Volume 13 | Issue 7 |

 Abstract

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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Creative Commons Attribution 4.0 and The Open Definition

 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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 Keywords

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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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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 Abstract

Attention-Deficit/Hyperactivity Disorder (ADHD) is a widely recognized neurodevelopmental condition that typically manifests during childhood and is characterized by persistent patterns of inattention, hyperactivity, and impulsivity. These behavioral patterns often interfere with a child's academic performance and social development, making early detection and intervention crucial. In the school environment, teachers are often the first to notice signs of ADHD, placing them in a pivotal position for initiating referrals and implementing support strategies. However, this responsibility demands a sufficient level of awareness and understanding of the condition. The present study was conducted to assess the level of awareness about ADHD among primary school teachers in Pundri town of District Kaithal, Haryana. Additionally, the research aimed to compare the awareness levels between teachers working in government and private schools. A quantitative descriptive research design was adopted for this investigation. Data were collected using a structured questionnaire developed to evaluate teachers' knowledge across multiple domains, including symptoms, causes, diagnosis, classroom implications, and management techniques related to ADHD. The sample consisted of a diverse group of primary school teachers selected through stratified random sampling from both government and private educational institutions. The findings revealed that while most teachers had heard of ADHD, their in-depth understanding of the disorder was limited. Common misconceptions about the causes and treatments were prevalent, and many teachers lacked clarity on how to effectively manage ADHD-related behaviours in the classroom. Furthermore, the comparative analysis indicated that private school teachers demonstrated a slightly higher level of awareness than their government school counterparts. This disparity may be attributed to variations in teacher training opportunities, institutional resources, and professional development exposure. The study underscores the urgent need for structured awareness campaigns, targeted training modules, and inclusion of ADHD-related content in pre-service and in-service teacher education programs. Enhancing teachers' capacity to identify and support students with ADHD is not only essential for improving academic outcomes but also for fostering an inclusive and equitable learning environment.

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 Keywords

Attention-Deficit / Hyperactivity Disorder (ADHD), Primary School Teacher, Neurodevelopmental Disorder

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study investigates the psychological well-being of students during the pre-examination and post-examination phases. This Study reveals a significant increase in anxiety and stress levels of the students before exams, during Exam and after Exams. The study leads effective mental health support during these critical periods. Through a mixed-methods approach using standardized psychological assessment tools and student interviews, the findings highlight the urgent need for better emotional support and coping strategies tailored to exam phases. It examines the psychological effects such as stress, anxiety, sleep disturbances, and coping mechanisms used by students. The study identifies key differences in students' emotional well-being before and after exams and suggests interventions to support mental wellness.

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 Keywords

Study, Exam, Mental Health, Stress, Anxiety Level.

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Creative Commons Attribution 4.0 and The Open Definition