Authors: Ruchi Gandhi

Abstract: Religious tourism is one of the most important and significant sectors of the Indian tourism industry. It plays a major role in contributing to economic growth, employment generation, infrastructure development, cultural preservation, and regional development. Gujarat is the one of the states from India which has rich religious heritage and some of them are known worldwide such as Somnath, Dwarka, Ambaji, Dakor and Palitana. However, some other religious destinations are underdevelopment though they have tourism potential. One such destination is Kameshwar Mahadev Temple, situated on the bank of the Ambika River in Gadat village, Navsari District, Gujarat. The main purpose of this research is to investigate the potential for sustainable development of Kameshwar Mahadev Temple as a regional religious tourism destination. The study evaluates the temple’s historical significance, geographical setting, tourism resources, visitor characteristics, existing infrastructure, environmental attributes, and socio-economic context. Furthermore, it examines opportunities and constraints associated with tourism development through SWOT analysis and sustainable tourism assessment frameworks. The research uses the mixed-method approach which is based on secondary data, demographic analysis, tourism statistics, infrastructure assessment, policy review, and qualitative evaluation. Findings indicate that Kameshwar Mahadev Temple possesses significant strengths including religious importance, strategic accessibility, natural landscapes, cultural heritage, and an established visitor base. Nevertheless, deficiencies in tourism infrastructure, accommodation facilities, sanitation, destination marketing, and community participation continue to constrain its development potential. This study introduces a master plan for tourism that combines better roads and facilities, environmental protection, community involvement, smart marketing, and teamwork among local authorities. The findings show that focusing on sustainable tourism can turn the Kameshwar Mahadev Temple into a major regional pilgrimage site. This development will boost the local economy and create jobs for residents while fully protecting the surrounding natural resources.

DOI: http://doi.org/10.5281/zenodo.21033316

Published by: vikaspatanker

Authors: Kester Nwinuazor Neemana, Victor Dugbor

Abstract: The interaction between soil and structure (SSI) is a key factor in determination of the dynamic response of foundation systems under cyclic and impact loading. However, most of the previous studies concentrated on the conventional concrete foundations and the effects of other sustainable materials are rarely studied under complex loading condition. Further, few research has focused on the interaction between cyclic and impact loading in a nonlinear-SSI model. The aim of this study is to overcome these shortcomings by developing an advanced nonlinear numerical model for comparing the SSI performance of geopolymer and conventional foundations under combined cyclic and impact loading. The model incorporated soil stiffness degradation, damping characteristics of the soil materials and introduces a novel Damage Accumulation Index (DAI) to quantify progressive deterioration. Using MATLAB simulation approach, transient and steady state dynamic responses were captured in time domain analysis. The results shows that geopolymer foundations outperform the conventional foundations in all the important parameters. In particular, the peak displacement was reduced by ~4.69% while the reduction in velocity and acceleration responses was ~7.62% and the stiffness degradation was ~6.54%, respectively. Moreover, geopolymer foundations have energy dissipation capacity of about 7.35% higher. The proposed DAI model also shows that the cumulative damage was reduced by ~27.33%. These results verify a better damping and better stiffness retention capacity and a better resistance to dynamic loading effects of geopolymer foundations. The study confirms that geopolymer foundation offers a promising sustainable alternative for infrastructure subjected to cyclic, impact, and seismic loading conditions.

DOI: https://doi.org/10.5281/zenodo.21032123

Published by: vikaspatanker

Authors: Rushikesh Falke, Vishal Bagal, Pranav Bartakke

Abstract: Choosing the right career path is a critical decision for students and often requires personalized guidance based on their interests, skills, and abilities. This paper proposes an Intelligent Questionnaire-Based Career Path Recommendation System that utilizes the Random Forest machine learning algorithm to recommend suitable career options. The system collects user responses through a structured questionnaire covering personality traits, technical skills, academic interests, aptitude, and career preferences. The collected data are processed and analyzed using a trained Random Forest model to predict the most appropriate career path. In addition to career recommendations, the system provides guidance on relevant skills and learning resources to enhance career readiness. A web-based interface enables users to complete the assessment and receive recommendations instantly. The proposed approach improves the accuracy and personalization of career guidance compared with traditional counseling methods. The experimental results demonstrate that the system provides reliable recommendations and supports students in making informed career decisions. The proposed framework is scalable and can be extended with real-time job market data and advanced AI techniques in future work.

DOI: https://doi.org/10.5281/zenodo.21031283

Published by: vikaspatanker

Authors: Shivam Namdev, Bhanu Pratap Singh

Abstract: The rapid increase in urbanization, public security challenges, and criminal activities has accelerated the development of intelligent surveillance systems for real-time violence detection and criminal activity identification. Traditional surveillance systems often depend heavily on manual monitoring, which limits detection efficiency, increases response time, and reduces reliability in complex environments. Recent advancements in deep learning, machine learning, computer vision, sensor networks, and predictive analytics have significantly improved automated surveillance capabilities for public safety management. This review presents an intelligent deep learning framework for violence detection and criminal activity identification in smart surveillance systems by analyzing recent developments in convolutional neural networks (CNNs), 3D-CNNs, ConvLSTM architectures, transfer learning, optimization techniques, and sensor-based monitoring systems. The framework integrates video analytics, spatiotemporal feature extraction, facial recognition, object detection, anomaly detection, and predictive threat analysis into a unified intelligent surveillance ecosystem. Furthermore, the study highlights the role of real-time monitoring, smart city technologies, and intelligent decision-support systems in improving public security operations. The review indicates that deep learning-based surveillance frameworks significantly improve violence detection accuracy, reduce false alarms, enhance predictive threat identification, and support automated emergency response systems in modern smart environments.

Published by: vikaspatanker

Authors: Prakash Gahora, Bhanu Pratap Singh

Abstract: The rapid growth of digital communication, cloud computing, Internet of Things (IoT), and smart infrastructures has significantly increased cybersecurity threats and network vulnerabilities. Traditional intrusion detection systems (IDS) often struggle to detect sophisticated and evolving cyber-attacks due to their dependence on static rule-based mechanisms. To address these limitations, Machine Learning (ML) and Explainable Artificial Intelligence (XAI) have emerged as promising solutions for intelligent and adaptive intrusion detection. This research explores the integration of ML and XAI techniques in intrusion detection systems to improve attack detection accuracy, transparency, and real-time threat response. The study reviews various machine learning approaches, including supervised learning, deep learning, reinforcement learning, and federated learning methods used in modern IDS frameworks. Additionally, the role of explainable AI in enhancing trust, interpretability, and decision-making within cybersecurity systems is examined. The proposed approach emphasizes intelligent threat detection, reduced false alarm rates, and improved adaptability in IoT, industrial, and distributed computing environments. The findings indicate that AI-driven IDS frameworks provide efficient and scalable cybersecurity solutions capable of addressing emerging cyber threats while ensuring transparency and reliability in security operations.

Published by: vikaspatanker

Authors: Jyoti Gahora, Bhanu Pratap Singh

Abstract: Brain tumors are among the most critical neurological disorders that require early and accurate diagnosis for effective treatment and improved patient survival. Magnetic Resonance Imaging (MRI) is widely used for brain tumor diagnosis because of its superior soft tissue visualization capability. However, manual tumor detection and classification are time-consuming and highly dependent on radiologists’ expertise. To overcome these limitations, this research proposes an intelligent MRI-based brain tumor detection and classification system using deep learning techniques. The proposed framework integrates preprocessing, segmentation, feature extraction, deep learning classification, and performance evaluation into a unified automated system. Initially, MRI images undergo preprocessing steps such as artifact removal, noise reduction, intensity normalization, and bias field correction to improve image quality. Segmentation techniques including thresholding, region growing, and watershed algorithms are then applied to isolate tumor regions from healthy brain tissues. Histogram-based, texture-based, and shape-based features are extracted to improve discriminative learning. The EfficientNetB3 deep learning model is employed for tumor and non-tumor classification due to its efficient feature learning and lightweight architecture. Hyperparameter tuning techniques such as optimized learning rate, batch size, dropout regularization, and data augmentation are used to improve classification performance and reduce overfitting. The proposed model achieves high performance with improved accuracy, precision, recall, and F1-score compared to existing approaches. Experimental results demonstrate that the proposed framework provides accurate and reliable brain tumor detection with enhanced segmentation and classification capability. The system also supports intelligent clinical decision-making and has the potential for future real-time healthcare applications.

Published by: vikaspatanker

Authors: Atharv Nitin Gore, Rushikesh Vijay Kolhe, Samarth Suresh Gaikwad, Professor Snehal Phate

Abstract: This paper proposes an AI-powered lead management platform designed to optimize sales pipeline efficiency through a Hybrid Machine Learning Classifier. The system incorporates multi-channel lead capture, XGBoost-based lead scoring, BERT-driven intent detection, lead deduplication, and CRM synchronization to enable real-time qualification and conversion of leads. With an F1 Score of 87% and AUC-ROC of 93%, it ensures a reliable and data-driven pipeline management experience. The system supports various lead sources including web forms, social media, email, and API integrations, making it highly adaptable for B2B and B2C enterprises. Ethical considerations are addressed through strong privacy safeguards, JWT-based authentication, and GDPR-compliant data management. Additionally, it minimizes manual sales effort, reduces lead response time by 89%, and enhances conversion rates by 79%. This solution establishes a reliable framework for secure, automated, and scalable lead management in digital marketing and sales operations. By leveraging advanced AI techniques such as XGBoost scoring, BERT intent detection, and fuzzy deduplication, the system effectively prioritizes high-value prospects to maximize pipeline conversion.

DOI: https://doi.org/10.5281/zenodo.20962801

Published by: vikaspatanker

Authors: Miss. Pemmanaboyina Durga Devi, Miss. Savarapu Suhasini

Abstract: Cloud computing has become the foundation of modern digital services by providing scalable, flexible, and cost-effective computing resources for organizations across various domains. Despite its widespread adoption, the increasing complexity of cloud infrastructures has introduced numerous security challenges, including unauthorized access, insecure configurations, application vulnerabilities, distributed denial-of-service (DDoS) attacks, and abnormal network activities. Conventional cloud security mechanisms primarily rely on rule-based detection techniques, which often struggle to identify sophisticated and previously unknown cyber threats in dynamic cloud environments. To overcome these limitations, this paper proposes an intelligent machine learning-based framework for cloud vulnerability detection and threat prevention. The proposed framework analyzes security-related information collected from system logs, network traffic records, cloud service activities, and vulnerability reports to identify malicious behavior and potential security risks. Comprehensive data preprocessing and feature engineering techniques are employed to improve data quality before training multiple machine learning models, including Decision Tree, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and Isolation Forest. The effectiveness of these algorithms is evaluated using performance metrics such as accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC analysis. Experimental results demonstrate that the Random Forest model achieves superior detection performance by accurately identifying cloud vulnerabilities while maintaining a low false alarm rate. The proposed framework enables real-time threat monitoring, intelligent anomaly detection, and adaptive security analysis, thereby improving the resilience, reliability, and overall protection of distributed cloud infrastructures against evolving cyber threats.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue3.479

Published by: vikaspatanker

Authors: Miss. Tatipaka Pooja, Miss. Savarapu Suhasini

Abstract: Accurate credit risk assessment is essential for financial institutions to minimize loan defaults and support effective lending decisions. Conventional loan evaluation processes largely depend on manual analysis of customer financial information, making them time-consuming, inconsistent, and susceptible to human bias. With the rapid advancement of machine learning, intelligent prediction models have emerged as efficient solutions for automating credit risk evaluation and improving decision-making accuracy. This paper presents an intelligent credit risk prediction framework that utilizes machine learning algorithms to classify loan applicants based on their probability of loan repayment or default. The proposed framework analyzes customer financial and demographic attributes, including credit history, checking account status, employment status, loan amount, loan duration, and applicant age. Data preprocessing techniques such as missing value handling, outlier removal, categorical feature encoding, and feature scaling are employed to enhance data quality before model training. Multiple machine learning algorithms, including Decision Tree, Random Forest, Support Vector Machine (SVM), Naïve Bayes, Multi-Layer Perceptron (MLP), and a Stacking Ensemble model, are implemented and comparatively evaluated using performance metrics such as accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC analysis. Experimental results indicate that the ensemble learning approach consistently outperforms individual classifiers by achieving higher prediction accuracy and improved generalization capability. The proposed framework provides a reliable, scalable, and data-driven solution for intelligent credit risk assessment, enabling financial institutions to improve loan approval decisions, reduce financial losses, and strengthen overall credit risk management.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue3.478

Published by: vikaspatanker

Authors: Mr. Chokkakula Chaitanya, Miss. Savarapu Suhasini

Abstract: Heart disease is one of the leading causes of mortality worldwide, making early and accurate diagnosis essential. This study proposes a next-generation heart disease prediction framework using Quantum Machine Learning (QML) and presents a comparative evaluation with traditional machine learning approaches. A clinical heart disease dataset containing attributes such as age, gender, blood pressure, cholesterol level, and heart rate is pre-processed, balanced, and divided into training and testing sets. Traditional algorithms, including Logistic Regression, Support Vector Machine (SVM), Naïve Bayes, Decision Tree, K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA), are compared with Quantum Machine Learning models for disease prediction. The models are evaluated using 5-fold cross-validation with accuracy, precision, recall, F1-score, and ROC-AUC as performance metrics. Results show that Logistic Regression and Linear Discriminant Analysis achieve the best performance among classical models, while Quantum Machine Learning demonstrates competitive prediction capability with improved feature representation. The proposed framework highlights the potential of QML as a scalable and intelligent solution for next-generation heart disease prediction and clinical decision support.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue3.477

Published by: vikaspatanker