Daily Archives: June 29, 2026

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