Volume 13 | Issue 9

 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.

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Blended learning, attitude, Standardization

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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.

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 Keywords

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

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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.

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 Keywords

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

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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.

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

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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.

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

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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.

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

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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.

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

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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.

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False Positives, True Negatives, Anomalies

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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.

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

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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.

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

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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.

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

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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.

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

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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

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

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Urban informality has become the defining feature of modern Indian cities, where rapid urbanization and rural-to-urban migration meet limited planning capacity. Jodhpur, the second city of Rajasthan, provides a crucial case to analyze the interaction between slum development, squatter settlements, and changing cultural landscape. Census of India (2011) reports that about 28.3% of the population of Jodhpur lives in slums, with about 1.2 lakh individuals living in more than 210 notified and non-notified clusters. These settlements, being primarily found on the periphery of the city and industrialized belts, depict both socio-economic vulnerabilities and resilience. This paper examines the geographical spread, socio-economic composition, and cultural dynamics of Jodhpur's informal settlements through a mixed-methods approach. Secondary Census data (2001-2011), reports of Jodhpur Development Authority (JDA), and satellite imagery were combined with primary surveys from five representative slum clusters: Rajiv Nagar, Indira Colony, Basni Kachchi Basti, Pratap Nagar, and Mandore fringe. The results show that almost 65% of the households are migrants from Barmer, Pali, and Jalore districts, and 45% are dependent on the informal labor markets such as construction, handicrafts, and street vending. These slums' cultural environment is different: even as spatial situations are characterized by poor housing, sanitation, and uneven water supply (only 32% of households use piped water), the communities have rich cultural practices. These are celebrated communally through festivals like Gangaur and Teej, local shrines and temples serve as foci of social bonding, vernacular modes of building adaptations--mud walls, tin roofs, recycled stone slabs--are modes of scarcity and creativity. GIS mapping reveals the locational exclusion of squatter settlements close to industrial belts and rail tracks, accentuating exclusion from formal urban development. The settlements also serve, however, as drivers of urban culture and economic life, adding to Jodhpur's labor force, handicraft exports, and street economies. The research concludes that slums and squatter settlements are not just areas of deprivation but also living participants in the cultural design of the city. Their contribution to Jodhpur's cityscape should be accepted and their role should be recognized in planning informal geographies into inclusive planning, housing policy, and cultural heritage practices.

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Significance of Research on Jodhpur's Informal Settlements, Research Area: Jodhpur City, Pattern of Distribution of Slums and Squatter Settlements, Interconnection between Urban Informality and Cultural Landscape, Study Objectives, Urban Informality in Jodhpur, Cultural Landscape of the Informal Settlements, Planning and Environmental Issues.

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Bangladesh:From Independence to Sheikh Hasina's Resignation and the Muhammad Yunus Era

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Key words: Bangladesh, Independence, Sheikh Hasina, Muhammad Yunus, Democracy, Political Transition, Governance, South Asia

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Aarthik Udarikaran Ke Pashchat Bharat Me Dairy Udhyog Ke Yogdaan Ka Adhyayan: Samay Srikhala Vishleshan

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Aarthik Udarikaran Ke Pashchat Bharat Me Dairy Udhyog Ke Yogdaan Ka Adhyayan: Samay Srikhala Vishleshan

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A study was conducted to assess the knowledge of people from Urban and Rural area regarding prevention of dengue fever. To assess the practice of people from Urban and Rural area regarding prevention of dengue fever. To find the association between knowledge and practice score regarding dengue fever with their selected demographic variables. METHOD :A comparative research design was selected for the study, setting for the study was two communities (Rural(Mujakheda) and Urban (Dhankour GZB. 60 Sample were selected from each community MAJOR FINDING OF THE STUDY Data represented depicted that 39(65%) that is majority of people had poor knowledge, 15(25%) of people had average knowledge and 6(10%) had good knowledge in Rural area. But in Urban area34 (57%) had average knowledge, 15 (25%) of people had good knowledge and 15(25%) had poor knowledge. This indicated that the people of Urban area had more knowledge as compare with Rural area population. Data represented show that the mean score of Rural (13.06) among people was lower than the Urban people score of mean (21.45) after assessment. This indicated that the people of Urban area had more knowledge as compare with Rural and urban area population. There was no significant association between level of knowledge score with the selected demographic variables. CONCLUSION. According to knowledge and practice rural area had low knowledge and practice according to urban.

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knowledge, Practise People, Assess, Dengue ,Urban & Rural

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The primary purpose of this research is to develop and assess polyherbal antidandruff shampoo based on natural sources. The herbs were extracted using simple decoction method. The most effective herbs such as - shikakai, reetha, neem, aloevera, belpatra, pomegranate, rose oil, glycerine, methyl paraben. Polyherbal shampoo is a hair care product made consisting of a variety of herbal extracts and natural ingredients. Polyherbal shampoos don't contain the scientific ingredients found in ordinary shampoos. Unlike normal shampoos which use a single synthetic component to cure our hair problems; polyherbal shampoos use several plant-based, natural components to ensure a holistic approach to hair health. Each herb offers its own unique benefits e.g. nourishment to the scalp, enhancement of shine, strengthening of hair. Polyherbal shampoo exists due to recent desire of holistic and natural hair care solutions. This shampoo is for people looking for effective hair care solutions that emphasize both hair health and a person's well-being. The variety of herbs also provides vitamins, minerals, and antioxidants while promoting faster hair growth, reducing dandruff, and preventing damage.

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Introduction, herbal shampoo, dandruff, classification of dandruff, antidandruff shampoo, ideal properties of polyherbal anti dandruff shampoo, benefits of polyherbal anti dandruff shampoo, plant profile, neem,belpatra,shikakai,reetha,aloevera, pomegranate

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Abstract: A field survey was conducted to observe the butterfly fauna of Kundapura region by random observation from December 2023 to November 2024. Butterfly diversity was studied at four different habitat types that included forest, plantation, crop lands, and wetland. A total of 123 butterfly species belonging to the families of Hesperiidae (28 species), Papilionidae (11 species), Pieridae (10 species), Nymphalidae (44 species) and Lycaenidae (30 species) were identified. Highest butterfly diversity and abundance was recorded from areas of dense forest (76.10% of individuals), while plantation (18.58% of individuals), cropland (4.05% of individuals) and wetland (1.27% of individuals) showed lower butterfly diversity and abundance. Maximum number of individuals and species of butterflies were recorded during the monsoon season (52.57% of individuals) followed by pre-monsoon (39.40% of individuals) and post-monsoon (8.03% of individuals). As the natural habitats are dwindling due to anthropogenic causes, the butterflies too are facing survival problems. We are hopeful that the present study on butterfly diversity of Kundapura Taluk may provide basic data for detailed long term study of biodiversity of this area.

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Keywords: Diversity, Forest, Crop land, Wetland, Plantation

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