Volume 14 | Issue 4 |

 Abstract

The investment environment has been significantly transformed by technological advancements and the increasing availability of real-time financial information. Investors now depend on instant access to market prices, news updates, volatility trends, technical indicators, and digital trading platforms to make faster and more accurate investment decisions. This study titled "An Analytical Study on the Impact of Real-Time Information on Investment Decisions with Reference to RTT NEWS" examines the influence of real-time information on investor behaviour, decision-making patterns, and investment preferences. The study is based on primary data collected from 178 respondents through a structured questionnaire, and statistical tools such as Percentage Analysis, Simple Average Method, Reliability Test, Mann-Whitney U Test, and Kruskal-Wallis Test were applied. The findings reveal that real-time financial information plays a vital role in investment decisions, particularly in market timing, portfolio adjustment, risk analysis, and profit forecasting. Factors such as volatility tracking, market trend analysis, technical indicators, and advisory services strongly influence investor choices. The study concludes that real-time information has become an essential component of modern investment decision-making by reducing uncertainty, improving efficiency, and enabling quick responses to market movements.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Real-Time Information, Investment Decisions, Investor Behaviour, Financial Markets, Market Timing, Portfolio Adjustment, Risk Analysis, Technical Indicators, Digital Trading Platforms.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

PCOS, Illness Perception, Coping Strategies, Psychological Health, Women.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Managing personal finances has become increasingly challenging with the growth of digital transactions and complex spending patterns, making traditional methods of tracking inefficient and time-consuming. This paper presents FinanSmartAI, an AI-based personal finance tracking and recommendation system designed to automate expense management and provide intelligent financial insights. The system utilizes machine learning techniques to categorize user expenses, analyze spending behavior, and predict future financial trends. Developed using modern web technologies, it offers a secure and user-friendly interface along with interactive data visualization tools for better financial understanding. By reducing manual effort and enabling data-driven decision-making, the proposed system enhances financial awareness and supports effective budgeting. The results indicate that integrating artificial intelligence into personal finance management can significantly improve accuracy, efficiency, and user engagement while laying a foundation for further advancements in intelligent financial systems.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Expense tracking , Predictive analytics ,Data Visualization

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid growth of online food delivery platforms has created a need for intelligent systems capable of optimising demand prediction and delivery efficiency. This paper presents CloudPredict, an end-to-end predictive analytics system integrating machine learning, backend APIs, and business intelligence dashboards. The system utilises real-world delivery data processed using R-based machine learning models, such as Random Forest and C5.0, along with K-Means clustering for customer segmentation. A Node.js backend enables API-based communication, while Power BI provides visualisation of insights. The results demonstrate high predictive accuracy and improved decision-making capabilities for logistics and marketing optimisation.

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 Keywords

Machine Learning, Food Delivery Systems, Random Forest, Predictive Analytics, Business Intelligence, Node.js, Power BI

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study introduces a hybrid optimization framework that integrates principles of Vastu Shastra with modern artificial intelligence techniques, including convolutional neural networks and genetic algorithms, for interior design generation. The proposed system translates traditional spatial guidelines into a rule-based scoring mechanism, while leveraging machine learning models to evaluate aesthetic quality and evolutionary algorithms to explore multiple design possibilities efficiently. The experimental results indicate a significant improvement in performance, achieving an average fitness score of 82.4 compared to a random baseline score of 61.2, reflecting a 34.7% enhancement. Furthermore, expert assessments reported a cultural compliance rating of 7.8 out of 10, while user feedback showed a 78% preference for the generated designs. These findings highlight the potential of combining traditional design philosophies with advanced AI methodologies, demonstrating that culturally informed and optimized interior layouts can be effectively generated using an integrated computational approach.

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 Keywords

Interior Design Optimization, Vastu Shastra, Convolutional Neural Networks (CNN), Genetic Algorithms (GA), Hybrid AI Framework, Cultural Computing, Space Planning, Evolutionary Computation, Aesthetic Evaluation, Smart Design Systems.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid proliferation of misinformation and fake news across digital media platforms poses a serious threat to public discourse, democratic processes, and societal well-being. Allcott and Gentzkow [2] demonstrated that social media served as a primary conduit for politically motivated fake news during the 2016 U.S. election, showing that the average American encountered at least one fake news story during the campaign. Balmas [4] further established that repeated exposure to misinformation leads to political alienation, cynicism, and feelings of inefficacy among citizens. Existing automated detection systems predominantly operate as "black-box" models, offering classifications without human-understandable justification. This paper presents TruthLens, a Flask-based web application that integrates machine learning with Explainable Artificial Intelligence (XAI) techniques for transparent and interpretable fake news detection. The system employs a Logistic Regression classifier trained on TF-IDF features extracted from the ISOT Fake News Dataset, achieving an accuracy of 98.67%. Explainability is provided through LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), which highlight the influential features driving each prediction. The pipeline further incorporates Named Entity Recognition (NER) using spaCy, real-time external fact-checking via the GNews API with cosine similarity scoring, and a hybrid decision fusion mechanism combining ML confidence scores with semantic similarity. The system is designed to be transparent, user-centric, and deployable in real-world misinformation detection scenarios.

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 Keywords

- Fake News Detection, Explainable AI, LIME, SHAP, TF-IDF, Logistic Regression, Named Entity Recognition, Fact Checking, Natural Language Processing, Flask, Misinformation.

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Creative Commons Attribution 4.0 and The Open Definition

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Mata Ni Pachedi is a traditional textile art form practiced by the Vaghari community of Gujarat, India. The term means "behind the Mother Goddess," as these painted cloths are used as backdrops in religious worship. Historically, marginalized communities who were denied entry into temples created these textiles as portable shrines, making them significant symbols of faith, identity, and resistance. This paper examines the stylistic features, cultural significance, and production techniques of Mata Ni Pachedi. The compositions typically feature a central goddess figure such as Durga or Kali, surrounded by narrative elements arranged symmetrically. The use of natural dyes and handcrafted processes highlights its sustainable nature. In contemporary times, this art form has expanded beyond ritual use into fashion and design, raising questions about preservation and adaptation. The study emphasizes the importance of maintaining its cultural authenticity while exploring its relevance in modern design practices.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Mata Ni Pachedi, sacred textiles, Gujarat, natural dyes, sustainability, Indian folk art, block printing

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Creative Commons Attribution 4.0 and The Open Definition

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ABSTRACT This study focuses on exploring effective teaching and learning methods at the undergraduate level-Bachelor of Education from the student teachers perspective. The primary objective of the study is to evaluate the effectiveness of various teaching-learning methods, strategies, techniques and activities used in undergraduate education. The sample consisted of 240 Bachelor of Education students. A survey research method was employed, and data were collected through interviews. The collected data were analyzed using appropriate statistical techniques. The study examined student teachers' perceptions of the most effective and interesting teaching-learning methods, along with the reasons for their preferences. The major reasons identified include comprehensive explanation of the topic by the teacher, opportunities for direct observation, time efficiency, active student teacher participation, and improved learning effectiveness. However, student teachers did not favor certain methods due to factors such as being time-consuming, highly activity-oriented, and requiring a high level of creativity. Overall, student teachers opinions and ratings regarding effective and interesting teaching-learning methods, strategies and activities provide valuable insights for suggesting improvements in the teaching-earning process.

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Creative Commons Attribution 4.0 and The Open Definition

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teaching, learning, effective teaching-learning methods, approaches, strategies, techniques, activities, student teachers etc.

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Creative Commons Attribution 4.0 and The Open Definition

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This study examines the short and long run relationships among crude oil prices, gold prices, and the Indian stock market (NIFTY 50) over January 2000 - January 2026 (312 monthly observations). Using Johansen cointegration, Vector Error Correction Model (VECM), Granger causality, BEKK GARCH volatility spillovers, and variance decomposition, we find: (1) a long run equilibrium binds the three markets; (2) unidirectional Granger causality from oil to stock returns (p=0.015), but no causality from gold to stocks; (3) oil explains 16.8% of 12 month stock return forecast variance, gold only 4.3%; (4) gold acts as a conditional safe haven (positive returns in 5 of 7 extreme crash months, 71% success); (5) a structural break post 2022 doubled oil's impact on stocks; (6) adding 40% gold to an equity portfolio improves Sharpe ratio by 12.5% and reduces volatility by 18%. Oil provides no diversification benefit. The study offers actionable insights for investors and policymakers.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Crude oil, Gold, NIFTY 50, Cointegration, Volatility spillover, Safe haven, India.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Given the subjective nature of traditional techniques using set percentages or expert judgment, cost contingency estimation remains one of the hardest tasks when it comes to project planning. The influence of project properties, geographical location, contractor capacity, and prior cost overrun tendencies can hardly be taken into consideration by such techniques. To the best of this author's knowledge, there has been no paradigmatic framework developed specifically for cost contingency estimation in Egypt, while contemporary research indicates the potential of artificial intelligence approaches for construction cost estimation. Thirteen predictive factors grouped into project factors, geographic factors, and contractor risk and capacity factors are proposed as the basis of a framework for cost contingency prediction through machine learning. Data gathering on projects, data pre-processing, selection of predictors, development of a machine learning model, and decision support constitute the four steps of the recommended approach. Predictors are used to estimate a cost contingency percentage using a conceptual artificial neural network. In addition, the current paper lists some key shortcomings of existing literature, including insufficient number of samples, varying validation methods, poor generalizability, and absence of interpretability. Finally, the paper suggests some new avenues of research such as artificial intelligence, building information modeling, and Monte Carlo simulations. The proposed paradigm serves as a systematic basis for future empirical research.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial neural network; Conceptual framework; Construction projects; Cost contingency; Machine learning.

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Creative Commons Attribution 4.0 and The Open Definition