ONLINE PAPER PUBLISHING - Volume 6 - Issue 10, October 2023 Edition
All listed papers are published after full consent of respective author or co-author(s).
For any discussion on research subject or research matter, the reader should directly contact to undersigned authors.
ADVANCED HR MANAGEMENT SYSTEM: OPTIMIZING RECRUITMENT, ASSESSMENT, RETENTION, AND PERFORMANCE WITH MACHINE LEARNING
Authors: RISHIKOPAN. S; SIVARUJAN.S; RAMYA.M; SANDUNI PERERA; JIVISHAN.T
Abstracts: This research paper explores four critical human resources (HR) components, focusing on leveraging advanced technology to enhance organizational performance, employee retention, and recruitment processes. The system include developing an effective reward system using machine learning techniques, employee retention prediction, recommended systems for candidate selection, and designing an automated online examination (OE) system. The first component delves into the impact of rewards on employee performance and outlines a methodology the second component focuses on employee retention prediction, emphasizing its significance in identifying factors contributing to turnover and implementing effective strategies. General parameters and key features are discussed, supported by research studies conducted by leading companies. The third component addresses the challenges faced by organizations in finding suitable candidates. It explores the use of recommended systems, leveraging social networks, and the scalability of databases. The fourth component focuses on designing and implementing an automated OE system customized based on the applicant's profile and job role. This system streamlines the recruitment process by evaluating applicants' competence and abilities. It involves collecting applicant information, identifying related keywords, and creating an intuitive interface for OE. Organizations can enhance performance, improve employee retention, and streamline recruitment by integrating AI, machine learning, data analysis, and natural language processing techniques. These advancements provide valuable insights and tools for organizations seeking to optimize their HR strategies and drive overall success.
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Cite this Article: Rishikopan. S; Sivarujan.S; Ramya.M; Sanduni Perera; Jivishan.T , "ADVANCED HR MANAGEMENT SYSTEM: OPTIMIZING RECRUITMENT, ASSESSMENT, RETENTION, AND PERFORMANCE WITH MACHINE LEARNING", International Journal of Advanced Research and Publications (IJARP), http://www.ijarp.org/online-papers-publishing/oct2023.html, Volume 6 - Issue 10, October 2023 Edition, 1-7 #ijarporg
MUSIC MOODY - FACIAL RECOGNITION AND VOICE RECOGNITION TO DETECT MOOD AND RECOMMEND SONGS
Authors: K. THARMIKAN, HEISAPIRASHOBAN.N, M.A. MIQDAD ALI RIZA, R.R. STELIN DINOSHAN, THUSITHANJANA THILAKARTHNA
Abstracts: This project aims to develop a comprehensive music recommendation system that provides personalized song suggestions based on the user's individual tastes and current emotional state. The system incorporates four main components: mood detection using live voice recognition techniques, collaborative filtering for playlist generation, multiclassification of songs based on mood, and base and frequency feature extraction. The real-time voice recognition module analyzes the user's voice to extract features like pitch, volume, and tone, which are then used to determine the user's mood state. This information is fed into the mood detection and song recommendation module, which employs a neural network trained on a large dataset of labeled audio recordings to predict the user's mood. Also utilizes collaborative filtering techniques, considering the user's music preferences, listening history, and similarities with other users, to generate personalized song playlists. Additionally, a multiclassification approach using base and frequency features is employed to classify songs into mood categories such as happy, sad, calm, and energetic. This classification allows for better organization and recommendation of songs based on their emotional characteristics. Overall, this project offers a comprehensive approach to personalized music recommendation, leveraging voice recognition, collaborative filtering, and song mood classification to provide users with relevant and enjoyable song suggestions based on their individual tastes and emotional states.
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Cite this Article: K. Tharmikan, Heisapirashoban.N, M.A. Miqdad Ali Riza, R.R. Stelin Dinoshan, Thusithanjana Thilakarthna , "MUSIC MOODY - FACIAL RECOGNITION AND VOICE RECOGNITION TO DETECT MOOD AND RECOMMEND SONGS", International Journal of Advanced Research and Publications (IJARP), http://www.ijarp.org/online-papers-publishing/oct2023.html, Volume 6 - Issue 10, October 2023 Edition, 8-14 #ijarporg
COMPARATIVE ANALYSIS OF THE QUALITY OF PORTLAND CEMENT SOURCED FROM DANGOTE CEMENT FACTORY OBAJANA AND BUA CEMENT FACTORY SOKOTO
Authors: NWAHA, D. O1, CHUKWUJAMA, I. A2, SANI J. E.3 AND MOHAMMED, I. S.4
Abstracts: This study emphasizes the critical role of high-quality cement in ensuring the durability and structural integrity of concrete-based construction. The quality of cement significantly impacts a building's compressive strength, workability, and overall performance. Portland cement, a key binding agent in concrete production, is examined in this study through a comparison of BUA Cement from the Sokoto factory and Dangote Cement from the Obajana cement factory. Various physical, chemical, and mechanical tests were conducted according to British standards, involving 30 cubes and 12 beams. Testing occurred at intervals of 7, 14, 21, and 28 days, with selected beams subjected to sulfuric acid immersion. The findings suggest that Dangote Cement excels in terms of compressive strength, crushing strength, workability, and durability, essential attributes for construction. On the other hand, BUA Cement demonstrates faster setting times, enhanced scheduling flexibility, and superior overall performance. The study highlights BUA Cement's higher content of key chemical components, though the differences between compositions are relatively small. These findings emphasize the importance of selecting the right cement type based on specific construction requirements.
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Cite this Article: Nwaha, D. O1, Chukwujama, I. A2, Sani J. E.3 and Mohammed, I. S.4 , "COMPARATIVE ANALYSIS OF THE QUALITY OF PORTLAND CEMENT SOURCED FROM DANGOTE CEMENT FACTORY OBAJANA AND BUA CEMENT FACTORY SOKOTO", International Journal of Advanced Research and Publications (IJARP), http://www.ijarp.org/online-papers-publishing/oct2023.html, Volume 6 - Issue 10, October 2023 Edition, 15-19 #ijarporg