Volume 13 | Issue 5 |

 Abstract

Railway track safety is a critical concern, with foreign object intrusions posing significant threats to operational efficiency and human life. Traditional surveillance systems often rely on manual monitoring, which is error-prone and delayed in response. This paper proposes a novel, AI-powered foreign object detection system using Convolutional Neural Networks (CNNs) and YOLOv5 for real-time surveillance. High-resolution video feeds from CCTV cameras are analyzed to identify threats such as animals, vehicles, debris, and humans on railway tracks. The model is trained on annotated datasets under various conditions to ensure high accuracy and robustness. Results show a detection accuracy of over 96%, with real-time alert generation for prompt action. The system also integrates object tracking and automated notifications to control centers. Deployment tests validate its effectiveness in enhancing railway safety. This research sets a foundation for scalable, intelligent monitoring in railway environments.

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 Keywords

Railway Safety, Foreign Object Detection, YOLOv5, Convolutional Neural Networks, Deep Learning, Real-Time Surveillance, Object Tracking, Intrusion Detection System

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Creative Commons Attribution 4.0 and The Open Definition

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The paper focus on hybrid work. After covid 19 the work culture and structure has been changed completely. To understand what is student expectation towards job market. The primary and secondary data is used in preparing the paper. Google forms are used to collect data. 55 convenient samples are used in the paper. MBA student from Mysore are taken. To know about hybrid work and Job among students. Percentage analysis is used to analyze the data. The results indicate that students have confusion, less preparation regarding the Job and ready to work in Hybrid jobs.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Students, Hybrid work, Job preparation,Job and Environment.

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Creative Commons Attribution 4.0 and The Open Definition

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The concept of unemployment is very negative term for the society, this unemployment problem play pivotal role in all Indian state the present try to find out root cause of unemployment the term youth unemployment is modern phenomena this study define first trends and pattern of youth unemployment in Odisha and sectoral generating the unemployment in Odisha, second, the study show that the cause and consequences of unemployment in Odisha and why youth unemployment increased rapidly in Odisha it is also try to find out the why the agriculture production and labour force participation decrease day by day ,another important aspect pointed out the male rural youth unemployment increase in comparison to urban unemployment. The unemployment is increase because of the rapid growth of population and poverty and unskilled education, etc. This present paper aims to examine the sweep and patterns of youth unemployment in Odisha. The present research paper explains that, it is assertive to know the overall joblessness rate trends in Odisha. Because it will not only help to compare with the youth unemployment rate trends, but also it will provide a proper direction for explaining the reasons and determinants of youth unemployment in Odisha. The sectoral rural and urban youth unemployment trends and its pattern by demographic (age and sex), and socio-economic (by caste and income groups), and by level of education is explained in this research paper

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 Keywords

Agriculture, Education, Poverty, Rural, Urban, and Unemployment

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract Background: Arterial Blood Gas (ABG) interpretation is an essential skill for nurses working in critical care settings. Despite its significance in patient care, many staff nurses lack sufficient knowledge in interpreting ABG results, which can impact clinical decision-making and patient outcomes. This study aims to evaluate the effectiveness of a planned teaching programme on enhancing the knowledge of staff nurses regarding ABG interpretation. Aim: To assess the effectiveness of a planned teaching programme on the knowledge of ABG interpretation among staff nurses in selected hospitals in Ujjain, Madhya Pradesh. Methods: A quasi-experimental pre-test post-test design was used. The study included 200 staff nurses working in selected hospitals in Ujjain, selected through purposive sampling. A structured knowledge questionnaire was administered before and after the teaching intervention to assess the nurses' knowledge. The data were analyzed using descriptive and inferential statistics, including paired t-tests to compare the pre- and post-test knowledge scores. Results: The study found a significant improvement in the knowledge of ABG interpretation post-intervention. The pre-test mean knowledge score was 11.65 (SD = 3.12), and the post-test mean score was 18.34 (SD = 2.85), with a mean difference of 6.69. The paired t-test revealed a statistically significant difference (t = 24.58, p < 0.001), indicating a marked improvement in knowledge following the planned teaching programme. Conclusion: The planned teaching programme effectively enhanced the knowledge of staff nurses regarding ABG interpretation. Regular in-service education programmes focused on critical competencies like ABG interpretation can improve clinical performance and patient care outcomes.

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Keywords: Arterial Blood Gas, ABG Interpretation, Nursing Education, Staff Nurses, Knowledge Assessment, Planned Teaching.

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Creative Commons Attribution 4.0 and The Open Definition

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This research explores the strategic implementation of predictive analytics to enhance sales forecasting, inventory management, and production planning. Historical sales data were analyzed using methods like exponential smoothing and ARIMA, with ARIMA (1,1,0) emerging as the most effective model. The analysis revealed seasonal demand fluctuations, peaking in May and dropping in December. Predictive analytics significantly boosts operational efficiency but faces challenges such as data inconsistencies and market volatility. Recommendations include improving data collection, integrating external variables, and staff training. These steps support better decision-making and adaptability. The study offers a model for other mid-sized manufacturers..

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Predictive analytics, sales forecasting, inventory management, production planning, ARIMA, exponential smoothing, quantitative research, seasonal demand, digital transformation, manufacturing industry.

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Creative Commons Attribution 4.0 and The Open Definition

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This research investigates the level of job satisfaction among employees at Butterfly Gandhimathi Appliances Ltd, Pudupakkam. with the primary aim of identifying the key factors that contribute to employee satisfaction and dissatisfaction. Understanding these factors is essential for enhancing employee performance and reducing staff turnover. A structured questionnaire was used to collect data 232 employees. Data analysis was conducted using percentage analysis, correlation, mann whitney U-test, chi-square tests. The findings offer recommendations to improve salary, promotion and workload expectations for better employee job satisfaction.

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satisfaction, regular training, increase salary and reduce workload.

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Creative Commons Attribution 4.0 and The Open Definition

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Students with Disabilities often face significant barriers in accessing quality education, particularly in STEM disciplines. Effective strategies, research and practice gaps are highlighted in this systematic review, which examines the current state of STEM education for students with disabilities. Selected studies on the relationship between disability and STEM education were systematically reviewed and compiled in accordance with PRISMA guidelines. The analysis focused on five key themes: career development and guidance, evidence-based practices, inclusive teaching and teacher support, tailored curricula and assistive technologies, and addressing gaps in existing research and practices. The results highlight the importance of career-oriented counselling, team-based teacher support, and customised approaches in promoting STEM education for students with disabilities. To ensure educational equity, educators should continually adapt and improve their methods, providing all students with equal opportunities to succeed. However, the impact of STEM education on post-secondary outcomes for students with disabilities and develop additional evidence-based strategies to facilitate their STEM learning and the present study is limited to selected studies and Western-centric focus.

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STEM education, students with disabilities, inclusive education, PRISMA

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Creative Commons Attribution 4.0 and The Open Definition

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Custodial violence remains a critical issue in India, undermining the integrity of the criminal justice system and violating human rights. This study investigates the prevalence and patterns of custodial deaths, evaluates the existing legal frameworks, and assesses the impact of custodial violence on both victims and society. The research adopts a doctrinal methodology, analyzing statutes, case law, judicial pronouncements, and national and international human rights frameworks to provide a comprehensive understanding of the issue. The study aims to examine the legal provisions in India, such as the BNS, BNSS, and guidelines issued by the Supreme Court, alongside international human rights conventions, to determine their effectiveness in preventing custodial torture and deaths. The objectives of this research include analyzing the prevalence and patterns of custodial deaths, examining the existing legal mechanisms, and exploring the broader implications of custodial violence as a violation of fundamental human rights. Additionally, the study seeks to propose reforms for addressing the gaps in law enforcement practices, judicial oversight, and institutional safeguards. In conclusion, the study underscores the necessity for stronger legal provisions, effective enforcement mechanisms, and institutional accountability to prevent custodial violence. It further emphasizes the critical role of the Supreme Court in shaping judicial directives and ensuring the protection of human rights in custodial settings.

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Custodial Violence, Human Rights, Supreme Court, Legal Framework, Custodial Deaths

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract: We are developing an on-line voting system by taking advantage of centralized database witha web interface. The main concept of this project is to build a website , which will be able to allow people to cast their vote through on-line. Time saving , working load reduced , information available at time and it provides security for data. In a democratic country like India we are not getting 100% of voting. People are not ready to poll their vote because of many factors like people can't go to the polling stations to cast their vote(especially aged persons and physically challenged people). People may be at remote places .There are several issues with traditional paper based voting like ridging votes during election , insecure or inaccessible polling stations , inadequate polling materials and also inexperienced personnel. This On-line Voting System seeks to address the above issues. With this system , the citizens may get ample time during the voting period. Every citizen is registered first and all the details are managed at centralized database .And at the time of elections the citizens will be login through their credentials and cast their vote.

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KEYWORDS--Online Voting, Secure Authentication, OTP, Aadhar-based Login, E-Voting System, Biometric Verification, Database Security, Electronic Election, Web-Based Voting

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Creative Commons Attribution 4.0 and The Open Definition

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Stroke is one of the leading causes of death and long-term disability worldwide, making early risk prediction critical for timely intervention and prevention. Traditional methods of stroke risk assessment often rely on clinical evaluations and static risk scores, which may not fully capture the complex interplay of multiple risk factors. In this study, we propose and evaluate the use of advanced machine learning (ML) techniques for stroke risk prediction based on demographic, lifestyle, and medical data. Various supervised learning algorithms, including Logistic Regression, Random Forest, Support Vector Machine, and Gradient Boosting, were applied to a publicly available stroke dataset. The models were assessed using key performance metrics such as accuracy, precision, recall, F1-score, and area under the ROC curve (AUC). Our experimental results demonstrate that machine learning techniques can effectively enhance the predictive accuracy of stroke risk assessment compared to traditional methods. Furthermore, feature importance analysis revealed significant predictors contributing to stroke risk, enabling more interpretable and data-driven decision support for healthcare practitioners. The study highlights the potential of ML-based models as robust, scalable, and non-invasive tools for stroke risk prediction, paving the way for personalized preventive healthcare strategies.

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 Keywords

Stroke Prediction, Machine Learning, Logistic Regression, Random Forest, Support Vector Machine, Gradient Boosting, Risk Assessment, Healthcare Analytics, Predictive Modeling, Data-Driven Decision Support

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Creative Commons Attribution 4.0 and The Open Definition