Volume 13 | Issue 9 |

 Abstract

Digital marketing has revolutionized the way businesses interact with consumers, particularly among younger, tech-savvy demographics such as college students. This study aims to investigate the impact of digital marketing on the consumer buying behaviour of college students in Kerala. The specific objectives include examining the influence of social media platforms on purchase decisions, assessing the role of online advertisements in shaping brand preference, analyzing the relationship between digital marketing strategies and customer loyalty, and evaluating consumer trust and satisfaction in online purchases. Using a structured questionnaire, primary data will be collected from students across various colleges in Kerala. The data will be analyzed using statistical tools such as correlation and regression analysis to test the hypotheses, including the null hypothesis (H?) that there is no significant relationship between digital marketing strategies and consumer buying behaviour, and the alternative hypothesis (H?) that social media marketing has a significant positive impact on purchase decisions. The findings are expected to provide valuable insights for marketers and educational institutions on leveraging digital channels effectively to engage with student consumers, enhance brand loyalty, and drive informed purchasing behaviour.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Digital Marketing, Consumer Behaviour, Social Media, Online Advertising, Brand Loyalty, College Students, Kerala.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Chattisgarh rajya ke pramukh parytan evam dharmik sthal

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Chattisgarh rajya ke pramukh parytan evam dharmik sthal

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Continuous monitoring of vital parameters is essen- tial in modern healthcare, particularly for patients with chronic illnesses and elderly populations. Conventional wearable devices typically operate at fixed sampling rates, resulting in unnecessary energy consumption and reduced device lifetime. This paper presents a Python-based simulation framework for dynamic mode switching in health monitoring embedded systems. The proposed approach employs a Finite State Machine (FSM) to adaptively transition between Normal, Alert, and Critical states based on variations in heart rate (HR) and blood oxygen saturation (SpO2). The framework was implemented using open- source Python libraries (NumPy, SciPy, and Matplotlib) for signal generation, threshold detection, and real-time visualization. Simulation results demonstrate that the system conserves energy during stable conditions while ensuring responsiveness during abnormal events. Unlike prior studies that rely heavily on MATLAB/Simulink, this work highlights Python as a cost- effective and flexible platform for rapid prototyping of embedded healthcare solutions, with strong scalability toward IoT integra- tion and predictive analytics.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Health Monitoring, Embedded Systems, Dy- namic Mode Switching, Python Simulation, Finite State Machine, SpO2, Heart Rate, Energy-efficient Healthcare.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The integration of Artificial Intelligence (AI) in education presents transformative opportunities and significant challenges. AI technologies, such as adaptive learning systems, intelligent tutoring, and automated grading, offer personalized learning experiences, enhanced engagement, and improved efficiency in educational settings. These innovations enable educators to tailor instruction to individual student needs, facilitate real-time feedback, and reduce administrative burdens, thereby fostering a more effective and inclusive learning environment. However, the adoption of AI in education also raises critical challenges, including data privacy concerns, ethical considerations, and the potential for exacerbating educational inequalities. Additionally, there is a need for substantial teacher training and infrastructural support to effectively implement AI-driven tools. This paper explores the dual aspects of AI integration in education by examining its potential to revolutionize teaching and learning while addressing the ethical, practical, and technological hurdles that must be overcome to realize its full potential.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Intelligent Tutoring Systems, Personalized Learning Educational Technology, Virtual Classrooms, Data Privacy, Ethical Considerations, Educational Inequality.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The persistent challenge of non-performing assets (NPAs) has had a profound impact on the financial stability and profitability of the Indian banking sector. This study undertakes a comparative analysis of NPAs and key performance indicators--net profit margin (NPM) and net interest margin (NIM) between selected public sector banks (SBI, UBI, Canara Bank) and private sector banks (ICICI, HDFC, Axis Bank) over the five-year period from FY 2021 to FY 2025. The results reveal that private sector banks consistently outperform public sector banks in terms of NPM and NIM, with significant statistical differences across most years. While public sector banks have shown improvement in managing NPAs, the gap in asset quality remains significant in the later years of the study. Furthermore, regression findings confirm a strong negative relationship between NPAs and both NPM and NIM, underscoring the critical role of asset quality in financial performance.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Non-Performing Assets (NPAs), Public Sector Banks, Private Sector Banks, Net Profit Margin, Net Interest Margin, Financial Performance

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Economic inequality continues to shape the trajectory of national and global development. The Kuznets Curve hypothesis, introduced by Simon Kuznets in 1955, suggests an inverted-U relationship between inequality and economic growth. In early stages, inequality tends to rise as industrialisation favours capital-intensive sectors. However, beyond a certain income threshold, improved education, democratic redistribution, and labour participation contribute to an eventual decline. While this model remains influential, it often overlooks micro-level interventions that may influence the inflection and trajectory of inequality--especially among socially marginalised populations.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Inequality in education

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Among the important and essential principles of Green Chemistry Catalysis is the most valuable one. Green chemistry is the application of a set of principles that eradicates or minimizes the use or formation of hazardous materials in the formulation, execution and utilization of chemical products and emphasizes vulnerability minimization as the desired quality while developing new synthetic protocols. In recent years, nanotechnology, specifically nanocatalysis has become an emerging field of science due to its higher activity, selectivity and productivity as compared to conventional catalysts. Due to the exclusive structural and electronic characteristics of the nano materials and the exceptionally large surface area to volume ratio impart unique catalytic properties to nano particles. In this article we will be discussing the latest advances in the field of nano-catalysis and surfactant mediated generation of Nano Pockets which have opened new ways to explore synthetic methodologies for generation of novel functional materials.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Green Chemistry, Atom Economy, Nano Catalysis, Aqueous Solution Organic Synthesis, Surfactant Combined Catalyst

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Accurate classification of petrological samples is fundamental to geological investigations and resource exploration. Traditional methods relying on manual lithographic analysis are time-consuming and subject to expert bias. This study presents a novel deep learning framework for automated classification of Lithographical thin-section images. Utilizing convolutional neural networks (CNNs), the model was trained on a curated dataset encompassing diverse rock types, including igneous, sedimentary, and metamorphic classes. Data augmentation and transfer learning techniques enhanced model generalizability and performance. The proposed system achieved an overall classification accuracy exceeding 90%, outperforming conventional machine learning baselines. The results highlight the potential of deep learning to streamline petrological workflows, reduce human error, and enable large-scale geological mapping. Future work aims to integrate geochemical data and expand the model's applicability to field-acquired samples

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Accuracy, Convolution, Deep Learning, Image, Lithography, Neural Network.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Beti Bachao, Beti Padhao (BBBP) scheme, initiated by the Government of India in January 2015, is a flagship national program designed to address two critical issues: the Declining child sex ratio (CSR) and the promotion of education and empowerment of the Girl child. The scheme adopts a convergent approach, integrating the efforts of the Ministry Of Women and Child Development, Ministry of Health and Family Welfare, and Ministry Of Education. Despite extensive publicity through mass media campaigns, community Meetings, and school programs, studies across India reveal that awareness levels and Accurate understanding of the scheme's objectives vary considerably, particularly in rural And socio-economically disadvantaged regions. In the context of Bihar, where sociocultural norms, literacy rates, and economic constraints influence women's empowerment And gender equality, it becomes imperative to assess the community's knowledge Regarding BBBP and identify the socio-demographic factors that influence it.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Beti Bachao Beti Padhao, knowledge assessment, rural community, sociodemographic factors, awareness programs, Bihar, gender equality, girl child education.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This Liver cancer remains one of the most significant causes of cancer-related mortality globally, necessitating fast and accurate diagnosis. Traditional diagnostic approaches, such as biopsies and imaging, are often invasive and time-consuming. Deep learning has emerged as a promising solution in the medical imaging domain due to its ability to automate feature extraction and deliver high accuracy. This study focuses on using Convolutional Neural Networks (CNNs) and other deep learning models to detect liver cancer from CT and MRI scans. The proposed framework incorporates feature extraction, classification, and segmentation to enhance diagnostic precision. Experimental results show that the deep learning models outperform traditional machine learning techniques in metrics such as accuracy, recall, and F1-score. The findings support the integration of AI-based systems to assist radiologists in early detection and improve patient outcomes.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

CNNs, Liver Cancer, Deep Learning, CT and MRI Segmentation.

  License

Creative Commons Attribution 4.0 and The Open Definition