Volume 14 | Issue 4 |

 Abstract

The significance of artificial intelligence is increasing in academia, particularly in India. With this, the awareness of AI is being called into question. There is a need to assess students' level of AI for adapting learning, identify the factors influencing their awareness, and investigate the challenges they face in using these tools. The purpose of this study is to examine the level of awareness among students about AI in education and its impact on students' creativity. This employed a quantitative approach, distributing a questionnaire both offline and online via Google Forms. A survey of undergraduate, postgraduate, and research scholars was conducted through offline and online Google forms to measure the perceptions of students toward the usage of Gen AI as a learning tool. Data were collected from 102 students using a structured five-point Likert scale and analyzed using descriptive statistics, including percentages and averages, and by applying the t-test, one-way ANOVA, and Levene's test. This study found that there is no significant difference among students on the basis of gender, age, or academic level. It showed a strong level of awareness of GenAI, including ChatGPT, among students in education, particularly for adaptive learning purposes. Despite providing valuable insight into students' perception of generative AI, the study had some limitations, indicating largely non-significant differences across gender, age, area, and academic level, suggesting limited variability in responses, which may reduce the accuracy of the statistical test to distinguish group variability. However, the study also identifies some major challenges, including data privacy, transparency, and trust issues, which create barriers to the adaptation of Gen AI among students in education.

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 Keywords

Generative AI, Natural Language Processing, Creativity, ChatGPT, Awareness

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Creative Commons Attribution 4.0 and The Open Definition

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The integration of Artificial Intelligence (AI) into educational platforms has transformed the traditional learning experience into a more personalized and efficient process. This paper presents the design and development of an AI-Integrated Learning Management System (LMS) that enhances teaching and learning through intelligent automation and data-driven insights. The system supports essential academic functionalities such as course management, assignment handling, quizzes, attendance tracking, and performance analysis. AI components analyze student behavior, identify learning patterns, and provide personalized recommendations to improve academic outcomes. Additionally, an AI-powered chatbot offers real-time support for academic queries and platform navigation. Predictive analytics helps in identifying at-risk students, enabling early intervention by instructors. The system is developed using React.js, Node.js, MongoDB, and Python-based AI services, ensuring scalability, security, and efficiency. The proposed LMS provides a smart, adaptive, and interactive learning environment suitable for modern educational institutions.

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 Keywords

Artificial Intelligence, Learning Management System, Personalized Learning, Chatbot, Predictive Analytics

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Gramin kshetra ke Ucch Madhyamik Istar ke Vidhyarthiyon ke Parivarik Watavran ka Unke Mansik Swasthya par Padhne wale Prabhav ka Adhyayan.???? ????-???? ???? ???? ???? ?? ????-???? ??? ???????? ??? ?? ??? ??? ????? ???? ??? ?? ????? ??? ?????? ?? ???????? ????? ?? ??? ???? ?? ? ?????? ?? ???????? ????? ?? ?????? ???? ??? ??? ???? ?? ?????? ???? ?? ? ?????? ?? ????? ??? ?????? ?? ????????? ???? ??? ???? ?????? ???????? ?? ???-??? ??????? ???? ??? ??? ???? ?? ????????? ???????? ????? ?? ????? ?? ?? ?? ????????? ???????? ?? ?????? ???? ?? ?? ??? ???? ????????? ???????? ????? ?? ????? ?? ?? ?? ????????? ???????? ?? ??????? ?? ?????? ???? ??? ???? ?? ????????? ????? ??? ?????? ?? ?????????? ?????? ???? ?? ? ???? ?? ?????? ????? ????????? ??????? ?? ??????? ???????? ???? ?? ? ?? ???? ?? ?????? ????????? ?? ??? ?????? ?? ??? ?????????? ?? ?????????? ????????? ???? ?? ? ???? ???? ??? ?? ???????? ??????? ?? ?? ???? ????? ???? ???? ????? ?? ?????? ???? ??????? ??? ???? ?? ?????? ?? ???? ??, ???? ?? ?????? ????? ?? ???? ?? ???? ??? ???? ??, ?? ??? ????? ?? ?????? ?? ??? ???? ????-????? ??????? ??????? ??? ?? ??????? ???? ?? ? ??????? ?? ????????? ?????? ?? ????? ???????? ??????? ????????? ?????? ????????? ?? ?????? ???? ??? ???????? ??? ???????? ????????? ??????? ?????? ??? ??????????? ?????????? ?? ?????? ?? ?? ??? ????????? ??????? ?????? ??? ????? ????? ?????????? ??? ?????? ??????? ?? ???? ???? ??? ?????? ?? ??????? ????????? , ????????, ????????? ?? ??????? ??????? ?????? ??????? ?? ??????? ???? ?? ??? ???? ?????? ???? ???

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Gramin kshetra ke Ucch Madhyamik Istar ke Vidhyarthiyon ke Parivarik Watavran ka Unke Mansik Swasthya par Padhne wale Prabhav ka Adhyayan.

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Mukhapaka (stomatitis) is an oral mucosal disorder characterized by pain, burning sensation, and ulceration, affecting daily activities and quality of life. Ayurveda recommends Kavala therapy for oral disorders, and Spatika Churna is described for its anti-inflammatory, healing, and astringent properties. This randomized controlled trial evaluated the efficacy of Spatika Churna Jala Kaval in patients with Mukhapaka. Patients were randomly divided into a trial group (Spatika Churna Jala Kaval) and a control group (conventional treatment). Assessment included subjective parameters like pain and burning sensation, and objective parameters like ulcer size and erythema. The trial group showed statistically significant improvement compared to the control group, indicating that Spatika Churna Jala Kaval is an effective, safe, and economical treatment for Mukhapaka.

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Mukhapaka, Stomatitis, Spatika Churna, Jala Kaval, Kavala, Ayurveda

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract: The pharmaceutical industry is one of the most energy-intensive industrial sectors due to its reliance on controlled environments, continuous production processes, and stringent regulatory standards. This research evaluates operational carbon emissions in pharmaceutical industries located in Aurangabad, India, with a focus on identifying key decarbonization challenges. The study adopts the Greenhouse Gas (GHG) Protocol framework to classify emissions into Scope 1, Scope 2, and Scope 3 categories and analyzes emissions across site, building, and process levels. Three industrial clusters--Waluj, Shendra, and Chikalthana MIDC--are selected as case studies representing large, medium, and small-scale industries respectively. The findings reveal that Scope 2 emissions dominate due to electricity consumption from HVAC systems and cleanroom operations, while Scope 1 emissions arise from fossil fuel-based utilities. Hotspot analysis identifies HVAC systems, granulation processes, and utilities as major contributors. Benchmarking results show that small-scale industries exhibit the highest emission intensity due to inefficient infrastructure, while large-scale industries perform relatively better but remain dependent on grid electricity

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Decarbonization; Pharmaceutical Industry; Operational Carbon; HVAC Systems; Benchmarking; Emission Hotspots.

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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This paper explores the idea of the ideal state as articulated in Dastan-i Ram u Sita, the Persian adaptation of the Ramayana composed by the seventeenth-century scholar Mulla Masih Panipati during the reign of the Mughal emperor Jahangir. Focusing on Panipati's portrayal of Ramrajya, this study argues that the text presents a Mughal vision of ideal governance, one that resonates closely with Indo-Persian political thought, rather than with Hindu theological doctrine alone. In this Persian retelling, Ramrajya is not imagined as a mythical or purely divine realm, but as a site of ethico-moral order grounded in justice, restraint, and righteous kingship. This paper demonstrates that Panipati reinterprets the concept of Ramrajya through Islamic ethical values such as justice (?adl), trust (amanat), patience (sabr), and submission to divine will. Lord Rama is portrayed not only as an incarnation of Vishnu but also as a divinely guided ruler whose authority rests on moral conduct and responsibility toward his subjects. This vision of kingship closely parallels Mughal political ideals, in which the stability of the state depended upon the ruler's ethical discipline and commitment to justice. The ideal state described in the text thus resembles the Islamic conception of Dar ul-Islam, a realm characterised by peace, moral order, and just governance under a righteous sovereign. By analysing Ramrajya in Panipati's Persian Ramayana, this paper highlights how literary adaptation functions as a medium of political reflection in Mughal India. It suggests that Dastan-i Ram u Sita offers not only a reinterpretation of an ancient Indian epic but also a subtle articulation of Mughal ideals of statecraft, authority, and moral order.

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 Keywords

Dar ul-Islam, Dastan-i Ram u Sita, justice, kingship, Mughal political thought, Mulla Masih Panipati, Persian, Ramayana, Ramrajya

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Creative Commons Attribution 4.0 and The Open Definition

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Nowadays, most of our daily activities like banking, shopping, and communication happen online. Because of this, protecting user accounts has become very important. Many systems still depend only on passwords or fixed multi-factor authentication, which are not always secure and sometimes make the login process difficult for users. So, the attackers can easily access it without the user knowledge. In this project, we propose an Adaptive Multi-Factor Authentication system that works based on the risk level of each login attempt. Instead of asking the same verification every time, the system checks details like user location, device, and IP address and the behaviour of the user. Based on this, it decides whether to allow access directly or ask for extra verification such as OTP, email verification. We have created a web application by testing the real time actions. From the results, it is clear that this approach improves both security and user experience in a better way and different from compared to traditional methods.

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Creative Commons Attribution 4.0 and The Open Definition

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Adaptive authentication, Multi-Factor Authentication (MFA), Cloud Security, Risk-Based Authentication, Secure Cloud Authentication, User Risk Assessment, OTP (One-Time Password)

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Creative Commons Attribution 4.0 and The Open Definition

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Continuous Integration and Continuous Deployment (CI/CD) pipelines have become a fundamental component of modern software development practices, enabling rapid development, testing, and deployment of applications. However, as software projects grow in complexity and scale, CI/CD pipelines often encounter performance bottlenecks, increased build times, resource inefficiencies, and higher failure rates. These challenges can slow down development cycles and negatively impact software delivery efficiency. This research paper investigates the optimization of CI/CD pipelines using data-driven analytics to improve pipeline performance and reliability. The study proposes an analytical framework that collects and analyzes pipeline execution metrics such as build duration, test execution time, failure frequency, resource utilization, and deployment success rates. By applying statistical analysis and monitoring tools, the framework identifies performance bottlenecks and inefficiencies within different stages of the CI/CD pipeline. Based on the insights obtained from the analytics process, optimization strategies such as parallel execution of tasks, intelligent caching, resource allocation adjustments, and automated failure detection are implemented to enhance pipeline performance. A prototype CI/CD environment was developed using modern DevOps tools including Git- based version control, automated build systems, containerization technologies, and monitoring frameworks. Experimental evaluation demonstrates that the proposed analytics-driven optimization approach significantly reduces pipeline execution time, improves build reliability, and enhances overall deployment efficiency. The results show measurable improvements in development productivity and system performance. The findings of this research highlight the importance of integrating analytics into CI/CD workflows to enable continuous monitoring and data-driven decision making. The proposed approach provides practical guidelines for software engineering teams seeking to improve pipeline efficiency and accelerate software delivery in modern DevOps environments

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CI/CD Pipeline, DevOps, Continuous Integration, Continuous Deployment, Pipeline Optimization, Build Automation, Data Analytics, Software Delivery, Performance Monitoring, DevOps Tools

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Creative Commons Attribution 4.0 and The Open Definition

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In developing countries, the issue of accessibility to healthcare is serious because of language diversity, the lack of medical workers, disorders in the emergency system, and the high cost of treatment. This study presents a Multilingual Artificial Intelligence-Based Healthcare Support System that is meant to improve the initial disease detection process, enhance healthcare information, organize emergency response, and facilitate telemedicine. The system has a multilingual Natural Language Processing (NLP) system that enables users to communicate in English, Hindi, and Marathi both through text and voice. Its disease prediction model uses an ensemble machine learning model based on a combination of the Random Forest, Support Vector machine, and Deep Neural Network models, and is trained on a dataset consisting of 773 diseases, 377 symptoms. The system was implemented with a microservice-based architecture (Python and FastAPI) and containerized with Docker and Kubernetes, which guarantees its scalability, reliability, and high availability. According to experimental results, the ensemble model can predict with an accuracy of 88.2 percent and a high F1-score, in addition to system-level testing showing that the ensemble model can operate steadily even with more than 100,000 simultaneous users, response time of less than 400 ms, and multilingual processing accuracy of over 92 percent. This suggested platform is a scalable and viable solution to deliver early medical advice, emergency management, and remote medical care, especially in resource-limited settings.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

AI in healthcare, Multilingual NLP, Disease Detection, Telemedicine, Emergency Response (Technologies), Healthcare Accessibility, Digital Health platform.

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Creative Commons Attribution 4.0 and The Open Definition