A Portland cement pervious concrete PCPC mixes made with various types of amounts of aggregate, cementitious materials, admixtures were evaluated. Workability, Compressive strength and Permeability of concrete were tests. Five factors included here are water-cement ratio, cement aggregate ratio, recycle aggregate, sand and grades considered in orthogonal test. Adopts tagauchi's approach with an L16(215) OA and two-level factors.

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This research investigates the transformative impact of Artificial Intelligence (AI) on modern marketing practices, exploring the revolutionary tools and applications reshaping the industry. Employing a mixed-methods approach, combining quantitative analysis of survey data with qualitative insights from interviews and case studies, this study provides a comprehensive understanding of AI's current utilization and its tangible effects on marketing outcomes. Findings reveal AI's potential to significantly enhance marketing efficiencies, personalize customer experiences, and drive measurable results through advanced analytics and automation. However, the study also underscores the critical importance of addressing ethical considerations, ensuring data quality, and recognizing the indispensable role of human oversight in AI-driven marketing strategies. By identifying research gaps in integrated AI frameworks and the longitudinal effects of AI on consumer behavior, this research offers actionable recommendations for practitioners, aiming to harness AI's transformative power and maintain a competitive edge in an increasingly AI-infused marketing landscape. Ultimately, this study contributes to a deeper understanding of AI's role in shaping the future of marketing and provides a foundation for developing effective, ethical, and value-driven AI strategies.

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Growth and development of economy triggers expansion in urbanization. Municipal Solid Waste (MSW) management is one of the essential services for maintaining quality of life in urban areas and for ensuring better standards of health and sanitation. Presently, this service falls short of the desired level, as systems adopted are outdated and inefficient. Institutional weakness, shortage of human and financial resources, improper choice of technology, inadequate coverage and lack of short- and long-term planning are responsible for the inadequacy of service. This often induces migration of population from rural & semi urban areas to big towns and cities. Unless a proper planning is undertaken well Solid Waste Management 8 ahead of time, the uncontrolled growth in urbanization can strain municipal infrastructures like water supply, sewage and solid waste disposal resulting in deterioration of environment and public health problems. This study examines the solid waste management (SWM) practices and waste characteristics in Lucknow, Uttar Pradesh, India. Creation of waste is natural by human being but being human it needs proper disposal .The solid waste is ugly in appearance , creates foul odour , creates nuisance and ultimately creates unhygienic conditions Improper management of solid waste leads to unhygienic conditions which results in spread of diseases It evaluates the waste generation rates, composition, collection, transportation, and disposal methods, providing insights into the challenges faced by the Lucknow Municipal Corporation (LMC). Recommendations are proposed for improving SWM through sustainable practices and infrastructure development. This research contributes to the growing body of knowledge on urban waste management in rapidly developing cities. the uncontrolled growth in urbanization can strain municipal infrastructures like water supply, sewage and solid waste disposal resulting in deterioration of environment and public health problems. For maximum efficiency and effectiveness of this service, it is necessary to tackle this problem systematically by analyzing the present scenario of the MSW management and come forward with a cost-effective system which ensures adequate level of MSW management services to all class of citizens of Lucknow.

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Agriculture is India's backbone, as agriculture is the primary occupation of 50 percent to 55 percent of the country's population. As a result, it is essential to introduce new agricultural implementation. The project's ultimate purpose is to create a multipurpose agricultural vehicle capable of performing tasks such as spraying, grass cutting, and tilling. The modification entails the creation of a small, compact piece of equipment. The project is about developing a machine that makes cultivation much easier. The vehicle's chassis has been designed in such a way that it is suitable for operations.

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Effective crowd management is crucial for large temples to ensure a safe and enjoyable experience. However, managing massive crowds can be daunting. This project proposes a comprehensive crowd management system leveraging advanced technologies like IR sensors, automated walkway escalators, and ultrasonic sensors. The system aims to optimize crowd flow, reduce congestion, and enhance devotee satisfaction. This proposed system aims to address these challenges by integrating cutting-edge technologies, including IoT sensors, servo motors, and web applications.

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Construction waste management is a critical aspect of sustainable development, aiming to minimize environmental impact and promote efficient resource utilization. The increasing volume of construction and demolition waste contributes significantly to land pollution, resource depletion, and carbon emissions. Effective waste management strategies, including waste reduction, recycling, and proper disposal, play a vital role in mitigating these challenges. This study explores innovative and sustainable techniques for construction waste management, focusing on modern technologies, policy frameworks, and industry best practices. By integrating advanced waste management solutions, the construction industry can enhance sustainability, reduce costs, and contribute to a circular economy. The research highlights the importance of stakeholder collaboration, regulatory enforcement, and technological advancements in achieving a more sustainable and responsible construction sector.

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Advancements in ultra-precision machining have driven significant innovations in single-point cutting tools, nano-cutting strategies, and process optimization techniques. This review consolidates findings from experimental and simulation studies on tool material development, structured surface texturing, cutting mechanics, and nano-scale deformation mechanisms. Emphasis is placed on diamond tool modification, tool edge geometry, surface integrity, and molecular dynamics simulations. The review highlights critical parameters influencing tool performance, wear resistance, subsurface damage, and cutting efficiency, providing insights into future development trends in ultra-precision manufacturing and nanomachining.

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Inconel is a nickel-based alloy known for its ability to endure extremely high temperatures while maintaining most of its mechanical strength. Inconel super-alloys are challenging to machine due to their robustness at high cutting temperatures and sensitivity to heat generated by conventional methods like drilling, milling, and grinding. These traditional techniques struggle with poor surface quality, high tool wear, and unexpected tool failure, largely due to the alloy's low thermal conductivity. To address these challenges, non-traditional methods are employed to effectively machine super-alloys. Laser machining for Inconel, a non-traditional technique is employed which takes into account factors like depth of cut, laser power, distance from the laser to the workpiece, cutting speed, and feed rate. Key outputs, such as surface roughness, surface finish, cutting forces, machining temperature, etc. are closely monitored. This review paper aims to provide an overview of various experiments conducted on Inconel using Laser beam machining and Laser-assisted machining.

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The synthetic pathways and in-depth of synthesis and antimicrobial study a series of six 4-((E)-benzylidene)-2-((E)-styryl) oxazol-5(4H)-one (5) derivatives can be described. These moieties were prepared by condensation of substituted aromatic aldehydes (4) and cinnamoylglycine (3) with acetic anhydride in the presence ZnO as a catalyst. The compound (3) was prepared from cinnamoylchloride (2) with glycine and cinnamoylchloride (2) can be obtained from cinnamic acid and thionylchloride in the presence of MDC at 5-100C. All the six evaluated by advanced spectral data (1HNMR, 13CNMR & LCMS) and the structure of the all-titled derivatives can be estimated by elemental analysis. In addition to evaluation of ant-microbial properties against four bacterial strains as well as two fungal strains.

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The practice of farming today is strange and unusual. The world demands food but the land and water continue to dry up. According to an uncle from my hometown farming is a life-sustaining occupation yet it removes all life energy from people. Thus, we decided to develop a system through our combination of swarm intelligence and artificial intelligence systems. Dragonfly-like drones position themselves among fields and detect weary crops as well as insect pests before applying precise/targeted chemical applications. We used our laptop as a starting point to create the system before we applied it to a tiny agricultural field. During an inquiry with my local uncle he responded with "The flying robot shortens the morning chores yet it remains a financial burden" The current expensive costs do not stop this innovative system from defining future agricultural development.

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The project "Empowering Farmers with Online Shopping" is made to help farmers buy farming products easily through the internet. Today, many farmers face problems in getting good quality seeds, fertilizers, and tools. Sometimes, they have to travel far or depend on shopkeepers who charge high prices. This project gives farmers a better option by creating a simple online shopping system just for them. Through this system, farmers can buy all the things they need for farming—like seeds, fertilizers, pesticides, pipes, farming tools, and machines—from their home or village using a mobile phone or computer. They do not have to go to big cities or markets. This saves time, money, and energy. The platform is designed in a simple way, so even farmers who are not used to using the internet can understand it easily. It will be available in local languages to help more farmers use it comfortably. The products will have pictures, prices, and descriptions, so farmers can choose the right items. They can also read reviews from other farmers who already used the product. The system will show prices clearly, so there is no confusion or cheating. By buying directly from the seller, farmers can avoid middlemen and get products at better prices. This can help increase their profit and reduce their farming costs. The platform can also give helpful tips, such as how to grow crops better, how to use fertilizers, or how to protect plants from insects. This information will be shared in simple language and videos, so farmers can learn and improve their farming skills.

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With the rapid proliferation of IoT devices, ensuring network security has become a critical challenge. Traditional security measures often fail to detect sophisticated cyber threats targeting IoT networks. This project, IoTShield: Intelligent Intrusion Detection System for IoT Networks, proposes an advanced intrusion detection mechanism leveraging machine learning to classify network traffic as either malicious or normal. IoTShield is trained on the TON_IoT dataset and integrates Support Vector Machines (SVM) and Artificial Neural Networks (ANN) to enhance detection accuracy. The system allows users to upload IoT network traffic data, processes it using pre-trained models, and generates detailed classification reports and accuracy metrics. Additionally, feature selection techniques like Symmetric Uncertainty are applied to optimize model performance and reduce false positives. Designed for scalability, IoTShield can be deployed in enterprise IoT environments to monitor thousands of connected devices in real-time. By providing automated intrusion detection and visual analytics, the system strengthens IoT security, mitigating potential threats before they impact critical infrastructure.

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Healthcare accessibility remains a persistent challenge in many underdeveloped regions, including India, where the public health infrastructure is overburdened and private healthcare often fails to penetrate rural and remote areas. As an alternative model, cooperative hospitals—rooted in community ownership and participatory governance—offer a promising solution. This study explores the role of community involvement in enhancing the effectiveness of cooperative hospitals, with a specific focus on Malappuram district in Kerala, a region known for its robust cooperative movement and diverse population. Through a case study approach, supported by both qualitative and quantitative data, the research investigates how cooperative hospitals in Malappuram engage with the local community, foster trust, and impact health outcomes. The findings highlight that active community participation not only tailors healthcare services to local needs but also cultivates trust, accountability, and sustainability in service delivery. However, challenges such as resource constraints, administrative limitations, and scaling community engagement persist. The study contributes to the limited but growing body of literature on cooperative healthcare models in India and underscores the importance of participatory frameworks in strengthening healthcare delivery, particularly in socioeconomically diverse regions. The insights from Malappuram offer valuable lessons for policymakers, healthcare practitioners, and cooperative institutions aiming to improve healthcare inclusion through community-driven models.

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In the money related administrations industry, credit card extortion is a major issue. Each year, credit card extortion costs billions of dollars in misplaced revenue. Due to privacy concerns, there are not numerous thinks about on the examination of genuine credit card data. In this ponder, credit card extortion is recognized utilizing machine learning techniques. First, standard models are employed. Next, cross breed approaches that combine lion's share voting with AdaBoost are used. The viability of the demonstrate is surveyed utilizing a freely open credit card information set. After at that point, a budgetary institution's genuine credit card information set is examined. To encourage assess the algorithms' versatility, clamor is too presented into the information samples. The lion's share vote approach has great exactness rates in distinguishing credit card extortion occurrences, concurring to the trial information.

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The study presents a machine learning-based drug recommendation system designed to reduce medical prescription errors by leveraging patient reviews and sentiment analysis. By integrating data from various sources, the system predicts drug effectiveness and suggests the best medication for specific medical conditions. The approach combines natural language processing, supervised learning, and data analysis to extract insights from user feedback. Experimental results demonstrate the system's capability to enhance decision-making in drug prescriptions, ensuring improved patient outcomes. The paper also outlines limitations and proposes directions for future work, such as expanding datasets and improving real-time adaptability. The increasing reliance on digital platforms for healthcare guidance has highlighted the potential of machine learning in reducing medical prescription errors. This study proposes a drug recommendation system that uses patient reviews and sentiment analysis to suggest appropriate medications for specific conditions. Leveraging datasets with over 160,000 reviews, the system employs natural language processing and machine learning algorithms to classify sentiment and rank drugs based on effectiveness. Experimental results validate the system's ability to provide accurate recommendations, demonstrating its potential to enhance healthcare delivery, reduce prescription errors, and improve patient outcomes.

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This project presents an Aptitude Quiz Website built to assist users in preparing for various competitive exams and job assessments. The website hosts a variety of aptitude-based multiple-choice questions (MCQs) categorized by topic and difficulty level. It includes features such as a real-time countdown timer, instant feedback, scoring system, and performance analytics. The main objective is to provide a self-paced, user-friendly, and interactive learning platform that improves critical thinking and problem-solving skills through regular practice. The website is responsive, lightweight, and accessible from any device with a web browser.

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Fire and smoke detection is a critical component of early warning systems aimed at preventing the rapid spread of fires in both urban and forest environments. Traditional detection methods, such as thermal sensors and manual monitoring, often suffer from limitations including delayed response times, environmental dependencies, and high maintenance costs. In this study, we propose a real-time fire and smoke detection system utilizing the YOLOv8 deep learning model. The system is trained on a dataset of over 11,000 annotated images sourced from Roboflow, enabling high-accuracy detection in diverse scenarios. The proposed method incorporates a structured pipeline comprising image preprocessing, normalization, model training, and alert generation. Experimental results demonstrate that the YOLOv8 model achieves a mean average precision (mAP@50) of 0.926, with a precision of 0.954 and recall of 0.848, outperforming previous YOLO variants. This system offers a robust solution for real-time fire and smoke detection, with applications in forest surveillance, urban safety, and industrial monitoring.

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The Underground Cable Fault Distance Locator using Arduino is a tool that helps identify and fix problems in underground power cables. Since these cables are hidden, it can be difficult to find faults quickly. Quick detection is important to save time and reduce repair costs. This project makes use of an Arduino microcontroller, voltage divider circuits, and basic principles of Ohm's law. It finds problems in cables by measuring changes in resistance. A series of resistors act like the underground cable, and we simulate a fault by creating a short circuit at various points. When a fault is detected, the system calculates the fault's distance and displays it on an LCD screen. This system is both affordable and practical for power companies. It can locate faults accurately, thereby reducing downtime and improving efficiency. This makes it a valuable tool for maintaining and managing power distribution networks effectively.

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The most straight forward synthetic route was involved preparation of 2-(5-bromo-6-methylpyridin-3-yl)-5-phenyl-1, 3, 4-oxadiazole (5a-5h) from the cyclodehydration of 2-(5-bromo-6-methylpyridin-3-yl)-5-phenyl-1, 3, 4-oxadiazole (5a-5h) from the cyclodehydration of 5-bromo-6-methylnicotinohydrazide (3) with substituted aromatic aldehyde(4) which typically requires harsh reagents such as I₂/K₂CO₃ in followed by grinding method . The intermediate (3) can be obtained from the compound (2) with hydrazine hydrate and an intermediate (2) was prepared by the reaction between 5-bromo-6-methylnicotic acid with thionyl chloride in non-polar medium. The required derivatives can be evaluated by advanced spectral analysis such as ¹HNMR, ¹³CNMR and LCMS and the structural derivatives determined by elemental analysis. The biological activity of the titled analogous examined against antimicrobial studies.

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Handyman: A Household Solution System is an innovative platform designed to simplify and enhance the process of finding and hiring skilled professionals for household maintenance, repairs, and improvement services. The system provides an efficient, user-friendly interface that enables homeowners to connect with verified handymen for a variety of tasks, including plumbing, electrical work, carpentry, painting, appliance repair and general home maintenance. This solution incorporates key features such as user registration, service categorization, real-time availability tracking, online booking, secure payment options, and a rating/review system to ensure quality service delivery. Additionally, it offers automated reminders, job status tracking, and emergency service requests to cater to urgent household needs.

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Music generation has gained significant traction with the advent of deep learning techniques, particularly through the use of recurrent neural networks (RNNs) like Long Short-Term Memory (LSTM) networks. The Extended Long Short-Term Memory (xLSTM) model enhances traditional LSTMs by introducing innovations such as exponential gating and modified memory structures, which are particularly beneficial for capturing the complexities of musical composition. This review paper explores the application of xLSTM in music generation, detailing its architecture, advantages, and performance compared to traditional LSTMs and other state-of-the-art models.

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Computer network and information technology fields are changing at a fast pace, inducing enormous change in the means of people's learning, work, and way of living. The COVID-19 pandemic has further increased the rate of the change towards online learning, making digital examination systems inevitable to evaluate students' performance. The planned system, Quizzology, aims to overcome the drawbacks of conventional manual testing approaches and cater to the growing demand for contactless, paperless exams. This system seeks to automate the process of examination through a completely automated platform that conserves time, minimizes human effort, and provides fast and accurate results. It enables institutions or organizations to administer multiple tests for any number of candidates at once, promoting effective test administration and assessment. The application also provides features to include new topics, create customized tests, and generate random question sets to avert cheating. Additionally, the system is environmentally friendly as it does away with paper-based exams and is platform-independent using RESTful APIs, making it available on Windows, Linux, Mac, and Android platforms. It also provides data security and real-time performance monitoring. The online quiz system is beginner-friendly as well as advanced user-friendly, making it a perfect tool for learning in academia, placement preparation, and skill evaluation during difficult times such as the pandemic.

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Noise pollution has been a major concern for us these days, which causes various issues like headaches, increased stress levels, sleeplessness at night, hearing impairments etc. The people living in urban areas are more prone to it and almost all of us could have been exposed to noise pollution, especially when we are travelling. One of the major contributors of noise pollution is the fixed volumes of the vehicles that we use for travelling. These fixed volumes have high range of sound which causes irritation to the public when they are travelling in congested and traffic prone areas. This paper presents an IoT-powered Smart Environmental Adaptive Horn, which adjusts the sound output based on the surrounding environmental noise levels like in sensitive areas the sound will be low and in others it will be adjusted based on certain criteria like the closeness of the nearby vehicle in such a way that it is not a disturbance to the public. This system uses “IoT based sensors” and “GPS” to analyze the surrounding noise levels and detect the kind of environment in which the vehicle is moving and modulates the horn volume based on the collected data. This approach offers a sustainable solution to noise pollution while enhancing road safety and public health. Its adaptability shows its potential to transform and control the urban noise pollution especially during travel.

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Managing shared expenses is a dull undertaking that frequently results in errors, misunderstandings, and miscommunications in contemporary social and professional contexts. Traditional approaches to informal settlements and manual expense monitoring are opaque and prone to human error. This paper presents a Group Expense Tracking System that automates and simplifies the process of real-time expense recording, dividing, and settlement in order to allay these worries. Users of the platform can add participants, make their own groups, and assign expenses to each member. A dynamic dashboard that delivers insights into users' balances and previous settlements allows users to interact with the system, which is built with a scalable, microservices-based architecture and enables high availability, fault tolerance, and safe data handling. The Group Expense Tracking System offers a dependable and easy-to-use way to manage group expenses by fusing sophisticated debt management, real-time communication, and strong security measures.

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The real estate industry is undergoing a transformative shift with the integration of Artificial Intelligence (AI). This paper presents an AI-based real estate web application designed to revolutionize property search, valuation, and customer interaction. The system leverages advanced AI technologies, including Natural Language Processing (NLP) for user interaction, machine learning algorithms for personalized property recommendations, and predictive analytics for accurate property valuation. By integrating these technologies into a unified platform, the proposed web application addresses the inefficiencies of traditional real estate processes, such as manual property searches, subjective valuations, and limited customer support. Experimental results demonstrate that the system achieves an 85% accuracy rate in matching user preferences with available properties, while the AI-powered chatbot responds to queries in under 2 seconds, significantly enhancing user satisfaction. This study highlights the potential of AI to streamline real estate operations, improve decision-making, and enhance user experience. Future enhancements include mobile application deployment, edge AI integration, and dataset expansion to further improve system robustness and scalability. This research contributes to the growing field of AI in real estate, offering a practical and scalable solution for modern property management.

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