Getting access to safe and clean drinking water is still a major problem in the world, especially in areas with poor infrastructure and resources. Conventional water filtration techniques can be expensive, especially in distant locations, and also call for significant energy inputs. This research study offers a novel solution that integrates solar panels, batteries, and a Reverse Osmosis (RO) system to use solar energy for water purification in order to address these issues. By using photovoltaic panels to capture solar energy, the suggested method turns sunlight into electrical power. Water purification operations can be powered sustainably and ecologically with the help of this renewable energy source. Because the electricity produced is stored in batteries, it can run continuously even at night or during times of low solar radiation, guaranteeing constant availability to clean water. The RO purification technology, which successfully eliminates pollutants from water such as bacteria, viruses, dissolved solids, and other impurities, is essential to the system's operation. RO filtration produces great purification efficiency by using semi-permeable membranes, resulting in potable water that is acceptable for drinking and other household uses. The design, installation, and performance assessment of the solar-powered water purifying system are examined in this research article. To determine whether a system is feasible and has the potential to be widely adopted, important factors such system efficiency, reliability, and cost-effectiveness are examined. Furthermore, in comparison to traditional purification techniques, the system's sustainability and capacity to lower carbon emissions are assessed by environmental impact studies. To illustrate the usefulness and efficiency of the solar-powered water filtration system in actual environments, field tests and case studies are provided. These studies demonstrate its scalability, flexibility in responding to changing environmental conditions, and favorable socioeconomic effects, especially in marginalized areas without access to potable water.

Read More...

The rapid growth of road infrastructure in India has significantly increased energy consumption, greenhouse gas emissions, and natural resource use associated with pavement construction and maintenance. In rural and semi-urban regions, where road connectivity is expanding rapidly, the selection of pavement type plays a crucial role in determining long-term environmental and economic sustainability. This study presents a comprehensive Life Cycle Assessment (LCA) of two commonly adopted pavement systems—rigid (cement concrete) and flexible (bituminous)—constructed in rural areas of Maharashtra, India. A cradle-to-grave framework was adopted, covering material production, transportation, construction, use, maintenance, and end-of-life stages. Primary data were collected from two real road projects and combined with India-specific embodied energy and carbon emission coefficients to estimate energy consumption, CO₂ emissions, and resource utilization per kilometre of road. Results indicate that although rigid pavements involve relatively higher initial material-related energy due to cement usage, their longer design life and minimal maintenance requirements result in substantially lower annualized energy consumption and greenhouse gas emissions compared to flexible pavements. The study concludes that rigid pavements represent a more sustainable alternative for long-term rural road development and emphasizes the importance of integrating LCA-based methodologies into pavement planning and policy-making.

Read More...

India is an agricultural country where the agriculture sector is the backbone of the economy, contributing to more than 40% of the country's GDP [1]. The agricultural industry continues to struggle with various issues, including limited water availability, shifting climate patterns, and decreased yields caused by traditional and inefficient farming methods. Hence, it is essential to integrate advanced technologies into agriculture to improve efficiency and boost crop output. This project proposes an IoT-enabled Smart Agriculture Monitoring System designed to automate and streamline crop management for better productivity. System uses various sensors to monitor environmental conditions in real- time. The data collected is processed by a microcontroller and transmitted wirelessly to a web application that provides farmers with visualized information about their crops. This system is developed to be cost-effective and user-friendly, enabling farmers to remotely track crop conditions and make informed decisions to support optimal growth. The system delivers real-time insights into crop conditions, assisting farmers in making smart choices about irrigation, fertilizer application, pest management, and optimal harvesting periods. As a result, it can contribute to higher yields, lower expenses, and greater overall profitability. The project also has future implications, including the integration of machine learning and artificial intelligence technologies to further optimize crop management. With the increasing demand for food production this project offers a potential solution for promoting sustainable agriculture while tackling the pressing issues of climate change and food security.

Read More...

Pedestrian recognition plays a crucial role in modern surveillance systems, particularly in applications related to public safety and security monitoring. However, recognizing pedestrians in low-light or night-time conditions remains a significant challenge due to poor illumination, noise, and low contrast. Traditional surveillance systems often fail to provide reliable results under such conditions. This paper presents a real-time pedestrian recognition framework specifically designed for lowlight environments. The proposed system integrates low-light image enhancement techniques with deep learning-based pedestrian detection models to improve recognition accuracy. Contrast enhancement and noise reduction are applied as preprocessing steps, followed by a real-time object detection model to identify pedestrians efficiently. Experimental observations indicate that the proposed approach improves detection accuracy while maintaining real-time performance, making it suitable for enhanced surveillance applications.

Read More...

Concrete pavements, both pervious and non-pervious, play a crucial role in transportation infrastructure. However, their traditional compositions rely heavily on natural aggregates and ordinary Portland cement (OPC), which contribute to high material costs, significant carbon emissions, and rapid depletion of natural resources. Recent research explores the use of cost-efficient and sustainable materials such as supplementary cementitious materials (SCMs), recycled aggregates, industrial by-products, geopolymer binders, ceramic waste, and crumb rubber to improve performance while reducing cost and environmental impact. Pervious concrete (PC) offers additional hydraulic benefits, including stormwater infiltration and runoff reduction, while non-pervious concrete (NPC) serves high-load applications requiring high strength and durability. This review synthesizes recent findings on mix design, mechanical performance, durability, and sustainability outcomes of both pavement types incorporating low-cost alternative materials. The paper highlights practical benefits, limitations, and research needs for future adoption in transportation and urban development.

Read More...

The rapid global shift toward electric vehicles has created an urgent need for more intelligent and reliable charging infrastructure management. ChargeMate addresses this challenge by introducing an AI-driven platform designed to streamline the process of locating and scheduling vehicle charging. By leveraging real-time data and predictive machine learning algorithms, the system accurately forecasts station availability and identifies the most convenient locations.

Read More...

The rapid expansion of computer networks, cloud services, and internet-based applications has significantly increased the frequency and complexity of cyber-attacks. Intrusion Detection Systems (IDS) are essential security mechanisms designed to detect unauthorized access, misuse, and malicious activities in networked environments. However, traditional IDS approaches often suffer from limitations such as high false alarm rates, poor scalability, and ineffective detection of novel attacks. To overcome these challenges, intelligent hybrid models integrating feature selection and machine learning classifiers have been widely explored. This paper presents a comprehensive review of Rough Set Theory (RST)–based Naïve Bayes Tree (NB-Tree) approaches for Intrusion Detection Systems. Rough Set Theory is an effective mathematical tool for handling uncertainty and redundancy in high-dimensional datasets, while NB-Tree classifiers combine probabilistic learning with decision tree structures to enhance classification accuracy. The integration of RST with NB-Tree improves detection accuracy, reduces false positives, and lowers computational complexity. This review critically analyzes existing literature, identifies research gaps, outlines a methodological framework, discusses expected outcomes, and highlights future research directions. The study concludes that RST-based NB-Tree models offer an efficient, interpretable, and scalable solution for modern intrusion detection environments.

Read More...

The Fast Growth of the Aviation Industry in India, this has led to more travel alternatives, making airline choice a complex one for passengers. Passengers have to take several factors into account such as ticket price, journey time, and overall service quality while deciding upon an airline. Currently, there are many airlines Flight booking websites mainly concentrate on integer filters such as cost and stops, often failing to incorporate qualitative areas like customer satisfaction with passengers, among others perception. Further, this proposed Airline Customer Offer Recommendation system using Machine Learning and applying Sentiment Analysis to assist in passenger decision making informed travel decisions. The system combines "structured flight data with unstructured customer" reviews to develop sentiment-based airline recommendations. Natural Language Processing techniques applied to retrieve sentiment polarity from passenger reviews, which is then aggregated at the airline level to show the level of service provided. Many machine learning classifiers, including Logistic Regression, Support Vector Machine, Random Forest, Naive Bayes, XGBoost algorithms are implemented and evaluated. To improve robustness, ensemble models are also developed. Experimental results demonstrate that incorporating sentiment-based features significantly enhances recommendation accuracy and reliability. The proposed system provides balanced, data-driven, and user-centric airline suggestions, improving overall passenger decision confidence.

Read More...

Sugarcane's importance in India stems from its role as a vital cash crop providing raw materials for a large agro-based industry, generating significant employment and income for farmers and laborers, and contributing to the national economy through sugar, jaggery, and ethanol production. As the source of the second-largest agro-based industry after textiles, it supports rural economies, offers export potential, and contributes to India's goal of producing bio-fuels and saving foreign exchange by reducing crude oil imports through ethanol blending. The productivity and quality of sugarcane, a crucial commodity for the world's sugar industry, are greatly impacted by a number of illnesses. Effective management and prevention methods depend on fast and accurate disease detection. To find problems like rust, red rot, mosaic, wilt, and ratoon stunting disease, sugarcane disease detection uses both conventional visual examination and contemporary machine learning (ML) approaches, especially deep learning for image analysis. Deep learning (DL) models, trained on images of diseased leaves, can predict diseases with high accuracy (e.g., 96%), enabling farmers to take timely action using mobile applications to mitigate losses.

Read More...

Crowd management and public safety have become major challenges in urban areas due to increasing population density and large public gatherings. Traditional surveillance and manual monitoring methods are often inefficient and slow in emergency situations. This intelligent surveillance system enables lost person identification and real-time crowd density monitoring using AI-based computer vision techniques. By analyzing CCTV feeds, the system helps authorities locate missing individuals, prevent overcrowding, and generate timely alerts, thereby improving public safety and supporting smart city infrastructure.

Read More...

Managing personal finances efficiently has become increasingly challenging due to the rise in digital transactions and the absence of systematic expense monitoring tools. Traditional methods such as manual record keeping, notebooks, or spreadsheets are time-consuming, error-prone, and lack analytical capabilities, making it difficult for users to understand their spending behavior. To address these challenges, this project presents the design and development of LifeSync, a web-based expense tracking application aimed at simplifying and improving personal financial management. LifeSync provides a secure and user-friendly platform that enables users to record, categorize, and analyze their income and expenses in real time. The system supports user authentication to ensure data privacy and allows users to enter financial transactions with details such as amount, category, date, and description. All records are securely stored in a structured database, enabling efficient retrieval and processing of financial data. The backend dynamically computes total income, total expenses, savings, and category-wise expenditure, while the frontend presents this information through interactive dashboards and graphical visualizations such as pie charts and bar graphs. The application is developed using modern web technologies including HTML, CSS, JavaScript, Chart.js, and a server-side framework such as Flask or PHP, along with SQLite or MySQL for database management. The system is lightweight, responsive, and compatible across multiple devices, ensuring ease of access and usability. Offline data storage capability enhances reliability and user trust by reducing dependence on continuous internet connectivity. Experimental testing using real-world expense data demonstrates that LifeSync provides accurate calculations, fast response time, and clear financial insights. The application effectively improves financial awareness, supports better budgeting decisions, and encourages disciplined spending habits. LifeSync serves as a practical and scalable solution for personal expense management and can be further enhanced with features such as predictive analytics, mobile integration, and automated financial recommendations.

Read More...