Volume 14 | Issue 4 |

 Abstract

Digital marketing has become an important tool for communication, product promotion, customer engagement, and business growth. In India, digital technology is expanding rapidly through smartphones, internet services, and digital payment systems. However, many rural areas are still unable to use digital tools effectively for business purposes. Rural Akola is also facing a similar situation.

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 Keywords

Digital Marketing, Rural Akola, Adoption, Awareness, Farmers, Youth, Rural Development.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Bharat Stage 6.2 automotive emission norms draw attention to exploring alternative fuels like Compressed Natural Gas (CNG), which offers cleaner combustion and reduced emissions. However, maintaining the BS 6.2 emission compliance levels requires a fine balance between combustion stability and after-treatment performance. The purpose of this review is to systematically analyze previous research related to the influence of air-fuel ratio on engine performance, emission formation, and catalyst conversion efficiency in spark-ignition CNG engines. The review spans from early experimental works in the 2000s to recent studies incorporating advanced control strategies, catalyst optimization, and BS VI calibration technologies. Several studies have analyzed ?, but most either focus only on combustion or only on catalyst performance, or often use single-cylinder test engines or older measurement techniques. Very few studies combine ? variation, multi-cylinder engines, and BS 6.2-compliant emission testing. This review summarizes key findings from existing research, trends, identifies technological progress, discusses research gaps that remain unexplored, and highlights the need for integrated studies that link ?, engine combustion, and catalyst efficiency for modern CNG engines.

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 Keywords

CNG engine, Air-Fuel Ratio (?), NOx, Methane Slip, Three-Way Catalyst (TWC), ECU Calibration, air-fuel ratio, BS 6.2, emissions.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

With the rapid increase in urban vehicles, traffic violations such as overspeeding and non-compliance with safety rules have become a major cause of road accidents. Manual traffic monitoring systems require significant manpower and are often inefficient in detecting real-time violations. To address these challenges, an intelligent and automated traffic monitoring solution is required. TrafficGuard - Intelligent Traffic Violation Detection & Management System is an AI-powered system designed to detect traffic violations using computer vision and deep learning techniques. The system utilizes YOLOv8 object detection for identifying vehicles from video streams and calculates vehicle speed using frame-based distance tracking. If a vehicle exceeds the specified speed limit, the system automatically generates a challan and sends an email notification to the vehicle owner. The system also includes a web-based payment portal developed using Streamlit, allowing users to securely pay fines online using a unique challan ID. An Admin Dashboard provides real-time statistics, revenue analysis, and violation management features. The system ensures automation, accuracy, transparency, and scalability in traffic violation detection and management. The assessment is conducted through a web-based interface developed using Streamlit, where users answer the generated questions in real time. The system automatically evaluates responses, calculates scores, and performs performance analysis using data analytics techniques. Detailed assessment reports are generated in PDF format and delivered via email, enabling users to review their strengths and areas for improvement. SkillGauge AI improves efficiency, scalability, and fairness in interview assessments, making it suitable for recruitment processes, training institutions, and students seeking to evaluate and enhance their technical capabilities.

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 Keywords

Traffic Violation Detection, YOLOv8, Computer Vision, Deep Learning, Speed Estimation, Automated Challan System, Streamlit, Web-Based Application, Email Notification, Admin Dashboard, Data Analytics, Online Payment System, AI-Based Assessment, Skill Evaluation, Interview Automation.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In the contemporary globalized and technologically driven world, effective English communication skills have become essential for engineering students to succeed in academic, professional, and multicultural environments. However, many technical students often face challenges in developing fluency, confidence, and accuracy in communication due to limited exposure and traditional language teaching methods. With the rapid advancement of digital technologies, Artificial Intelligence (AI) has emerged as a transformative force in the field of education, particularly in language learning and teaching. AI-driven tools such as intelligent chatbots, speech recognition systems, automated writing assistants, and adaptive learning platforms provide innovative approaches to enhance language learning experiences. These technologies enable students to practice language skills interactively while receiving instant feedback, thereby improving their listening, speaking, reading, and writing abilities. In technical education, where students often focus more on subject-specific knowledge, AI-based language learning tools can play a crucial role in strengthening their communication competence. This paper explores the role of AI-driven English language teaching in enhancing the communication skills of engineering students. It examines how AI technologies support personalized learning, increase learner engagement, and create dynamic classroom environments that promote effective communication. The study highlights that integrating AI with traditional pedagogical practices can significantly improve students' communicative abilities and confidence. The paper concludes that AI-driven language teaching offers promising possibilities for the future of English language education. When used effectively, AI can complement the role of teachers and contribute to developing globally competent engineering graduates with strong communication skills.

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 Keywords

Artificial Intelligence, English Language Teaching, Communication Skills, Engineering Students, AI-Assisted Learning, Language Pedagogy

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Phishing attacks remain one of the most persistent and evolving threats to internet security, causing significant financial losses and compromising sensitive data worldwide. To address this challenge, Web Integrity Shield presents an intelligent, real-time phishing detection system aimed at safeguarding users by proactively identifying malicious websites before harm occurs. By combining traditional URL-based heuristics with advanced machine learning, the system provides a proactive layer of defense that evolves alongside emerging phishing tactics. The proposed solution employs hybrid AI architecture integrating multiple technologies for accuracy and scalability. Initially, a machine learning model is trained in Python using a comprehensive dataset containing URL-based heuristic features. To further enhance detection, the system leverages Selenium WebDriver for automated, headless browser analysis within a secure, sandboxed backend. This enables dynamic extraction of features from the website's DOM structure, JavaScript execution patterns, and visual layout. The enriched feature set is then processed by the trained model, serialized into ONNX format, and deployed through a Java Spring Boot backend. A lightweight JavaScript browser extension functions as the client, performing quick feature extraction and querying the backend API for instant predictions. This modular architecture ensures scalability, high accuracy, and separation of data science and deployment. The system delivers real-time protection against evolving phishing threats while remaining efficient and user-friendly

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 Keywords

Artificial Intelligence, Machine Learning, Phishing Detection, Cybersecurity, Web Security, Feature Extraction, Ensemble Learning, Real-Time Systems, Browser Extensions, ONNX Runtime, Selenium WebDriver

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Urbanization is one of the most widespread forms of land-use change worldwide, radically reshaping natural environments and generating new urban ecosystems with a heavy human footprint. Urbanization exposes wildlife to new resource landscapes, light and noise pollution, diminished predator presence, and human contact. The purpose of this study is to explore the diversity of wildlife responses in urban areas, including nesting and feeding adaptations. Through a qualitative, secondary-data-based review, we searched peer-reviewed journals, books, government documents, and conservation databases for information about urban wildlife using thematic content analysis. The review presents evidence of vertebrate species' nesting adaptations by using buildings, bridges, roofs, and other urban structures, and adding non-natural materials, like plastic, wire, and fabric, to their nests. Feeding adaptations include greater dependence on human-provided food sources, such as discarded human food and waste and landscaping, as well as diversification of omnivorous and opportunistic feeding habits. The results suggest that behavioral adaptability and plasticity predict species' success in urban areas, allowing certain generalist species (pigeons, crows, rats, squirrels, foxes, and macaques) to flourish. On the other hand, specialists, large carnivores, and disturbance-prone species are generally absent from dense urban areas. The changes in behavior have profound ecological consequences, such as changes in food webs, increased interspecific competition and a greater potential for human-wildlife conflicts. The study recommends that biodiversity-friendly urban planning and design, with the inclusion of green infrastructure (such as wildlife corridors and nesting sites) and proper waste management, are necessary to facilitate human-wildlife co-existence.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Urban wildlife; Behavioral adaptation; Nesting habits; Diet change; Urban ecology; Human-wildlife interaction

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Mouth dissolving tablets offer a practical alternative to traditional solid dosage forms, enabling rapid disintegration and dissolution in the mouth without the need for water. This property is especially beneficial for patients with swallowing difficulties, such as those in paediatrics, geriatrics, or bedridden populations. Domperidone, a dopamine antagonist commonly used to treat nausea, vomiting, and gastrointestinal disorders, is often administered in tablet form, which may pose challenges for certain patient groups due to its size and delayed onset of action. Formulating Domperidone into mouth dissolving could enhance patient compliance and improve therapeutic outcomes by ensuring faster absorption. The present study involves two innovative techniques namely sublimation and effervescence for developing Domperidone mouth dissolving tablets. The sublimation method utilizes a volatile material to create a porous structure, while the effervescence technique generates gas through an acid-base reaction to facilitate rapid tablet disintegration. Both approaches aim to optimize the dissolution and absorption of Domperidone, thus offering a promising solution for improving patient care, particularly for those requiring prompt relief from gastrointestinal symptoms.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Mouth dissolving tablets, Domperidone, Sublimation method, Effervescent method.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Unexpected machine failures in modern industrial environments remain a critical challenge, leading to production downtime, increased operational costs, and safety risks. Traditional maintenance strategies based on fixed schedules often fail to capture complex degradation patterns present in sensor data. This paper presents a predictive maintenance (PdM) framework that leverages supervised machine learning to classify machine health states and detect potential failures. The proposed system utilizes the AI4I 2020 Predictive Maintenance Dataset, which consists of multivariate sensor data including Air temperature, Process temperature, Rotational speed, Torque, and Tool wear. Three supervised learning models--Support Vector Machines (SVM), Random Forest (RF), and XGBoost--are trained and evaluated on this dataset. A hybrid ensemble approach using a Voting Classifier is implemented to improve prediction robustness and overall classification performance. To enhance interpretability, feature importance analysis derived from the Random Forest model is used to identify key contributing factors. The analysis highlights Torque and Tool wear as the most significant indicators of machine failure, providing actionable insights for maintenance decision-making. Furthermore, a Streamlit-based graphical user interface (GUI) is developed to enable interactive and near real-time prediction using user-provided inputs. Experimental results demonstrate that the hybrid model outperforms individual models in terms of accuracy, making the proposed system a practical solution for predictive maintenance in Industry 4.0 environments.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Predictive Maintenance, Hybrid Learning Framework, Fault Diagnosis, Feature Engineering, Supervised Learning, Model Evaluation, F1-Score

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Chronic ankle instability (CAI) is a common musculoskeletal condition characterized by sequential ankle sprains, impaired proprioception and reduced postural stability. Although the ankle joint can be dealt with as the principal target of the classic rehabilitation interventions, the recent studies have shown that the proximal muscle activities particularly the gluteal ones should be regarded as a significant source of lower limb stability. At the same time, one of the most ubiquitous aspects of contemporary sedentary living has been associated with the reduction in muscle activity specifically in the gluteal region namely; extended sitting.

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 Keywords

Chronic Ankle Instability (CAI), Postural Stability, Gluteal Inhibition, Prolonged Sitting, Y Balance Test, Electromyography (EMG), Neuromuscular Control, Kinetic Chain, Balance Deficits, Physiotherapy Rehabilitation.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study examines the role of play in enhancing cognitive development among children aged 2-6 years. Using a survey-based mixed-method approach, data were collected from parents to analyze relationships between play engagement, screen time, and cognitive outcomes. The findings indicate that increased play is positively associated with improvements in attention, memory, language, and problem-solving abilities. In contrast, excessive screen time is linked to reduced attention and behavioral challenges. The study highlights the importance of promoting play-based learning and regulating screen exposure to support healthy cognitive development in early childhood.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords: play, cognitive development, early childhood, screen time, learning

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Creative Commons Attribution 4.0 and The Open Definition