Volume 13 | Issue 8 |

 Abstract

The widespread propagation of fake news via digital means gravely impairs constructive civil discourse, thus making detection systems essential. This work presents a machine learning-based fake news detection framework implementing NLP and text classification techniques. A labeled dataset of real and purposely fabricated news articles is preprocessed by lowercasing, removal of punctuation, and normalization of tokens, and feature extraction is performed using a TF-IDF vectorizer. The dataset is then split into training and testing sets to achieve the strong evaluation purposes. Four classification models of Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB) are trained and tested under the evaluation criteria of accuracy, precision, recall, and F1-score. The experimental results reveal that a pair of ensemble classifiers, namely RF and GB, are the best performers in fake news detection. Further, the system permits manual testing of any custom text inputs-from-the-fly to make it more usable in real-life settings. From the results, it can be inferred that NLP combined with ensemble machine learning can provide a scalable and automated tool for the efficient identification of fake news. Future enhancements can include deep learning and large language models to improve accuracy and the ability to adapt.

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 Keywords

Fake News Detection, Machine Learning, NLP, Text Classification, TF-IDF, Logistic Regression, Decision Tree, Gradient Boosting, Random Forest, Misinformation, Model Evaluation, Ensemble Learning, Automated News Verification.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rise of digital technologies has led to the exponential growth of data, necessitating advanced analytical frameworks to manage, process, and derive meaningful insights. Big Data, characterized by its high volume, velocity, and variety, serves as a foundation for informed decision-making across sectors such as business, healthcare, finance, and e-commerce. Concurrently, decision trees have emerged as intuitive tools for classification and prediction, offering a transparent and structured approach to decision-making. This paper explores the fundamental concepts of Big Data, its classifications, and key applications, and demonstrates the construction and use of decision trees through practical case scenarios. Additionally, the study incorporates the use of EdrawMind, an open-source visualization tool, to develop clear and interpretable decision tree diagrams that enhance understanding and presentation. The study highlights how these tools collectively contribute to data-driven decisions and outlines their potential for academic and industrial applications.

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 Keywords

Big Data, Decision Trees, Data Analytics, Data-Driven Decision-Making

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 Abstract

Main objectives of Ayurveda have been divided into two aspects namely: Swasthasya Swathya Rakshanam - To protect and maintain the health of a healthy person (Preventive and Social medicine) Aturasya Vikar Prashamanam - To treat the disease and giving relief to sick person (Therapeutics). According to Ayurveda, Ahar (Diet) plays very important role for good physical and mental health. If we follow proper rules for consumption of food then it acts as amruta (nectar) otherwise it may produce various health issues. Now a day due to changes in life style we cannot maintain to take diet in proper quantity and in proper time. Ayurveda text books elaborate about proper way of consumption of food. This study was done to converse on proper way of consumption of food as it is one of most important pillars of human life

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 Keywords

Upsthambha,Ahar, Swasthya, Ayurveda, Atur,Amrut

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 Abstract

In Ayurveda, the treatment of bone fractures is known as Bhagna Chikitsa, which is a part of both surgical and para-surgical care. This concept was developed in detail by Acharya Sushruta, who classified fractures (Kandbhagna) and based on their type, shape, and the extent of displacement. Ayurveda offers a variety of treatment methods for managing such injuries. Traditional techniques like Kushabandh, Chakrayoga, Taila Droni, and Alepa are useful in specific cases. However, the core methods used include Bhagna Sthapana (realigning the bone), Bandhana (immobilization), and Sukhchestaprasara (gradual restoration of movement), often supported by surgical or para-surgical procedures. Alongside these, Ayurvedic and modern systems of medicine also provide symptomatic treatments to reduce pain and swelling. This holistic approach reflects the strength and relevance of Ayurvedic methods in managing fractures, even in today's medical practice.

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 Keywords

Ayurveda, Surgery, Bhagna, Fracture.

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Creative Commons Attribution 4.0 and The Open Definition

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Tadasana, or Mountain Pose, is a foundational standing posture in yoga that emphasizes proper alignment, balance, and body awareness. This article explores the anatomical and Ayurvedic perspectives of Tadasana, highlighting its benefits for physical health and mental clarity. From an anatomical standpoint, Tadasana engages multiple muscle groups, including the core, legs, and back, while promoting the alignment of the feet, knees, pelvis, spine, shoulders, and head. This alignment encourages a neutral spine, prevents joint strain, and enhances stability. In Ayurveda, Tadasana is viewed as a practice that promotes the balance of the three doshas--Vata, Pitta, and Kapha--through grounding, centering, and energy flow regulation. The pose supports the body's Prana (vital energy) and Agni (digestive fire), fostering overall health and well-being. By cultivating Sattva (mental clarity) and balancing the body's energy, Tadasana helps improve posture, mental focus, and physical strength. This holistic understanding of Tadasana underscores its significance in both yoga practice and Ayurvedic health, offering practitioners a pathway to physical and emotional equilibrium.

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 Keywords

Tadasana, Mountain Pose, Ayurvedic health, Agni, digestive fire

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The study analytically examines the impact of determinants of government expenditure on higher education in Rajasthan by considering the time series data for the period 1980-2023. Autoregressive Distributed Lag (ARDL) Approach given by Toda and Yamamoto (1995) has been applied. Sources of data collected for the considered variables are EPWRF Indian Time Series and Economic Review of Rajasthan. Total expenditure on education has been used as dependent variable while Net State Domestic Product, Pupil Teacher Ratio, Number of Teachers and Total enrolment have been used as independent variables. ARDL bound test confirms the pre sence of the long-run relationship between the variables. Pupil Teacher Ratio and Net State Domestic Product show the positive significant effect on expenditure on higher education in the long run. Total Expenditure on higher education with lag 1 has been found significant in effecting the current total expenditure on higher education similarly current Net State Domestic Product and itself with lag 1 have the statistically significant impact on the total expenditure on higher education in short- run.

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 Keywords

Higher Education, Government Expenditure, Autoregressive Distributive Lag Model, Rajasthan.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study investigates the impact of a structured 12-week physical activity program on the span of attention in boys diagnosed with Attention Deficit Hyperactivity Disorder (ADHD), aged 11 to 14 years. Forty participants were randomly assigned into two equal groups: an experimental group that underwent a regular physical activity regimen and a control group that continued their usual routine. A tachistoscope-based test was administered pre- and post-intervention to measure attention span. Each test involved recalling alphanumeric characters flashed briefly on a screen, scored out of 50. Data were analyzed using paired and independent t-tests, and one-way ANOVA. Statistical analysis revealed significant improvement in the experimental group (t = 58.02, p < 0.001), while the control group showed a smaller yet statistically significant gain (t = 13.08, p < 0.001). The experimental group's post-test scores were significantly higher than the control group (t = 28.39, p < 0.001), and ANOVA results confirmed significant differences across all groups (F = 510.71, p < 0.001).

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 Keywords

: ADHD, attention span, physical activity, tachistoscope, cognitive function, children, intervention strategies

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Creative Commons Attribution 4.0 and The Open Definition

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With the rising depletion of natural river sand and growing environmental concerns, artificial sand (AS) has gained attention as a sustainable replacement in concrete production. This study investigates how Carbon Nanotubes (CNTs) and Steel Fibers (SFs) can work together to enhance the mechanical properties of high-performance concrete made entirely with artificial sand. Concrete samples were prepared using CNTs at 1%, 2%, and 3% by weight of cement, alongside a fixed 5% volume of steel fibers. Standard tests for compressive, flexural, and splitting tensile strength were conducted to evaluate performance. The results revealed a notable improvement in strength and post-cracking behaviour due to the hybrid reinforcement. The mix containing 2% CNTs offered the most consistent improvement across all metrics. However, at 3% CNTs, reduced workability and strength gains were observed, likely due to nanoparticle agglomeration. Steel fibers played a key role in improving crack resistance and enhancing flexural toughness across all concrete batches. Overall, the combination of CNTs and SFs proved to be a promising solution for developing sustainable, high-performance concrete using artificial sand.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Carbon Nanotubes (CNTs), Steel Fibers (SFs), Artificial Sand (AS).

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Predicting house prices accurately is essential for stakeholders like buyers, sellers, and investors in the real estate sector. Conventional appraisal techniques often depend on personal judgment, which can result in variability. This study employs machine learning algorithms to examine diverse property features-- including location, size, and amenities--to improve the precision of predictions. Techniques such as linear regression, decision trees, and neural networks are evaluated for their performance. The results demonstrate that machine learning offers a reliable, data-driven solution for estimating property values.

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 Keywords

Index Terms--Real Estate Pricing, Machine Learning, Linear Regression, Decision Trees, Neural Networks

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Creative Commons Attribution 4.0 and The Open Definition

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This study explores the impact of social media on consumer buying behavior in the Fast-Moving Consumer Goods (FMCG) sector, with a specific focus on Karnataka, India. Given the rapid growth of digital platforms like Instagram, Facebook, WhatsApp, and YouTube, the research investigates how these channels influence brand discovery, trust, product engagement, and purchase decisions among consumers. Using a descriptive research design, data was collected from 400 respondents across urban, semi-urban, and rural districts. Statistical tools such as ANOVA and correlation were applied to examine platform-specific effects, consumer perceptions, and the role of promotional content. Findings reveal that Instagram and WhatsApp are particularly influential in driving impulse buying and peer-based recommendations, while YouTube and Facebook support brand research and reviews. The study also highlights significant demographic variation in digital engagement. This research fills key gaps by analyzing region-specific consumer behavior and providing strategic insights for FMCG marketers to optimize platform-based marketing in emerging markets.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Consumer Buying Behavior, FMCG (Fast-Moving Consumer Goods), Digital Marketing, Influencer Marketing, Instagram Ads, Facebook Promotions

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Creative Commons Attribution 4.0 and The Open Definition