Volume 14 | Issue 4 |

 Abstract

This paper aims to investigate the role of educational technology in enhancing the teaching learning process, addressing a critical need to understand how higher education academicians navigate its integration in the post-COVID era. A growing body of research suggests that technology enhances engagement and personalization, yet little is known about how academicians navigate post-COVID integration. This study addresses this need by providing the key insights on the impact of educational technology in enhancing the teaching- learning process from the perspective of 120 higher education academicians by administering a survey questionnaire. Findings reveal that a vast majority do integrate digital tools such as slide presentations, Learning Management System (LMS) platforms, and video conferencing into their teaching and report that these tools improve student engagement, enhance understanding, and allow quicker feedback. However, challenges like limited training, unreliable infrastructure, time constraints, and learner distractions were also reported. These findings affirm educational technology's potential to strengthen teaching practices but emphasize the necessity for targeted training and infrastructure support to sustain student engagement.

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 Keywords

Educational Technology; Teaching-Learning; Academician Survey; Student Engagement; Technology Integration

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 Abstract

Stroke is still a serious health issue and affects a large number of people every year. In many situations, it does not just lead to death but also leaves patients with long-term problems such as reduced mobility, memory issues, or emotional stress. These effects make daily life difficult and also increase the load on healthcare systems. Because of this, predicting stroke risk at an early stage becomes important, as it gives some time for preventive steps to be taken. In this work, a system is developed to estimate the risk of stroke using artificial intelligence techniques. The main idea is to provide some level of early support to doctors by using patient data, rather than depending only on symptoms that appear later. While working on this, one practical issue was the lack of proper datasets. Most datasets that were available were either incomplete or did not match the required parameters. So instead of relying on limited data, a synthetic dataset was created based on medically accepted ranges. While doing this, an attempt was made to keep the data realistic, but it is clear that it may not fully capture real-world variations. The dataset includes common risk factors such as age, gender, hypertension, heart disease, glucose level, BMI, and smoking habits. Before using the data, some preprocessing steps were necessary. Missing values had to be handled, categorical data was converted into numerical form, and scaling was applied where needed. During this step, it became clear that some models, especially Logistic Regression, were sensitive to how the data was scaled. Without proper normalization, the results were not stable. Three models were tested: Logistic Regression, Decision Tree, and Random Forest. These were selected mainly because they are widely used and easier to understand in healthcare-related problems. While testing, the Decision Tree model gave good results on training data but did not perform as consistently on test data, which indicated overfitting. Random Forest, on the other hand, gave more stable results across both training and testing. This is likely because it combines multiple decision trees, which helps reduce overfitting. Among all the models, Random Forest performed better overall, giving an accuracy close to 92%. It also showed better balance between precision and recall compared to the other models. Based on these observations, it was selected for the final system. The system itself is divided into stages such as input, preprocessing, prediction, and output. It provides a simple indication of risk, which can help in early-level decision-making. In future, this system can be connected to hospital records or wearable devices for continuous monitoring. However, one limitation of this work is that the model is trained only on synthetic data, so its performance with real patient data may vary. Overall, this work shows that machine learning can be useful as a supporting tool for early stroke risk prediction. At the same time, more testing with real clinical data is required before it can be used in practical situations.

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 Keywords

Stroke prediction, Machine learning, Artificial intelligence, Random Forest, Early diagnosis, Healthcare, Classification, Risk assessment.

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 Abstract

Traditional methods of measuring and recreating objects are often slow and less accurate, especially when dealing with complex geometries. Manual drawing or modeling can lead to loss of fine details and errors in reconstruction. To overcome these limitations, modern 3D scanning techniques use sensors, lasers, and cameras to capture detailed information quickly and efficiently.

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 Keywords

3 D drawing, laser sensor, Arduino uno, camera module

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid adoption of cloud computing in healthcare has improved data accessibility and scalability but introduced significant security challenges. Traditional encryption techniques, such as RSA and ECC, are vulnerable to potential attacks from quantum computers. This paper proposes a quantum-safe cloud storage framework for securing electronic health records (EHRs) using post-quantum cryptographic algorithms. The system integrates lattice-based encryption for secure data storage and quantum-resistant key exchange mechanisms to protect sensitive medical data. Additionally, role-based access control and data integrity verification techniques are implemented to ensure authorized access and prevent tampering. The proposed model enhances confidentiality, integrity, and long-term security of healthcare data against both classical and quantum threats. Experimental analysis demonstrates improved resilience with acceptable computational overhead, making it suitable for real-world healthcare applications.

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 Keywords

Quantum-safe security, Post-quantum cryptography, Cloud storage, Healthcare data security, Electronic Health Records (EHR), Data privacy, Lattice-based cryptography, Digital signatures, Secure key management, Role-Based Access Control (RBAC), Data integrity, Encryption, Cloud security.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

: Looking closely at Uber and Swiggy shows how numbers shape work in India's growing gig scene. Because more people are joining, smart tracking helps balance who needs rides or food with those offering it. Instead of just linking one rider to one driver, grouping requests cuts down waiting, sometimes slashing empty stretches between trips from an hour and a half to far less. With maps that spot busy zones, drivers head where demand spikes, avoiding slow spots. On delivery apps, layered prediction models guess order surges before they happen, getting meals out faster - often in half an hour or less. Workers earn more when systems run smoothly, feeding into wider economic gains already adding up to over 1% of national output. Yet pay can still swing wildly week to week, leaving some struggling to plan ahead. Personal details get collected nonstop, raising worries about who sees what. Rules haven't kept pace, so safety nets often fall short when problems hit. Even as tech pushes progress, fairness lags without updated policies catching up

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 Keywords

Business Analytics, Gig Economy, Uber, Swiggy, Workforce Demographics, Demand Forecasting, Platform Efficiency, Urban India

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 Abstract

Context: The efficacy, determination, and wellbeing of the healthcare personnel have a significant impact on the provision of healthcare. Both clinical and non-clinical hospital workers are essential to maintaining patient safety, high-quality services, and efficient operations. Nevertheless, despite growing workload demands and organizational difficulties, there are still few systematic evaluations of employee satisfaction, especially in mid-sized private hospitals. Goals: To evaluate the level of staff satisfaction at Gold Rush Hospital, to identify key factors influencing employee experience such as workload, teamwork, communication etc; and to provide data-driven recommendations for improving workforce engagement and hospital performance. Methodology: 35 hospital staff from both clinical and non-clinical departments participated in a cross-sectional descriptive research that was carried out utilizing purposive sampling. A five- point Likert scale-based structured questionnaire was used to gather data. Descriptive statistics, regression analysis, and correlation analysis were used in the statistical study to look at the connections between workplace characteristics and satisfaction. Finding: Overall, the mean score for employee satisfaction was moderate (M = 2.71, SD = 1.24). While safety (M = 2.68) and workload management indicated concerns, teamwork (M = 2.91) and communication (M = 2.91) shown comparatively greater satisfaction levels. Workload and satisfaction had a substantial negative link (r = -0.62), although there were favorable correlations with cooperation (r = 0.65), supervisory support (r = 0.58), and communication (r = 0.60). According to regression analysis, these factors accounted for 68% of the difference in employee satisfaction. Key issues identified included excessive woekload, inadequate recognition syatem. Conclusion: The study shows that a variety of organizational, administrative, and operational factors affect hospital worker satisfaction. To improve staff well-being and service quality, workload imbalance must be addressed, leadership practices must be strengthened, and recognition mechanisms must be improved. It is advised to use organized interventions and ongoing monitoring in order to develop a motivated and long-lasting healthcare personnel.

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 Keywords

Keywords: Staff Satisfaction, Healthcare Workforce, Employee Engagement, Workload, Teamwork, Hospital management, Organizational Performance.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The fast growth of Artificial Intelligence has made it possible to create highly realistic synthetic media known as deepfakes. These manipulated files may appear in the form of altered videos, cloned voices, or fully generated multimedia content. Although such technology can be useful in filmmaking, gaming, education, and accessibility tools, it also creates major risks when used for fraud, misinformation, identity theft, or public manipulation. Existing manual verification techniques are often slow and ineffective against advanced AI-generated content. This research paper introduces DeepShieldX, an intelligent framework designed to detect deepfakes in video, audio, and combined multimedia sources. The proposed model uses facial inconsistency analysis, motion pattern learning, voiceprint verification, spectrogram classification, and multimodal fusion techniques for accurate prediction. The framework is practical, scalable, and suitable for real-world deployment using open-source technologies. Experimental findings indicate that combining both audio and video signals provides stronger performance than using a single source alone.

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 Keywords

Deepfake Detection, Multimedia Security, Video Forgery, Audio Forgery, Artificial Intelligence, CNN, LSTM, Transformer.

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In today's data-centric business environment, effective data analysis plays a crucial role in enhancing decision-making and operational efficiency. This project presents a comprehensive study of Super Store Data Analysis using Amazon Web Services (AWS), which provides a scalable and cost-effective cloud computing platform. The system leverages Amazon S3 for secure and reliable data storage, AWS Glue for Extract, Transform, Load (ETL) operations, Amazon Redshift for high-performance data warehousing, and AWS QuickSight for advanced data visualization. The objective of this project is to analyze retail datasets to identify trends in sales, customer behavior, product performance, and regional demand patterns. The proposed solution ensures efficient data processing, reduced latency, and real-time analytics capabilities. Experimental observations demonstrate improved query performance, enhanced scalability, and reliable insights generation. This approach significantly improves business intelligence processes and supports strategic planning. The integration of cloud technologies provides flexibility, security, and performance advantages over traditional data processing systems, making it suitable for modern retail analytics applications.

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 Keywords

Business Intelligence, AWS, Super Store Data Analysis, Retail Analytics, ETL, Amazon S3, AWS QuickSight, Strategic Planning.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In today's volatile financial markets, investors are increasingly concerned not only about maximizing returns but also about managing the risks associated with their portfolios. This study examines strategic portfolio construction and riskadjusted performance evaluation using selected bluechip stocks listed on the National Stock Exchange (NSE) of India. Ten largecap stocks from banking, information technology, FMCG, telecom and infrastructure sectors were selected to build a diversified equity portfolio. Daily closing price data from 2014 to 2026 were collected from Yahoo Finance and analysed using Python for return calculation, risk measurement and portfolio simulation. The study adopts Modern Portfolio Theory (MPT) as its theoretical base and applies Monte Carlo simulation to generate a large number of portfolio combinations with different weight allocations. For each simulated portfolio, expected return, volatility, Sharpe Ratio, Sortino Ratio, Beta and Maximum Drawdown were computed to assess performance from a riskadjusted perspective. The results show that a welldiversified portfolio combining banking and IT stocks achieves an attractive balance between return and risk, with an annual expected return of around 18.9 per cent, volatility of 15.7 per cent and a Sharpe Ratio of 0.885. The optimal portfolio identified has a Beta slightly below one, indicating marginally lower volatility than the overall market, while still delivering competitive returns. The findings highlight the importance of diversification, systematic asset allocation and the use of quantitative tools in constructing efficient portfolios. The study also demonstrates how integration of Python for analytics and Power BI for visualization can make portfolio analysis more practical and decisionoriented for investors, analysts and students.

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 Keywords

Portfolio construction, riskadjusted performance, Modern Portfolio Theory, Monte Carlo simulation, Sharpe Ratio, NSE bluechip stock

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Creative Commons Attribution 4.0 and The Open Definition

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This paper will explore the connection between Corporate Social Responsibility (CSR) and organisational commitment, and specifically the mediating effect of Green Training and Development (GTD), which is an important dimension of Green Human Resource Management (GHRM). Based on the Social Exchange Theory and the Ability-Motivation-Opportunity (AMO) model, the research hypothesises that CSR activities lead to an increased perception of organisational support among employees, which subsequently increases their commitment, based on environmentally oriented training practices. A structured questionnaire was used to collect data from 487 service employees. The hypotheses were tested with the help of Structural Equation Modelling (SEM) with Smart PLS. The results indicate a strong positive relationship between CSR and organisational commitment, and that Green Training and Development mediates this relationship. The research paper is important to the literature in that it combines the CSR and GHRM viewpoints and provides practical implications for organisations looking to help improve employee commitment by using sustainable HR practices.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

CSR, Green HRM, Organisational commitment, Training and development

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Creative Commons Attribution 4.0 and The Open Definition