Volume 14 | Issue 4

 Abstract

The digital transformation of financial systems has significantly reshaped entrepreneurial finance, altering how startups access capital, manage risk, and scale operations. The emergence of fin tech platforms, crowd funding models, venture capital networks, angel investment ecosystems, and data-driven financial tools has reduced traditional barriers to funding while introducing new regulatory and strategic complexities. This study examines the impact of digital financial innovation and alternative funding mechanisms on venture growth dynamics. Using a structured survey of startup founders and analytical review of contemporary literature, the research evaluates the relationship between fin tech adoption, financial literacy, regulatory environment, and venture scalability. The findings suggest that digital financial integration enhances funding accessibility and operational efficiency, while regulatory challenges and limited financial capability remain constraints. The study contributes to entrepreneurial finance literature by integrating digital transformation, funding diversification, and venture performance into a unified analytical framework.

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 Keywords

Entrepreneurial Finance, Digital Transformation, Fin Tech, Venture Capital, Crowd funding, Startup Growth, Financial Innovation, Regulatory Environment.

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Artificial Intelligence (AI) and automation are rapidly transforming industries, with marketing among the most impacted. This study, explores how these technologies are reshaping customer engagement, strategic decision-making, and business operations. In today's competitive market, businesses must enhance efficiency, deliver personalized experiences, and adapt quickly. AI and automation help achieve these goals by minimizing manual tasks and enabling data-driven marketing, allowing marketers to analyze large datasets, predict customer behavior, and execute highly targeted campaigns. The research outlines the core applications of AI and automation in marketing, including data analytics, CRM, content generation, and programmatic advertising. AI has improved audience segmentation and campaign targeting, resulting in increased customer satisfaction and higher conversion rates. Automation further supports operations by managing repetitive tasks such as email marketing, social media scheduling, and analytics reporting, enabling teams to focus on strategic priorities. Despite these benefits, the study identifies key challenges including high initial investment, ongoing technical support requirements, and ethical concerns around data privacy and algorithmic bias. Employees also highlight the importance of maintaining authentic human connections amid increasing automation. The study recommends a phased adoption approach, beginning with small pilot projects to build organizational confidence, alongside continuous employee training to bridge skill gaps. Ultimately, a balanced strategy that integrates AI efficiency with human creativity and oversight is essential for sustainable success in digital marketing.

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 Keywords

Artificial Intelligence, Automation, Personalization, Customer Engagement, Data Privacy, Marketing Strategy

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The increasing need of universal mental health support in the global context, in particular, in the high-density urban environment, presupposes that the range of digital processes is to be developed in such a way that it became user-friendly and cost-effective to a great extent. The paper at hand is on the design and architectural implementation of a proof-of-concept, artificial intelligence (AI) 3D conversational agent (CA) that will be intended to provide initial mental health advice and psychoeducational services. This system is based on the concepts of Natural Language Processing (NLP), especially which give more emphasis on reliability and predictability of dialogue rather than on generative competencies. The invention of the solution must be the creation of such a simple and deterministic dialogue core with a complex 3D graphical interface with an embodied (avatar) avatar which is able to synchronize lip movement and emotive gestures. This Human Computer Interaction (HCI) emphasis is in a bid to develop more user interaction, solve the poor adherence issue in the popular text-only systems, and so as to develop stronger sense of therapeutic presence. In a critical discussion on the deep-rooted ethical problems of accountability, privacy of data, and the constraints of artificial mimicry, in a therapeutic context, the report describes the three-layered nature of the system, formalizes non-functional requirements (NFRs), and functional requirements. The test of the implementation is how the final form of dialogue can be revived in order to assist the majority of people with mental support in a way that is immersive and readily accessible.

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 Keywords

Conversational Agents (CAs), Digital Mental Health, 3D Visualization, Rule-Based Chatbot, Human-Computer Interaction (HCI).

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Nagaland State Rural Livelihood Mission (NSRLM) is a centralized scheme which provides financial assistance and funds to the Self Help Groups. SHGs need start up fund for their income generating activities and mutual benefit of the group members. The NSRLM provides "revolving fund" for their startup and at the same time provides micro loans at a minimum rate of interest. The main objective of NSRLM is to alleviate poverty especially for rural women by creating self employment and accelerate various income generating activities which promote collective sustainable income and reduce poverty in rural areas. The study has made an attempt to find the role of NSRLM in women empowerment especially in decision making and income generating activities of the group members with sample size of 291. The logistic multiple regression model has been used to study the functional relationship.

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Self Help Groups, Revolving fund, Women Empowerment

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Around the world, brain tumors are becoming the leading cause of mortality. The inability to undertake a timely tumor diagnosis is the primary cause of this pandemic. Brain cancer diagnosis is a crucial procedure that relies on the expertise and experience of the doctor. Radiologists must use an automated tumor classification model to find brain cancers. The current model's accuracy has to be improved to get suitable therapies. Radiologists can consult various computer-aided diagnostic (CAD) models in the literature on medical imaging to assist them with their patients. Previous research has widely used CNN models for tumor detection and classification, which typically require large datasets. This research proposed the Caps-VGGNet hybrid model, which integrates the CapsNet model with the VGGNet model by adding the layers of VGGNet. The presented model addresses the challenge of requiring large datasets by automatically extracting and classifying features. The suggested algorithm's effectiveness was assessed using the Brats-2020 and Brats- 2019 dataset, which contains high-quality images of brain tumors. Compared to other conventional and hybrid models, the empirical outcomes of the suggested model indicate that it exhibited the highest level of effectiveness and superior efficacy in terms of accuracy, specificity, and sensitivity. Specifically, the presented hybrid model attained an accuracy of 0.99, a specificity of 0.99, and a sensitivity of 0.98 on the Brats20 dataset.

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Key words : Brain tumor detection, MRI detection, Image detection, image preprocessing

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The integration of Artificial Intelligence (AI) with Heritage Building Information Modelling (HBIM) represents a paradigm shift in the digital conservation of historic structures. This paper explores at theoretical methodology for using AI to create and enhance digital twins (3D digital models) of the Nampally Serai, which is a building made during the 1919 of late Asaf Jahi dynasty. Using a multi-phase approach combining drone-based photogrammetry, point cloud processing, deep learning-based damage detection, and parametric HBIM modelling, the study develops an AI-enriched digital twin of the structure. The resulting AI-HBIM environment enables scenario based structural analysis, predictive deterioration modelling, and evidence to driven conservation planning. The paper concludes that AI-integrated HBIM substantially enhances the accuracy, efficiency, and interpretive depth of heritage conservation workflows, offering a scalable and replicable model for the management of Hyderabad's endangered historic precincts.

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Artificial Intelligence, Heritage BIM, Nampally Serai, Digital Twin, Conservation, Nizam-era Architecture

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This study presents a unified multidimensional numerological framework for predicting success and failure across diverse domains. The model integrates key parameters including Personality Number (P), Destiny Number (D), Name Number, Parent Number, Root Number, Mahadasha (MD), Antardasha (AD), Pinnacle, Personal Year (PY), Event Number, and Event Date. A comparative analysis of 52 case studies (26 successful and 26 unsuccessful) is conducted to evaluate the influence of alignment, timing, and strength factors. The findings establish that success is governed by multidimensional alignment and dominant factor interaction, while failure results from structural and timing misalignment. The study proposes a Unified Prediction Model (UPM) for systematic and practical application.

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A Unified Multidimensional Numerological Model for Predicting Success and Failure Across Domains Using Parent Number and Event-Date Synchronization

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The IT industry is one of India's fastest-growing sectors, yet maintaining high employee engagement remains a persistent challenge for organisations. This study examines employee engagement and its impact on organisational productivity at Pantech Solutions, Semmancheri, Chennai. The primary objective is to assess the level of employee engagement, identify key influencing factors, and evaluate the relationship between engagement and productivity. Primary data were collected from 103 respondents using a structured questionnaire based on a Likert-scale instrument. Statistical tools including Percentage Analysis, Mean Score Analysis, Pearson Correlation, and Chi-Square tests were employed. Key findings reveal that 90.3% of respondents agree that high employee engagement contributes to overall organisational performance, and 75.7% confirm that engagement positively influences individual productivity. Critical gaps were identified in employee recognition (51.5% feel unappreciated), participative management (59.2% feel their opinions are not valued), and workload management (46.6% report unreasonable workloads). The study concludes that while engagement levels are moderate to high overall, targeted interventions in recognition, communication, and workload distribution are essential for sustaining and enhancing organisational productivity.

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Employee Engagement, Organisational Productivity, Job Satisfaction, Work Environment, Training and Development, Pantech Solutions, IT Industry.

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The quick development of immersive technologies has changed employee training methods because these technologies create interactive spaces that enable employees to learn through direct experience. The research conducted so far has not established which factors drive employee participation in these particular environments. The research study investigates how two factors technological readiness and immersive engagement contribute to employee training participation while the study investigates how self-determination functions as a bridge between these two elements. The study develops a conceptual framework that combines technological elements with psychological aspects and experiential components based on Self-Determination Theory and Technology Readiness and Immersion theories. Researchers used a quantitative research design to study 240 employees who had done technology-based training after they completed a structured questionnaire. The researchers used Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the gathered data. The research found that technological readiness as well as immersive engagement both positively influenced employee training participation. The process of self-determination functions as a mediator between technological readiness and engagement since it connects both technological readiness and engagement together with immersive engagement and engagement together. The research study examines engagement outcomes in immersive training environments through the technology adoption lens because it changes the existing literature about technology adoption. The study shows how training effectiveness improves when organizations combine their technological capacity with the psychological factors that drive their workforce. Organizations that want to create effective immersive training programs should pay attention to technological readiness and employee motivation according to the research findings.

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 Keywords

Technological Readiness, Immersive Experience, Self Determination, Employee Engagement in Training, Metaverse, PLS SEM

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Nephrotoxicity and kidney injury remain major clinical challenges worldwide, contributing significantly to morbidity and mortality. Experimental animal models are indispensable tools for understanding renal pathophysiology and evaluating the safety of drugs. This review summarizes various animal models used to study nephrotoxicity, including drug-induced, chemical-induced, ischemia-reperfusion, surgical, and disease-based models. Mechanisms of injury, biomarkers, evaluation methods, limitations, and recent advancements are also discussed. Despite limitations in translation, animal models continue to play a critical role in nephrology research and drug development.

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Nephrotoxicity,Animal model, Kidney injury,Renal Toxicity,Experimental model

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Childhood trauma represents a significant developmental challenge that can have long-lasting effects on an individual's emotional, psychological, and social functioning. Experiences such as abuse, neglect, or exposure to adverse family environments during early life can interfere with the formation of secure attachments and healthy emotional regulation. These early disruptions often extend into adulthood, influencing how individuals perceive, approach, and maintain interpersonal relationships. The present study seeks to examine the impact of childhood trauma on adult interpersonal relationships through an in-depth qualitative exploration. A phenomenological research design was employed to understand the lived experiences of individuals who reported exposure to childhood trauma. Participants within the age range of 18 to 45 years were selected using purposive sampling. Data were collected through semi-structured interviews, allowing participants to openly share their personal experiences, emotional patterns, and relational challenges. The collected data were analysed using thematic analysis to identify recurring patterns and meaningful insights related to interpersonal functioning. The analysis revealed several consistent themes across participant narratives. Many individuals reported persistent difficulties in trusting others, often accompanied by a fear of betrayal or abandonment. Emotional dysregulation emerged as another prominent theme, with participants describing challenges in managing intense emotions such as anger, anxiety, and sadness within relationships. Communication barriers, including difficulty expressing emotions and avoidance of conflict, were also commonly observed. Additionally, several participants indicated a diminished sense of self-worth, which further affected their ability to engage in secure and fulfilling relationships. Despite these challenges, the study also highlighted the presence of resilience and adaptive coping mechanisms among participants. Strategies such as self-awareness, emotional distancing, seeking social support, and engaging in personal growth practices were reported as ways to manage relational difficulties. These findings suggest that while childhood trauma can significantly influence interpersonal dynamics, individuals possess the capacity to adapt and work towards healthier relational patterns over time. Overall, the study emphasizes the profound and multifaceted impact of childhood trauma on adult relationships. It underscores the importance of trauma-informed approaches in psychological practice, particularly those that address both emotional healing and relational functioning. By providing a deeper understanding of individual's lived experiences, this research contributes to the existing body of knowledge and highlights the need for continued exploration in this area. Future research may expand on these findings by incorporating diverse populations and longitudinal perspectives to better understand the long-term progression of trauma-related relational patterns.

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Psychological impact

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Oxalic acid (C?H?O?) is an important dicarboxylic acid widely used in textiles, metallurgy, pharmaceuticals, and wastewater treatment. This paper presents a comprehensive review and experimental approach for the production of oxalic acid from sugarcane juice and molasses. The study focuses on oxidation methods using nitric acid, nitrogen oxides, and permanganate. Experimental data including yield, kinetics, and process parameters are discussed. The maximum yield reported from molasses-based oxidation reaches up to ~78-79% of theoretical yield under optimized conditions.

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Oxalic acid, Dicarboxylic acid, Sugarcane juice, Molasses, Oxidation methods, Nitric acid, Nitrogen oxides, Permanganate, Yield, Kinetics, Process parameters, Experimental data, Theoretical yield, Optimized conditions, Wastewater treatment, Textiles, Metallurgy, Pharmaceuticals.

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Ayurveda is a holistic science that emphasizes the importance of Dinacharya (daily regimen), Rtucharya (seasonal regimen), and Ahara Vihara (diet and lifestyle) in maintaining good health and preventing diseases. In today's sedentary lifestyle, irregular dietary habits and adverse work environments have contributed to the rise of various chronic and lifestyle-related disorders. As a result of this continuous exposure to incompatible foods, toxins, and improper routines, various systemic changes are occurring in our body, and their action can be explained on the basis of concept of "Dushivisha". The key factors which influence the effects of Dushi Visha are Dooshita Desha (vitiated environment),

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Ayurveda, lifestyle disorder, dushivisha,agadtantra

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Historic buildings often undergo adaptive reuse, requiring the integration of modern building services such as electrical wiring and plumbing networks. However, conservation research has primarily focused on architectural fabric while neglecting the technical infrastructure embedded within heritage structures. This study examines strategies for the conservation and adaptive reuse of historic electrical and plumbing systems in heritage buildings through a systematic literature review using the PRISMA screening method. Twenty research papers were identified, including ten Western and ten Indian studies. After applying inclusion criteria related to heritage conservation, adaptive reuse, and building services integration, the literature was analysed thematically. The review reveals that Western research largely focuses on energy retrofitting and environmental performance, while Indian studies emphasise broader heritage conservation frameworks. Despite these contributions, limited research specifically addresses the conservation of historic electrical and plumbing infrastructure. The study identifies this gap and proposes future research directions focused on documentation, reversible interventions, and conservation-compatible service integration in heritage palace architecture such as Kollapur Palace.

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Adaptive reuse; Electrical systems; Historic infrastructure; Plumbing systems; Retrofitting

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Thuja occidentalis L. is a species of the Cupressaceae family that has been widely researched as regards its aerial part, most commonly leaves and twigs. Nevertheless, one of the least explored roots is the root system even though it has been used in traditional medicine in many indigenous and folk practices. This review summarizes the existing information on the phytochemical composition of the root, pharmacognostic, ethnobotanical, extract, and pharmacological properties. Particular emphasis is put on the comparison of root and aerial parts chemistry and bioactivities. The root has a different chemical profile with reduced thujone and increased phenolics and sesquiterpenes. Adventitious root morphology and resin duct anatomy are pharmacognostic features that are described. Aerial part uses are opposed to conventional uses of digestive and genitourinary disorders. Improved extraction methods to include Soxhlet, ultrasound-assisted and green solvent methods have been shown to yield better bioactivities. Antimicrobial, anti-oxidant, and anti-inflammatory potential are examples of biological activities that help to support ethnomedicinal claims. In spite of the insights, there is still a gap in the research between standardization, mechanistic research and clinical validations, which underscores the root as an untapped therapeutic resource that is subject to further research.

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Thuja occidentalis, phytochemistry, pharmacognosy, traditional medicine, extraction methods, bioactive compounds, comparative analysis.

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This study explores the influence of brand labels on consumer trust in India's food market. In an environment characterized by increasing consumption of packaged foods and rising concerns regarding safety and authenticity, brand labels act as critical signals that guide consumer decision-making. The research integrates established branding theories, including Aaker's Brand Equity Model and Keller's Customer-Based Brand Equity framework, to examine how elements such as brand names, visual design, and labeling cues contribute to trust formation. Primary data was collected through a structured questionnaire administered to respondents across major urban centers, including Delhi, Mumbai, and Bengaluru. The study evaluates key variables such as brand perception, label interpretation, trust indicators, and purchase behaviour. Statistical tools including descriptive analysis and regression are employed to assess relationships between label attributes and consumer trust. The findings are expected to demonstrate that effective brand labeling significantly enhances consumer trust, which in turn influences purchase intention and brand loyalty. The research contributes to the existing literature by providing insights specific to the Indian food sector and offers practical implications for marketers aiming to build credible and trustworthy brand identities.

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Brand Labels, Consumer Trust, Food Market, Packaging, Brand Equity, Purchase Behaviour

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Automated road divider detection is a crucial component in intelligent transportation systems and autonomous driving applications. This project presents a real-time detection system using the advanced deep learning model YOLOv12 (You Only Look Once). The system is designed to accurately identify road dividers from images and video streams under varying environmental conditions such as lighting changes, shadows, and traffic density. The proposed approach leverages computer vision and object detection techniques to process input data, detect road dividers, and highlight them with bounding boxes. YOLOv12 enables high-speed and efficient detection by analyzing the entire image in a single pass, making it suitable for real-time applications. The model architecture is optimized to balance accuracy and computational efficiency, ensuring effective performance even on resource-constrained devices. The dataset used for training includes diverse road scenarios such as highways, urban roads, and rural environments, which improves the robustness of the system. Data augmentation techniques such as rotation, scaling, and brightness adjustment are applied to enhance model generalization. The system is evaluated using performance metrics like precision, recall, and mean Average Precision (mAP) to ensure reliability. Experimental results indicate that the proposed system achieves high detection accuracy with low latency, making it suitable for practical deployment. Additionally, the model demonstrates strong performance in detecting partially visible or occluded road dividers. This solution can be integrated into advanced driver assistance systems (ADAS), traffic monitoring systems, and autonomous vehicles to enhance road safety and navigation efficiency. Furthermore, the system can be extended by incorporating additional features such as lane detection, obstacle detection, and real-time alert mechanisms for drivers. With continuous improvements and integration of emerging technologies, automated road divider detection has the potential to play a significant role in the development of safer and smarter transportation systems.

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Automated Road Divider Detection, YOLOv12, Object Detection, Computer Vision, Deep Learning, Intelligent Transportation Systems, Autonomous Vehicles, Real-Time Detection, Image Processing, Bounding Box Detection, Traffic Monitoring, ADAS (Advanced Driver Assistance Systems), Convolutional Neural Networks (CNN), Road Safety, Smart Transportation, Video Analysis, Feature Extraction, Model Training, Dataset Annotation, Edge Computing, Embedded Systems, Scene Understanding, Urban Traffic Management,

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This paper proposes an intelligent Career Bridge Platform that bridges the gap between academic education and industrial needs by offering personalized skill assessment, learning paths, and career recommendations. The architecture of the platform is composed of a FastAPI backend with PostgreSQL database management for handling users' profile information, job history, and technical knowledge. The proposed framework utilizes Google Gemini (Generative AI) to assess users' skills and knowledge based on resume data, GitHub portfolio, LinkedIn profile, and eLearning records. From the assessment results, the Career Bridge Platform offers adaptive technical quizzes and constructs a personalized learning roadmap consisting of Foundation, Intermediate, and Advanced stages. The framework also incorporates the use of Adzuna Job Search API to offer users real-time job recommendations based on their domain, competencies, and learning roadmap topics. A mixed job matching method that employs both keyword matching rules and generative AI models to assign scores to candidates enhances the accuracy of job recommendation. Some of the other innovative capabilities of this platform include project-based learning, follow-up quiz , performance analytics, and remote proctoring.

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Artificial Intelligence, Career Recommendation System, Skill Gap Analysis, Personalized Learning, Generative AI, FastAPI, PostgreSQL, Job Recommendation, Resume Analysis, Adaptive Quiz System, Learning Roadmap Generation, Proctoring System, Web-Based Application

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User behaviour analysis through opinion mining employs natural language processing to identify and quantify subjective information in textual data. This paper presents a systematic comparative evaluation of sentiment classification methods applied to product reviews from major e-commerce and food delivery platforms including Amazon, Flipkart, and Zomato. We evaluate five major methodological approaches: lexicon-based methods (VADER), classical machine learning models (TF-IDF with Naive Bayes and SVM), word embedding techniques (FastText and GloVe), deep learning architectures (CNN, LSTM, and BiLSTM), and state-of-the-art transformer models (BERT, DistilBERT, RoBERTa, ALBERT, and XLNet). Extensive experiments conducted on large-scale public datasets demonstrate that transformer-based models achieve the highest accuracy rates (92-96%), while FastText provides a highly competitive baseline (~93-95%). We conduct comprehensive qualitative error analysis using confusion matrices and employ explainability tools (LIME and SHAP) to understand model decision-making processes and identify failure patterns. Additionally, we address critical ethical considerations including data privacy protection, algorithmic bias detection, and fairness across demographic groups. Our findings establish a rigorous benchmark and provide a methodological blueprint for reproducible user behaviour analysis research, offering practitioners evidence-based guidance for selecting appropriate methods across different application contexts and performance requirements. Sent User behaviour analysis research.

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Sentiment Analysis, Opinion Mining, Product Reviews, Transformer Models, BERT, RoBERTa, Explainability, Machine Learning, Natural Language Processing.

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