Volume 13 | Issue 9

 Abstract

The advent of digital technology has revolutionized the marketing approach of Fast-Moving Consumer Goods (FMCG) companies. Digital marketing tools such as social media advertising, influencer marketing, content marketing, and data-driven campaigns have enabled FMCG brands to reach broader audiences, increase consumer engagement, and drive higher sales growth compared to traditional methods. This study assesses the impact of digital marketing on FMCG growth by examining its influence on brand visibility, consumer purchase behavior, and market competitiveness. The findings highlight how effective use of digital platforms strengthens brand loyalty, enhances revenue, and creates sustainable competitive advantages for FMCG companies operating in a dynamic market environment.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Digital Marketing; FMCG Growth; Consumer Behavior; Brand Loyalty; Social Media Marketing; E-Commerce; Online Advertising; Influencer Marketing

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study reports the green synthesis of Balanites aegyptiaca fruit pulp extract with Titanium dioxide (TiO?) nanoparticles. The biosynthesized extract from the fruit pulp resulted in a solution with a honey-like colour. UV-Vis spectroscopy revealed a sharp absorption peak at 304 nm, characteristic of bioactive compounds present in fruit pulp extract. The FTIR spectrum of biosynthesized fruit pulp extract with TiO2 Nps clearly shows different bioactive compounds (flavonoids, alkaloids, terpenoids, phenolic compounds, etc) present in the green synthesized fruit pulp extract of Balanites aegyptiaca. XRD showed anatase-phase TiO? with an average crystallite size of 10-30 nm. The SEM revealed a highly porous nanostructured surface with pores and particles in oval shape, sizes in the range of 50-100 nm. while EDS confirmed elemental purity with a Ti:O atomic ratio close to 1:2, suggesting the presence of oxygen vacancies that can enhance photocatalytic activity. The results establish green synthesis as a sustainable and efficient approach for producing biosynthesized products with potential applications in photocatalysis, antimicrobial activity, anticancer, anti-inflammatory, antioxidant etc.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Balanites aegyptiaca fruit pulp extract, Titanium dioxide nanoparticles

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Alcoholic cirrhosis, structured teaching programme, knowledge, students, Indore

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Stroke is one of the major causes of death and permanent disability in the world, and it generates an astronomical amount of scientific information that is ever expanding. The current practice of manually synthensizing this information is proving more difficult and it is a barrier to achieving timely insight generation and evidence-based decision making. This research presents a new machine reading framework that combines LDA topic modeling with Open IE to use stroke-related biomedical literature published between 2000 and May 2025 in an arteriosclerosis-related randomized controlled trial. This research curated a corpus of 179,219 PubMed abstracts by using stroke-specific MeSH terms and using the biomedical natural language processing tools to preprocess text and recognize an entity. Ten significant thematic clusters that consist of neuroimaging, acute reperfusion therapies, neuroinflammation, rehabilitation, and AI-driven stroke prediction became apparent in the analysis. Open IE captured over 4.1 million subject-predicate-object triplets, allowing querying by structure around comorbidities, the performance of drugs, and currently emerging risk variables like COVID-19. The current study noted an extreme increase in the number of studies that apply deep learning and large language models (LLMs) to predict stroke after 2022. Topic coherence scores and expert reviews were used in validating the model to ascertain the reliability of results. This was lastly achieved through the creation of an interactive web-based dashboard allowing them to visualize topic trends and explore the extracted knowledge to provide researchers with a dynamic way to discover literature. This paper proves the capability of machine reading to make literature review scalable and data driven and thus assist in faster discovery and better clinical decision-making in stroke research.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

brain stroke; machine reading; topic modeling; Open IE; biomedical NLP; stroke prediction; large language models

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

.The study focuses on analyzing consumer preferences and satisfaction towards eco-friendly products, a rapidly growing sector in the modern marketplace. With increasing global awareness of environmental degradation, climate change, and sustainable consumption, customers are gradually shifting from conventional goods to environmentally friendly alternatives. The objective of this study is to examine the factors influencing consumer choices, including awareness levels, product attributes, pricing, accessibility, and perceived quality. It also explores the extent to which these factors contribute to customer satisfaction and loyalty. Using both primary and secondary data, the research employs statistical tools such as Chi-Square tests to analyze the relationship between demographic variables and eco-friendly product adoption, and ANOVA to assess variations in satisfaction levels across different consumer groups. The findings reveal that although consumer interest in eco-friendly products is increasing, barriers such as higher costs, limited availability, and misconceptions about effectiveness remain significant. The study suggests that improving affordability, enhancing distribution channels, and promoting consumer education can significantly boost adoption and satisfaction. By providing empirical evidence, this study contributes to both academic literature and practical strategies for marketers, policymakers, and producers seeking to enhance sustainable consumption patterns.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Eco-Friendly Products, Consumer Preference, Green Marketing, Customer Satisfaction, Sustainable Consumption, Environmental Awareness

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Preparing for competitive examinations is often accompanied by heightened stress, anxiety, sleep loss, and social isolation, which collectively threaten students' mental well-being. This review identifies key risk factors--such as performance pressure, perfectionism, and sleep deprivation--and highlights protective influences like social support, structured study routines, and healthy coping strategies. Evidence shows that cognitive-behavioral interventions, study-skills training, and relaxation practices can effectively reduce test anxiety and improve performance. Institutions and policymakers play a crucial role in providing counselling, psychoeducation, and equitable support. Balancing academic rigor with student well-being requires coordinated individual, educational, and systemic interventions.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

competitive examinations, student well-being, test anxiety, sleep deprivation, interventions, academic stress

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Attitude towards blended learning refers to an individual's perceptions, feelings, and predispositions whether positive or negative towards the integration of traditional face-to-face teaching methods with online or digital learning environments. It reflects the extent of acceptance, satisfaction, and engagement demonstrated when participating in or supporting blended learning approaches. The present study aimed to develop a standardized scale for measuring attitudes towards blended learning among B.Ed. trainees. The initial pool of items was subjected to item analysis, and low-validity items were eliminated. The final version of the scale consists of 50 items, each rated on a five-point Likert scale. Both the validity and reliability of the scale were established.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Blended learning, attitude, Standardization

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The world of digital marketing is changing as a result of the quick uptake of automation and artificial intelligence (AI) technology. Businesses in India are rapidly using AI-driven solutions for chatbots, predictive analytics, targeted campaigns, and consumer segmentation, especially in up-and-coming commercial centers like Pune. This study investigates the scope and effects of automation and artificial intelligence on digital marketing strategies used by Pune-based companies. The study explores important facets of AI integration, perceived advantages, difficulties, and consumer engagement results using a mixed-method approach that includes surveys, interviews, and secondary research with 80 marketing experts. Research shows that companies using AI-powered solutions have better ROIs, more effective client acquisition, and higher levels of engagement than those using conventional techniques. However, issues including insufficient knowledge, worries about data privacy, and expensive implementation expenses continue to exist. Initiatives for skill development, government-sponsored programs for digital adoption, and cross-sector partnerships are among the suggestions. In addition to laying the groundwork for further research on AI-driven marketing in mid-tier Indian cities, this study offers marketers and policymakers practical insights.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence (AI), Automation, Digital Marketing, Pune Businesses, Customer Engagement, Predictive Analytics, Chatbots, Personalization, ROI

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The largest environmental challenges that Northern India faces today is the landslides. In this paper, a physical modeling method based on load - settlements curve to find the load carrying capacity of micro piles. Parametric study of highway embankment by CBR type arrangements which involves manually loading arrangements, proving ring & dial gauge. Method of static point bearing micro piles in sandy soil has been developed. The formulation for calculating the load - settlement curve by proving ring and dial gauge. Special attention is given to determine the spacing of micro piles in different arrangements (single row, double row and staggered or zigzag pattern of micro piles) with the aim of achieving the maximum load carrying capacity of the micro piles arrangements. Thus, a new design method for micro piles for earthen slope stabilization is proposed that includes details about choosing arrangements of the micro piles with existing highway embankment, selecting micro plies arrangements with maximum load carrying capacity, and considered end bearing piles. Evaluating the angle for staggered piles, calculating the spacing required to stabilize the slope. The utilization of the technique, to a highway embankment land slide which was manually modeled to have experimentation in Punjab region, India, is described, and manipulating the data show which envisage the change of soil slope definitely stopped by obtaining a suitable outcomes of the embankment is observed, proved the good response of this of the technique.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Manually modeled soil embankment; Dial Gauge, Proving ring, CBR Type Arrangements: Stabilizations, Micro piles, Piles capacity, Load-settlement curves.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Orthognathic surgery has evolved from traditional two-dimensional (2D) planning methods to advanced digital approaches that integrate virtual surgical planning (VSP), computer-assisted navigation, and patient-specific implants (PSIs). VSP enables three-dimensional (3D) evaluation of dentofacial deformities, virtual osteotomies, and precise simulation of skeletal repositioning, thereby enhancing diagnostic accuracy and surgical predictability. Computer-assisted navigation provides real-time intraoperative spatial guidance, ensuring accurate translation of the virtual plan and minimizing risks such as nerve injury or malpositioning. Clinical studies demonstrate sub-millimetric accuracy in maxillary positioning, improved facial symmetry, reduced reoperation rates, and high patient satisfaction compared to conventional workflows. Although initial costs, training requirements, and integration of multimodal imaging remain challenges, these technologies improve efficiency, reduce intraoperative adjustments, and are particularly cost-effective in complex or high-volume cases. Emerging applications of augmented and virtual reality promise to further expand the role of digital navigation in orthognathic surgery.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Orthognathic surgery, Virtual surgical planning, Computer-assisted navigation, Accuracy, Oral and Maxillofacial Surgery

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The aim of this paper is to bring out the core concept of Bhakti in Dvaita Philosophy advocated by Sri Madhvacharya, which paves way as a means to Liberation. The purpose of this paper is to analyse and highlight the significance of Bhakti through which the Mumukshu can attain Salvation.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Bhakti, Isvara, Vairagya, Mahatmya, Upasana, Moksa, Jnana, Karma, Isvara-Prasada, Liberation, Samsara, mukta, Salvation.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

A well designed and maintained water distribution network system is a cornerstone of modern society, underpinning public health, economic stability and quality of life. Its primary goal is to deliver a reliable supply of water with appropriate quality, quantity and pressure to satisfy the basically needs, etc. The study presents an in-depth analysis of pipe network modeling using EPANET for the District Metered Area (DMA) Gumadam, focusing on the optimization of hydraulic parameters and network performance evaluation. The primary objective was to assess the adequacy of the existing water supply infrastructure in terms of pressure distribution, flow velocities, and head losses at various junctions and pipe segments within the DMA. Comprehensive simulations were performed to evaluate the network, involving junction elevations, required demand, and pressure heads to ensure consistent delivery across all parts of the zone. Key findings indicated considerable variations in pressure and flow rates that were attributed to pipe diameters, material types (primarily HDPE of varying diameters), lengths, and elevations at junction nodes. Further, the study identified critical junctions and pipes prone to head losses, serving as focal points for future interventions to enhance hydraulic efficiency and operational sustainability. The research underscores the vital importance of DMA-based modeling for urban water distribution planning, facilitating targeted infrastructure improvements and energy savings. Through detailed tabulation and systematic analysis, the work provides practical recommendations for system upgrades and efficient resource allocation. Overall, the investigation demonstrates that EPANET-based modeling is a robust tool for optimizing water supply systems, ensuring reliable service and supporting long-term planning initiatives.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords: EPANET, District Metered Area (DMA), hydraulic parameters, network performance evaluation, pressure distribution, flow velocities, head losses, resource allocation, pressure heads, HDPE, operational sustainability.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Early and accurate diagnosis of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) is vital for effective patient care and timely intervention. Magnetic Resonance Imaging (MRI) serves as a powerful modality for detecting structural brain changes, but traditional manual analysis is time-consuming and subjective. This study presents a novel hybrid framework that integrates the deep Convolutional Neural Network VGG16 with the local feature extraction capability of the Scale-Invariant Feature Transform (SIFT) algorithm to classify AD and MCI from MRI scans. The approach employs a feature fusion strategy that combines global high-level features from VGG16 with fine-grained local features from SIFT, eliminating the need for complex preprocessing steps like manual segmentation. Performance evaluation using confusion matrix-derived metrics demonstrates the framework's strong discriminative power. The model achieved an accuracy of 97.60%, sensitivity of 98.00% and specificity of 97.20%, highlighting its efficiency. These results confirm the model's high accuracy, robustness, and practicality, making it a promising tool for integration into clinical decision-support systems to facilitate early and reliable diagnosis of Alzheimer's Disease.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Learning, Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), Feature Fusion, Scale-Invariant Feature Transform (SIFT)

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper gives an overview about how the anomalies are detected in the network data containing the bytes sent and received and session duration while a bit of data is being transferred. The anomalies in the data play an important role in detecting security breaches, performance issues and abnormal behaviours while the network is being transmitted. The identification of anomalies face challenges, particularly when it comes in finding and managing false positives i.e., incorrectly flagged activities as abnormal or malicious. This paper aims in detecting all the anomalies in the given data and finding false positives in the data set. This paper gives different views about the different methods to detect anomalies in the given network data based on the false positive rates. We discuss different algorithms that are used in this detection including machine-learning algorithms. Here we also discuss the causes, and additionally we display the graph of the anomalies in the data. The analysis is used for improving the effectiveness of Intrusion Detection Systems for future.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

False Positives, True Negatives, Anomalies

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Utilizing pre-trained models allows the network to effectively recognize intricate patterns in medical images while minimizing training duration. The dataset is divided into four categories: normal, benign, malignant, and uncertain. Data preprocessing methods like image resizing and normalization were employed to enhance the model's performance and avoid overfitting. Moreover, data augmentation was applied to guarantee the model's ability to manage novel, unseen data effectively. The model's efficiency was demonstrated via visual representations of essential classification attributes and performance measures. The integration of CNNs and transfer learning presents a scalable and efficient approach for detecting lung cancer, delivering considerable benefits to healthcare practitioners who depend on AI-based diagnostic tools to improve patient results.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Lung Cancer Prediction, Convolutional Neural Networks (CNN), Transfer Learning, Deep Learning, Medical Image Classification, CT Scans, Artificial Intelligence in Healthcare

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The National Pension System (NPS) is an essential mechanism for safeguarding retirement security for Indian citizens, especially those in the unorganised sector. Since its establishment in 2004, the NPS has experienced substantial modifications, resulting in the launch of a new scheme with improved attributes. This study offers a comparative analysis of the previous and current NPS schemes, emphasising four critical variables: tax advantages, returns, risk-adjusted returns, and participation rates. The research employs a mixed-methods approach, integrating qualitative surveys and quantitative analysis to evaluate the financial advantages, risk profiles, and subscriber engagement of each scheme. The research indicates that the new NPS scheme provides enhanced financial returns, superior tax benefits, improved risk-adjusted returns, and increased participation rates relative to the previous scheme. The findings indicate that the new NPS scheme offers a more appealing risk-return profile and enhanced accessibility, leading to greater participation. Policymakers and financial institutions can utilise these insights to improve the scheme's efficacy. Future research may investigate the effects of global economic fluctuations on NPS returns and analyse the demographic variables affecting NPS participation.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

National Pension System (NPS), tax benefits, returns, risk-adjusted returns, participation rate, comparative analysis, new pension schemes, old pension scheme.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Healthcare datasets frequently exhibit severe class imbalance, where critical conditions represent minority classes, posing significant challenges for traditional machine learning approaches. This research investigates the development and implementation of interpretable ensemble methods using Python libraries to address class imbalance in healthcare applications while maintaining model transparency for clinical decision-making. Our proposed framework integrates advanced resampling techniques with ensemble learning algorithms, incorporating explainable AI components through SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to provide clinically meaningful insights. The methodology combines traditional ensemble approaches like Random Forest and Gradient Boosting with novel weighted voting mechanisms specifically designed for healthcare contexts. Through extensive experimentation on multiple healthcare datasets including diabetes prediction, heart disease diagnosis, and cancer detection, our framework demonstrates superior performance metrics . The implementation leverages core Python libraries including scikit-learn, pandas, numpy, and specialized packages like imbalanced-learn, making it accessible for healthcare data scientists. Results indicate that our interpretable ensemble approach not only addresses class imbalance effectively but also provides actionable insights that align with clinical knowledge, potentially reducing diagnostic errors and improving patient outcomes in real-world healthcare settings.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Class Imbalance, Ensemble Methods, Healthcare Analytics, Interpretable Machine Learning, SHAP, Python Libraries, Clinical Decision Support.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The modern job market is characterized by rapidly evolving skill requirements, making it challenging for students and professionals to identify and address the specific gaps in their knowledge for the desired career paths. This paper presents the Skill Gap Analyzer, a full-stack web application designed to provide a clear, data-driven, and personalized analysis of a user's skill set against industry requirements. The system compares user input skills against a comprehensive database of skills required for various job roles. The core features of the visualization of this "skill gap" through a series of interactive charts, which offers an intuitive understanding of the user's strengths and weaknesses. Furthermore, the application generates a dynamic learning path by fetching relevant high-quality educational resource from public APIs like YouTube for each identified missing skill. The project is implemented using a modern tech stack, featuring a React.js front-end, a Python FastAPI backend, and a MongoDB database, demonstrating a robust and scalable architecture.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Skill Gap, Career Development, Full-Stack, React, FastAPI, Data Visualization, REST API

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The banking environment is transforming from standard online banking to feature-rich mobile applications for fund transfers, bill payments, cross-border remittances, robo-advice, wealth management, etc. The allure of artificial intelligence-based financial innovations to reduce transaction costs has seen banking services undergo rapid transformation. Financial institutions are now deploying artificial intelligence (AI) and machine learning (ML) tools to meet growing customer demand for better, safer and more convenient ways to manage their money. The prime objective of this research is to analyse the customer perception towards AI enhanced features internet banking. The samples for this study were the general public from various districts in Tamil Nadu and it was selected using convenience sampling method. Mediation analysis is used to prove the two hypotheses. Direct Effect Hypothesis H1: AI-enhanced internet banking features will have a positive impact on customer perception and Mediation Hypothesis H2: The relationship between AI-enhanced internet banking features and customer perception will be mediated by Customer Experience. Exploratory factor analysis (EFA) using SPSS version 26, Confirmatory factor analysis (CFA), Structural Equation Modelling (SEM), Mediation analysis using AMOS 23 was undertaken to evaluate the proposed hypotheses

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Customer experience, Customer perception, Internet Banking.

  License

Creative Commons Attribution 4.0 and The Open Definition