Volume 14 | Issue 4

 Abstract

Road traffic accidents are a problem that causes deaths and bad injuries all over the world. When Road traffic accidents happen it is really important that people get help away. The old way of watching CCTV cameras is not very good because it mostly relies on people watching them and this can be very slow. The Smart Traffic Accident Detection and Alert System is a way to do things. It uses computer programs to look at CCTV pictures and find Road traffic accidents. This system uses a Convolutional Neural Network to figure out if a Road traffic accident happened. It also uses a YOLO-based object detection model to find the car and draw a box around it. When the Smart Traffic Accident Detection and Alert System thinks a Road traffic accident happened and it is pretty sure it writes down what happened and tells a person to check. The person has 30 seconds to look at it through a web page. If the person does not do anything the Smart Traffic Accident Detection and Alert System will send out emergency messages again and again. The Smart Traffic Accident Detection and Alert System is a way to help people who get hurt in Road traffic accidents. It can find Road traffic accidents quickly. It can get help to people who need it. The Smart Traffic Accident Detection and Alert System is an improvement, over the way of watching CCTV cameras.

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 Keywords

Smart traffic systems; accident detection; deep learning; YOLO; computer vision; intelligent transportation systems (ITS); emergency response automation; real-time surveillance; metadata extraction; cloud integration; Flask-based deployment; multi-camera monitoring; decision tagging; analytics dashboard; road safety.

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Many small businesses face difficulty in moving to digital platforms. The travel and tourism industry is one of the areas which could benefit from utilizing digital transformation to improve both customer reach as well as the efficiency of providing services. This research paper outlines both the design and development of a new user centered and scalable web application for tour and travel companies. This system will allow small business owners to handle all aspects of their business including managing customer's inquiries and bookings via a single web based interface. The application has been built using HTML, CSS, and JavaScript on the front end and has Firebase for managing authentication (signing up) and databases, while EmailJS has also been implemented to provide email confirmation of bookings and inquiries. With the newly developed application, users have the capability to sign up, sign in, browse through travel packages, and make reservations respectively in a fast and efficient manner. The reservations are stored in an encrypted format and can be retrieved in real time by business owners. This research paper provides a concise and functional application for small business owners that have a formalized structure supporting the digitalization of their operations. As a result, the user experience is enhanced, manual operations are reduced and overall business efficiency is markedly increased. This proposed solution is also scalable and contains the capability of additional modifications such as payment systems and administrative dashboards to be added in the future

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 Keywords

Web Application, Travel Website, Firebase, EmailJS, User Centric Design, Digital Business

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 Abstract

Invasive fungal infections (IFI) are now recognized as a serious global health threats placing a heavy burden on health care system. Treating thus infections is difficult because the available antifungal drugs often cause side effects , have limited effectiveness and face rising resistance. Over the past 30 years,researchers have carried out extensive testing of antifungal drugs.These efforts have been crucial in creating treatment guidelines for serious fungal infections. Most of these guidelines are based on well known and highly cited clinical trials. While we have made progress in developing antifungal medicines that are more effective and less toxic than older options .Early detections and targeted treatment This growing challenge highlights the urgent need for new antifungal medications. This review explores the latest advancements in antifungal drug development, focusing on innovative compounds that are either being tested in clinical trials or are likely to be evaluated soon. These new agents stand out because of their novel mechanisms of action, broader antimicrobial coverage, and improved pharmacokinetic properties. Together, these qualities hold promise for more effective treatment options than traditional drugs.

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 Keywords

: Invasive fungal infections (IFIs), Global health concern, Drug resistance, Clinical trials, Pharmacokinetics, Improved patient outcomes

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 Abstract

The proliferation of deepfake videos synthesized through deep generative models such as Generative Adversarial Networks (GANs) and neural autoencoders poses critical threats to digital security, media integrity, political stability, and public trust. Existing detection approaches either focus exclusively on spatial artifacts within individual frames using CNNs, or on temporal inconsistencies using recurrent networks, but rarely integrate both. This paper presents a novel hybrid deep learning framework combining Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) for robust, video-level deepfake detection. The proposed system employs EfficientNetB0 as the spatial feature extractor to capture per- frame facial artifacts, while a two-layer Bidirectional LSTM models temporal inconsistencies across a sequence of 20 uniformly sampled frames. Face regions are detected using the OpenCV Haar Cascade detector with 25% bounding box padding. The model is trained and evaluated on the FaceForensics++ benchmark comprising 7,000 videos across six manipulation categories: Deepfakes, Face2Face, FaceShifter, FaceSwap, NeuralTextures, and DeepFakeDetection. Class imbalance (1:5 real-to-fake ratio) is addressed through Weighted Random Sampling, label smoothing, and stratified splitting. Training employs AdamW optimization, Cosine Annealing scheduling, and PyTorch Automatic Mixed Precision on an NVIDIA Tesla T4 GPU. Experimental evaluation yields a test accuracy of 92.4%, F1-Score of 91.7%, and AUC- ROC of 0.967, demonstrating the superiority of joint spatial- temporal learning over frame-only and temporal-only approaches. A Flask-based real-time web application is developed for practical deployment, providing binary REAL / FAKE verdicts with confidence scores and REST API integration for downstream systems.

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 Keywords

Deepfake Detection, CNN-LSTM, EfficientNetB0, Bidirectional LSTM, FaceForensics++, Video Forensics, Temporal Analysis, Face Manipulation, GAN, Deep Learning, OpenCV, Flask, Real-Time Detection

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This study explores motivational HR practices in Mumbai startups and the contextual factors shaping their adoption. Drawing on pilot survey data (N=35) and secondary data from 661 Mumbai startups, the analysis identifies flexible work arrangements, regular recognition, and career growth communication as the most prevalent practices (mean scores 4.00-4.26). Conversely, equity-based incentives such as ESOPs exhibit relatively lower adoption (mean 2.91), indicating a cautious approach to ownership dilution among early-stage ventures. Contextual analysis highlights high operational costs, intense competition for skilled talent, and funding constraints as key ecosystem-level drivers of HR design choices. Bootstrapped startups (42.9 percent) display a greater reliance on non-monetary and relational motivators compared with venture-funded firms that can complement these with financial incentives. With a 71.4 percent active survival rate in the pilot sample, the findings suggest that strategically aligned motivational HR practices constitute an important lever for startup resilience and sustainability in Mumbai's dynamic but demanding environment.

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 Keywords

Motivational HR practices, startup survival, Mumbai ecosystem, employee retention, entrepreneurial HRM

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 Abstract

The advent of COVID-19 created an unprecedented divide, forcing a radical re-evaluation of this centuries-old tradition. With the closure of holy shrines and strict social distancing protocols, the traditional narrative of physical pilgrimage became impossible. This led to a profound shift, giving rise to new concepts of spiritual practice. The pandemic accelerated the adoption of technology, giving birth to the "virtual pilgrimage," where devotees could participate in online darshans and live-streamed aartis. This decoupling of the spiritual experience from a physical location is a key change. Furthermore, the enforced isolation encouraged a focus on "interior pilgrimage," a more personal and introspective journey of faith. This paper explores the contrasting pilgrimage narratives across these two phases: the pre-pandemic era, defined by physical presence and community, and the post-pandemic landscape, characterised by a hybrid model of spiritual practice. By analysing these narratives, this study aims to illuminate how Tirth Yatra has adapted, demonstrating the resilience of faith in the face of modern challenges and highlighting the new dimensions of spirituality brought about by connectivity, communication, technology, and introspection.

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 Keywords

Keywords: Tirth Yatra, Pilgrimage, COVID-19, Virtual Darshan, Spiritual Narratives, Hybrid Faith, Indian Temples

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Agro-waste fibers, Natural dyes, Mordanting techniques, Eco-friendly home decor, Sustainable craftsmanship, Value-added products

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 Abstract

Military supply chain logistics in complex and contested environments depends critically on timely, accurate terrain assessment and adaptive route planning. Traditional methods relying on manual reconnaissance or satellite optical imagery often fall short in adverse weather and low-visibility conditions. This paper presents an integrated Explainable Ar- tificial Intelligence (XAI) framework that fuses Sentinel-1 (S1) Synthetic Aperture Radar (SAR) and Sentinel-2 (S2) multispec- tral satellite imagery for automated terrain classification and intelligent military route optimization. A lightweight Convolu- tional Neural Network (CNN) is trained to classify terrain into four operationally relevant categories--urban, grassland, barren- land, and agricultural--with confidence estimation. Gradient- weighted Class Activation Mapping (Grad-CAM) is applied to provide pixel-level visual explanations of the model's decisions, supporting transparency and trust in mission-critical deploy- ments. Route safety is scored dynamically based on terrain type across three candidate paths, and an integrated Large Language Model (Qwen2.5) generates natural-language military logistics justifications. The complete system is deployed as a web-based interactive application built using Django, Bootstrap, HTML5, CSS3, JavaScript, and SQLite3. Experimental results on the OpenSARUrban and SEN12 datasets demonstrate 97.4% terrain classification accuracy alongside meaningful and actionable route recommendations. The proposed framework advances trustwor- thy AI for defense logistics and sets a foundation for real-time battlefield supply chain intelligence.

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 Keywords

Synthetic Aperture Radar, Explainable AI, Grad-CAM, Military Supply Chain, Terrain Classification, Con- volutional Neural Network, Route Optimization, Sentinel-1, Sentinel-2, Large Language Models, Django, Deep Learning

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 Abstract

Social media platforms such as Instagram have experienced rapid growth over the past decade. However, this expansion has also introduced significant risks, including fake profiles, impersonation attacks, spam conversations, and phishing attempts. Malicious actors frequently exploit deceptive profile information and social engineering methods, making it genuinely difficult for ordinary users to spot fraudulent accounts. Existing detection solutions are mostly controlled by the platforms them- selves and operate as closed, opaque systems that offer very little transparency or direct control to individual users.

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 Keywords

Fake account detection, Instagram authenticity, machine learning, TF-IDF, Random Forest, XGBoost, Naive Bayes, heuristic analysis, social media security, risk scoring.

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 Abstract

The ancient Uttarapatha, meaning the "Northern Road," represents one of the earliest extensive trade networks in the Indian subcontinent, serving as a vital artery for interaction between South Asia, Central Asia, and the wider Eurasian region. Predating the widespread recognition of the Silk Route, this corridor facilitated connections among prominent urban centres, including Taxila, Mathura, Varanasi, and Pataliputra. Through these linkages, it enabled not only commercial exchange but also the movement of ideas, cultures, and religious traditions.

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 Keywords

Uttarapatha, Silk Route, Ancient Indian Trade Routes, Trans-Asian Trade Networks, Cultural Exchange, Central Asia, Connectivity, Economic History.

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In Jain meditative wisdom, a mantra is defined as a word or phrase in which the power of pure souls is inherent. Central to this tradition is the sacred mantra Om Arham Namaha. As stated in the Arham A??a?ga Yog ?atakam (v. 25), "????? ???????? ??????? ??????????????????" ? "Arham is the seed-letter principle, the single supreme essence of all mantras." This study explores the linguistic, symbolic, and transformative dimensions of this foundational sound

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Om Arham Namaha, B?ja-ak?ara, Arham Dhyan Yog, Jainism & Meditation, Muni Pra?amya Sagar, Contemporary Yoga Studies

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 Abstract

Formula 1 (F1) motorsport operates at the intersec- tion of high-speed engineering and data-driven decision making, where fractions of a second determine competitive outcomes. Despite the existence of publicly accessible telemetry libraries such as FastF1 and structured APIs like the Ergast Developer API, the raw data they expose remains largely inaccessible to students, independent analysts, and motorsport enthusiasts due to the technical complexity involved in extraction and interpretation. This paper presents the design and development of an interactive web-based platform that consolidates F1 telemetry and race data into a structured analytical dashboard. The system integrates data pipelines built on FastF1 and Ergast, performs preprocessing to normalize lap times, telemetry channels, and sector timings, and exposes processed results through a responsive frontend built with ReactJS. Core analytical features include lap-time differential computation, multi-driver telemetry overlays, speed- versus-distance profiles, sector performance comparisons, and consistency metrics derived from statistical measures of lap- time variance. The architecture follows a modular layered design encompassing an API controller, data processor, database storage layer, and visualization module. Evaluation confirms that the platform accurately renders historical race sessions and provides meaningful performance insights in an intuitive visual format. The work demonstrates practical application of data engineering, dynamic visualization, and web application development within the domain of real-world motorsport analytics.

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 Keywords

Formula 1, FastF1, Telemetry Analysis, Race Data Visualization, Lap Time Comparison, Driver Performance, Ergast API, Web Dashboard, Data Preprocessing, Interactive Charts

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 Abstract

Understanding customer behavior in online shop- ping environments plays a central role in shaping marketing strategies and building personalized user experiences. This paper presents a machine-learning-based framework that leverages clickstream behavioral data to perform customer segmentation and deliver targeted product recommendations. The study utilizes a publicly available e-commerce dataset from Kaggle, which contains detailed records of user interactions such as product browsing, add-to-cart actions, and purchase completions. From this dataset, a set of behavioral features is derived that collec- tively captures spending patterns, order frequency, engagement intensity, and sensitivity to discounted products. These features serve as inputs to the K-Means clustering algorithm, an unsuper- vised machine learning technique that partitions customers into groups sharing similar behavioral characteristics. To improve the interpretability of the resulting clusters, Principal Component Analysis (PCA) is applied to reduce the multi-dimensional feature space into three principal components representing spending and order intensity, engagement behavior, and discount sensitivity. The reduced representation enables effective visualization of customer clusters and supports analysis of inter-group behavioral differences. Leveraging the identified customer segments, the system generates segment-specific product recommendations and delivers them through an automated email notification pipeline. The proposed approach demonstrates how clickstream-based behavioral analytics, combined with unsupervised learning and dimensionality reduction, can support meaningful personalization and targeted marketing in e-commerce environments.

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Clickstream Data, Customer Segmentation, K-Means Clustering, Principal Component Analysis, Personalized Recom- mendations, E-Commerce, Behavioral Analytics, Unsupervised Machine Learning

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 Abstract

Inguinal hernia is one of the most common surgical conditions seen in adult males, especially among those involved in strenuous physical work. Direct inguinal hernia occurs due to weakness of the posterior wall of the inguinal canal and may extend into the scrotum in long-standing cases. The coexistence of inguinal hernia in a patient with a past history of abdominal tuberculosis is uncommon and may create diagnostic and management challenges. We present the case of a 39-year-old male who complained of right inguinoscrotal swelling for 5-6 months associated with mild pain. The swelling was reducible and had a positive cough impulse. Ultrasonography confirmed a right direct inguinoscrotal hernia with a 1.2 cm defect and protrusion of bowel loops on Valsalva maneuver. The patient had a past history of abdominal Koch's, ascites, and diagnostic laparoscopy, for which anti-tubercular treatment had been completed. Routine laboratory investigations were within acceptable limits for surgery. The patient underwent right open hernioplasty with polypropylene mesh under spinal anesthesia. The intraoperative and postoperative period was uneventful. The patient recovered well and was discharged in stable condition. This case highlights successful management of direct inguinoscrotal hernia with mesh hernioplasty in a patient with previous abdominal tuberculosis and supports the effectiveness of conventional surgical treatment in such presentations.

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 Keywords

Keywords: Inguinal hernia, Direct inguinal hernia, Inguinoscrotal hernia, Hernioplasty, Mesh repair, Abdominal tuberculosis

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Creative Commons Attribution 4.0 and The Open Definition

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College Event Management, Event Registration, Razorpay Integration, Payment Verification, QR-Code Entry Pass, Attendance Tracking, Analytics Dashboard, CSV Reporting, React, Spring Boot, MySQL, JWT, ZXing QR, html5-qrcode, Chart.js

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In today's digital era, online recruitment platforms have made job searching easier, but they also present challenges such as irrelevant job recommendations and the increasing presence of fraudulent job postings. To address these issues, this project proposes a Resume-Centric Job Recommendation and Fake Job Detection System using machine learning techniques. The system analyzes uploaded resumes by extracting text from PDF files and identifying key skills and competencies through keyword processing. It then matches these skills with job descriptions obtained from real-time job search APIs. To measure the relevance between a candidate's profile and job descriptions, TF-IDF (Term Frequency-Inverse Document Frequency) is used for feature extraction, and cosine similarity is applied to compute a similarity score, enabling accurate and personalized job recommendations. Additionally, the system enhances user safety by detecting fraudulent job postings using a Logistic Regression classifier trained on labeled job data to identify suspicious patterns and classify listings as genuine or fake. The complete system is implemented using the Flask framework, providing an interactive and user-friendly web interface where users can upload resumes and filter job results based on criteria such as location and role. Experimental results demonstrate that the proposed system improves recommendation accuracy while significantly increasing trust and reliability in online recruitment platforms by effectively identifying fake job listings.

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Resume-Centric Job Recommendation, Fake Job Detection, Logistic Regression, Machine Learning, TF-IDF Vectorization, Cosine Similarity, Skill Matching, Recruitment Fraud Analysis,Flask-Based Web System.

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This study examines how employer branding influences both the attraction of potential candidates and the retention of existing employees. As competition for skill talent intensifies, organizations increasingly rely on strong and authentic employer branding to differentiate themselves in the job market. The research explores key factors which shapes employee perceptions and hiring decisions. Using a descriptive research design and primary data collected from about 50 respondents, the study analyzes how employer branding affects job seekers application intentions and employees' willingness to remain with their current organizations. The findings reveal that a positive employer brand significantly enhances talent attraction, strengthens engagement, and reduces turnover, while inconsistencies between the brand message and actual employee experience negatively impact trust and retention. The study concludes that organizations must invest in transparent communication, employee well-being, career development, and strong leadership to build a credible employer brand that supports long-term workforce stability. Future research may explore industry-wise comparisons and the evolving impact of remote and hybrid work on employer branding strategies.

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Keywords- Employee Retention, Employee Experience, Employer Branding, Talent Attraction, Turnover Reduction.

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 Abstract

ABSTRACT Knee joint pain is a common musculoskeletal problem among the geriatric population, significantly affecting their mobility and quality of life. This study aimed to assess the effectiveness of hot water application with Epsom salt versus hot water application alone on the level of knee joint pain among geriatrics in a selected area at Karungal, Kanyakumri District. A true experimental research design with a quantitative evaluative approach was adopted. The study included three groups: control group, experimental group I (hot water application with Epsom salt), and experimental group II (hot water application alone). The independent variables were hot water application with Epsom salt and hot water application, while the dependent variable was the level of knee joint pain. Data were collected using a structured interview schedule, including demographic variables and a numerical pain rating scale. The tool's reliability was confirmed using the test-retest method (r = 0.9). A pilot study established feasibility. The conceptual framework was based on the Gate Control Theory by Melzack and Wall. The findings revealed a significant reduction in knee joint pain levels in both experimental groups compared to the control group. However, experimental group I showed greater effectiveness than experimental group II. Statistical analysis (unpaired 't' test) indicated a significant difference between the two interventions. There was no significant association between post-test pain levels and selected demographic variables. The study concluded that hot water application with Epsom salt is more effective than hot water application alone in reducing knee joint pain among geriatrics. The intervention is simple, cost-effective, and can be recommended as a non-pharmacological method for pain management in elderly individuals.

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Knee joint pain, Geriatrics, Hot water application, Epsom salt, Non-pharmacological intervention, Pain management, Musculoskeletal disorders, Numerical pain rating scale, Gate Control Theory, Experimental study, Elderly care, Hydrotherapy

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 Abstract

In recent years, the shift toward online work has increased significantly, especially after the COVID-19 pandemic. While working remotely provides flexibility, it has also led to higher stress levels due to continuous screen usage, workload pressure, and reduced social interaction. This paper presents a stress analysis and care prediction system designed for online workers. The system observes user behavior through facial expressions, typing patterns, and physiological signals to understand stress levels in real time. A combination of deep learning and machine learning techniques is used to improve the system's performance. The model is implemented using a Raspberry Pi setup, making it practical for real-world usage. Compared to traditional methods, this approach focuses on multiple inputs instead of relying on a single parameter, which helps in achieving better accuracy. The system also provides feedback to users so they can take necessary actions to manage their stress. Overall, this work aims to provide a simple and effective solution to support the well-being of people working in digital environments.

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Stress Detection, Machine Learning, Facial Recognition, Real-Time Monitoring, Raspberry P

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Creative Commons Attribution 4.0 and The Open Definition

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Ensuring transparency and reliability in electoral processes is essential for maintaining public trust in democratic systems. Conventional voting methods, including paper-based ballots and electronic voting machines, have raised concerns regarding security, transparency, and potential manipulation of results. This study proposes a blockchain-enabled electronic voting system designed to improve the integrity and auditability of digital elections. The system leverages Ethereum smart contracts to securely record votes on a decentralized ledger, ensuring that once a vote is submitted it cannot be modified or removed. Voter authentication is carried out through MetaMask wallet verification, which supports the enforcement of a one-voter- one-vote policy while preserving user anonymity. The architecture combines a web-based voting interface with blockchain-based smart contracts to manage vote submission and storage in an immutable ledger. Experimental implementation demonstrates that the proposed approach enhances transparency, reduces the possibility of vote tampering, and enables verifiable election results. The system can serve as a practical framework for conducting secure digital elections in educational institutions, organizational environments, and other controlled voting scenarios.

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 Keywords

lockchain, Electronic Voting, Smart Contracts, Ethereum, MetaMask.

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Creative Commons Attribution 4.0 and The Open Definition