Volume 13 | Issue 5 |

 Abstract

This study investigates employee perspectives on HR digitalization within manufacturing organizations in the context of Industry 4.0. It focuses on key factors such as ease of use, training effectiveness, and communication quality, and how these influence employee satisfaction and adoption of digital HR systems. A structured questionnaire was administered to 132 employees, and data were analyzed using SPSS, employing Mann-Whitney U, Kruskal-Wallis H, and Friedman tests. Findings reveal that while employees recognize the benefits of digitalization--such as improved communication and work-life balance--they also face challenges like technical difficulties and limited training. The study concludes that prioritizing employee feedback, continuous support, and system usability is essential for successful HR digital transformation

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 Keywords

HR digitalization, Industry 4.0,System usability, Training effectiveness, Digital transformation and Employee satisfaction .

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

A Go-kart is a small four wheeled vehicle. Go-kart, by definition, has no suspension and no differential. They are usually raced on scaled down tracks but are sometimes driven as entertainment or as a hobby by non-professionals. Our goal is to design and fabricate the go-kart that offers exceptional performance, driver comfort, and safety. The primary focus is on creating a lightweight kart that delivers impressive performance. Adherence to the rulebook of Edge line Championship is mandatory and significantly influences our objectives. The go-kart, which is propelled by a rear- wheel internal combustion engine without suspension or differential, has been designed in accordance with standard principles. All critical factors and design parameters have been considered, resulting in a thoroughly failure-analysed and ergonomically optimized go-kart. Comprehensive calculations have been conducted for each component, followed by 3D modelling and simulation using professional software tools. The analysis was carried out with different iterations and finally the optimum design of each part of the Go kart was done so as to obtain the optimum result.

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 Keywords

Go-kart, Design, Frame, Analysis, Steering System, Braking System, Engine, Transmission, Innovation.

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Leaf detection is a critical task in agriculture, aiding in the early identification of plant leaf to ensure optimal benefits. This paper presents a comprehensive approach to automating leaf detection using advanced image processing and deep learning techniques in Python. The methodology involves pre-processing the input images to enhance features and extract meaningful information. Subsequently, a Convolutional Neural Network (CNN) model is trained on a curated dataset comprising healthy plant leaves. The CNN learns to classify leaves into respective categories, enabling automated detection. The trained model is evaluated based on various metrics such as accuracy, precision, assess its benefits. In this paper, the performance of a pre-trained ResNet34 model in detecting disease is investigated. Additionally, a real-world application of the model is demonstrated through predictions on unseen leaf images. The results showcase the efficacy of the proposed approach in accurately identifying plant leaf, laying the foundation for further advancements and integration into agricultural practices.

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 Keywords

Image Processing, Convolutional Neural Networks (CNN) , Leaf Detection

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Deep learning has transformed medical imaging, particularly cancer detection. This paper introduces a Vision Transformer (ViT)-based approach for ovarian cancer classification. Unlike CNN-based models such as ResNet-50, ViTs utilize self-attention mechanisms to enhance feature extraction interpretability. The proposed model incorporates Swin Transformers and hierarchical feature fusion techniques, achieving superior classification accuracy. Evaluations on hematoxylin and eosin (H&E) stained histopathology slides reveal that ViTs outperform conventional models, achieving 99.2% accuracy, 99.1% sensitivity, and 99.0% specificity. These findings suggest significantly that ViTs improve early cancer detection rates and assist pathologists in reliable diagnostics.

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 Keywords

Ovarian Cancer, Vision Transformer, Swin Transformer, Medical Imaging, Deep Learning, Histopathology, Self-Attention Mechanism, AI in Healthcare.

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This paper presents Campus Commute, a campus-exclusive ride-sharing application designed to provide secure, efficient, and affordable transportation solutions for college students and faculty members. Unlike commercial ride-hailing platforms, Campus Commute restricts its users to verified members of the college community, ensuring a safer and more familiar commuting environment. The system integrates real-time ride booking, GPS tracking, student ID verification, and emergency support features. It leverages modern mobile technologies to enhance user experience while promoting eco-friendly travel through ride-sharing among peers.

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 Keywords

College, Riding, Pooling

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Automatic gender classification and age detection may be a fundamental task in computer vision which has recently attracted immense attention. Here, a new type of intelligent system which is based on several neural networks connected on two levels of classification with an efficient and simple Convolutional Neural Network (CNN) model architecture by considering gender and age estimation as a multi-label classification problem. The proposed model is trained and then evaluated on the publicly available audience benchmark dataset. The model first performs feature extraction on the input image which can classify eyes, lips, beard, hair etc supporting these featured the model will classify the gender as male or female. Here we have used the Haar Cascade for feature extraction purpose. Age is estimated with the assistance of Caffe Model. We have used various detection methods for analysis like skin colour Segmentation and lip detection and many more. The CNN model is built by using VGG-16 architecture. The CNN model is then trained using epochs, where each epoch contains a certain number of training images. To remove distorted and unwanted images, the loss Gauss function is used. For testing, the input image is given by the user. The model makes the predictions to estimate age, gender and emotion of that input image by comparing with the trained images.

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 Keywords

Convolutional Neural Network, Recurrent Neural Network, Support Vector Machines, Deep EXpectation, Generative Adversarial Network, Conformal Embedded Analysis

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Creative Commons Attribution 4.0 and The Open Definition

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The study was conducted with an aim to assess and improve the knowledge and attitude of school teachers regarding first aid skills, to promote safety in school environments and encourage prompt and effective management of emergencies. A descriptive research design was used for the study in order to evaluate the existing knowledge and attitude regarding first aid among teachers of selected schools at Charar-I-Sharief, Budgam, Jammu and Kashmir. A total of 60 teachers were selected through convenient sampling technique from the accessible population. Data was collected using structured questionnaires and attitude scale for assessing knowledge and attitude respectively. The main study was conducted over a period of four weeks. The data collected was analyzed using descriptive and inferential statistics.

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Knowledge, Attitude, First Aid, CPR, Wound, Bleeding

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The present paper examines the effects of new economic reforms on the livelihoods and development of marginalized groups in India. Marginality addresses the socio-cultural and human issues faced by individuals from diverse segments of society, including Scheduled Castes, Scheduled Tribes, impoverished farmers, laborers, artisans, and others. Agriculture serves as a fundamental component of the Indian economy, with many marginalized groups in rural areas relying on it as their traditional means of sustainable livelihood. The livelihood project serves as a strategic initiative aimed at empowering individuals to enhance their economic value, optimize production systems, and promote social justice. The policies of New Economic Reforms (NER, 1991) have multiple dimensions that have affected vulnerable communities both favorably and unfavorably. Since the emergence of LPG, there has been an increase in GDP growth and a favorable impact on various factors. These measures have also resulted in the excessive exploitation of natural resources in rural India, upon which the marginalized masses depend. Finally, this paper includes necessary recommendations for future research work.

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 Keywords

New economic reforms, marginalised groups, liberalization, privatization, globalization, sustainable livelihood

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In the ever-evolving landscape of education, traditional learning management systems (LMS) often fail to cater to the diverse needs of students pursuing different career paths. Pathwise Academy is an adaptive learning platform designed to provide personalized learning experiences for college students. This research paper presents the architecture, methodologies, and potential impact of Pathwise Academy in assisting students to navigate multiple career options while still in college. The system leverages machine learning (ML) algorithms to analyze student progress, recommend tailored learning paths, and integrate real-world career insights. By combining data analytics, career-based recommendations, and interactive learning modules, Pathwise Academy aims to bridge the gap between academic learning and career readiness. This study highlights the significance of personalized education in improving student engagement, retention, and employability. The research further evaluates the performance of the system through empirical analysis, user feedback, and industry relevance. The findings indicate that adaptive learning frameworks like Pathwise Academy can significantly enhance students' ability to make informed career decisions, ultimately reducing dropout rates and increasing job market readiness.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Personalized Learning, Learning Management System (LMS), Artificial Intelligence, Career Guidance, College Education, Machine Learning, Adaptive Learning

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Creative Commons Attribution 4.0 and The Open Definition

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The aim of this study is to explore the potential of sentiment analysis on election news article titles using machine learning techniques and to determine the most effective methods for text representation in this context. Traditionally, sentiment analysis has relied on part-of-speech tagging and word polarity counts, which work well in broad domains and when large labeled datasets are unavailable. However, in more specific domains with pre-labeled data, supervised learning methods are more suitable. This thesis evaluates the performance of a Convolutional Neural Network (CNN) and a Support Vector Machine (SVM) on various datasets, which were designed to capture different linguistic features

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 Keywords

Sentiment Analysis Feature Selection of Newspaper Articles on Election

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Creative Commons Attribution 4.0 and The Open Definition