505 20 16488 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine Learning-Driven Energy Management for Electric Vehicles in Renewable Microgrids Description An account of the resource The surge in demand for sustainable transportation has accelerated the adoption of electric vehicles (EVs). Despite their benefits, EVs face challenges such as limited driving range and frequent recharging needs. Addressing these issues, innovative energy optimization techniques have emerged, prominently featuring machine learning-driven solutions. This paper reviews work in the areas of Smart EV energy optimization systems that leverage machine learning to analyse historical driving data. By understanding driving patterns, road conditions, weather, and traffic, these systems can predict and optimize EV energy consumption, thereby minimizing waste and extending driving range. Concurrently, renewable microgrids present a promising avenue for bolstering power system security, reliability, and operation. Incorporating diverse renewable sources, these microgrids play a pivotal role in curbing greenhouse gas emissions and enhancing efficiency. The review also delves into machine learning-based energy management in renewable microgrids with a focus on reconfigurable structures. Advanced techniques, such as support vector machines, are employed to model and estimate the charging demand of hybrid electric vehicles (HEVs). Through strategic charging scenarios and innovative optimization methods, these approaches demonstrate significant improvements in microgrid operation costs and charging demand prediction accuracy. The Authors, published by EDP Sciences, 2024. Creator An entity primarily responsible for making the resource Sharon Sophia J.; Winster Praveenraj D.D.; Al-Attabi K.; Bijlwan S.; Nagar M.; Ikhar S. Source A related resource from which the described resource is derived E3S Web of Conferences, Vol-540 Publisher An entity responsible for making the resource available EDP Sciences Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1051/e3sconf/202454002023" target="_blank" rel="noreferrer noopener">https://doi.org/10.1051/e3sconf/202454002023</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198118499&amp;doi=10.1051%2Fe3sconf%2F202454002023&amp;partnerID=40&amp;md5=c2fb913d5c44ea2d670748cea6b725fc" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198118499&amp;doi=10.1051%2fe3sconf%2f202454002023&amp;partnerID=40&amp;md5=c2fb913d5c44ea2d670748cea6b725fc</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 25550403 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sharon Sophia J., School of Business and Management, CHRIST (Deemed to Be University) Bangalore Yeshwantpur Campus, India; Winster Praveenraj D.D., School of Business and Management, CHRIST (Deemed to Be University) Bangalore Yeshwantpur Campus, India; Al-Attabi K., The Islamic University, Najaf, Iraq; Bijlwan S., Department of Computing Sciences, Uttaranchal School of Computing Sciences, Uttaranchal University, Dehradun, 248007, India; Nagar M., Department of Computer Science &amp; Engineering, IES College of Technology, IES University, Madhya Pradesh, Bhopal, 462044, India; Ikhar S., Yashika Journal Publications Pvt Ltd, Wardha, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine Learning-Enabled NIR Spectroscopy. Part 3: Hyperparameter by Design (HyD) Based ANN-MLP Optimization, Model Generalizability, and Model Transferability Subject The topic of the resource Artificial Neural Network Multilayer Perceptron (ANN-MLP); data-driven modelling; design of experiments (DoE); hyperparameter optimization; model generalizability; model lifecycle management; model transferability; near infrared (NIR); process monitoring; statistical process control (SPC); target drift detection Description An account of the resource Data variations, library changes, and poorly tuned hyperparameters can cause failures in data-driven modelling. In such scenarios, model drift, a gradual shift in model performance, can lead to inaccurate predictions. Monitoring and mitigating drift are vital to maintain model effectiveness. USFDA and ICH regulate pharmaceutical variation with scientific risk-based approaches. In this study, the hyperparameter optimization for the Artificial Neural Network Multilayer Perceptron (ANN-MLP) was investigated using open-source data. The design of experiments (DoE) approach in combination with target drift prediction and statistical process control (SPC) was employed to achieve this objective. First, pre-screening and optimization DoEs were conducted on lab-scale data, serving as internal validation data, to identify the design space and control space. The regression performance metrics were carefully monitored to ensure the right set of hyperparameters was selected, optimizing the modelling time and storage requirements. Before extending the analysis to external validation data, a drift analysis on the target variable was performed. This aimed to determine if the external data fell within the studied range or required retraining of the model. Although a drift was observed, the external data remained well within the range of the internal validation data. Subsequently, trend analysis and process monitoring for the mean absolute error of the active content were conducted. The combined use of DoE, drift analysis, and SPC enabled trend analysis, ensuring that both current and external validation data met acceptance criteria. Out-of-specification and process control limits were determined, providing valuable insights into the models performance and overall reliability. This comprehensive approach allowed for robust hyperparameter optimization and effective management of model lifecycle, crucial in achieving accurate and dependable predictions in various real-world applications. Graphical Abstract: [Figure not available: see fulltext.]. 2023, The Author(s). Creator An entity primarily responsible for making the resource Ali H.; Muthudoss P.; Chauhan C.; Kaliappan I.; Kumar D.; Paudel A.; Ramasamy G. Source A related resource from which the described resource is derived AAPS PharmSciTech, Vol-24, No. 8 Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1208/s12249-023-02697-3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1208/s12249-023-02697-3</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178941492&amp;doi=10.1208%2Fs12249-023-02697-3&amp;partnerID=40&amp;md5=ddd3b1f0e43bb7503c4dfc66120e77ee" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178941492&amp;doi=10.1208%2fs12249-023-02697-3&amp;partnerID=40&amp;md5=ddd3b1f0e43bb7503c4dfc66120e77ee</a> Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Relation A related resource ISSN: 15309932; PubMed ID: 38062329 | CS; 2023-2024; vOL-1; 00185-00186 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Ali H., Christ (Deemed to Be University), Karnataka, Bangalore, 560029, India; Muthudoss P., A2Z4.0 Research and Analytics Private Limited, Tamilnadu, Chennai, 600062, India, NuAxon Bioscience Inc., Bloomington, 47401-6301, IN, United States, School of Pharmaceutical Sciences, Vels Institute of Science, Technology &amp; Advanced Studies (VISTAS), Velan Nagar P.V. Vaithiyalingam Road Pallavaram 600117, Tamilnadu, Chennai, India; Chauhan C., The Machine Learning Company, Pune, India; Kaliappan I., School of Pharmacy, Hindustan Institute of Technology and Science (HITS), Padur, Tamilnadu, Chennai, 603 103, India; Kumar D., Department of Pharmaceutical Engineering &amp; Technology, IIT (BHU), Uttar Pradesh, Varanasi, 221011, India; Paudel A., Research Center Pharmaceutical Engineering GmbH (RCPE), Inffeldgasse 13, Graz, 8010, Austria, Graz University of Technology, Institute of Process and Particle Engineering, Inffeldgasse 13/3, Graz, 8010, Austria; Ramasamy G., Christ (Deemed to Be University), Karnataka, Bangalore, 560029, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Singhal, Abhinav; Seema; Saeed, Abdulkafi Mohammed; Nirwal, Sonal; Das, Soumik; Chaudhary, Anjali Title A name given to the resource Machine learning-enhanced heat and mass transfer study of elliptic motion in piezoelectric thermoelastic plates using Green-Naghdi III and three-phase-lag theories Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived International Communications in Heat and Mass Transfer;Volume;172;Issue;;Article No.;110482; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.icheatmasstransfer.2026.110482" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.icheatmasstransfer.2026.110482</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105027527965?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105027527965?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Singhal A., Christ University, Bengaluru, 560029, India; Seema, Christ University, Bengaluru, 560029, India; Saeed A.M., Department of Mathematics, College of Science, Qassim University, Buraydah, 51452, Saudi Arabia; Nirwal S., Centre for Mathematical Needs, Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, India; Das S., School of Physical Sciences, Amrita Vishwa Vidyapeetham, Mysuru Campus, Karnataka, 570026, India; Chaudhary A., Department of Management, College of Business Administration, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia Description An account of the resource Rayleigh-type surface waves in piezoelectric (PE) solids are pivotal for acoustic sensors, microelectromechanical systems (MEMS), and non-destructive evaluation. However, classical thermoelastic models fail under high heat flux due to the assumption of infinite thermal signal speeds, which limits their accuracy in coupled thermo-mechanical systems. To capture finite-speed and memory-dependent thermal effects, the Rayleigh wave propagation in a transversely isotropic (TI) PE half-space using generalized theories (such as Green-Naghdi type III (GN-III) and three-phase-lag (TPL)) is studied in this paper. The analytical formulation under varied electrical and thermal boundary conditions has been obtained. Secular equations are derived to characterize phase velocity, attenuation, and specific energy loss. A regression-based machine learning (ML) surrogate model is trained by using an analytical dataset to provide rapid predictions of wave parameters. Additionally, a confusion matrix classifier is applied to identify boundary conditions from simulated wave response features. The results demonstrated that the phase velocity increases with inclination angle and stabilizes with wave number, whereas attenuation and specific loss vary strongly by boundary condition (e.g., minimal in shorted-isothermal cases). The ML surrogate successfully reconstructed analytical predictions with minimal residual error, and the confusion matrix demonstrates accurate classification performance and validates the diagnostic potential of the framework. The novelty of this paper lies in integrating dual thermoelastic theories with machine learning, merging mechanics, heat transfer, and intelligent computing. These findings enable enhanced SAW sensor designs for precise gas/chemical detection, low-loss NDE tools for aerospace composite defect identification, and real-time diagnostics in biomedical ultrasonics for clearer imaging and efficient energy harvesting. 2026 Elsevier Ltd Subject The topic of the resource Attenuation and phase velocity; Computational mechanics; Confusion matrix; Elliptic motion energy wave; GreenNaghdi III; Heat and mass transfer; Machine learning; Multiphysics simulation; Piezoelectric thermoelasticity; Three-phase-lag theory Publisher An entity responsible for making the resource available Elsevier Ltd Relation A related resource ISSN: 7351933; CODEN: IHMTD Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine Learning's Transformative Role in Human Activity Recognition Analysis Subject The topic of the resource Activity Recognition; Artificial In telligence (AI); Computer Vision; Deep Learning; Human-Computer Interaction (HCI); Machine Learning; Motion Analysis; Pattern Recognition; Video Surveillance Description An account of the resource Human action recognition (HAR) is a burgeoning field of computer vision that seeks to automatically understand and classify the intricate movements performed by humans. From the graceful leaps of a ballerina to the decisive strides of a surgeon, HAR aims to decipher the language of motion, unlocking a plethora of potential applications. This abstract delves into the core of HAR, highlighting its key challenges and promising avenues for advancement. We begin by outlining the various modalities used for action recognition, such as RGB videos, depth sensors, and skeletal data, each offering unique perspectives on the human form. Next, we delve into the diverse set of algorithms employed for HAR, ranging from traditional machine learning techniques to the burgeoning realm of deep learning. We explore the strengths and limitations of each approach, emphasizing the crucial role of feature extraction and model selection in achieving accurate recognition. Challenges in Human Action Recognition (HAR), such as intra-class variations, inter-class similarities, and environmental factors. Ongoing efforts include robust feature development and contextual integration. The paper envisions HAR's future impact on healthcare, robotics, video surveillance, and augmented reality, presenting an invitation to explore the transformative world of human action recognition and its potential to enhance our interaction with technology. 2024 IEEE. Creator An entity primarily responsible for making the resource Darshan R.; Janmitha S.N.; Deekshith S.; Kulkarni P.; Rajesh T.M.; Gurudas V.R. Source A related resource from which the described resource is derived Proceedings of InC4 2024 - 2024 IEEE International Conference on Contemporary Computing and Communications Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/InC460750.2024.10649391" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/InC460750.2024.10649391</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203794903&amp;doi=10.1109%2FInC460750.2024.10649391&amp;partnerID=40&amp;md5=5a973d0f2f68afdaad0624c3e9b80880" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203794903&amp;doi=10.1109%2fInC460750.2024.10649391&amp;partnerID=40&amp;md5=5a973d0f2f68afdaad0624c3e9b80880</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835038365-2 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Darshan R., Dayananda Sagar university, Department of CSE, Bengaluru, India; Janmitha S.N., Dayananda Sagar university, Department of CSE, Bengaluru, India; Deekshith S., Dayananda Sagar university, Department of CSE, Bengaluru, India; Kulkarni P., Dayananda Sagar university, Department of CSE, Bengaluru, India; Rajesh T.M., Dayananda Sagar university, Department of CSE, Bengaluru, India; Gurudas V.R., CHRIST University, Department of CSE, Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine learningbased approaches for enhancing human resource management using automated employee performance prediction systems Subject The topic of the resource Artificial intelligence; Decision making; Employee performance; Human resource; Machine learning; Random Forest Description An account of the resource Purpose: This study focuses on enhancing the accuracy and efficiency of employee performance prediction to enhance decision making and improve organisational productivity. By introducing advance machine learning (ML) techniques, this study aims to create a more reliable and data-driven approach to evaluate employee performance. Design/methodology/approach: In this study, nine machine learning (ML) models were used for forecasting employee performance: Random Forest, AdaBoost, CatBoost, LGB Classifier, SVM, KNN, XGBoost, Decision Tree and one Hybrid model (SVM + XGBoost). Each ML model is trained on an HR data set covering various features such as employee demographics, job-related factors and past performance records, ensuring reliable performance predictions. Feature scaling techniques, namely, min-max scaling, Standard Scaler and PCA, have been used to enhance the effectiveness of employee performance prediction. The models are trained to classify data, predicting whether an employees performance meets expectations or needs improvement. Findings: All proposed models used in the study can correctly categorize data with an average accuracy of 94%. Notably, the Random Forest model demonstrates the highest accuracy across all three scaling techniques, achieving optimise accuracy, respectively. The results presented have significant implications for HR procedures, providing businesses with the opportunity to make data-driven decisions, improve personnel management and foster a more effective and productive workforce. Research limitations/implications: The scope of the used data set limits the study, despite our models delivering high accuracy. Further research could extend to different data sets or more diverse organisational settings to validate the models effectiveness across various contexts. Practical implications: The proposed ML models in the study provide essential tools for HR departments, enabling them to make more informed data driven decisions with regard to employee performance. This approach can enhance personnel management, improve workforce productivity and fostering a more effective organisational environment. Social implications: Although AI models have shown promising outcomes, it is crucial to recognise the constraints and difficulties involved in their use. To ensure the fair and responsible use of AI in employee performance prediction, ethical considerations, privacy problems and any biases in the data should be properly addressed. Future work will be required to improve and broaden the capabilities of AI models in predicting employee performance. Originality/value: This study introduces an exclusive combination of ML models for accurately predicting employee performance. By employing these advanced techniques, the study offers novel insight into how organisations might transition from a conventional evaluation method to a more advanced and objective, data-backed approach. 2024, Emerald Publishing Limited. Creator An entity primarily responsible for making the resource Uppal A.; Awasthi Y.; Srivastava A. Source A related resource from which the described resource is derived International Journal of Organizational Analysis Publisher An entity responsible for making the resource available Emerald Publishing Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1108/IJOA-07-2024-4643" target="_blank" rel="noreferrer noopener">https://doi.org/10.1108/IJOA-07-2024-4643</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208648589&amp;doi=10.1108%2FIJOA-07-2024-4643&amp;partnerID=40&amp;md5=2a6ff2e9a716209b7c9761a9f1e09907" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208648589&amp;doi=10.1108%2fIJOA-07-2024-4643&amp;partnerID=40&amp;md5=2a6ff2e9a716209b7c9761a9f1e09907</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 19348835 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Uppal A., Symbiosis Law School, Noida, Constituent of Symbiosis International (Deemed) University, Noida, India; Awasthi Y., School of Commerce, Finance and Accountancy, Christ University, Bengaluru, India; Srivastava A., Department of Management, Institute of Technology and Science, Ghaziabad, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Uppal, Aakanksha; Awasthi, Yashmita; Srivastava, Anubha Title A name given to the resource Machine learningbased approaches for enhancing human resource management using automated employee performance prediction systems Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Journal of Organizational Analysis;Volume;33;Issue;8;pp.2307-2346 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1108/IJOA-07-2024-4643" target="_blank" rel="noreferrer noopener">https://doi.org/10.1108/IJOA-07-2024-4643</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85208648589?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85208648589?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Uppal A., Symbiosis Law School, Noida, Constituent of Symbiosis International (Deemed) University, Noida, India; Awasthi Y., School of Commerce, Finance and Accountancy, Christ University, Bengaluru, India; Srivastava A., Department of Management, Institute of Technology and Science, Ghaziabad, India Description An account of the resource Purpose This study focuses on enhancing the accuracy and efficiency of employee performance prediction to enhance decision making and improve organisational productivity. By introducing advance machine learning (ML) techniques, this study aims to create a more reliable and data-driven approach to evaluate employee performance. Design/methodology/approach In this study, nine machine learning (ML) models were used for forecasting employee performance: Random Forest, AdaBoost, CatBoost, LGB Classifier, SVM, KNN, XGBoost, Decision Tree and one Hybrid model (SVM + XGBoost). Each ML model is trained on an HR data set covering various features such as employee demographics, job-related factors and past performance records, ensuring reliable performance predictions. Feature scaling techniques, namely, min-max scaling, Standard Scaler and PCA, have been used to enhance the effectiveness of employee performance prediction. The models are trained to classify data, predicting whether an employees performance meets expectations or needs improvement. Findings All proposed models used in the study can correctly categorize data with an average accuracy of 94%. Notably, the Random Forest model demonstrates the highest accuracy across all three scaling techniques, achieving optimise accuracy, respectively. The results presented have significant implications for HR procedures, providing businesses with the opportunity to make data-driven decisions, improve personnel management and foster a more effective and productive workforce. Research limitations/implications The scope of the used data set limits the study, despite our models delivering high accuracy. Further research could extend to different data sets or more diverse organisational settings to validate the models effectiveness across various contexts. Practical implications The proposed ML models in the study provide essential tools for HR departments, enabling them to make more informed data driven decisions with regard to employee performance. This approach can enhance personnel management, improve workforce productivity and fostering a more effective organisational environment. Social implications Although AI models have shown promising outcomes, it is crucial to recognise the constraints and difficulties involved in their use. To ensure the fair and responsible use of AI in employee performance prediction, ethical considerations, privacy problems and any biases in the data should be properly addressed. Future work will be required to improve and broaden the capabilities of AI models in predicting employee performance. Originality/value This study introduces an exclusive combination of ML models for accurately predicting employee performance. By employing these advanced techniques, the study offers novel insight into how organisations might transition from a conventional evaluation method to a more advanced and objective, data-backed approach. 2024 Emerald Publishing Limited Subject The topic of the resource Artificial intelligence; Decision making; Employee performance; Human resource; Machine learning; Random Forest Publisher An entity responsible for making the resource available Emerald Publishing Relation A related resource ISSN: 19348835; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine Learningcloud-Based Approach to Identify and Classify Disease Subject The topic of the resource Healthcare; Hybrid Decision Making; Internet of Things; Machine Learning Model Description An account of the resource The term "Internet of Things"(IoT) describes the process of creating and modeling web-related physical objects across computing systems. IoT-based healthcare applications have offered multiple real-time products and benefits in recent years. For millions of people, these programmers provide hospitalization can get regular medical records and healthy lives. The introduction of IoT devices in the health sector has several technological developments. This study uses the IoT to construct a disease diagnostic system. Wearable sensors in this system initially monitor the patient's sympathy impulses. The impulses are then sent by a network environment to a server. In addition, a new hybrid approach to evaluation decision-making was presented as part of this research. This technique starts with the development of a set of features of the patient's pulses. Based on a learning approach qualifications are neglected. A fuzzy neural model was used as a diagnostic tool. A specific diagnosis of a particular ailment, such as the diagnosis of a patient's normal and abnormal pulse or the assessment of insulin issues, would be modeled to assess this technology. 2022 IEEE. Creator An entity primarily responsible for making the resource Gupta B.; Alagdeve V.D.; Padmaja P.; Coumaressin T.; Reddy V.N.K.; Kumar R.G. Source A related resource from which the described resource is derived MysuruCon 2022 - 2022 IEEE 2nd Mysore Sub Section International Conference Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/MysuruCon55714.2022.9972738" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/MysuruCon55714.2022.9972738</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145351972&amp;doi=10.1109%2FMysuruCon55714.2022.9972738&amp;partnerID=40&amp;md5=202b4522bf13ec354806263182b8f1d1" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145351972&amp;doi=10.1109%2fMysuruCon55714.2022.9972738&amp;partnerID=40&amp;md5=202b4522bf13ec354806263182b8f1d1</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-166549790-9 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Gupta B., Siddhant College of Engineering, Department of I.T., Pune, India; Alagdeve V.D., Yeshwantrao Chavan College of Engineering, Department of Electronics Engineering, Nagpur, India; Padmaja P., Teegala Krishna Reddy Engineering College, Department of Electronics and Communication Engineering, Hyderabad, India; Coumaressin T., Sri Manakula Vinayagar Engineering College, Puducherry, India; Reddy V.N.K., Shri Jagdishprasad Jhabarmal Tibrewala University, Department of Computer Science &amp; Engineering, Jhunjhunu, India; Kumar R.G., School of Engineering and Technology Christ (Deemed to Be University), Computer Science and Engineering, Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine LearningEnabled NIR Spectroscopy. Part 2: Workflow for Selecting a Subset of Samples from Publicly Accessible Data Subject The topic of the resource artificial neural network-multilayer perceptron (ANN-MLP); data quality; machine learning; NIR spectroscopy Description An account of the resource Abstract: An increasingly large dataset of pharmaceuticsdisciplines is frequently challenging to comprehend. Since machine learning needs high-quality data sets, the open-source dataset can be a place to start. This work presents a systematic method to choose representative subsamples from the existing research, along with an extensive set of quality measures and a visualization strategy. The preceding article (Muthudoss et al. in AAPS PharmSciTech 23, 2022) describes a workflow for leveraging near infrared (NIR) spectroscopy to obtain reliable and robustdata on pharmaceutical samples. This study describes the systematic and structured procedure for selecting subsamples from the historical data. We offer a wide range of in-depth quality measures, diagnostic tools, and visualization techniques. A real-world, well-researched NIR dataset was employed to demonstrate this approach. This open-source tablet dataset (http://www.models.life.ku.dk/Tablets) consists of different doses in milligrams, different shapes, and sizes of dosage forms, slots in tablets, three different manufacturing scales (lab, pilot, production), coating differences (coated vs uncoated), etc. This sample is appropriate; that is, the model was developed on one scale (in this research, the lab scale), and it can be great to investigate how well the top models are transferable when tested on new data like pilot-scale or production (full) scale. A literature review indicated that the PLS regression models outperform artificial neural network-multilayer perceptron (ANN-MLP). This work demonstrates the selection of appropriate hyperparameters and their impact on ANN-MLP model performance. The hyperparameter tuning approaches and performance with available references are discussed for the data under investigation. Model extension from lab-scale to pilot-scale/production scale is demonstrated. Highlights: We present a comprehensive quality metrics and visualization strategy in selecting subsamples from the existing studies A comprehensive assessment and workflow are demonstrated using historical real-world near-infrared (NIR) data sets Selection of appropriate hyperparameters and their impact on artificial neural network-multilayer perceptron (ANN-MLP) model performance The choice of hyperparameter tuning approaches and performance with available references are discussed for the data under investigation Model extension from lab-scale to pilot-scale successfully demonstrated Graphical Abstract: [Figure not available: see fulltext.]. 2023, The Author(s). Creator An entity primarily responsible for making the resource Ali H.; Muthudoss P.; Ramalingam M.; Kanakaraj L.; Paudel A.; Ramasamy G. Source A related resource from which the described resource is derived AAPS PharmSciTech, Vol-24, No. 1 Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1208/s12249-022-02493-5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1208/s12249-022-02493-5</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146104488&amp;doi=10.1208%2Fs12249-022-02493-5&amp;partnerID=40&amp;md5=c6810fee3b90926dcfbe92fa893e2a65" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146104488&amp;doi=10.1208%2fs12249-022-02493-5&amp;partnerID=40&amp;md5=c6810fee3b90926dcfbe92fa893e2a65</a> Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Relation A related resource ISSN: 15309932; PubMed ID: 36627410 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Ali H., Christ (Deemed to Be University), Karnataka, Bangalore, 560029, India; Muthudoss P., A2Z4.0 Research and Analytics Private Limited, Tamilnadu, Chennai, 600062, India; Ramalingam M., Chettinad School of Pharmaceutical Sciences, Chettinad Academy of Research and Education, Chettinad Health City, Tamilnadu, Chennai, 603103, India; Kanakaraj L., Chettinad School of Pharmaceutical Sciences, Chettinad Academy of Research and Education, Chettinad Health City, Tamilnadu, Chennai, 603103, India; Paudel A., Research Center Pharmaceutical Engineering GmbH (RCPE), Inffeldgasse 13, Graz, 8010, Austria, Institute of Process and Particle Engineering, Graz University of Technology, Inffeldgasse 13/3, Graz, 8010, Austria; Ramasamy G., Christ (Deemed to Be University), Karnataka, Bangalore, 560029, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machine Transliteration of Handwritten MODI Script to Devanagari using Deep Neural Networks Subject The topic of the resource Calamari OCR; CRNN; Deep Neural Networks; Machine Transliteration; MODI script Description An account of the resource The transliteration process involves transcribing words from the source language into the target language that uses a different script. Language and scriptural hurdles can be overcome via transliteration systems. There is a demand for automated transliteration systems due to the existence of several languages and the growing number of multilingual speakers. This study focuses on the Machine Transliteration of handwritten MODI script to Devanagari. MODI script was the official script for Marathi till 1950. Although Devanagari has, since then, taken over as the Marathi languages official script, the MODI script has historical significance as large volumes of its manuscripts are preserved in libraries across different parts of India. However, MODI into Devanagari transliteration is a difficult task because MODI script documents are complex in nature and there is no standard dataset available for the experiment. Machine Transliteration can be approached either as a Natural Language Processing task or as a pattern recognition task. In this research work, the transliteration task is carried out using the pattern recognition technique. The transliteration of MODI script to Devanagari is implemented using Convolutional Recurrent Neural Network (CRNN) based Calamari OCR, which is open-source software. An accuracy of 88.14% is achieved in character level matching of each word in the MODI to Devanagari transliteration process. When considering the entire word matching, the accuracy achieved is 61%. Machine Transliteration of MODI script documents results in the retrieval of large repositories of knowledge from ancient MODI manuscripts. (2024), (Research Institute of Intelligent Computer Systems). All rights reserved. Creator An entity primarily responsible for making the resource Joseph S.; George J. Source A related resource from which the described resource is derived International Journal of Computing, Vol-23, No. 2, pp. 219-225. Publisher An entity responsible for making the resource available Research Institute of Intelligent Computer Systems Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.47839/ijc.23.2.3540" target="_blank" rel="noreferrer noopener">https://doi.org/10.47839/ijc.23.2.3540</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198112575&amp;doi=10.47839%2Fijc.23.2.3540&amp;partnerID=40&amp;md5=ed628b7621e33c9b685b93388b5ef488" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198112575&amp;doi=10.47839%2fijc.23.2.3540&amp;partnerID=40&amp;md5=ed628b7621e33c9b685b93388b5ef488</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 17276209 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Joseph S., Carmel College of Arts Science and Commerce for Women, Goa, India; George J., Christ University, Delhi NCR Campus, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Reddy, Gudisa Vathsalya; Meghna, P.; Ramesh, Nandana; Jayapandian, Natarajan Title A name given to the resource Machine-Learning Based Sleep Pattern Analysis Using Linear Regression Algorithm Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Electrical Engineering;Volume;1420 LNEE;pp.131-142 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-6406-1_11" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-6406-1_11</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105019639852?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105019639852?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Reddy G.V., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India; Meghna P., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India; Ramesh N., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India; Jayapandian N., Department of Computer Science and Engineering, Christ University, Kengeri Campus, Bangalore, India Description An account of the resource This article is investigating the connection between sleep patterns and concentration spans among university students while exploring the potential influence of MyersBriggs Type Indicator (MBTI) personality types on these aspects. The primary objective is to understand how sleep duration affects students ability to maintain focus and how their personality traits might interact with this relationship. Data was collected from university students aged 1619 using a multiple-choice form. The key variables analyzed were age, MBTI personality types, sleep duration, concentration span, and effective study ranking. Pearson's correlation was employed to examine these relationships. Additionally, a linear regression model was developed to predict concentration span based on sleep hours. The findings revealed a strong positive correlation 0.758 between sleep duration and concentration span, suggesting that increased sleep is associated with longer concentration spans. A moderate positive relationship 0.249 was also observed between concentration span and effective study ranking. However, the analysis showed a negligible relationship ? 0.008 between MBTI personality types and concentration span, indicating that within the context of this study, personality type does not significantly influence concentration span. This research emphasizes the critical role of sleep in academic settings and challenges the assumption that personality types significantly impact concentration span and sleep patterns. The linear regression model developed provides a predictive tool for understanding the impact of sleep on concentration, underscoring the importance of adequate sleep for academic success. This research is contributing to the broader understanding of factors influencing student performance and offers practical insights for optimizing study habits and educational strategies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Concentration span; Linear regression; Machine learning; MyersBriggs type indicator; Sleep pattern Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18761100; ISBN: 978-981966405-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Machining Characteristics Evaluation of Al7075TiB2 In Situ Composite Using Abrasive Water Jet Machining with Varied Test Parameters Subject The topic of the resource Abrasive water jet; Al7075TiB₂; In situ process; Surface roughness; VMRR Description An account of the resource The study delves into the abrasive water jet (AWJ) cutting of an Al7075TiB2 metal matrix composite that was synthesized in situ. The primary goal is to investigate how variations in three key process parameters, namely, stand-off distance (SOD) ranging from 0.5 to 2.5mm, abrasive flow rate (100 to 300gmin), and traverse speed (100 to 500mmmin), affect three critical performance metrics: volumetric material removal rate (VMRR), dimensional accuracy, and surface roughness (SR). The study's findings were represented graphically, highlighting the relationships between these responses and the aforementioned process parameters. Scanning electron microscopy (SEM) was also used to examine the machined surfaces. It was discovered that increasing traverse speed resulted in significant increases in surface roughness, VMRR, and dimensional errors. An increase in the SOD, on the other hand, resulted in an increase in surface roughness, VMRR, and a decrease in dimensional accuracy. Furthermore, increasing the abrasive flow rate resulted in lower surface roughness and dimensional accuracy while achieving a higher VMRR. 2023, The Institution of Engineers (India). Creator An entity primarily responsible for making the resource Kavya J.T.; Keshavamurthy R.; Ravishankar M.K.; Pradeep Kumar G.S. Source A related resource from which the described resource is derived Journal of The Institution of Engineers (India): Series D Publisher An entity responsible for making the resource available Springer Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s40033-023-00597-1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s40033-023-00597-1</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178937550&amp;doi=10.1007%2Fs40033-023-00597-1&amp;partnerID=40&amp;md5=05dc99f4d44f01b177a87907191a98d7" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178937550&amp;doi=10.1007%2fs40033-023-00597-1&amp;partnerID=40&amp;md5=05dc99f4d44f01b177a87907191a98d7</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 22502122 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kavya J.T., Department of Automobile Engineering, Dayananda Sagar College of Engineering, Bangalore, 560111, India; Keshavamurthy R., Department of Mechanical Engineering, Dayananda Sagar College of Engineering, Bangalore, 560111, India; Ravishankar M.K., Department of Automobile Engineering, Malnad College of Engineering, Hassan, 573202, India; Pradeep Kumar G.S., Department of Mechanical and Automobile Engineering, Christ University, Bengaluru, 560074, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kavya, J.T.; Keshavamurthy, R.; Ravishankar, M.K.; Pradeep Kumar, G.S. Title A name given to the resource Machining Characteristics Evaluation of Al7075TiB2 In Situ Composite Using Abrasive Water Jet Machining with Varied Test Parameters Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Journal of The Institution of Engineers (India): Series D;Volume;106;Issue;1;pp.145-155 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s40033-023-00597-1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s40033-023-00597-1</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85178937550?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85178937550?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kavya J.T., Department of Automobile Engineering, Dayananda Sagar College of Engineering, Bangalore, 560111, India; Keshavamurthy R., Department of Mechanical Engineering, Dayananda Sagar College of Engineering, Bangalore, 560111, India; Ravishankar M.K., Department of Automobile Engineering, Malnad College of Engineering, Hassan, 573202, India; Pradeep Kumar G.S., Department of Mechanical and Automobile Engineering, Christ University, Bengaluru, 560074, India Description An account of the resource The study delves into the abrasive water jet (AWJ) cutting of an Al7075TiB2 metal matrix composite that was synthesized in situ. The primary goal is to investigate how variations in three key process parameters, namely, stand-off distance (SOD) ranging from 0.5 to 2.5 mm, abrasive flow rate (100 to 300 g min), and traverse speed (100 to 500 mm min), affect three critical performance metrics: volumetric material removal rate (VMRR), dimensional accuracy, and surface roughness (SR). The studys findings were represented graphically, highlighting the relationships between these responses and the aforementioned process parameters. Scanning electron microscopy (SEM) was also used to examine the machined surfaces. It was discovered that increasing traverse speed resulted in significant increases in surface roughness, VMRR, and dimensional errors. An increase in the SOD, on the other hand, resulted in an increase in surface roughness, VMRR, and a decrease in dimensional accuracy. Furthermore, increasing the abrasive flow rate resulted in lower surface roughness and dimensional accuracy while achieving a higher VMRR. The Institution of Engineers (India) 2023. Subject The topic of the resource Abrasive water jet; Al7075TiB₂; In situ process; Surface roughness; VMRR Publisher An entity responsible for making the resource available Springer Relation A related resource ISSN: 22502122; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource MADeGen: Multi-Agent based Deep Reinforcement Learning for Sequential Keyphrase Generation Subject The topic of the resource Actor-critic; Adaptive reward; Extraction agent; Generation agent; Keyphrase generation; Multi-agent; Reinforcement learning Description An account of the resource Keyphrase generation is an essential tool in the field of natural language processing for information retrieval, document summarization, and text recommendation applications, extracting succinct and representative phrases from the text document. Traditional keyphrase extraction methods applied the supervised or unsupervised learning fail to capture the sequential keyphrase generation in a dynamic environment. The keyphrase generation approaches lack focus on explicitly discriminating the present and absent keyphrases, leading to the inadequate generation of semantically rich absent keyphrases. Hence, this work utilizes the potential benefits of reinforcement learning with the design of a distinguished reward function for present and absent keyphrases for sequential decision-making in the keyphrase generation. Thus, this work presents a novel keyphrase generation system, MADeGen, utilizing Multi- Agent Deep Reinforcement Learning (MADRL). In particular, a multi-agent reinforcement system collaboratively enables the generation of representative and coherent keyphrases by the evaluation metric-aware cooperative reward function analysis and adaptively training the agents. The proposed MADeGen incorporates two major phases, such as multi-agent modelling and actor critic-based policy optimization towards accurate keyphrase generation. In the first phase, the proposed approach designs two learning agents, including the extraction agent and generation agent, with the incorporation of a pre-trained language model. In the multi-agent system, the generation agent is the finetuned version of the extraction agent with the integration of the Wikipedia source. Secondly, the evaluation-aware adaptive reward function is designed to evaluate each agent's generated keyphrases with reference to ground-truth keyphrases. In subsequence, the cooperative reward analysis triggers the actor critic-based policy optimization for the generation agent in the multi-agent system to precisely generate the semantically relevant keyphrases with the assistance of an external web source. Experimental results on several benchmark datasets, such as Inspec, PubMed, and wiki20, illustrate the effectiveness of the proposed MADeGen compared to the existing keyphrase extraction models, yielding state-of-the-art performance in keyphrase extraction tasks. The proposed MADeGen proves its higher performance in the present as well as absent keyphrase extraction as 0.367 and 0.438 F1-score, respectively, while testing on the Inspec dataset. (2024), (Intelligent Network and Systems Society). All Rights Reserved. Creator An entity primarily responsible for making the resource Jose J.; Soundarabai P.B. Source A related resource from which the described resource is derived International Journal of Intelligent Engineering and Systems, Vol-17, No. 6, pp. 69-85. Publisher An entity responsible for making the resource available Intelligent Network and Systems Society Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.22266/ijies2024.1231.07" target="_blank" rel="noreferrer noopener">https://doi.org/10.22266/ijies2024.1231.07</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207918938&amp;doi=10.22266%2Fijies2024.1231.07&amp;partnerID=40&amp;md5=9a190da7e40910600039335ed126548d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207918938&amp;doi=10.22266%2fijies2024.1231.07&amp;partnerID=40&amp;md5=9a190da7e40910600039335ed126548d</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 2185310X Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jose J., Christ Deemed to be University, Bangalore, India; Soundarabai P.B., Christ Deemed to be University, Bangalore, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kumar, Satishchandra; Majumdar, Anjali; Dhulla, Tejal Title A name given to the resource Madhavrao Babasaheb Ghorpade (19262019) Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Psychology in India: Stories of Eminence;pp.197-201 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4324/9781003591634-32" target="_blank" rel="noreferrer noopener">https://doi.org/10.4324/9781003591634-32</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105012664187?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105012664187?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kumar S., University Department of Applied Psychology and Counselling Center, University of Mumbai, India; Majumdar A., School of Psychological Sciences, Christ University, Bengaluru, India; Dhulla T., S.P. Jain School of Global Management, Mumbai, India Description An account of the resource This chapter highlights the significant contributions of M.B. Ghorpade to the field of psychology in India, emphasizing his foresight and dedication to documenting the evolution of this discipline. Ghorpades work included the publication of essential reading materials on psychological testing and abnormal psychology, along with numerous research papers and articles in various journals. His influential publications include An Introduction to Experimental Psychology, Essentials of Psychological Testing, Essentials of Psychology, Essentials of Social Psychology, Industrial Psychology, and Introduction to Modern Psychotherapy. Through his scholarly endeavours, Ghorpade ensured that future generations have access to a well-grounded understanding of psychological principles and practices. 2025 selection and editorial matter, Braj Bhushan; individual chapters, the contributors. Publisher An entity responsible for making the resource available Taylor and Francis Relation A related resource ISBN: 978-104036672-1; 978-103296995-4; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online https://archives.christuniversity.in/files/original/c4f1f9458c5ee0b79a5473c3f4531cbd.jpg 4441e0cee3bc69d222960e8e4d165bf9 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Exhibit Still Image A static visual representation. Examples include paintings, drawings, graphic designs, plans and maps. Recommended best practice is to assign the type Text to images of textual materials. Original Format The type of object, such as painting, sculpture, paper, photo, and additional data Engraved solid pure silver ingots, layered with pure 24-carat gold Physical Dimensions The actual physical size of the original image Stamp size is 33.50 mm x 33.50 mm Gold Replica is 2.2mm thick with diamond cut perforations Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Madhubala Subject The topic of the resource Stamp ingot collection Description An account of the resource Madhubala stamp was originally issued on 18 March, 2008. The Department of Posts issued the commemorative stamp on Madhubala, one of the most enduring legends of Indian cinematic history. Creator An entity primarily responsible for making the resource Hallmark Group Limited Source A related resource from which the described resource is derived Pride of India Collection Publisher An entity responsible for making the resource available Authority of India Post Date A point or period of time associated with an event in the lifecycle of the resource 2008-12-14 Format The file format, physical medium, or dimensions of the resource JPG Type The nature or genre of the resource Image Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Arunmozhi, M.; Sunitha, R.; Rajalakshmi, D.; Mohan E, Syam Title A name given to the resource MADTRAS: Dataset for aspect-based sentiment analysis of movie reviews in Tamil Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Data in Brief;Volume;63;Issue;;Article No.;112073; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.dib.2025.112073" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.dib.2025.112073</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105017782327?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105017782327?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Arunmozhi M., Department of Computer Science, Pondicherry University, Puducherry, Kalapet, 605014, India; Sunitha R., Department of Computer Science, Pondicherry University, Puducherry, Kalapet, 605014, India; Rajalakshmi D., Department of Computer Science, Pondicherry University, Puducherry, Kalapet, 605014, India; Mohan E S., Department of Computer Science, Pondicherry University, Puducherry, Kalapet, 605014, India, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India Description An account of the resource The rise of online platforms has led to a growing trend of people expressing their thoughts and emotions in their native languages. Movies have been a predominant topic of discussion on online platforms where people reflect on various aspects of movies. Aspect-based Sentiment Analysis (ABSA), a computational technique, assists in examining the sentiments hidden in these discussions. Two challenges arise when attempting to use ABSA to identify sentiments in movie reviews written in the Indian regional language Tamil; the former being the unavailability of potential Tamil movie review datasets and the latter being the difficulty that arises due to the agglutinative nature of Tamil Language. This work addresses the first challenge by curating an annotated movie review dataset in Tamil, MADTRAS (Dataset for Aspect-based Sentiment Analysis of Movie Reviews in Tamil). The quality of the dataset is ensured through content and annotation evaluation. To prove the efficiency of the dataset, the multilingual BERT (mBERT) was used, and the performance was compared with other Deep Learning(DL) models. 2025 The Authors Subject The topic of the resource ABSA; Correlation; Data curation; mBERT; Transformers Publisher An entity responsible for making the resource available Elsevier Inc. Relation A related resource ISSN: 23523409; Language A language of the resource English Type The nature or genre of the resource Data paper Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online https://archives.christuniversity.in/files/original/9eb342437f631a8bd59f033f4184ec21.pdf dde82a12ed3c662bc8488bc699fa6513 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource MPHIL Mphil Mphil Thesis Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource MAGAZINE COVER: AN ARTISTIC EXPRESSION AND A METHOD TO COMMUNICATE WITH THE READERS Creator An entity primarily responsible for making the resource Ramesh Apurva Date A point or period of time associated with an event in the lifecycle of the resource 2012 Source A related resource from which the described resource is derived Media Studies Description An account of the resource The dissertation aims at going deeper than what meets the eyes, in terms of design and creativity with the help of semiotic analysis. The researcher conducted semiotic analysis of eight issues of Bloomberg Businessweek and Wired magazine??s cover pages, in an attempt to decode and deconstruct the nuances of the cover page. Stylistically all the cover pages considered for the semiotic analysis are covers that have several connotations and denotations. These aspects are generally neglected by the readers and there has been a constant need to decipher the underlying meaning. A lot of creative thought that goes into the creation of these cover pages as it involves a combined effort of the editorial and the design team with best of creative minds coming together. Here the attempt by the researcher is too look into that aspect of the cover which goes beyond the obvious. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Yhome, Lucy Keneikhrienuo; Mudaliar, Meghna Christina Title A name given to the resource MAGIC AND TERROR IN EASTERINE KIRES ECOLOGICAL FICTION: Indigenous Naga Ecofeminism and Conservation Ethics Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived The Routledge Companion to Global Womens Writing;pp.213-224 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4324/9781003365730-23" target="_blank" rel="noreferrer noopener">https://doi.org/10.4324/9781003365730-23</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022539789?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022539789?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Yhome L.K., Department of English and Cultural Studies, Christ University, Bangalore, India; Mudaliar M.C., Christ University, India Description An account of the resource Indigenous women across the globe are front-line environmental activists implementing sustainable living practices and conservation through their activism and narratives. Indigenous women writers from Nagaland dominate published creative work from the region, making creative writing a space of resistance and representation. Native or Indigenous knowledge systems revolve around ecocultural practices of sustainability and conservation ethics. The Tenyimia worldview of the Angami Nagas of Nagaland opens up possibilities of ecological ethics and sustainable living through its knowledge systems. A minority Indigenous community in the Northeast region of India, the Angami Nagas represent a worldview that offers sustainable living practices and means of forest conservation through narratives that incorporate magic and terror. Easterine Kire, a renowned writer from Nagaland, has revived the eco-culture of the community through her representation of the Tenyimia worldview, offering insights into Indigenous ecofeminist views through her narratives, which she terms Peoplestories.' The present chapter investigates how magic and terror in Easterine Kires fiction represent forms of Indigenous knowledge that help define ecological ethics. The study applies an Indigenous ecofeminist approach to Easterine Kires work which invokes magic and terror through forest spirits, river spirits, and environmental legends such as the Tekhumevi, or were-tiger, to offer a re-imagination of the ecological spaces traditionally reflected through the communitys oral narratives. 2025 selection and editorial matter, Ina C. Seethaler and Tripthi Pillai; individual chapters, the contributors. Publisher An entity responsible for making the resource available Taylor and Francis Relation A related resource ISBN: 978-104035365-3; 978-103243105-5; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Magical mushroom Ganoderma-A Promising treatment for cancer Description An account of the resource [No abstract available] Creator An entity primarily responsible for making the resource Nandi S.; Paul A.M.; Nag A.; Acharya K. Source A related resource from which the described resource is derived Ganoderma: Cultivation, Chemistry and Medicinal Applications, Vol-1, pp. 168-188. Publisher An entity responsible for making the resource available CRC Press Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003354789-10" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003354789-10</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185098120&amp;doi=10.1201%2F9781003354789-10&amp;partnerID=40&amp;md5=8eeda0fd91ae8965ab9b0a479a1b62ec" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185098120&amp;doi=10.1201%2f9781003354789-10&amp;partnerID=40&amp;md5=8eeda0fd91ae8965ab9b0a479a1b62ec</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-100384727-4; 978-103239761-0 | LS; 2023-2024; Vol-2; 0821-0846 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Nandi S., University of Calcutta, Kolkata, India; Paul A.M., Christ (Deemed to be University), Bangalore, Karnataka, India; Nag A., Christ (Deemed to be University), Bangalore, Karnataka, India; Acharya K., University of Calcutta, Kolkata, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Magnetic coupling across the antiferromagnetic-antiferromagnetic interface Subject The topic of the resource antiferromagnetism; low-energy electron diffraction; magnetic coupling; thin films Description An account of the resource We investigate the magnetic coupling across the antiferromagnetic-antiferromagnetic (AFM-AFM) interface for the prototypical CoO-NiO bilayer system where the bulk Nl temperature (T N ) of NiO is higher than that of CoO. Using the temperature-dependent exchange-scattered electron intensities from the surface AFM lattice, the surface T N of CoO was estimated as a function of the CoO/NiO film thicknesses. Our results show that the surface T N of CoO layers is enhanced significantly from its bulk T N value and approaching the T N of the NiO layers, as the thickness of the CoO layers is reduced to the monolayer limit. Thus, thinner CoO layers are found to have higher T N than thicker layers on NiO, contrasting with the expected finite-size behavior. In addition to the short-range magnetic exchange coupling at the CoO-NiO interface, we observe the existence of a longer-range magnetic coupling across the interface, mediated by the magnetic correlations. Thus, the magnetic proximity effect is attributed to a combination of a short-range and a weaker long-range magnetic coupling, explaining the long AFM order propagation length in AFM-AFM superlattices and bilayers. Further, our results indicate a new approach to tune the AFM Nl temperature by varying the individual layer thickness of the bilayer system through the magnetic proximity effect. 2021 IOP Publishing Ltd. Creator An entity primarily responsible for making the resource Jena B.B.; Kar A.; Barman S.; Mandal S.; Menon K.S.R. Source A related resource from which the described resource is derived Journal of Physics D: Applied Physics, Vol-54, No. 32 Publisher An entity responsible for making the resource available IOP Publishing Ltd Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1088/1361-6463/ac02fb" target="_blank" rel="noreferrer noopener">https://doi.org/10.1088/1361-6463/ac02fb</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107871709&amp;doi=10.1088%2F1361-6463%2Fac02fb&amp;partnerID=40&amp;md5=1cef7dcf4dadf7dc3ab9bb7f69b430c2" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107871709&amp;doi=10.1088%2f1361-6463%2fac02fb&amp;partnerID=40&amp;md5=1cef7dcf4dadf7dc3ab9bb7f69b430c2</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 223727; CODEN: JPAPB Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jena B.B., Surface Physics and Material Science Division, Saha Institute of Nuclear Physics, HBNI, 1/AF Bidhannagar, Kolkata, 700064, India; Kar A., Surface Physics and Material Science Division, Saha Institute of Nuclear Physics, HBNI, 1/AF Bidhannagar, Kolkata, 700064, India; Barman S., Department of Physics, Raja Peary Mohan College, 1 Acharya Dhruba Pal Road, Uttarpara, Hooghly, West Bengal, 712258, India; Mandal S., Physics and Electronics Department, CHRIST (Deemed to Be University), Bangalore, 560029, India; Menon K.S.R., Surface Physics and Material Science Division, Saha Institute of Nuclear Physics, HBNI, 1/AF Bidhannagar, Kolkata, 700064, India