and CPS in the smart grid. First and foremost, a decentralized and democratized energy market is made possib.e b. b.ockchain, which enab.es safe and effective peer-to-peer energy trade among prosumers. This increases sustainab.lity b. encouraging the use of renewab.e energy sources and reducing dependency on centralized power generation. In conclusion, there is enormous potential for improving sustainab.lity through the smart grid's integration of b.ockchain technology with cyb.r-physical systems.

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Employing stacked ensemb.e approach for time series forecasting

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International Journal of Information technology (Singapore) 2021年第5期13卷 2075-2080页

作者： Sharma, Nonita Mangla, Monika Mohanty, Sachi Nandan Pattanaik, Chinmaya Ranjan CSE Department Dr. B. R. Ambedkar National Institute of Technology Jalandhar Punjab India CSE Department Lokmanya Tilak College of Engineering Navi Mumbai India Department of Computer Engineering College of Engineering Pune Pune India Department of Computer Science and Engineering Ajay Binay Institute of Technology Cuttack Odisha India

This manuscript presents a novel stack-b.sed multi-level ensemb.e model to forecast the future incidences of conjunctivitis disease. b.sides predicting the frequency of conjunctivitis, the proposed model also enhances accuracy through the use of the ensemb.e model. A stacked multi-level ensemb.e model b.sed on Auto-ARIMA (Autoregressive Integrated Moving Average), NNAR (Neural Network Autoregression), ETS (Exponential Smoothing), HW (Holt Winter) is proposed and applied on the dataset. Predictive analysis is carried out on the collected dataset and further evaluated for various performance measures. The result shows that the various error metrics of the proposed ensemb.e is decreased significantly. Considering the RMSE (Root Mean Square Error) error values, for instance, are reduced b. 39.23%, 9.11%, 19.48%, and 17.14% in comparison to Auto-ARIMA, NNAR, ETS, and HW model in that order. This research concludes that the accuracy of the forecasting of diseases can b. significantly increased b. applying the proposed stack generalization ensemb.e model as it minimizes the prediction error and hence provides b.tter prediction trends as compared to Auto-ARIMA, NNAR, ETS, and HW model applied discretely. © 2021, b.arati Vidyapeeth's Institute of computer Applications and Management.

关键词： Ensemb.e modeling Exponential smoothing Neural network auto regression Time series forecasting

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b.as Protected Attrib.tes Data b.lancing using Map Reduce 6

Bias Protected Attributes Data Balancing using Map Reduce

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6th International Conference on Electronics, Communication and Aerospace technology, ICECA 2022

作者： Pandya, Darshanab.n Dipakkumar Gupta, Namrata S. Jadeja, Ab.ijeetsinh Patel, Rasik D. Degadwala, Sheshang Vyas, Dhairya Sankalchand Patel University Department of Computer Science Gujarat Visnagar India Smt. B. K. Mehta It Centre Bca College Gujarat Palanpur India College of Engineering Sankalchand Patel University Gujarat Visnagar India Sigma Institute of Engineering Gujarat Vadodara India The Maharaja Sayajirao University of Baroda Gujarat Vadodara India

ISBN: (纸本)9781665482714

In the discipline of Data Mining (DM), researchers occasionally disregard the necessity to maintain a b.lanced distrib.tion on a dataset. This is prob.ematic since maintaining a b.lanced distrib.tion is essential. It is possib.e that the result of the process of categorizing things will b. significantly influenced b. this factor. Most classifiers are designed to function b.sed on the fundamental premise that the distrib.tion of the data is approximately b.lanced. Therefore, the efficacy of the classification technique has simply dropped, and it is required to address this issue to find a solution to the prob.em that has b.en presented. This research will construct a Fuzzy clustering using SMOT approach for the purpose of estab.ishing lab.ls for minority classes. The goal of this technique is to prevent the model from developing b.as b.sed on any of the protected attrib.tes, such as gender, race, etc. This strategy makes advantage of parallel processing to provide a method for achieving a b.lance in the distrib.tion of the dataset. b.cause of this, the classification findings will b. enhanced in terms of the accuracy with which they categorize. The metrics of accuracy, sensitivity, and specificity are ob.ained from the various comb.nations of methods, and they are then compared to the various machine learning models that are currently in existence. © 2022 IEEE.

关键词： Data mining

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Impact of Author indexing from the Co-authorship Relation

Impact of Author indexing from the Co-authorship Relation

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International Interdisciplinary Conference on Mathematics, engineering and science (MESIICON)

作者： Sovan b.attacharya Ayan b.nerjee Arkaprava Mazumder Sub.ata Nandi Dept. Computer Science and Engineering Dr. B. C. Roy Engineering College Durgapur India NIT Durgapur Durgapur India Dept. Computer Science and Engineering National Institute of Technology Durgapur Durgapur India

As the the quality of research and spectrum of the pub.ications are improving day b. day, the citation parameter h-index [1] alone seems not sufficient to measure authors' performance. The citation is the relationship b.tween two papers when one paper refers to another paper. In way to resolve the said issue, in this paper we have introduced a new author indexing parameter, $\mathb.{h}_{c}$ -index b.sed on the influence of the co-authors' h-index level. Our newly introduced index $(\mathb.{h}_{c})$ looks through the co-authorship genealogy tree of an author and sub.equently measures the related citation impacts created b. every coauthors. This mechanism gives more accurate measurement for calculating author's citation index and the same we have ob.erved in our experimental analysis, where we have seen sub.tantial impact improvement while comparing actual h-index with $\mathb.{h}_{c}$ -index for top 10 authors from Web.of Universities 1 1 https://***/enlhlargerthan100 ranking.

关键词： Mathematics Indexing

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Efficient Method for Personality Prediction using Hyb.id Method of Convolutional Neural Network and LSTM 4

Efficient Method for Personality Prediction using Hybrid Met...

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4th International Conference on Electronics and Sustainab.e Communication Systems, ICESC 2023

作者： Sujatha, N. Pramod, S. b.atla, Shruti Thulasimani, T. Kant, Ravi Chauhan, Amit B. M. S. College of Engineering Department of Mathematics Karnataka Bengaluru India Nitte Meenakshi Institute of Technology Yelahanka Bangalore Department of Mathematics Karnataka India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Bannari Amman Institute of Technology Sathyamangalam Department of Mathematics Tamilnadu Erode India Shoolini University Cse Department Himachal Pradesh Solan India Department of Life Sciences Karnataka Bengaluru India

ISBN: (纸本)9798350300093

Users' contrib.tions and the emotions conveyed in status updates may prove invaluab.e to studies of human b.havior and character. A numb.r of other research have taken a similar approach, and the field itself is still growing. The goal of this proposed is to create a technique for deducing a user's personality traits b.sed on their social media profiles. Among the many customer services now availab.e on SNSs are media and recommendations of user involvement. The need to give internet users with more specialized and customized services that meet their specific requirements, which sometimes depend heavily on the users' inner personalities, is significant. However, there hasn't b.en much work done on the psychological analysis that's needed to deduce the user's inner nature from their outward activities. In this instance, LSTM-CNN was fed pre-processed and vectorized text documents. SNF is used for feature extraction. The proposed method employs CFS for the purpose of Feature Selection. Finally, LSTM-CNN was used to train the model. While CNN is good at extracting features that are independent of time, LSTM is b.tter at capturing long-term dependencies. comb.nation of features for personality prediction, the LSTM-CNN model is superior to the individual models. © 2023 IEEE.

关键词： Forecasting

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Assessments on linearized feedb.ck control in regulating electrical stimulations for revival of Sitting-to-Standing movement on sub.ects with injuries of the spinal cord

Assessments on linearized feedback control in regulating ele...

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8th IET Smart Cities Symposium (SCS 2024)

作者： M. Ahmed M. H. Muhammad b. S. K. K. Ib.ahim M. S. Huq Abubakar Tafawa Balewa University P. M. B. 0248 Bauchi Nigeria Department of Electrical and Computer Engineering Gulf University for Science & Technology Hawally Kuwait School of Computing and Digital Media London Metropolitan University London UK

ISBN: (数字)9781837243105

Functional Electrical Stimulation (FES) entails utilising a suitab.e form of electric current for reviving functions. Literature showed that applying FES for Sit-to-Stand (STS) restoration needed improved performance towards achieving clinical acceptab.lity which imposed challenges for researchers. Control systems have b.en applied for providing solutions and results were highly encouraging. Therefore, to contrib.te in that vein the study the evaluation of fatigue, spasms and tremors on the Linearized Feedb.ck Control (LFC) scheme. LFC was a nonlinear control method which is characterized b. b.ing rob.st. The integral/Time Integral amount of currents, Integral/Time Integral Rate of Change in Current, RMSE, Integral/Time Integral of Tracking Error and Maximum Control Signal without disturb.nce, fatigue, spasm and tremor are all b. far higher or with the PID compared to the LFC; making it the worse. The parameters are greater than the threshold specified for the restored movement. Results indicated that the LFC had b.tter and superior performance in comparison to the Proportional-Integral-Derivative (PID) for the restoration of the movement function. The LFC again portrayed a high level of rob.stness and disturb.nce rejection capab.lities and also was stab.e. Hence, the nonlinear control method was successfully applied for FES-induced STS restoration. Results therefore portrayed the suitab.lity of LFC for the achievement of disturb.nce rejection. The assessments showed that most likely the LFC might with further research lead to remarkab.e results when applied to such or even similar systems.

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Challenges in Cyb.r Physical Social Systems and Internet of Things

Challenges in Cyber Physical Social Systems and Internet of ...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： S. Revathi J. Raja M Mohanraj K. Malathi b.lasub.areddy Mallala R.G. Vidhya Department of Computer Science Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Business Systems Sri Eshwar college of engineering Coimbatore India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telangana India Department of ECE HKBK College of Engineering Bangalore India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

This research analyses the complex dynamics of Cyb.r-Physical-Social Systems (CPSS), encompassing cyb.r-physical systems, cyb.rsecurity, the Internet of Things (IoT), and social media. b. exploring the interactions among these elements, this study analyses the vulnerab.lities, risks, and potential b.nefits within the CPSS framework. This research study analyses the evolving landscape of cyb.rsecurity b. considering various aspects such as data integrity, privacy in the IoT era, and the influence of social media on user b.haviour and influence dynamics. The study also introduces innovative strategies to enhance CPSS resilience, including methods to mitigate cyb.r threats and enhance overall system integrity. Additionally, we investigate how social media shapes CPSS b.haviours, offering valuab.e insights into influence dynamics. This research advances the comprehensive understanding of CPSS to support future studies and advancements in the protection and improvement of interconnected systems.

关键词： Data privacy Social networking (online) Shape Media Real-time systems Internet of Things Protection Predictive analytics Monitoring Resilience

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An Energy-Efficient Clustering with Mob.le Sink and Rendezvous Nodes for Data Collection in IoT-b.sed Wireless Sensor Networks

An Energy-Efficient Clustering with Mobile Sink and Rendezvo...

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2022 International Interdisciplinary Conference on Mathematics, engineering and science, MESIICON 2022

作者： Chaurasiya, Sandip K b.swas, Arindam b.nerjee, Rajib School of Computer Science University of Petroleum & Energy Studies Energy Acres Building UK Dehradun248007 India School of Mines Kazi Nazrul University West Bengal Asansol India Dr. B. C. Roy Engineering College Department of Ece West Bengal Durgapur India

ISBN: (纸本)9781665470728

The inclusion of mob.le sink(s) and rendezvous nodes has facilitated the data collection in Internet-of-Things-b.sed wireless sensor networks to a great extent. In the sink-mob.lity-b.sed clustered network enriched with rendezvous nodes, normal sensor nodes do not require to transmit their readings to the distant b.se station. Instead, they can transmit their data to the nearb. mob.le sink/rendezvous nodes saving their respective limited energy. Such a facility allows the saved energy to b. utilized in further network operations, prolonging the overall network lifetime. However, clustering the nodes leading to energy-efficient operations in the aforementioned network scenario is still a challenging task. In this work, an existing scheme optimized-LEACH has b.en improved b. formulating energy-aware clusters in the network. Various simulations estab.ish the supremacy of the proposed scheme in terms of nodes' decay rate, average energy per node in the network, throughput, and nodes' distrib.tion in the clusters. Experiments confirm that the proposed scheme achieves gains up to 167.56% in data packet delivery under varying network configurations. © 2022 IEEE.

关键词： Energy efficiency

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The frontiers of chemical imaging

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science advances 2024年第50期10卷 eadu9069页

作者： Cheng, Ji-Xin Warren, Warren S. Department of Biomedical Engineering Department of Electrical and Computer Engineering Boston University Boston MA 02215 United States Senior Deputy Editor Science Advances Duke University James B. Duke Distinguished Professor Durham NC United States

Most of our knowledge ab.ut the chemical content in cells, tissues, or materials comes from analysis of fixed samples, b.t such in vitro assays can only detect the presence of molecules. Spatial and temporal dynamics information of metab.lites or b.omarkers is often crucial to understanding whether a detected species is the cause or consequence of an activity. Thus, in situ and in vivo molecular analysis (in most cases, without additional lab.ling), can decipher the function of molecules rather than simply detecting their presence.

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