https://hmjournals.com/journal/index.php/JAIMLNN/issue/feedJournal of Artificial Intelligence,Machine Learning and Neural Network (JAIMLNN) ISSN: 2799-11722024-02-01T05:08:07+00:00Editor in Chiefeditor.jaimlnn@gmail.comOpen Journal Systems<p>The <strong>Journal of Artificial Intelligence, Machine Learning and Neural Network (JAIMLNN) having ISSN: 2799-1172</strong> is a double-blind, peer-reviewed, open access journal that provides publication of articles in all areas of Artificial Intelligence, Machine Learning and Neural Network and related disciplines. The objective of this journal is to provide a veritable platform for scientists and researchers all over the world to promote, share, and discuss a variety of innovative ideas and developments in all aspects of <strong>Artificial Intelligence, Machine Learning and Neural Network.</strong></p>https://hmjournals.com/journal/index.php/JAIMLNN/article/view/3564Fruits Leaf Disease Detection Using Convolutional Neural Network2024-01-24T10:15:35+00:00Deepak Panthadeepakmphilit@gmail.com<p>Due to the traditional agricultural system, losses of millions of money have been loses every year. Farmers were always ready in agricultural work without risking their lives. If smart methods can be adopted in the agricultural system, the farmers will not have to suffer much damage. Using machine learning and testing with Convolutional Neural Network algorithm (mobileNet method), in this research to find out the actual accuracy, 3642 photos of apple leaves of Kaggle dataset and CSV files are used. In this paper, using Python language with the help of Jupyter notebook, Eposes has been tested 15 times to create confusion metrics. In this paper, precision, recall, f1_ score and average accuracy have been found and studied. An average accuracy of 95 percent has been obtained from the study. 95% accuracy is considered as a good result of the test using machine learning. By adopting this method, we can also give more motivation to the agricultural sector.</p>2024-02-01T00:00:00+00:00Copyright (c) 2024 Authorshttps://hmjournals.com/journal/index.php/JAIMLNN/article/view/3587Quantitative Assessment on Investigation on the Impact of Artificial Intelligence on HR Practices and Organizational Efficiency for Industry 4.02024-01-29T09:18:53+00:00Dr. Shweta Kulshresthashweta.rkcet@gmail.com<p>In the rapidly evolving landscape of Industry 4.0, the integration of Artificial Intelligence (AI) into Human Resources (HR) practices has emerged as a pivotal factor in enhancing organizational efficiency. This research study delves into the multifaceted implications of AI adoption within HR departments and its overarching impact on the operational efficiency of organizations. In the era of Industry 4.0, characterized by advanced automation, connectivity, and data-driven decision-making, AI technologies are playing an increasingly significant role in reshaping traditional HR functions. This research aims to quantitatively assess the extent to which AI-driven HR practices influence employee recruitment, retention, development, and overall human capital management. By analyzing data from a diverse set of organizations across different industries, this study seeks to identify patterns, trends, and best practices related to AI integration in HR. The research methodology involves a combination of surveys, data analysis, and case studies to collect and analyze quantitative data on AI adoption in HR practices and the subsequent impact on organizational efficiency. Key performance indicators (KPIs) such as employee productivity, cost effectiveness, and strategic alignment are scrutinized in order to ascertain the correlation between AI in HR and organizational success. Preliminary findings indicate that AI-driven HR practices are facilitating more streamlined and data-informed decision-making processes, allowing organizations to make better-informed talent-related choices. The insights gained from this study will be instrumental in guiding organizations in optimizing their HR functions through AI integration, enabling them to adapt and thrive in the Industry 4.0 landscape. Additionally, this research contributes to a deeper understanding of the evolving dynamics between AI, HR practices, and organizational efficiency, with implications for strategic decision-making and policy development in the context of Industry 4.0.</p>2024-02-01T00:00:00+00:00Copyright (c) 2024 Authorshttps://hmjournals.com/journal/index.php/JAIMLNN/article/view/3588Material Selection and Optimization of Torsion Bar Suspension for Military Vehicle in Case of Tank T-552024-01-29T09:21:55+00:00Ebisa Kejela Melkaebisakj@gmail.com<p>This project focuses on the analyzing different materials for torsion bar suspension system for Tank T-55 for optimizing its performance for cross country mobility and ride comfort. This suspension system is aimed to improve wheel travel and angle of twist on all terrain conditions from rough to flat surfaces. The different materials studied are carbon steel and alloy steel for their suitability as torsion bar and proposed de-sign is accomplished through the material selection and analytical calculation with analysis for shear stress, total deformation and strain. alloy steel is considered as alternative material for torsion bar based on the result of its good strength in shear stress and store maximum energy in the case of strain energy.</p>2024-02-01T00:00:00+00:00Copyright (c) 2024 Authorshttps://hmjournals.com/journal/index.php/JAIMLNN/article/view/3589A Predictive Study of Machine Learning and Deep Learning Procedures Over Chronic Disease Datasets2024-01-29T09:25:33+00:00 Nimay Sethtarunagrawal705@gmail.com<p>People's health and well-being are not given priority in the technological and Internet-savvy world we live in. People are becoming worse because they don't regularly attend the hospital for checkups due to job and unanticipated events. Most people nowadays suffer from one or more chronic illnesses, such as diabetes, hypothyroidism, heart disease, breast cancer, and dermatology. According to the World Health Organization (WHO), these chronic illnesses account for half of all fatalities in most nations and are the main cause of premature mortality. Patients who are identified early on potentially have their condition stop progressing. Many dispersed studies clearly demonstrated that conventional approaches to diagnosing chronic illnesses are prone to prejudice and heterogeneity among physicians, making it difficult to promptly and precisely diagnose problems. Still, Despite the availability of up-to-date information and a variety of machine learning-based methods, there have been enormous published efforts demonstrating that machine learning (ML)/deep learning (DL) based approach can considerably enhance the timely estimation of various health conditions. However, precise diagnosis of such diseases remains a difficulty. There are many machine learning-based techniques and current knowledge available, however despite this, a great deal of published research has shown that machine learning/deep learning based approach can considerably enhance the timely estimation of various health conditions. However, precise diagnosis of such diseases remains a difficulty. In order to tackle this problem, this work uses the UCI/KAGGLE ML/DL disease dataset to evaluate various ML/DL procedures and explores how different machine learning algorithms forecast chronic diseases. Accuracy and confusion matrix are used to verify the results. In order to help inexperienced researchers comprehend the disease prediction function of ML/DL-based techniques and determine the direction of Upcoming research, this study also discusses the advantages and disadvantages of accessible disease prediction schemes.</p>2024-02-01T00:00:00+00:00Copyright (c) 2024 Authorshttps://hmjournals.com/journal/index.php/JAIMLNN/article/view/3590Psychological Impact of AI: Understanding Human Responses and Adaptations2024-01-29T09:27:30+00:00Ayush Kumar Ojhaayushkumarojha484@gmail.com<p>This research explores the psychological impact of artificial intelligence (AI) on individuals, aiming to understand and analyze human responses and adaptations in the context of advancing AI technologies. Examining the intersection of psychology and AI, our study delves into the cognitive, emotional, and behavioral implications that arise as AI systems become integrated into various aspects of daily life. Through empirical investigations and comprehensive literature reviews, we aim to elucidate the evolving dynamics of human-AI interaction, shedding light on both positive and potentially challenging psychological outcomes. The findings contribute to a deeper understanding of the intricate relationship between humans and AI, providing valuable insights for developers, policymakers, and mental health professionals as society navigates the transformative landscape of technological integration.</p>2024-02-01T00:00:00+00:00Copyright (c) 2024 Authors