In response to the growing incidence of the disease, this study creates a machine learning pipeline for diabetes pre diction utilizing the Pima Indians Diabetes database. This paper presents the analysis of outliers and its effects on the performance of prediction model. In this study, we included Naive Bayes, Random Forest, Decision Trees, KNN, and Logistic Regression. The analysis gives the selection of optimal predictive method, significant features identification, and the effects of class imbalance. Based on clinical factors such as blood pressure, insulin, glucose, and BMI, we performed this study. Preprocessing the data is a part of the pipeline to handle outliers and missing values and provide objective training. Performance measurements are used, with an emphasis on F1-score because of class imbalance. These metrics include accuracy, precision, recall, and F1-score. We analyze the results on the basis of these performance parameters and concluded that Random Forest attaining the maximum accuracy and F1 score.

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This project focuses on creating a real-time ride-sharing platform designed to offer a more efficient, sustainable, and accessible mode of transport by linking passengers with drivers heading in similar directions. This project focuses on creating a seamless user experience through an app that allows users to request rides, match with available drivers, track their journey in real time, and share costs. Built using a robust tech stack, the app features a secure user authentication system, dynamic location tracking with GPS, and an intuitive interface to manage ride details. Key features include real-time notifications, fare estimation, in-app chat functionality, and user ratings, fostering a trustworthy and interactive platform. The app also integrates a payment gateway, ensuring secure and convenient transactions for users. This project provides a sustainable alternative to traditional travel, reducing traffic congestion and carbon emissions through shared rides. By applying state-of-the-art technologies and adhering to high software quality standards, this real-time ride-sharing app has the potential to transform urban mobility and set a foundation for future improvements in ride-sharing.

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The project titled "Direct Market Access for Farmers & Equipment Rental System" aims to create an innovative platform that connects farmers directly with consumers and businesses, streamlining the sale of agricultural products while facilitating the rental of essential farming equipment. This dual-function system addresses the challenges faced by farmers in accessing markets and optimizing resource utilization. By providing a direct marketplace, the platform enables farmers to showcase their produce, set competitive prices, and engage with buyers without intermediaries, thereby maximizing their profits. The integrated equipment rental feature allows farmers to access a variety of machinery and tools on-demand, reducing the financial burden of purchasing expensive equipment outright. Utilizing user-friendly interfaces and advanced algorithms, the system ensures a seamless experience for both farmers and consumers. Features such as real-time inventory tracking, secure payment options, and customer feedback mechanisms enhance trust and transparency in transactions. This project not only supports local economies by promoting direct sales but also fosters sustainable agricultural practices by enabling efficient resource sharing. Overall, the "Direct Market Access for Farmers & Equipment Rental System" represents a significant step forward in empowering farmers, improving market access, and enhancing the overall efficiency of the agricultural supply chain.

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This study attempts an economic evaluation of solid waste management (SWM) in Uttar Pradesh, specifically in the city of Lucknow. It analyzes waste generation, composition, collection systems, and disposal practices, offering valuable perspectives on the SWM challenges and capabilities within the city. Results show that about 47% of waste is organic waste, making the proper separation between different types of waste essential to reduce transport costs and reduce the impact on the environment. Despite positive step-changes in waste collection, such as the roll-out of a range of vehicles and smart bins, challenges like wet waste disposal or under-delivery on waste-to-energy solutions remain challenges. This way, analysis of the impact of MSW disposal highlights adverse effects on soil and water supply. The study calls for better practices, like the use of liners to reduce leachate contamination. As the study compares the systems of Lucknow against those of Delhi, it highlights a gap between the two cities, where Lucknow can benefit from a more economical, yet effective waste-to-energy systems based on this study, while in the longer run, it reiterates the necessity of integrated waste management system for sustainable economic and environmental development.

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The rise of unhealthy snacking habits and an increasing focus on health-conscious consumption have led to a growing demand for nutritious, minimally processed snacks. This study investigates the formulation, nutritional composition, and consumer acceptability of Moringa Power Bites, a functional snack designed to provide a balanced nutritional profile while aligning with global trends in plant-based and sustainable food products. Moringa oleifera, commonly referred to as the Miracle Tree, is a nutrient-rich superfood containing essential amino acids, fiber, vitamins, minerals, and phytochemicals. The study aims to maximize the nutritional benefits of moringa while maintaining desirable sensory attributes such as taste, texture, and appearance. A total of ten formulations were developed using moringa powder, dates, peanuts, orange juice, and a mix of seeds (sunflower, pumpkin, and flax seeds). These ingredients were selected for their complementary health benefits, including high protein content, natural sweetness (no added sugar), and fiber enrichment. The formulations underwent proximate and nutritional analyses to assess moisture, ash content, protein, fat, carbohydrates, fiber, iron, and vitamin C levels. The best three samples were identified based on their balanced macronutrient composition, with Sample 9 (3.5:6) emerging as the most optimal in terms of carbohydrates (58.5g), protein (5.23g), iron (17.2mg), and fiber (2.99g) per serving. Sensory evaluation was conducted using a 9-point Hedonic Scale, assessing attributes such as appearance, color, taste, flavor, texture, and overall acceptability. Among the samples, Sample 9 scored the highest, with an appearance rating of 8.1 and overall acceptability of 8.0, indicating strong consumer preference. Further statistical analysis using the t-test revealed significant differences between certain samples, confirming the importance of ingredient ratios in optimizing taste and texture. A consumer acceptability test involving 100 participants showed 51% of respondents expressing strong preference, 31% moderate preference, while only 3% expressed moderate dislike. The average acceptability score was 4.3 out of 5, demonstrating positive consumer perception. Additionally, microbial analysis (Total Plate Count using the Spread Plate Method) confirmed a low bacterial load (3.2 × 10⁶ CFU/g), ensuring the product's microbiological safety and shelf stability without requiring artificial preservatives. The findings suggest that Moringa Power Bites effectively address consumer demand for sustainable, plant-based, and functional foods. The product offers multiple health benefits, including enhanced digestion, improved heart health, balanced blood sugar levels, and immunity support, making it an ideal choice for health-conscious individuals.

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This paper presents a comparative analysis of three DC-DC converter topologies—Boost, Interleaved, and Quasi-Z-Source for electric vehicle (EV) charger applications. The primary objective is to assess their effectiveness in reducing charging time, a key parameter for efficient EV infrastructure. With increasing EV adoption, optimizing charger design remains critical to support transportation needs. Simulations conducted in MATLAB/Simulink evaluated these converters charging a 320 V, 100 Ah lithium-ion battery from 20% to 80% state of charge (SOC) using a 110 V, 60 Hz grid input. Results indicate the Quasi-Z-Source converter achieves the shortest charging time of 1.35 hours, outperforming the interleaved converter at 1.5 hours and the Boost converter at 1.62 hours. This 11-18% advantage arises from its enhanced boost factor and lower switching stress, improving energy transfer efficiency. The Boost converter, despite simplicity, demonstrates slower charging due to discontinuous output, while the interleaved converter reduces ripple but offers limited voltage gain. These findings highlight the Quasi-Z-Source converter's suitability for fast-charging systems, offering quantitative insights for selecting topologies in EV charger design and advancing infrastructure efficiency.

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The visibility of well-known figures like celebrities and social media influencers has drastically increased due to the increase usage of social media platforms, thus making them susceptible to online harassment and doxxing. Doxxing entails exposing personal information about individuals without their consent, mostly with an aim of intimidating or harassing them through unwanted attention, threats or physical harm. Despite its increasing rate, particularly among celebrities, there are no regulatory frameworks designed to combat doxxing in India. This absence emphasises serious legal issues regarding privacy rights and cyberbullying. This article examines the applicability of existing provisions on cybercrime law, privacy laws and defamation laws in major jurisdictions worldwide. After identifying the legal gaps, the researcher proposes specific changes that will help define doxxing more clearly and strengthen the legal framework aimed at protecting individuals from such assault on privacy.

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This project focuses on designing, analyzing, and fabricating an Articulated Foot Orthosis (AFO) to address foot drop, improving gait stability and mobility. Using CATIA V5, a 3D model was developed with an articulated ankle joint allowing adjustable dorsiflexion. Finite Element Analysis (FEA) in ANSYS assessed stress distribution and structural integrity under gait conditions. Materials like PLA, ABS, and CFRP were evaluated, with PLA selected for its superior mechanical properties and lightweight nature. The AFO was fabricated using 3D printing, ensuring a custom fit and enhanced comfort. Gait analysis demonstrated improved ankle dorsiflexion, foot clearance, and overall stability. This research underscores the potential of integrating CAD, 3D printing, and FEA to develop patient-centric AFOs, offering an effective solution for foot drop and gait impairments.

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This research paper introduces a Hybrid Deep Learning Method for Nail Health Classification that can precisely differentiate between healthy and unhealthy nails. The system uses a Convolutional Neural Network (CNN) for feature extraction, taking advantage of its capability to detect detailed patterns like edges, textures, and shapes necessary for medical image analysis. After feature extraction, three different classifiers—Decision Tree, Gradient Boosting, and K-Nearest Neighbors (KNN)—are used to carry out the classification process. These classifiers were chosen judiciously based on their complementary strengths: Decision Tree for interpretability and low computational cost, Gradient Boosting for high accuracy and robustness against overfitting, and KNN for its ability to capture local patterns in the data. The dataset includes images of healthy and diseased nails, divided into training and validation sets to provide strong model evaluation. For consistency, all images were resized to 150x150 pixels, and data augmentation methods were used to improve model generalization. The CNN model was built with three convolutional layers and max-pooling layers, ending with a fully connected layer for feature extraction. These are the high-level features which were then passed on as inputs to the three classifiers, and all three classifiers were trained and tested upon the same dataset. The models scored individually 87.57% for Decision Tree, 87.57% for Gradient Boosting, and 88.65% for KNN. In order to improve overall classification performance, the results of all three classifiers were combined through majority voting to create a hybrid model. This ensemble method takes advantage of the diversity of the classifiers by efficiently balancing their strengths while reducing their individual weaknesses. The hybrid model performed better than each individual classifier, proving the efficacy of this combined strategy. A user-friendly interface was created to present the input nail image with its classification output, allowing for real-time assessment of nail health. The interface not only promotes ease of use but also the detection of disease at an early stage, thus preventing unnecessary doctor consultations. The models, that is, the CNN and the three classifiers, were stored in deployable forms (.h5 and.pkl files) for ease of use and scaling up for subsequent studies or medical purposes. This work makes an addition to the area of medical image analysis by proving the efficiency of integrating CNN-based feature extraction with various classifiers within a combined framework. The system presented is a sound, precise, and efficient automated nail health screening solution, with its potential impact seen in public health, particularly in resource-limited environments. The findings highlight the feasibility of employing artificial intelligence in health diagnostics, making way for continued innovation in automatic disease detection and medical decision-making systems.

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The classification of marine species is essential for biodiversity conservation and ecological monitoring. In this study, we compare three deep learning models—Standard Convolutional Neural Network (CNN), Transfer Learning using ResNet50, and Squeeze-and-Excitation CNN (SE-CNN)—to evaluate their effectiveness in identifying marine species. A dataset consisting of 16,616 images of six marine species was used for training and testing. The models were assessed based on accuracy, computational efficiency, and class-wise performance analysis. Experimental results indicate that SE-CNN outperforms the traditional CNN model, while ResNet50 achieves high accuracy with minimal training effort. Our findings provide insights into selecting optimal models for marine species classification and contribute to AI-driven ecological monitoring solutions.

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This research investigates machine learning-based disability detection of oral cancer combined with metastasis management strategies. The research evaluates the diagnostic capabilities of ResNet-50 CNN, SVM, and Random Forest algorithms through assessment of image-histopathological data and patient CSV information. The dataset contains two measurement variables which include metastasis status and organ dimensions. The outcomes demonstrated that CNN(ResNet-50) scored the best identification rate of 90.08% compared to SVM 86.26% and Random Forest 74.81%. Machine learning techniques show effective potential in oral cancer diagnosis according to the obtained results that lead to early detection and better healthcare results.

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Artificial intelligence technologies exhibit major opportunities for strengthening the process of marine disease observation and with prevention strategies. Classic methods that monitor diseases along with their management prove to be both ineffective and resource-consuming as well as delayed in their responses. The research establishes an investigation to build AI-based strategies which would enhance detection of marine diseases while forecasting outbreaks. The Environmental Protection Interactive Centre in Hong Kong provided historical data regarding environmental conditions like temperature, pH, salinity, turbidity, dissolved oxygen and nitrate nitrogen parameters. The prediction of disease outbreaks relied on the implementation of Random Forest alongside Support Vector Machine (SVM) and Logistic Regression and Long Short-Term Memory (LSTM) and XGBoost machine learning models which processed these parameters. The classification system followed rules that identified diseases as either coral disease or parasitic disease or environmental stress disease or bacterial infection. The model performance evaluation relied on accuracy scores together with confusion matrices as well as feature correlation heat maps and ROC curves. The disease outbreak prediction results showed promise with the use of LSTM allowing it to work with both time-related connections and sequential data. Early ecological incident prevention measures become achievable due to AI systems monitoring marine ecosystems. The study demonstrates AI's ability to handle marine disease observation which helps protect on-going marine ecosystems and their biodiversity.

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A simple and an efficient general procedure have been enhanced for the synthesis of 5-phenyl-5,7,8,9-tetrahydro-6H-[1,3,4] thiadiazolo[2,3-b]quinazolin-6-onefused analogous and biological properties. This analogous can be synthesized from compound (3) with dimedone and arylaldhyde in the presence of Lews acid catalyst (ZrOCl₂) and the compound (3) can be prepared by the 2,4-dinitrobenzoic acid with thiosemicarbaide in the presence protic acid medium. All products have been characterized by ¹H NMR and ¹³C NMR spectroscopy, and mass spectrometry. This method provides versatile advantages of organo-catalyst such as economical, nontoxic, highly stable, readily and easily available as well as solvent-free reaction conditions.

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The Smart Shopping Trolley is an innovative solution aimed at enhancing the retail shopping experience by automating the billing process, providing real-time budget management, and detecting product expiration dates. The system utilizes RFID technology or barcode scanners to automatically read product details as items are added to the trolley, eliminating the need for manual checkout scanning. A built-in budget alert feature allows customers to set a spending limit, providing notifications as they approach or exceed their budget, thereby promoting mindful shopping. Additionally, the system checks the expiration dates of perishable items, alerting users if any product is nearing its expiry, which reduces waste and ensures product freshness.

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Our nation's growing energy demands lead to frequent power outages in rural areas. This is due to both high-power consumption by factories and the limited availability of non-renewable energy sources. Adopting a hybrid renewable energy system for electricity generation is a well-known, quick, reliable, and cost-effective solution for rural households. A solar-wind energy combination can significantly reduce electricity dependency in remote areas. However, wind speed fluctuates between day and night, impacting the horizontal axis wind turbine's output. To address this, a vertical axis wind turbine with a C-type blade has been introduced to generate power at low wind speeds. By integrating the C-type blade wind turbine with solar photovoltaic, a hybrid system has been developed.

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Generally, for welding operations in maintenance. And manufacturing we use chamfering machines but still we. Are using manually operated chamfering machines and for. Every operation we have to perform requires human setup. Noise: This manual interference is more time-consuming. Consuming and having more opportunities for physical injuries to occur. Human labor in close proximity to it. The primary objective of this paper is to. Compile a list of all potential approaches for automating the task. Operated chamfering machines to automated chamfering. Machine, enhance efficiency of them and diminishes human. Disturbance during the functioning of the equipment. This paper presents the conclusion of our result. Various previously employed techniques for sharpening chamfers. Machine are talked about. Analysis has is done considering. Exploring Different Methods and Discovering a New Approach Procedure that will be useful for the execution of beveling. Machine by utilizing hydraulic mechanisms. In this endeavor we are. Producing the portable chamfering device. Which can fit in pipes of different sizes, the project will. The main components of the system are divided into two parts, with the first part being the installation of the mounting. The spring-loaded machine should be able to set. The second part of the system is mounted on a variable pipe diameter. And creating the rotating device for grinding the surface. That chambering in various angles should be feasible. The: Machine has been designed with mathematical precision and precision to ensure safety. Cad model is developed for ease of manufacturing.

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The import-export industry faces challenges in transparency, logistics efficiency, and secure transactions. This paper presents Import and Export, a web-based platform designed to optimize international trade through a scalable and technology-driven approach. The platform integrates real-time pricing mechanisms, dynamically calculating costs including taxes, duties, and shipping charges to ensure financial transparency. A live shipment tracking system enhances logistics management by providing real-time monitoring of orders. To facilitate global transactions, the platform incorporates secure payment gateways, including UPI and credit/debit card support. Additionally, a digital marketplace connects businesses with suppliers and customers worldwide, fostering an integrated trade ecosystem. Built using the MERN stack (MongoDB, Express.js, React.js, Node.js) with cloud-based deployment on Render and Vercel, the platform ensures scalability, high availability, and seamless user experience. By addressing key trade challenges, this solution enhances efficiency, security, and accessibility in international commerce.

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The Bluetooth-controlled mini forklift project is designed to demonstrate remote vehicle control using a smartphone. The system integrates an Arduino microcontroller, Bluetooth module (e.g., HC-05), motor drivers, and IR sensors for obstacle detection. The user sends movement commands via a smartphone app, enabling forward, backward, left, right, and lifting actions. The project aims to enhance automation and wireless control applications, offering a practical solution for material handling in small-scale environments.

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In this study carried out to a biological potent activity of a series 2-(3-methoxy-9H-carbazol-9-yl)-N-phenylacetamide derivatives. These derivatives were synthesized from 2-(3-methoxy-9H-carbzol-9-yi) acetyl chloride treated aromatic primary amines in the presence of strong organic base such as triethylamine and MDC RT condition and the compound (4) also prepared by 3-methoxy-9H-Carbazole (3) with chloroacetylchloride in the presence of potassium carbonate in acetone as solvent at 50℃. The intermediate (3) was obtained by 4-methoxy aniline treated with 1,2-dichloro benzene in the presence of CuI₂ in Cs₂CO₃. The titled analogous can be evaluated by spectral tecniquich such as ¹HMNR, ¹³CNMR and LCMS. The structure of the desired compounds was determined by elemental analysis. In addition to, compounds were examined for in-vitro antimicrobial activity against bacterial strain and fungal strains.

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We live in a time full of new tech and fast changes in how we learn. The idea of using games for learning has become a strong way to change the old way of teaching in classrooms. This study looks into the big chance of using games and how they work to make learning full of action, fun, and very much into it. These ideas are built on well-known theories about learning, like learning by doing, learning in place, and learning through experiences. Learning through games suits good with the ideas of making your own knowledge, understanding things in context, and learning by hands-on work. The desire to play games comes from a theory that says people are driven by the need to make their own choices, be good at what they do, and feel connected to others – things that good games already have. Using games for learning can work in many ways in schools and other places. In normal classrooms, games help make hard ideas clear or can even fill up a whole course. In work training, the military, and teaching health, serious games and virtual play are used to teach real skills and know-how in a safe place where mistakes are okay. This paper shows two good examples of games in learning. The first one is about the game "Civilization," which is used to teach history. It shows how effective it is in getting players into the past, how societies grow, and how they make choices. The second example is about "Foldit," an online game that has helped a lot with scientific research on proteins by using players' skills in solving problems. But this study also knows games for learning face challenges. These include the hard work of making and growing the games, adding them to courses, worries about who can get to them, and figuring out how to check if learning is happening.

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