The AI-Enabled Water Well Predictor is a machine learning-based solution aimed at accurately predicting optimal drilling locations for water wells. This project leverages artificial intelligence to analyze vast datasets, including geological, hydrological, environmental, and meteorological data, to pinpoint areas with the highest likelihood of accessible groundwater. By integrating multiple data sources, the AI model identifies patterns and correlations that are difficult to detect through traditional methods, significantly increasing the reliability of well placement predictions. In regions where water scarcity is prevalent, (...) especially in rural and underdeveloped areas, this solution can provide a cost-effective, data-driven approach to well drilling. The AI-Enabled Water Well Predictor reduces the risks and costs associated with drilling unproductive wells, enhancing the success rate of water access initiatives. The system empowers governments, NGOs, and local communities to make informed decisions, promoting sustainable water resource management and supporting efforts to improve public health, agriculture, and economic resilience in water-stressed regions. (shrink)

"Genius AI" is an innovative SaaS platform poised to revolutionize the landscape of artificial intelligence tools. This dynamic platform integrates a diverse array of AI functionalities, including conversation generation, code generation, music generation, and video generation, all in one centralized hub. At its core, Smart AI harnesses cutting-edge machine learning algorithms and neural networks to empower users with unparalleled capabilities. The conversation generation feature enables seamless interaction with users through natural language processing, facilitating engaging and lifelike conversations. At its core, (...) Smart AI harnesses cutting-edge machine learning algorithms and neural networks to empower users with unparalleled capabilities. The conversation generation feature enables seamless interaction with users through natural language processing, facilitating engaging and lifelike conversations. At its core, Smart AI harnesses cutting-edge machine learning algorithms and neural networks to empower users with unparalleled capabilities. The conversation generation feature enables seamless interaction with users through natural language processing, facilitating engaging and lifelike conversations. For developers and coders, Smart AI offers a ground breaking code generation tool, streamlining the process of software development by automatically generating code snippets tailored to specific requirements and programming languages. Moreover, music aficionados can leverage Smart AI's advanced algorithms to compose original melodies, harmonies, and rhythms, personalized to suit any style or genre. Meanwhile, the video generation functionality empowers content creators with the ability to effortlessly produce high-quality videos, complete with customizable elements and visual effects. With its user-friendly interface and scalable architecture, Smart AI caters to a broad spectrum of industries and applications, from e-commerce and marketing to entertainment and education. Whether enhancing customer engagement, accelerating software development, or fostering artistic expression, Smart AI unlocks endless possibilities for innovation and creativity. Join the forefront of AI-driven transformation with Smart AI and redefine the way we interact with technology. (shrink)

Although Internet of Things (IoT) devices are being rapidly embraced worldwide, there are still several security concerns. Due to their limited resources, they are susceptible to malware assaults such as Gafgyt and Mirai, which have the ability to interrupt networks and infect devices. This work looks into methods based on machine learning to identify and categorize malware in IoT network activity. A dataset comprising both malware and benign traffic is used to assess different classification techniques, such as Random Forest, XGBoost, (...) Logistic Regression, etc., for multi-class malware detection. After a great deal of empirical testing, XGBoost comes out on top, providing 99.9% recall and accuracy. Both known and unknown malware can be detected by the trained model with remarkable precision. The use of transfer learning, where the XGBoost model is used as a basis for the rapid construction of new malware detection models, is one of the major innovations put forth. This makes it possible to quickly adjust to new dangers. More information about how real-time network traffic can be monitored with the help of the created model to find irregularities and sound the alarm. An intelligent and proactive security solution for IoT environments is offered by the machine learning technique that is being discussed. This is an efficient defense against malware because of its high accuracy, low false positive rate, real-time detection capability, and adaptability to new malware varieties changing risks associated with IoT. The suggested methods will assist in securing susceptible IoT devices and networks from obstructive malware assaults. (shrink)

This dissertation presents the design and implementation of a comprehensive management system tailored for postgraduate programs. The primary objective is to streamline administrative processes, enhance student engagement, and facilitate effective communication between stakeholders, including students, faculty, and administrative staff. The system incorporates modules for course registration, grade management, scheduling, and document submission, utilizing a user-friendly interface that promotes accessibility and efficiency. Through a combination of database management, web technologies, and user-centered design principles, the system addresses common challenges faced by postgraduate (...) institutions, such as data silos and inefficiencies in information retrieval. A case study approach is employed to evaluate the system's effectiveness within a specific postgraduate program, highlighting improvements in operational workflows and user satisfaction. Feedback from users indicates a significant reduction in administrative burdens and an increase in overall productivity. This research contributes to the field of educational management systems by providing insights into best practices for system design and implementation, ultimately supporting the ongoing evolution of postgraduate education in an increasingly digital landscape. (shrink)

Urban centers are grappling with significant challenges in managing domestic waste due to increasing population density and consumption patterns. Common issues include irregular waste collection, inadequate waste segregation, and limited recycling initiatives. These shortcomings often stem from insufficient communication between residents and waste management authorities, as well as a general lack of public awareness regarding proper waste disposal methods. This paper proposes a comprehensive web-based platform aimed at enhancing domestic waste management practices. The system allows users to schedule waste collection, (...) promotes awareness through educational resources on waste segregation and recycling, and provides a mechanism for reporting concerns such as missed pickups or improper disposal. By facilitating better interaction between communities and waste management services, this solution addresses existing inefficiencies and encourages responsible waste management behaviors. Preliminary feedback indicates that users have engaged more actively with waste management processes, suggesting that digital intervention can lead to improved community participation and better environmental outcomes. (shrink)

The research introduces to the design and implementation of the Cybersecurity Enabled Smart Controller for the grid connected microgrid which uses the combination of the Web technologies and the authentication mechanism which enables real time monitoring with help of the Dashboards built using Chart.js which is a framework of JavaScript. Due to the role and connection of the Microgrids with the critical infrastructure they are vulnerable to the cyber threats. This project leverages the use of HTML, CSS, JavaScript and MongoDB (...) to create a secure and responsive system ensuring the operational resilience and data integrity. By focusing on simplicity the project uses user friendly dashboard for monitoring the microgrid data , controlling the grid crafted with HTML, CSS and JavaScript allowing the users to visualize the performance metrics using Chart.js for real time data representation. This methodology focuses on creating secure communication channels which enables effective data management through MongoDB providing a responsive experience on the web application. This approach allows users to monitor grid operations continuously and the access management effectively. The findings focused on creating a user friendly interface using the HTML, CSS, JavaScript which give users the access to monitor and control microgrid operations seamlessly. Utilizing the Chart.js enabled the users to visualize real time performance metrics which enables the operators to make decisions quickly addressing operational efficiencies. This paper proposes a role-based access control system (RBAC) to protect and manage the smart controller of a grid-connected microgrid. The design integrates a login page developed with HTML, JavaScript, and MongoDB for authentication and user management, with three distinct roles: Admin, Power Operator, and User. Each role has access to specific functions in the dashboard, reflecting their respective permissions. The backend was implemented using Node.js and Express to monitor network traffic, detect potential threats, and prevent unauthorized operations. The results demonstrate that the system provides effective protection against cyber threats while ensuring that different users can access the functionalities appropriate to their roles. (shrink)

Farmers face challenges with fair pricing and transparency due to reliance on middlemen and unclear product authenticity. Current solutions are fragmented and lack a unified approach. By developing a web-based application that empowers farmers by allowing them to set their own prices, categorize products as organic or inorganic, analyze price trends, and use secure payments. The platform will feature KYC authentication, nutrition reports, personalized food recommendations, a feedback system, and direct buyer engagement tools. This aims to enhance farmer autonomy, build (...) consumer trust, and create a transparent, fair agricultural market. (shrink)

This study explores effective digital marketing strategies that can enhance sales conversions in the competitive e-commerce landscape. As technology continues to transform business practices, especially in online retail, companies must adopt strategic marketing approaches to remain competitive. This qualitative research identifies successful strategies through the collection of data using methods such as listening and recording key information. The data was analyzed through processes of data reduction, data display, and conclusion drawing. The results highlight the critical role of digital marketing in (...) driving traffic, engagement, and ultimately, sales conversions for e- commerce platforms. The study emphasizes the combination of various techniques including search engine optimization (SEO), content marketing, social media marketing, paid advertising, user experience (UX) optimization, customer reviews, personalized content, video marketing, incentives, and cart optimization. These strategies, when effectively implemented, can significantly improve the customer journey, enhance user engagement, and drive overall sales performance. (shrink)

Pneumonia is a serious respiratory infection that poses significant health risks, particularly if not diagnosed and treated promptly. Traditional methods of pneumonia diagnosis rely on the manual interpretation of chest X-ray images by radiologists, a process that can be time-consuming, subjective, and error-prone, especially in regions with limited access to experienced medical professionals. To address these challenges, this study explores the development of an automated deep learning-based system for pneumonia detection using chest X-ray images. The results demonstrate that the deep (...) learning model can achieve high levels of accuracy, sensitivity, and specificity, making it a valuable tool for assisting radiologists in diagnosing pneumonia more quickly and reliably. Moreover, the system's scalability and ease of deployment make it particularly beneficial in resource-limited settings, where timely and accurate diagnosis is crucial. This research highlights the potential of deep learning to revolutionize medical diagnostics, improving patient outcomes through enhanced diagnostic accuracy and efficiency. (shrink)

The lack of intellectual property (IP) awareness among school students is a pressing concern in today’s digital age. With the increasing importance of innovation and creativity, understanding intellectual property rights (IPR) at an early age is critical for fostering an informed generation. This paper presents the development of an interactive web application that educates school students about various aspects of IPR. The platform includes a series of educational modules, quizzes, and interactive features designed to engage students in learning. This project (...) highlights the growing need for early IPR education and provides a scalable, accessible solution for schools. The primary aim of the project is to create awareness and provide resources to help students understand the importance of intellectual property in safeguarding their innovations. The application also includes administrative functionalities for tracking student progress, offering a holistic learning experience. (shrink)

Dredging operations are essential for maintaining navigable waterways, but determining the ideal time to dredge requires a multifaceted approach, incorporating both environmental and operational variables. This paper presents the Dredging Analysis and Decision Support System (DADSS), a data-driven solution that employs historical sedimentation data, weather patterns, and water flow statistics to optimize dredging decisions. The system leverages Random Forest Classifier and Regressor models to predict the need for dredging and estimate associated costs. Key inputs include sedimentation depth, water flow rate, (...) sediment type, and precipitation levels. By providing real-time insights and cost-effective guidance without reliance on physical sensors, this solution enhances waterway management efficiency while reducing environmental impact. Results demonstrate a clear optimization in dredging operations with a reduced frequency of unnecessary dredging. (shrink)

In this day and age, blood donations are a common occurrence, with millions taking the charge to provide blood to an extreme amount, even more so in India. Yet, over 12000 people die everyday due to a tardy blood donation or lack thereof. This might seem quite negligible in front of the absolutely over abundant population of India as a whole, but is still appalling when seen at a monthly or a yearly rate. Sure, there are several websites that facilitate (...) contact for blood donation and act as the middle ground for donors and patients alike, but they do not take in one of the most valuable sanguinary assets India has, Students. The aim of the website we are making using elements from Full Stack Web development. is to connect a large number of students across various schools and potentially various institutions to act as the bridge to furnish patients with the blood that befits their transfusion parameters. This reduces the overall period of time it takes to donate blood and can be a great implementation of time reduction to help those who are in a state of emergency , mostly during cases of copious blood loss and patients with severe hematological deficiencies. (shrink)

As Distributed Denial of Service (DDoS) attacks evolve, accurately detecting these threats becomes essential to ensuring network stability. Traditional methods often face challenges in recognizing adaptive DDoS patterns and balancing detection accuracy with false positives. This paper presents a machine learning-based framework leveraging Gaussian Naive Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and an Ensemble Random Forest classifier. Through an in-depth performance analysis using accuracy and AUC-ROC metrics, the hybrid model aims to provide a robust, scalable solution to enhance (...) network security. (shrink)

The establishment of a centralized Nasha Mukti (drug deaddiction) database is a vital initiative aimed at consolidating information from various sources to effectively combat substance abuse. By unifying data from deaddiction centers, healthcare providers, and law enforcement, this database will enhance coordination, improve intervention strategies, and support research and policy formulation. Ultimately, it seeks to create an integrated system to address substance abuse on both community and national levels.