Volume 13 | Issue 7 |

 Abstract

Festivals provide core activities that build on or are displayed in a frame of local culture and heritage. An identity is who or what a person or thing is. Our identity defines who we are. Culture is a set of norms and values that we may not even know we have because we learn them as part of growing up in a group that shares them. Identity includes culture and many other personal things about us. Festivals are a vibrant expression of a community's cultural heritage. Festivals foster a sense of unity and belonging among people. By celebrating unique cultural traditions, promoting unity, festivals contributes in the formation of a community's identity. Racially the Karbis belong to the Mongoloid group and linguistically they belong to the Tibeto-Burman group. Those Karbis residing in the plains of Kamrup and Morigaon district of Assam, and the Ri-Bhoi districts of Meghalaya identify themselves as the Dumrali or plains Karbis. Domahi is not only a festival or just an event of the Karbi community but it is rather a sentiment and an emotion for the Karbi folks. Domahi is the most colourful festivals where the community enjoys to their heart's content.

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 Keywords

Festivals, Identity, Belonging, Karbi, Domahi, Orality, Cultural diversity, Heritage.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study investigates the various challenges faced by dairy product manufacturers in Coimbatore city, a prominent hub for dairy production in Tamil Nadu. The research identifies key issues such as fluctuations in raw milk availability, high input costs, lack of advanced processing technology, inadequate cold chain infrastructure, and increasing competition from large-scale and multinational brands. Regulatory hurdles, workforce shortages, and shifting consumer preferences towards plant-based and health-conscious products further compound the challenges. The study utilizes primary data from local dairy units and secondary sources including government and industry reports.

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Advanced processing technology

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The present work was conducted at two tourist destinations i.e., Prayagraj and Ayodhya districts of Uttar Pradesh. This work aims to find out the price competitiveness and tourism experience. This study is based on primary data using a sample of 100 tourists on 10 different factors of price competitiveness and tourism experience together. The Price competitiveness in lodging, in restaurants, goods in stores, sports and recreation and Tourism experience in sightseeing, natural beauty, closeness to nature, cultural heritage and tranquil atmosphere were listed in questionnaire. Data obtained from tourist response for price competitiveness and tourism experience in Prayagraj and Ayodhya were analyzed and compared. According to the data it was observed that Tourists were found more satisfied with the price competitiveness in respect to restaurants, to lodging, to transport, to goods in stores and to price competitiveness in sports and recreation in Prayagraj than in Ayodhya.

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 Keywords

Tourism, destinations, competitiveness, tranquil atmosphere, lodging, price competitiveness

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 Abstract

With the rapid expansion of online marketplaces, providing users with tailored product recommendations has become critical. This study introduces a refined collaborative filtering model that leverages distributed big data technologies to enhance accuracy and scalability. By integrating optimized similarity computations with high-volume processing frameworks, the system mitigates data sparsity challenges and improves performance. Experimental evaluations demonstrate substantial gains over traditional recommendation systems in both precision and recall metrics.

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 Keywords

Index Terms--Product recommendation, collaborative filtering, big data, machine learning, user-item interaction, e-commerce.

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 Abstract

Modern sciences adopted the tool of Reductionism and Rene Descartes has been the most foundational thinker of Mechanistic Reductionism which founded a much bigger theoretical perspective of Newtonian mechanics in the next century. Rene Descartes laid the foundation by arguing that animals could be regarded as machines in some way and it was Newtonian mechanics which presented the laws of mechanical motion. Human quest for reductionist and unchangeable reality has given birth to the present gene centric fetishism biology. Present gene centric biological understanding has failed in understanding living organisms. Living organisms and living systems cannot be reduced to the orders of their genes and they are active agents with the capability to adjust to their environment.

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Liberation of Metaphysics, Science and Consciousness from Reductionism and Mechanistic paradigm

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 Abstract

In today's digitally driven education landscape, the availability of accessible learning resources is crucial for ensuring equitable participation among students of all abilities. Visually impaired learners, however, continue to encounter notable chal- lenges in engaging with digital content, which often restricts their academic progress and overall learning experience. Assistive technologies such as screen readers offer critical support by translating on-screen text, images, and interfaces into speech or Braille output. These tools help users with visual impairments interact with educational platforms and content with greater independence. One of the most widely recognized tools in this space is NonVisual Desktop Access (NVDA), a free and open-source screen reader designed for Microsoft Windows. NVDA stands out for its flexibility, ease of use, and strong community-based development, which continuously enhances its features. Unlike commercial alternatives that may be cost-prohibitive, NVDA's accessibility and adaptability make it particularly valuable in educational settings with limited technological resources. This study provides a detailed assessment of NVDA's function- ality, focusing on its utility in academic environments. It explores the software's performance in carrying out routine educational tasks such as navigating web pages, reading documents, and accessing learning management systems. The evaluation also includes a comparative analysis between NVDA and proprietary screen reading solutions, highlighting both its advantages and areas needing improvement. The findings suggest that NVDA is an effective and inclusive digital tool that significantly contributes to the empowerment of visually impaired students in modern education.

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 Keywords

NonVisual Desktop Access (NVDA), assistive technology, visual impairment, accessible e-learning, inclusive digital education, screen reading softwareassistive software

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 Abstract

Finding the right hospital can feel overwhelming, especially when you're dealing with a serious health issue or need specialized treatment. This project introduces a smart hospital recommendation system that uses machine learning to make the process easier, faster, and more personal. The system looks at a variety of important factors, like what other patients have said in reviews, hospital ratings, your location, treatment costs, the expertise of doctors, and what services are available. Then, it uses advanced algorithms to suggest hospitals that best match what you're looking for. What makes it even smarter is how it uses Natural Language Processing (NLP) to actually understand patient reviews. It can pick up on what people liked or didn't like about their hospital experience, and use that information to help others make better choices. In the end, this system is about helping people find the care they need without all the stress. It gives patients useful, personalized options and also helps reduce the workload for doctors and healthcare staff by automating part of the referral process, keywords: machine learning, and a clustering model

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 Keywords

Machine Learning Healthcare Analytics Recommender System Medical Decision Support Health Informatics Patient-Centered Care Smart Healthcare Machine Learning Specific: Supervised Learning Unsupervised Learning Classification Clustering

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 Abstract

The construction sector is quickly growing, and new methods are being used to enable quicker and more effective work in the field. In this industry, concrete is a key building material, but producing it consumes a lot of natural resources, which can be expensive and unsustainable in the long run. It is crucial to either recover these natural resources or discover different solutions that can lessen the issue in order to handle these difficulties. Currently, waste foundry sand--a by-product of the metal casting industry--is contributing to several environmental issues. However, utilising this trash in the construction of buildings may help to lessen the environmental impact. We can lessen the construction industry's negative environmental effects and advance sustainability by figuring out how to integrate leftover foundry sand into building materials. It is crucial to investigate the possibilities of used foundry sand as a useful substitute material for building construction.

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 Keywords

ESTIMATION AND COSTING OF A RESIDENTIAL BUILDING

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One of the sectors that have undergone tremendous change by the addition of innovative technologies is the tourism industry, specifically the introduction of Machine Learning (ML). The study describes and tests the creation and application of an ML-driven tourism management and recommendation system. Using methods like collaborative filtering, clustering and natural language processing the system can derive the interests of the travelers and can optimize the itineraries. Besides, it mitigates dynamics of cold-start problems, data sparsity, and user behavior. The suggested ML-based tourism system will benefit the decision-making of both travelers and tourism agencies to ensure the effective allocation of resources, the enhanced customer satisfaction, and the intelligent development of tourism. The conducted experiments prove that ML models are much more accurate and engaging than traditional rule-based systems. The paper advances the so-called intelligent tourism as a rapidly developing research area with the scalable and adaptive real-time recommendation and planning framework.

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Tourism Recommendation System With Machine Learning

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 Abstract

As digital banking becomes increasingly central to financial activity, the risk of fraud has grown significantly. Traditional fraud detection methods, which often rely on static rules, are proving insufficient against the sophisticated strategies of modern cybercriminals. This research focuses on utilizing machine learning (ML) approaches to enhance the detection of fraudulent behaviour in online banking systems. By examining extensive transaction datasets, ML models can learn to recognize unusual patterns that may signal fraud, allowing for timely and accurate detection. The system developed in this study applies supervised learning techniques such as logistic regression, decision trees, and random forest classifiers trained on labelled data to distinguish between legitimate and suspicious transactions. Through careful feature extraction and data pre-processing, the models achieve improved precision and recall. Findings indicate that machine learning offers a dynamic and efficient framework for combating fraud, ultimately strengthening the safety of online banking and fostering greater user confidence.

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 Keywords

Index Terms: Online banking, fraud detection, machine learning, financial fraud, Decision Tree, Random Forest, SVM, Neural Networks, XG Boost, Transactions, Risk Scoring, User Behaviour Analysis, Transaction Data, Time-Series Analysis, Python, Tensor Flow.

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