Volume 12 | Issue 4 |

 Abstract

The matter of dalit suppression has been of great concern towards constructing an ideal Indian nation. It has been a curse for the Indian nation since the beginning of the caste system. The outcaste or the so-called dalits have been oppressed and discriminated against in the hands of upper caste people of the society. Although some steps have been taken for their upliftment after the colonial era, still they're in a suppressed state. Still there is a change to come to build up a castle and class less Indian nation where nobody will be oppressed. There is the need for a change in the mindset of both the upper caste people and the dalits themselves to come. This paper tries to bring a link between Mulk Raj Anand's ''Untouchable" and Odia writer Manglu Charan Biswal's play "Bhukha (The Starved)" on dalit cause.

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 Keywords

Dalit, untouchable, starved, hunger, out-castes, oppressed, discrimination, change, hopeful.

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Creative Commons Attribution 4.0 and The Open Definition

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The escalating urban population in India has given rise to a surge in traffic congestion within cities, posing challenges for ambulances to navigate through densely populated streets. This issue is exacerbated by a general lack of public awareness regarding the critical importance of yielding to emergency vehicles. To address this problem, our research focuses on training the YOLOv8 model for effective ambulance identification amidst other vehicles on the road. YOLO, distinguished for its efficacy in object detection, notably excels in swift processing speeds and exceptional accuracy. This project emphasizes the utilization of YOLOv8, which demonstrates an 84.62% precision, a 75.93% recall, and an F1-score of 79.98% for ambulance detection and the application of deep learning methodologies for image segmentation, aiming to enhance emergency vehicle navigation in congested urban environments.

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 Keywords

Emergency vehicles, YOLOv8, object detection, deep learning, image segmentation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This research paper describes a system for predicting student performance and recommending courses using predictive analytics. The system utilizes machine learning models such as Linear Regression, Support Vector Regression (SVR), and Random Forest to accurately forecast student performance based on historical academic data, study habits, and course preferences. Moreover, it generates personalized course recommendations specific to each student's profile, academic goals, and learning needs. Experimental evaluation demonstrates the effectiveness of the proposed approach in predicting performance and providing relevant course suggestions, which can significantly enhance academic outcomes and student satisfaction.

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 Keywords

student, performance, prediction, recommendation, machine learning, linear regression

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The primary focus of the present study is to study the correlation between academic stress and academic achievement of Under Graduate students. The study was conducted by following descriptive survey method, in which a sample of 60 students (30 girls and 30 boys) were selected through equal number stratified random sampling procedure from Govt. Degree College, Sundargarh, Odisha. The tool used in the study for data collection was Academic Stress Scale developed by Rajendran and Kaliappan in 1990. After collecting data, product movement coefficient of correlation was applied to interpret the results. The study disclosed that there is negative correlation between academic stress and academic achievement of Undergraduate students.

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 Keywords

academic stress, academic achievement, undergraduate students

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Creative Commons Attribution 4.0 and The Open Definition

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The present study delves into the notion of cruelty as a basis for divorce in the Hindu Marriage Act and English Law. It first examines the definition, categories, legal provisions, and procedures for pursuing a divorce based on cruelty in each legal system. Next, it presents a comparative analysis that highlights the similarities and differences between the two legal systems. Lastly, the study addresses the impact on society, difficulties in establishing cruelty, potential future implications for legal systems, and recommendations for individuals who are thinking about filing for divorce based on cruelty.

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 Keywords

Cruelty, divorce, Hindu marriage Act, English law, society, Family law

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Creative Commons Attribution 4.0 and The Open Definition

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In the dynamic landscape of retail, the challenge of uncertain inventory decisions poses significant obstacles, leading to suboptimal stocking strategies, missed sales opportunities, and increased operational costs. This approach presents a novel approach to mitigate this challenge by integrating deep learning techniques, specifically convolutional neural networks (CNNs) implemented through Keras, with traditional machine learning algorithms such as Singular Value Decomposition (SVD). Leveraging image data, the CNN model accurately predicts demographic attributes like gender and age from customer images, augmenting the predictive capabilities of traditional methods. By harnessing these insights, retailers can optimize their inventory management strategies to stock items tailored to the preferences of diverse customer segments. The findings suggest that this integrated approach enhances inventory management efficiency, leading to improved customer satisfaction and cost savings. This approach contributes to advancing the state-of-the-art in retail inventory management, offering a promising avenue for retailers to adapt to evolving consumer demands in an increasingly competitive market

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 Keywords

Deep Learning, Convolution neural network(CNN),Inventory optimization, Consumer demands

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Creative Commons Attribution 4.0 and The Open Definition

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The assessment of light motor vehicle (LMV) fitness is crucial for ensuring vehicle safety, reliability, and optimal performance in the automotive industry. In this project, we propose a novel approach using machine learning algorithms to detect and classify LMV fitness levels based on a comprehensive analysis of vehicle data. Our methodology involves extensive data collection from onboard sensors, maintenance records, and driver behavior logs, followed by preprocessing, feature engineering, and model development stages. We explore a range of machine learning algorithms, including traditional methods and deep learning architectures, to build robust models capable of accurately predicting LMV fitness levels [3]. Through rigorous evaluation and validation, our models demonstrate promising performance metrics, with high accuracy, precision, recall, and area under the ROC curve (AUC-ROC). The integration of these models into real-world LMV monitoring systems offers practical benefit for proactive maintenance, safety enhancement, and regulatory compliance. Further research avenues include the integration of IoT devices, enhancement of model interpretability, dynamic model updating, scalability optimization, incorporation of external factors, and validation studies for regulatory compliance. Overall, our project contributes to advancing automotive health monitoring and underscores the importance of leveraging machine learning for LMV fitness detection in the automotive industry

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 Keywords

machine learning, image processing

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

As urbanisation accelerates globally, the demand for efficient parking solutions becomes increasingly critical. This research conducts a systematic literature review to comprehensively explore the transformative potential of stack-type multi-level car parking systems and their implications for future urban planning. The study fulfils three primary research objectives: evaluating the efficiency and space utilisation of stack-type systems, analysing their economic viability, and understanding their environmental impact. The results reveal that stack-type parking systems demonstrate superior efficiency metrics, economic viability, and reduced environmental impact compared to traditional structures. The comparative analysis underscores their transformative potential, while acknowledging structural complexities and scalability issues as challenges. The discussion delves into the implications for urban planning, recommendations for policymakers, and avenues for future research, emphasising the importance of public acceptance and smart technology integration. The conclusion highlights the need for continuous exploration, innovation, and interdisciplinary collaboration to successfully integrate stack-type parking solutions into the dynamic tapestry of future urban landscapes.

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 Keywords

REVOLUTIONISING URBAN PARKING

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Creative Commons Attribution 4.0 and The Open Definition

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Network embedding successfully maintains the network structure by assigning network nodes to low dimensional representations. A considerable amount of progress has recently been achieved in the direction of this new paradigm for network research. In this study, we concentrate on classifying, analyzing, and pointing out the future directions for network embedding techniques research. We begin by summarizing the purpose of network embedding. We talk about network embedding and how it relates to traditional graph embedding methods in a cognitive radio context. Following that, we give a thorough overview of a variety of network embedding techniques in a methodical way, including advanced information preserving network embedding techniques, network embedding techniques with side information, and approaches that preserve structure and properties. Additionally, many methods of network embedding assessment as well as certain practical online tools, such as network data sets and software, are explored. In our last section, we cover the foundation for utilizing these network embedding techniques to create a successful system and identify some possible future paths.

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 Keywords

Network Embedding , Beyond 5G Internet of Things , Machine learning , 5G

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Demand of higher education increasing day by day and the enrolment of students in higher education becomes top priority in this modern era. NPE 2020 has also given priority to quality higher education. Mainly higher education focuses to quality research and better education. Due to limited books and materials many students are facing problem to access better education. Keeping in view this problem, Open Educational Resources (OER) was introduced This OER reduces so many problems of students like high tuition costs, high price of books etc. This study was conducted on the perception of Teachers of Higher Education Institutions towards OERs. The objective of this study is perception of higher education teachers towards availability and use of OER in teaching learning processes. This study employed descriptive survey design under quantitative method and the investigator selected random sampling technique to collect sample using questionnaire through online Google form. Data analysis made through percentage method. The findings of this study reveals around 80% of higher education teachers strongly agreed of being aware of OERs and they are able to use it in their classes and also they are agreed of the importance of OERs in present classroom situations to improve the quality in education. Out of the total teachers 35% have disagreed on the facilities and scope provided by their institute to use OERs in teaching learning system.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Open Educational Resources, Perception, Copyright, Creative Commons, Technology, ICT.

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Creative Commons Attribution 4.0 and The Open Definition