Volume 14 | Issue 4 |

 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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 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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Creative Commons Attribution 4.0 and The Open Definition

 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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 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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 Keywords

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

 Abstract

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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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

 Abstract

The rapid integration of digital media into everyday life has significantly reshaped childhood experiences, particularly in urban environments where access to smartphones, tablets, and online platforms is widespread. While digital technologies provide valuable opportunities for learning, communication, and entertainment, concerns are increasingly being raised about their impact on children's psychological development and family relationships. One area of growing importance is the influence of screen exposure on parent-child emotional bonding, especially within the framework of attachment relationships. Grounded in Attachment Theory, which emphasizes the importance of early emotional bonds between caregivers and children for healthy socio-emotional development, this study investigates how digital media use affects mother-child attachment among school-aged children. Secure attachment, characterized by trust, emotional closeness, and effective communication, plays a critical role in shaping a child's emotional regulation, resilience, and interpersonal functioning. However, the increasing presence of digital devices in daily routines may interfere with opportunities for meaningful interaction and responsive caregiving, thereby influencing attachment quality. The present study focuses on children aged 6-12 years in Bengaluru, an urban setting marked by rapid technological adoption and changing family dynamics. This developmental stage is particularly significant as children gradually gain independence while still depending on parental emotional support. The research adopts a quantitative correlational design to examine the relationship between screen time and mother-child attachment, while also exploring the role of technoference--defined as interruptions in interpersonal interactions caused by digital device use--as a contributing factor. A sample of 120 mother-child dyads was selected using stratified sampling from schools and residential communities. Data were collected using standardized instruments measuring children's screen time (in terms of duration, frequency, and type of use), mother-child attachment (across dimensions such as emotional closeness, communication, trust, and responsiveness), and technoference in daily interactions. In addition to quantitative data, qualitative responses were gathered to better understand family experiences and behavioral patterns related to digital media use. Statistical analysis revealed a significant negative relationship between screen time and attachment quality, indicating that increased digital media use is associated with reduced emotional bonding between mothers and children. Screen time was also found to be a significant predictor of attachment outcomes, explaining a notable proportion of variation in attachment levels. These findings suggest that excessive engagement with digital devices may limit opportunities for face-to-face interaction, emotional exchange, and shared experiences that are essential for building secure attachment relationships. Importantly, technoference emerged as a strong influencing factor, further intensifying the negative impact of screen use. Frequent interruptions caused by mobile devices during conversations and routine family activities were found to reduce parental responsiveness and attentiveness. This disruption in real-time interaction weakens the emotional connection between mother and child, highlighting that not only the amount of screen time but also the context and timing of device use play a crucial role in shaping relational outcomes. The qualitative findings supported these results by providing insight into everyday family dynamics. Many mothers reported that children with higher screen engagement tended to show reduced interest in communication, family participation, and shared activities. Patterns such as emotional distancing, decreased verbal interaction, and a growing preference for solitary digital engagement were commonly observed. Traditional bonding activities, including storytelling, play, and shared meals, were often replaced or interrupted by screen use, contributing to a decline in emotional closeness. At the same time, the study identified important moderating factors that can help mitigate these negative effects. Parental involvement in children's digital activities--such as co-viewing, guided usage, and setting structured screen time limits--was associated with improved communication and stronger emotional connection. When digital media was used interactively and under supervision, its adverse impact on attachment appeared to be significantly reduced. This highlights the role of mindful parenting in promoting healthy digital habits while preserving relational quality. The findings also align with the displacement hypothesis, which suggests that time spent on digital devices often replaces time that could otherwise be devoted to direct social interaction. In the context of parent-child relationships, this displacement reduces opportunities for emotional engagement and shared experiences, both of which are fundamental to secure attachment formation. Furthermore, the study underscores that the quality of screen use matters; passive and unregulated consumption tends to have more negative effects compared to purposeful and interactive engagement. Despite its contributions, the study acknowledges certain limitations. The use of self-reported measures may introduce bias, and the cross-sectional design does not allow for causal conclusions. Additionally, the focus on an urban population limits the generalizability of findings to other settings. Future research could benefit from longitudinal approaches and inclusion of multiple caregivers to provide a more comprehensive understanding of family interactions in the digital age. In conclusion, this study highlights that excessive and unregulated screen time can negatively influence mother-child attachment by reducing emotional closeness, communication, and responsiveness. Technoference further exacerbates these challenges by interrupting meaningful interpersonal interactions. However, the findings also offer a hopeful perspective, demonstrating that active parental involvement and balanced digital practices can significantly buffer these effects. The study emphasizes the need for greater awareness among parents, educators, and mental health professionals regarding the impact of digital media on children's emotional development. Encouraging mindful technology use, promoting family interaction, and fostering consistent emotional engagement are essential steps toward maintaining strong parent-child relationships in an increasingly digital world. Ultimately, achieving a balance between technological benefits and human connection is key to supporting healthy developmental outcomes for children.

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 Keywords

- Screen time, attachment theory, mother-child relationship, techno Ference, digital media, child development, parental mediation, urban families.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Gratitude has been increasingly recognized as a significant construct within positive psychology, contributing to various aspects of mental health and well-being. However, limited research has examined how its influence varies across different stages of life. The present study explores the relationship between daily gratitude and key mental health outcomes--emotional well-being, life satisfaction, and perceived stress--among adolescents, adults, and older adults. A quantitative cross-sectional design was employed with a sample of 120 participants equally distributed across three age groups. Standardized instruments, including the Daily Gratitude-Mental Health Lifespan Scale (DG-MHLS), PANAS, Satisfaction With Life Scale (SWLS), and Perceived Stress Scale (PSS-10), were used. Data were analyzed using descriptive statistics, one-way ANOVA, Pearson correlation, and regression analysis. Findings revealed that gratitude is significantly associated with emotional well-being (r = .50, p < .01) and life satisfaction (r = .40, p < .01), but not significantly related to perceived stress (r = -.06, p > .05). Significant differences across age groups were observed for gratitude and positive mental health variables, while stress remained consistent. Regression analysis indicated that gratitude significantly predicts emotional well-being and life satisfaction but not stress. The findings highlight gratitude as a resource that enhances positive mental health outcomes, while its role in reducing stress appears limited. The study contributes to a lifespan understanding of gratitude and its psychological relevance.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Gratitude, Mental Health, Emotional Well-being, Life Satisfaction, Perceived Stress, Lifespan Development

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Creative Commons Attribution 4.0 and The Open Definition