Volume 13 | Issue 3 |

 Abstract

This study investigates the impact of advertising on consumers purchase decisions and trust in over-the-counter (OTC) pain relief drugs. The research tests the hypothesis that advertising significantly influences consumers purchasing decisions and conducted a Chi-square analysis to support this. The findings revealed that advertising has a noteworthy positive impact on consumers' choices when it comes to buying OTC pain relief drugs. Additionally, the study examines the influence of advertising on consumer trust in OTC pain relief drugs. Another Chi square analysis demonstrated that advertising significantly affects consumer trust in these products. These results underscore the critical role that advertisements play in shaping both the purchasing decisions and trust levels of consumers regarding OTC pain relief medications.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Direct-to-consumer (DTC) advertising, Over-the-counter (OTC) pain relief products, Consumer decision-making, Brand awareness, Perceived efficacy, Product efficacy, Risks and side effects, Consumer perceptions, Ethical implications, public health, Consumer behavior.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The transition to electric vehicles (EVs) in the paratransit sector is a critical step toward achieving sustainable urban mobility in India. Paratransit, which includes auto-rickshaws, shared taxis, and other informal transport modes, serves as a primary means of transportation for millions. However, despite the environmental benefits and government incentives, the adoption rate of EVs among paratransit owners and drivers remains low. This study explores the key factors influencing their intention to adopt EVs, considering economic, technological, infrastructural, policy, and social determinants. High upfront costs, inadequate financing options, and limited awareness pose significant economic barriers, while concerns over battery performance, range anxiety, and inadequate charging infrastructure further hinder widespread adoption. Additionally, inconsistent government policies and a lack of clear incentives contribute to uncertainty among stakeholders. Through a comparative analysis, this study highlights both the challenges and enablers in the EV transition for paratransit operators. The findings emphasize the need for improved financial support, expanded charging infrastructure, and targeted awareness campaigns to enhance adoption rates. Addressing these challenges will not only accelerate EV adoption but also contribute to India's broader goals of reducing carbon emissions and promoting sustainable urban transport solutions.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Electric Vehicles, Paratransit, Sustainable Urban Mobility, EV Adoption Barriers, Charging Infrastructure, Policy Incentives, Battery Technology, Financial Challenges, India's Transport Sector, Carbon Emissions Reduction

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Electrical load forecasting plays a crucial role in the efficient operation, planning, and management of power distribution systems. This project focuses on forecasting the electrical load for a 33/11 kV substation using an Artificial Neural Network (ANN). Traditional load forecasting methods often struggle with complex, non-linear relationships in power consumption patterns. ANN, with its ability to learn and adapt, provides an effective solution for accurate short-term and long-term load prediction. The proposed model is trained using historical load data, weather conditions, seasonal variations, and other influencing factors. By leveraging machine learning techniques, the ANN model captures intricate patterns in power demand, enhancing prediction accuracy. This improved forecasting helps in reducing energy wastage, optimizing power distribution, and ensuring reliable power supply. The results of this project demonstrate the effectiveness of ANN in predicting load demand with high precision, thereby assisting power utilities in efficient decision-making, load management, and infrastructure planning. This study highlights the potential of artificial intelligence in modern power systems, providing a data-driven approach to enhance the reliability and efficiency of electrical networks.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial neural networks ,Electric power load forecasting, Machine learning, Mean square error, Mean absolute percentage error.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The integrated plagiarism detection system for text and image-based content in documents is designed to enhance the accuracy and efficiency of identifying duplicate or copied content across various formats. With the increasing accessibility of digital information, the risk of plagiarism has grown, making it essential to develop robust mechanisms that can analyse both textual and visual data. Traditional plagiarism detection tools focus primarily on textual similarities, leaving a gap in detecting image-based content that may also be duplicated or manipulated. This system addresses this limitation by incorporating advanced text-matching algorithms alongside image recognition techniques to comprehensively analyse documents. The proposed system utilizes natural language processing and deep learning methods to detect similarities in textual content, while computer vision techniques help identify duplicated or modified images. By integrating these approaches, the system ensures a more comprehensive evaluation of plagiarism in academic, research, and professional documents. The implementation of such a system is valuable for universities, publishers, and businesses aiming to maintain content originality and intellectual integrity. The integration of multi-modal detection methods improves the overall reliability of the system, reducing false positives and enhancing the accuracy of plagiarism reports. This approach provides an effective solution for modern plagiarism detection challenges, ensuring fair and ethical use of digital content.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Plagiarism detection, Image plagiarism, Online data sharing, Document sharing, Plagiarism checking software, AI-powered tools, Digital collaboration, Academic integrity, Content attribution, Comparative analysis.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract This study evaluates the risk-adjusted performance of various investment avenues, including cryptocurrencies, traditional assets, and stablecoins, using key financial metrics such as the Sharpe Ratio, Sortino Ratio, Jensen's Alpha, and Maximum Drawdown. Findings suggest that gold, HDGO, and AMCFG offer the best risk-adjusted returns, making them ideal for risk-averse investors. BNB and ETH show strong returns but exhibit higher volatility, while USDT performs poorly due to its negative Sharpe Ratio and high downside risk. The study highlights the need for portfolio diversification, incorporating both high-return assets and stability-focused investments. Future research should explore machine learning models and sentiment analysis to enhance predictive accuracy in dynamic markets.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Risk-adjusted performance, Investment avenues, Cryptocurrencies, Traditional assets, Stablecoins, Sharpe Ratio, Sortino Ratio, Jensen's Alpha, Maximum Drawdown Portfolio diversification Volatility Risk-averse investors High-return assets Stability-focused investments Machine learning models Sentiment analysis Predictive accuracy, Dynamic markets.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Plastic pollution poses a severe threat to marine ecosystems, necessitating automated detection methods for effective mitigation. This study presents a Hybrid YOLO-SAM Model designed for automated plastic bottle detection in aerial imagery, integrating YOLOv8 for object detection and Segment Anything Model (SAM) for segmentation. A custom dataset, derived from UAV-captured videos, was processed into 1,700 labeled frames, with 200 manually annotated for training. Our model achieve d 95.2% mAP for detection and high segmentation accuracy, outperforming standalone models. This work contributes to environmental monitoring by providing an efficient deep-learning framework for real-time plastic waste detection, facilitating large-scale ecological assessments

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

UAV imagery, YOLO-SAM, deep learning, plastic bottle detection, environmental monitoring, segmentation, computer vision.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Stress is an inevitable part of human life. It is also faced by the students in the studies which is called as Academic stress. There is a perception in today's society that female college students experience higher levels of stress than their male counterparts.The study investigates on academic stress between male and female undergraduate (UG) College students. The research assessing the level of academic stress faced by them in completion of syllabus at college and in other various works. A sample of 120 students (60 males & 60 females) was assessed using the Academic Stress Scale (ASS).The findings supported the null hypothesis, in which there is no significant difference in the academic stress of male and female college students but while assessing the various measures of academic stress there is a difference in calculated mean score

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords- Academic stress, academic anxiety, academic frustration, academic pressure, academic conflict, Male & Female UG college students.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Numerous worldwide analyses have pointed towards an increasing adoption of sustainable energy which includes wind and solar sources in the coming timeline. But with their use, variables such as their intermittency could create serious storage, EVs and grid issues. Thus, it will be crucial in our future designs to predict the solar and wind power availability accurately. Therefore, the objective of the work is creation of AI's based tool for predicting solar and wind energy output based on 10 years energy datasets. The dataset incorporates meteorological parameters such as relative humidity, temperature, wind speed, solar irradiance and surface pressure. These parameters highly affected energy based load curves and therefore can be included in the prediction models. To enhance performance, the proposed models use a Long Short-Term Memory (LSTM) network to learn the time dependencies in the time-series data and Convolutional Neural Network (CNN) to learn spatial patterns and feature-level patterns. Integrating these two types of networks to form LSTM-CNN architecture improves performance by increasing accuracy. The tool is evaluated using standard metrics such as Mean Squared Error (MSE) and Mean Absolute Error(MAE) translated into the units of measurement of the particular EV or microgrid.As a result of our work, energy management is improved which leads to strengthened grid reliability and puts more emphasis on assisting clean energy transition. This work sets the foundation for further research into artificial intelligent based approaches for renewable energy forecasting that could assist in such areas as energy policy, EV deployment, smart grids.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Renewable Energy Forecasting,Solar Energy Prediction, Wind Energy Prediction, AI-Based Energy Tool, LSTM-CNN Architecture, Machine Learning, Smart Grids, Energy Management, Deep Learning, Sustainable Energy Transition.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The COVID-19 pandemic has completely changed the nature of the educational landscape. As a result, teachers, students, policymakers, and other stakeholders faced various challenges and situations that required them to adjust to those situations, in which psychological factors have played a specific role. The study of student's academic success is the central point of the entire educational landscape. Understanding the factors that impact students' academic success in the present moment is crucial for gaining insight into the educational environment. The present research paper assesses the impact of Academic Self-Efficacy (ASE) on the academic achievement of secondary level students without any disruption after the pandemic. Academic self-efficacy is a core component of social cognitive theory, which refers to a person's belief in this ability to complete academic tasks and achieve desired educational goals. Descriptive survey methodology has been used for the present study. The study has been conducted on a sample of 300 students using the simple random sampling method. The result of the study reveals that it is very important to train students with the necessary psychological factors that tend to enhance students' Self-Efficacy such as self-regulating learning, time management, regulation, collaboration and help seeking, etc. It is clear from the study that ASE gives students the ability to maintain high educational achievement without being affected by negative situations. Therefore, in the current era, the education system needs to be planned in a way that helps in increasing the ASE of students.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Academic Self-Efficacy(ASE), Academic Achievement(AA), Post-Pandemic Era, Secondary level students

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Shoulder pain, breathing dysfunction, thoracic mobility, diaphragmatic movement, musculoskeletal pain.

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Creative Commons Attribution 4.0 and The Open Definition