Volume 12 | Issue 11 |

 Abstract

The Indian government's vision to transform the nation into a developed economy by 2047, encapsulated in the Viksit Bharat 2047 Plan, can be significantly advanced through Artificial Intelligence (AI). This paper explores AI's transformative impact across key sectors, including digital governance, education, agriculture, and public welfare. Notable applications such as Digiyatra streamline aviation processes, while AI-driven platforms enhance educational inclusivity and provide real-time translation, mitigating geographical and linguistic barriers. Sector-specific tools like KissanGPT support agriculture by delivering critical information to farmers, and PolicyGPT simplifies complex health insurance policies for consumers. Government initiatives, including Pradhan Mantri Garib Kalyan Anna Yojana and the "One Nation One Ration Card" scheme, demonstrate AI's effectiveness in improving service delivery and managing resources efficiently. Additionally, the paper addresses the importance of bridging the skill gap to prepare the workforce for an AI-driven job market, highlighting projections of a 40% increase in AI and machine learning roles by 2027 from the World Economic Forum. By aligning technological advancements with national development goals, AI is positioned as a crucial element in realizing the Viksit Bharat vision by 2047, as emphasized by Prime Minister Narendra Modi.

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 Keywords

AI Integration, Innovation Ecosystem, AI Ethics, Digital Economy, Smart Cities.

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Advancements in herbal formulations, utilizing advanced delivery systems like polymeric nanoparticles, liposomes, phytosomes, and transfersomes, have significantly improved the effectiveness of plant-based medicines. These novel systems enhance solubility, bioavailability, and stability, protect against toxicity and degradation, and provide sustained release and better pharmacological effects. A key innovation is Phytosome technology, which combines phospholipids with plant extracts to create lipid-compatible complexes that boost bioavailability and stability. Recent research highlights the advantages of modern drug delivery systems (NDDS) in herbal medicine, showcasing improved therapeutic outcomes compared to traditional methods. These systems, including liposomes and solid lipid nanoparticles, enhance absorption and bioavailability. While challenges in extraction and standardization of herbal medicines have previously hindered innovation, the rise of herbal excipients--affordable, biodegradable, and stable plant-derived compounds--has enabled more effective delivery systems. These innovations are particularly beneficial in addressing common health issues and enhancing treatments, including more effective anticancer therapies, underscoring the potential of herbal medicines in modern medicine.

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INNOVATIVE HERBAL FORMULATION TECHNOLOGIES, HERBAL COMPOUND NANOENCAPSULATION,PRECISE PHYTOCHEMICAL TARGETED DELIVERY , INTELLIGENT HERBAL DRUG CARRIERS, SUSTAINED RELEASE SYSTEM FOR HERBAL MEDICINES

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Technology benefited us in every aspect of our life right from communication to education. New methods of teaching have been introduced which is known as smart board technology. In this aspect the present study holds importance. The objectives of the study were to find whether there is any significant difference in the perception towards usage of smart boards among male and female teacher educators and teacher educators belonging to private and government Institutions. Samples of 150 Teacher educators belonging to B.Ed colleges, Karnataka during the academic year 2024-2025 were selected. Data pertaining to the usage of smart boards were collected through stratified random sampling techniques through survey method. The tool used for the present study was smart board scale developed by Manjunath (2022) was adopted. The researcher visited the B.Ed colleges personally and it order to collect the data. The tools were administered to the selected samples under normal conditions. The separate variance model of t-test was used for testing the hypotheses for the significance of mean difference in the usage of smart boards' scores of various groups was compared. Findings revealed that Male and female teacher educators differ statistically in their perception towards usage of smart boards. Comparing to the mean values of male teacher educators [1.4492] is greater than female teacher educators [1.3870]. Hence male teacher educators have more perception towards usage of smart boards. Teacher educators belonging to government and private Institutions differ statistically in their perception towards usage of smart boards. Comparing to the mean values of teacher educators belonging to Private Institutions (1.4254) is greater than that of teacher educators belonging to government Institutions (1.3886). Hence teacher educators belonging to private Institutions has more perception towards usage of smart boards.

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 Keywords

Teacher Educators, Technology, Smart boards, ICT

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This project focuses on creating a predictive model for soil nutrient content by integrating MIR (Mid-Infrared) spectroscopy with advanced machine learning techniques. The objective is to enhance the accuracy, scalability, and affordability of soil nutrient analysis, which is pivotal for precision agriculture. This integration promises to provide a real-time, accessible solution that addresses existing gaps in traditional soil testing and contributes to sustainable farming practices.

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 Keywords

Predictive Modeling, Soil Nutrient, MIR Spectroscopy

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Creative Commons Attribution 4.0 and The Open Definition

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The global healthcare system is currently grappling with critical challenges, primarily due to a shortage of skilled medical professionals and an uneven distribution of medical resources. This deficiency is exacerbated in underdeveloped regions, leading to delayed diagnoses and treatments, as well as elevated mortality rates. High-profile crises, such as the COVID-19 pandemic, highlighted these systemic issues, demonstrating the urgent need for a more robust and equitable healthcare framework. To mitigate these challenges, the integration of Artificial Intelligence (AI) and Machine Learning (ML) into healthcare systems is proposed. The global healthcare AI market is expected to reach $188 billion by 2030, addressing the anticipated shortfall of nearly 10 million healthcare workers worldwide. AI offers significant potential benefits, including improved patient outcomes, reduced healthcare costs, and enhanced public health management. By leveraging advanced technologies, the healthcare sector can better navigate the evolving demands and complexities of modern medicine.

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Artificial Intelligence, Machine Learning, Medicine

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Cancer is the second most common cause of death in US accounting for 1 in 4 deaths.Cancer is a condition in which some of the body cells uncontrollably grow and spread all over the body.It can start anywhere in the body.Generally chemotherapy along with the cancer agents are used to reduce the growth of malignant cells.This therapy may be used to cure and produce palliative relief to patient and increase the quality of patient life.The importance of chemotherapy for cure of cancer is increasing especially with its use as adjuvants to local therapy. Cancer is the uncontrollable growth of cells that are abnormal everywhere in the body- cancer cell. It is also called as the malignancy. The causative agents are - chemicals, toxic compounds , exposures, ionizing radiation and some pathogens.Most of the cancers forms tumors but not all the tumors are cancerous. Cancer is treated by different types of chemotherapies, radiation and surgery. General terms are : benign and malignant.whereas the benign are non cancerous tumors and malignant s are cancerous tumors.

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Chemotherapy, cancer treatment, oncology, pharmacology, antineoplastic therapy

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Social media has emerged as a dominant force in adolescents' lives, significantly influencing how they perceive themselves and interact with others. This paper explores the dual impact of social media on adolescents' social identity and emotional well-being. While it fosters self-expression, connectivity, and inclusivity, it also contributes to challenges such as social comparison, cyberbullying, and mental health issues. Using a mixed-methods approach, the study examines the nuances of this relationship, offering recommendations to leverage the positive aspects of social media while minimizing its risks.

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Social media, Adolescents, Social identity, Emotional well-being, Cyberbullying, Self-expression

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Creative Commons Attribution 4.0 and The Open Definition

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In today's dynamic work environments, the management of complex projects involving diverse stakeholders--employees, managers, and clients--presents considerable challenges. Traditional project management tools often exhibit limitations in real-time adaptability, intelligent task prioritization, and seamless transparency. These deficiencies lead to inefficiencies, communication breakdowns, and project delays. Additionally, the inability to effectively automate workflows exacerbates issues such as bottlenecks, missed deadlines, and employee burnout. This paper addresses the need for a more sophisticated project management solution that incorporates artificial intelligence (AI) and machine learning (ML) technologies. By integrating AI/ML, the proposed system aims to enhance real-time decision-making, automate task assignments based on priority, and foster greater transparency and collaboration among all stakeholders.

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Project automation, AI/ML, task assignment, workflow creation, progress tracking, transparency, productivity, real-time communication.

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Technology has advanced rapidly in recent years. This has been a godsend for the people, making their lives easier. The advancement of technology has a significant impact on students. This has revolutionised the way students learn, providing them with access to vast amounts of information and resources. Remote study is now possible for anyone who has a digital device like a mobile or a laptop and an internet connection. While technology has many benefits for students, it can also be a major source of distraction. The constant influx of notifications and updates might make concentrating on activities and assignments difficult. When working on a laptop, multitasking is simple, such as flipping between tabs for different purposes or opening undesired applications during productive time. This takes up most of the student's considerable time, which could be better spent studying and learning. To address the challenge of distractions and disengagement in online learning, we propose a gamified educational web application that combines video lessons with interactive quizzes. Our platform is designed to enhance student focus, motivation, and retention by incorporating game-like elements such as rewards, progress tracking, and leaderboards. This solution aims to change how students engage with online educational content by creating a more interesting learning experience. By embedding quizzes within the video content and rewarding focused engagement, the application keeps learners actively involved and motivated throughout their educational journey. This innovative approach is aimed at improving focus, retention, and overall academic performance, offering a dynamic learning environment that not only helps students manage distractions but also enhances the quality of online education. The solution is poised to benefit students, educators, and institutions alike, potentially reshaping the future of e-learning. Our approach with this gamified educational platform is significant because it addresses a key issue that many existing online learning solutions fail to tackle: student engagement and focus. Traditional e-learning platforms provide access to content but often overlook how students interact with that content, especially when they are bombarded with distractions. By embedding quizzes directly within the video lessons, we ensure that students stay actively involved rather than passively consuming information. This solution has the potential to extend its impact beyond individual students, transforming how educational institutions deliver online content by making it more interactive, engaging, and tailored to student needs.

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technology advancement, online learning, remote study, distractions, multitasking, gamified, interactive quizzes, retention, active learning, e-learning, student-centred education

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Skin diseases pose a significant health concern worldwide, affecting millions of individuals. The accurate and timely diagnosis of these conditions is critical for effective treatment. This project presents a robust solution for skin disease classification using deep learning techniques, specifically the VGG16 architecture, implemented in MATLAB. The primary objective of this research is to develop a highly accurate and efficient model for the automated classification of skin diseases. The dataset used in this project is composed of five distinct classes of skin diseases, including Acne-cystic acne, biting fleas, diabetic blisters, spider bites, and vitiligo. Each class is carefully curated to represent a wide range of skin conditions, making the model versatile and capable of handling various dermatological challenges. The VGG16 architecture, a well-established convolutional neural network (CNN) model, is employed for its remarkable feature extraction capabilities. Transfer learning is applied to fine-tune the pre-trained VGG16 model on the skin disease dataset. The model is trained, validated, and tested using a rigorous cross-validation approach to ensure its reliability. One of the standout achievements of this project is the exceptional classification accuracy obtained. The model demonstrates an impressive accuracy of 98.08%, signifying its effectiveness in accurately identifying and classifying skin diseases. This high accuracy rate is crucial in reducing misdiagnoses and enhancing the overall quality of patient care. In addition to its high accuracy, the proposed system also offers real-time skin disease classification, making it a valuable tool for medical professionals and dermatologists. The user-friendly interface developed in MATLAB ensures ease of use and accessibility, allowing healthcare practitioners to make informed decisions swiftly and accurately. In summary, this project presents a comprehensive approach to skin disease classification using deep learning techniques, with a focus on the VGG16 architecture. The achieved accuracy of 98.08% demonstrates the model's capability to accurately classify various skin diseases, thus aiding in early diagnosis and effective treatment. This research contributes to the advancement Generalization to Real-World Settings: Models that perform well in controlled research settings may not always generalize effectively to real-world clinical environments. Differences in lighting, camera quality, and patient demographics can all affect the performance of skin disease classification models. Robust validation on diverse, real-world datasets is essential for successful deployment in clinical practice(ar5iv). Future Directions and Clinical Applications As deep learning models continue to evolve, several promising directions are emerging in skin disease classification research: Mobile Health Applications: With the increasing availability of smartphones equipped with high-resolution cameras, there is growing interest in developing mobile applications for skin disease detection. These apps can allow users to capture images of skin lesions and receive instant analysis, making early detection more accessible to the general public(SpringerOpen).Teledermatology: AI-powered teledermatology platforms are being developed to diagnose remote skin disease, particularly in underserved areas. By integrating deep learning models with telemedicine platforms, healthcare providers can offer more timely and efficient care to patients who may not have access to in-person dermatological services(SpringerLink)(ar5iv). Improved Model Interpretability: Future research will likely focus on enhancing the explainability of deep learning models to increase their acceptance among clinicians. Techniques like Grad-CAM (Gradient-weighted Class Activation Mapping) can help visualize which parts of the image influenced the model's decision, providing insights into the underlying reasoning behind each prediction(ar5iv) Key Words: Skin disease, Convolutional Neural Network, Random Forest, Feature Extraction, Deep Learning.

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abstract you will find

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