This research focuses on developing an advanced UAV detection system using state-of-theart YOLOv8 and YOLOv9 models. By training these models on diverse datasets, we aim to enhance real-time detection accuracy and precision under various environmental conditions. The system is designed to improve UAV safety and minimize bird collisions. Through rigorous benchmarking, we demonstrate significant improvements in detection performance, accuracy, and computational efficiency compared to existing approaches. A user-friendly web interface, built using HTML, CSS, and Flask, provides real-time visualization of detection (...) results and system performance. This innovative system holds immense potential for applications in defense, airspace safety, wildlife conservation, and other UAV-dependent sectors. (shrink)

The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and missing data handling are applied to ensure the model’s robustness. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. This approach not only aims to improve diagnostic accuracy but also provides a data-driven solution to assist healthcare professionals in making informed decisions. The outcome of this project can (...) contribute to better management of CKD, ultimately helping to reduce the burden on healthcare systems and improving patient care. (shrink)

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)

Chronic Kidney Disease (CKD) is a significant global health issue, often leading to kidney failure and requiring costly medical treatments such as dialysis or transplants. Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, (...) Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and missing data handling are applied to ensure the model’s robustness. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. (shrink)

The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and missing data handling are applied to ensure the model’s robustness. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. This approach not only aims to improve diagnostic accuracy but also provides a data-driven solution to assist healthcare professionals in making informed decisions. The outcome of this project can (...) contribute to better management of CKD, ultimately helping to reduce the burden on healthcare systems and improving patient care. (shrink)

By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and missing data handling are applied to ensure the model’s robustness. T.

Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD.

"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)

The application of this system can lead to higher crop yields, sustainable farming practices, and reduced risks associated with poor crop choices. Through rigorous evaluation using standard classification metrics, the model's performance demonstrates its potential to revolutionize farming practices by aiding farmers in making informed decisions. The system has the potential to be an invaluable tool for agricultural consultants, farmers, and policymakers, ensuring long-term sustainability and improved productivity.

Chronic Kidney Disease (CKD) is a significant global health issue, often leading to kidney failure and requiring costly medical treatments such as dialysis or transplants. Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, (...) Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. (shrink)

Chronic Kidney Disease (CKD) is a significant global health issue, often leading to kidney failure and requiring costly medical treatments such as dialysis or transplants. Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, (...) Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and missing data handling are applied to ensure the model’s robustness. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. This approach not only aims to improve diagnostic accuracy but also provides a data-driven solution to assist healthcare professionals in making informed decisions. The outcome of this project can contribute to better management of CKD, ultimately helping to reduce the burden on healthcare systems and improving patient care. (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)

The AWSAR Job Portal is developed to provide job seekers with a platform to register, log in, and apply for jobs with ease. The purpose of this project was to create a full-stack web application, ensuring smooth user experience and addressing common challenges such as responsiveness and compatibility across browsers. The project used technologies like React.js, Tailwind CSS for frontend development, and Node.js with Express.js for backend management. MongoDB was implemented for database storage. The key challenge faced was the unresponsiveness (...) of the 'Apply to a job' button post-login, which was successfully resolved through code optimization. This manuscript discusses the system's architecture, methodology, issues encountered, and future enhancements. (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)

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)

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)

An efficient SMS spam detection system is developed using the Multinomial Naive Bayes (MNB) algorithm. It employs a labeled dataset and extracts features with the term frequency-inverse document frequency (TF- IDF) method. The MNB algorithm classifies messages by modeling term probability distributions. Parameter tuning and pre-processing techniques like text normalization and stop-word removal enhance feature quality. Experimental results show high accuracy, precision, recall, and F1-score, making MNB suitable for real-time applications. The system provides a practical solution for SMS spam detection (...) and enhances mobile communication security. (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 Resume Scorer Application is an innovative platform designed to streamline the process of evaluating resumes for both users and administrators. It features two distinct interfaces: a user interface where individuals can upload their resumes to receive a score based on predefined criteria, including skillsets, experience, and alignment with job roles, and an admin interface for managing user submissions and overseeing the scoring process. The platform utilizes advanced parsing techniques to extract key details such as the applicant's name, email, and (...) phone number, while also offering skill recommendations and course suggestions for career enhancement. This application is intended to assist job seekers in refining their resumes and help hiring teams in assessing candidate fit more efficiently. (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)

Ayush Portal is a digital platform designed to support entrepreneurship in the Ayurveda, Yoga, Unani, Siddha, and Homeopathy (AYUSH) sectors. It offers mentorship, funding opportunities, regulatory guidance, and multilingual support to help startups succeed. The platform features a robust verification system using GST, CIN, and PAN numbers for secure services, along with tools like React-Pptx for creating pitch decks and a realtime dashboard for updates. Adhering to WCAG standards, Ayush Portal aims to empower entrepreneurs, investors, and mentors while driving innovation (...) in traditional medicine sectors. (shrink)

News Rakshak is an advanced web application that monitors, analyzes, and verifies news related to government schemes in regional media across multiple platforms, including 200+ websites, e-papers, and YouTube channels. The application leverages Natural Language Processing (NLP) for sentiment analysis and integrates Artificial Intelligence (AI) to identify and flag fake news and videos. Sentiments are classified as positive, neutral, or negative, rated on a scale from 0 to 5, providing Public Information Bureau (PIB) officers with actionable insights. The system uses (...) Optical Character Recognition (OCR) for extracting text from image-based news formats and delivers real-time alerts to officials via multiple channels. The user-friendly interface, equipped with filtering options, enables effective trend visualization. News Rakshak serves as a comprehensive tool for tracking and verifying government-related news sentiment and authenticity, ensuring accurate feedback from regional media. (shrink)

This research focuses on developing an online integrated platform designed to facilitate project collaboration among students from various universities and colleges. The platform aims to bridge the gap between students from different institutions, enabling them to work together on academic and research projects. The methodology involves designing an easy-to-use web interface that allows users to create, join, and manage projects. Key features include project tracking, communication tools, and resource sharing, all within a secure environment. The platform enables students from different (...) universities to collaborate on projects, bringing together diverse ideas and promoting innovation. The major outcomes of this project include increased accessibility for students, enhanced collaboration, and the ability to create high- quality projects by leveraging the strengths of diverse academic backgrounds. Overall, the platform aims to make the project process more efficient, inclusive, and resourceful for students. (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)

The judiciary system deals with various types of cases, including civil disputes and criminal trials, that require efficient management and strict adherence to procedures. Traditionally, handling these cases involves extensive paperwork, complex scheduling, and logistical challenges. These existing methods often lead to delays, high costs, limited accessibility, and inconsistent data management. To address these issues, we propose developing an online portal for case management and hearings. This e-portal aims to automate processes, reduce costs, improve accessibility, enhance transparency, and ensure consistent (...) data management. By implementing this solution, we aim to expedite case resolutions, lower expenses, provide better access to information for all parties, and create a more efficient legal system. (shrink)

This project focuses on the development of a decentralized e-wallet system that utilizes blockchain technology to enable secure and transparent cryptocurrency transactions. The e-wallet allows users to store, send, and receive cryptocurrency without relying on third-party intermediaries. The system is built on the Ethereum blockchain with Ganache serving as the local blockchain development and testing environment. User data, such as profiles and transaction history, is managed using MySQL. This project does not implement smart contracts or additional security features, instead relying (...) on JavaScript for both frontend and backend operations. The results from testing indicate that the system is efficient, scalable, and provides secure peer-to-peer transaction capabilities. (shrink)

This study focuses on developing a comprehensive sentiment analysis framework aimed at understanding sentiments expressed in social media posts, enhancing online reputation management for brands. Given the overwhelming volume of user-generated content across platforms, we instituted a methodical approach leveraging advanced machine learning techniques. Specifically, we used Python libraries such as TensorFlow for deep learning functionalities and PyTorch for natural language processing tasks. Our models classify sentiments into three categories: positive, negative, and neutral, while simultaneously analyzing trending patterns and user (...) opinions. The results demonstrate a significant classification accuracy, providing evidence for the framework’s efficacy in real-time social media monitoring. Ultimately, our findings emphasize the strategic role of sentiment analysis in improving brand reputation, enabling better consumer engagement, and informing marketing strategies. (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)

hatbots support users in conversing via text or voice and machine learning is used to generate answers. In particular, chatbots are more human-oriented and thus are used in e- commerce customer support, call centers, gaming and others’ applications. Chatbots have algorithms that allow them to reply to messages, whether by using logical algorithms or programmed ones or by understanding certain words in a given context in the future Numerous hospitals, nursing homes, and private clinics are beginning to implement chatbots as (...) part of the online services they offer as well. Such bots help visitors through the processes of finding the right doctors, booking appointments, and obtaining necessary care. Still, AI is not only in management, drudgery, or dull tasks, but a deeper understanding comes to everyone’s mind that it is also employed in danger zone professions like healthcare and therefore raises the question on whether such important duties can be given to machines instead of real workers. This system of healthcare chatbots is designed to provide 24/7 online healthcare assistance. It can respond to generic questions, to more pinpointed questions, engage in querying for potential leads, collate lead and sales information, and automatically send it to the sales department. Through a number of filtered questions, the user is guided in a way that helps comprehensively target the right information or services required by a patient or visitors. (shrink)

This research work presents a cost-effective facial recognition alert system using the ESP32 microcontroller to enhance real-time security. The system integrates high-resolution cameras with Python’s facial recognition libraries to identify individuals. When an unknown face is detected, it triggers an immediate alert via the ESP32, activating an alarm. This solution is both scalable and affordable, making advanced security accessible to small businesses and residential areas. The system improves overall security by automating surveillance, reducing the need for manual monitoring.

Enhanced E-auction Platform is an innovative online bidding system designed to bring the excitement of live auctions directly to users’ homes. Leveraging cutting-edge technologies such as React, Node.js, and MongoDB, Enhanced E-auction Platform offers a user-friendly platform that enables real-time bidding, strategic auction previews, determining suitable roles and seamless auction item management. Users can experience the thrill of competitive bidding and win extraordinary items from the comfort of their own space. Enhanced E-auction Platform’s advanced features ensure a dynamic and engaging (...) auction environment, setting it apart from traditional auction platforms. (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.

The increasing reliance on digital technologies within the power sector has introduced considerable cybersecurity risks, especially from malware and trojans. These threats can disrupt essential operations, manipulate grid functions, and compromise the integrity of energy systems, thereby endangering both economic stability and national security. This research aims to create a detection framework tailored to the specific challenges of the power sector. The proposed framework utilizes advanced methods such as behaviour based anomaly detection, machine learning algorithms, and both static and dynamic (...) analysis of software. By examining distinct patterns and signatures associated with malware and trojans targeting power sector software, this study seeks to enhance early detection capabilities and response strategies. Real-world case studies and simulations will be employed to evaluate the effectiveness of these detection techniques, highlighting the necessity of robust and adaptable security measures to protect critical energy infrastructure. (shrink)

This paper presents Kaizen, a sensor-driven mobile application developed to track and encourage physical activity through goal-setting, gamification, and personalized diet recommendations, tailored specifically to the Indian dietary context. Kaizen uses accelerometers, GPS, and gyroscopes to monitor user movements in real-time, offering an alternative to traditional fitness tracking methods that often lack cultural dietary considerations or personalized motivation systems. With a ranking system and interactive challenges, Kaizen aims to enhance user engagement and promote continuous improvement in health and fitness. Early (...) user testing indicates high responsiveness and motivational efficacy, suggesting broad potential for adoption and further development. (shrink)

. The surge in data traffic strains backbone networks, causing congestion, latency, and higher costs. This project proposes a data compression system tailored for backbone networks, utilizing Lempel-Ziv (LZ) and Huffman encoding to enhance processing time and network efficiency. Adaptive features allow the system to respond to network conditions, ensuring scalability and cost-effectiveness. The primary objective is to optimize network performance by reducing data packet size, increasing throughput, lowering latency, and reducing energy use.

This paper introduces an AI-based IT training system that enhances the learning process through personalized, real-time support and automated assessment mechanisms. Leveraging AI technologies such as chatbots and NLP, the system addresses the challenges posed by traditional IT training platforms that offer standardized content and delayed feedback. By automating assessments and providing context-aware assistance, the system improves learner engagement, supports skill development, and ensures timely feedback, paving the way for a scalable, interactive, and effective learning environment.