Browse
Recent Submissions
Item Graph based Retrieval Augmentation for Education and Learning(2024) DHARSHAN AN : 1NH20AI026; G SAI CHARAN: 1NH20AI031; JAYAVIBHAV NK: 1NH20AI035; K MANISH : 1NH20AI038Graph-based knowledge representation has emerged as a powerful technique for organizing and structuring information in a way that captures rich semantic relationships. By representing knowledge as a network of interconnected nodes and edges, graph structures allow for efficient encoding of complex conceptual associations and hierarchies. This approach has significant implications for information retrieval and generation tasks, particularly in the realm of retrieval augmented generation.Item AI- Trip Advisor [Smart Travel Itinerary planner](2024) DAIVIK SUCHIT -1NH20AI024, JEEVAN C B - 1NH20AI036 , M DATHRI VENKAT REDDY -1NH20AI054, LIKITH M -1NH20AI134State-of-the-art technologies—Language Models (LLMs) in particular—are used to revolutionize conventional travel planning procedures. The "AI Trip Advisor" web application uses cutting-edge LLMs to provide consumers with a smooth and customized travel schedule. The system takes into account a number of variables, such as the user's location, preferred destinations, travel dates, financial limits, traveling companions, and customized instructions, like adding rest days or certain activities. Intelligent Flight Recommendations, wherein the system proposes ideal flight options based on user preferences, including cost, travel time, and layovers, is one of the application's key features. Budgetary restrictions, user preferences, and accessibility to well-known sites are all taken into account when providing accommodation suggestions. Users can modify their trip schedules to suit their own interests and needs using customizable itineraries. Weather and Best Times to Visit recommendations optimize the overall travel experience by considering weather conditions. The application ensures a fair allocation of costs among trip companions by offering Expense Splitting for group travelers, which tackles the financial part of travel as well. The domain is distinguished by the notable progress in incorporating artificial intelligence into the process of travel planning. The program offers a clever and user-friendly solution while streamlining the planning process to save users time and effort. This novel strategy represents a revolutionary change in the way travel plans are tailored and optimized, and it is in line with the needs of contemporary travelers.is started, the writer must exercise ingenuity in figuring out how to fit the paragraph into their work.Item Personalized dietary guidance with AI(2024) New Horizon College of EngineeringIn today's world, that has an abundance of food options, customers frequently encounter difficulties in making knowledgeable nutritional choices. Sorting through the numerous choices that are available and each claiming to be the greatest for your health can be rather overwhelming. Acknowledging this difficulty, our project seeks to transform the relationship between nutrition and technology by focusing the requirements and welfare of the user. There is a shift in mindset in which people are empowered by technology to make personalised, well-informed decisions about their nutrition and diet in addition to receiving information.Item AI-Powered Video Conferencing App with Real-time Speech-to-Speech Machine Translation(2024) NHCEIn the contemporary landscape of communication, video conferencing has emerged as an indispensable tool for global interaction, revolutionizing the way individuals and organizations connect across vast distances. The proliferation of video conferencing platforms has enabled seamless collaboration, transcending geographical boundaries and fostering real-time dialogue among users. This technological advancement has facilitated everything from business meetings and educational sessions to social gatherings and telehealth consultations, making face-to-face interaction possible regardless of physical location. However, despite these advancements, a significant barrier persists: language differences. These linguistic obstacles can impede the free flow of ideas, hinder effective discourse, and ultimately limit the inclusivity and efficiency of virtual communication. Language barriers can lead to misunderstandings, reduce engagement, and prevent participants from fully contributing to discussions. Addressing this challenge is crucial for enhancing the accessibility and utility of video conferencing technologies, ensuring that they can truly serve a diverse, global audience by bridging the communication gap and fostering a more inclusive virtual environment.Item Automation of Statistical Aid to Data Engineering(2024) New Horizon College of EngineeringIn the realm of data cleaning, statistics plays a pivotal role in elucidating patterns, identifying anomalies, and guiding the selection of optimal strategies for enhancing data quality, statistics provides a systematic framework for analyzing and interpreting data, offering valuable insights that are instrumental insight that are instrumental in the data cleaning process.Item Web-Based Real-Time Child Surveillance System(2024) NHCEIn an increasingly busy world, guardians often face challenges in ensuring the safety and well-being of their children while away from home. To address this issue, we have developed a "Web-Based Real-Time Child Surveillance System" utilizing Python and Convolutional Neural Networks (CNNs) for real-time monitoring. With the use of a web application, guardians will be able to remotely watch their children's actions and receive real-time notifications. The monitoring system recognizes and categorizes a range of child behaviors, including walking, running, sitting, and falling. With the use of real-time processing and sophisticated machine-learning techniques, our technology provides parents and guardians with a dependable and effective way to monitor their kids while they are away.Item Stress Analysis and Prediction using Machine Learning with EEG(2024) NHCEStress, which is known to be the underlying cause of many of mental health disorders, arises from various of sources that have a clearly harmful effects on health. The consequences of stress are most noticeable in the life of a working professional who must handle the demands of increased management expectations, time management limitations, and family obligations. Proactive stress management is crucial since ignoring stress for a long time increases the likelihood of developing anxiety and depression. Physiological characteristic is very essential for diagnosing stress-related conditions and provide important information about the complex relationship between mental and physical health. The goal of this study is to investigate stress using EEG data, which is recognized for its reliability, accuracy, and precision. Advanced (ML) machine learning models, such as SVM, RF, Decision Tree (DT), and KNN, are implemented based on the intrinsic compatibility between stress signals and EEG data.Item Multiple Disease Detection(2024) NHCEThe rapid advancement of deep learning techniques has significantly impacted the field of medical diagnostics, offering promising solutions for multiple disease detection. This study explores the application of deep learning algorithms to accurately identify and diagnose multiple diseases from medical imaging and other healthcare data. The proposed system leverages convolutional neural networks (CNNs) and recurrent neural networks (RNNs), combined with advanced data preprocessing and augmentation techniques, to enhance diagnostic accuracy and efficiency.Item Deepfake Detection Using Multi Modal Approach(2024) NHCEDeepfake technology is a serious danger to the accuracy of information shared online in the age of digital communication. This artificial intelligence (AI) produced videos, which may accurately portray people saying or doing things they never did, have serious ramifications for digital media credibility, human rights, and public debate. Advanced techniques for deepfake detection are required due to their potential misuse for espionage, manipulation, coercion, and harassment. In order to overcome this difficulty, we have created a deepfake video detector by utilizing CNNs' capabilities. Our method examines video frames for minute discrepancies that are characteristic of deepfake footage, making use of CNN's powerful feature extraction capabilities. Our methodology provides a potential remedy for by concentrating on temporal irregularities and pixel-level differences that are frequently undetectable to the human sight. This effort not only advances technology in the battle against digital disinformation, but it also emphasizes how crucial cross-sector cooperation is to preserving the integrity of online media. Our results shed light on the direction of future studies and advancements in the industry and demonstrate how important sophisticated machine learning methods are to preserving the security and legitimacy of digital interactions. It's important to acknowledge, however, that the fight against deepfakes is an ongoing arms race. As deepfake creators develop more sophisticated techniques, so too must deepfake detectors. This necessitates continuous improvement of detection algorithms, collaboration between researchers and tech companies, and public awareness campaigns to equip users with critical thinking skills to spot potential deepfakes.Item Precision Farming with AI for Optimal Crop Yield and Health(2024) A.VIGNESH REDDY - 1NH20AI007; D.GIRIHAS REDDY - 1NH20AI023; DAKSHA TM - 1NH20AI025; KUSHAL KULANDAIVELU - 1NH20AI051In the Heart of India's Predominantly agrarian landscape, a groundbreaking web platform emerges as a beacon of transformative change. This Project, at the intersection of agriculture and technology, represents a monumental leap forward in empowering farmers and reshaping the country's economic landscape.Item Automated Office Meeting Summarization with NLP- Based Video Transciption and Speaker Diarization(2024) Akash B - 1NH20AI006; C Sumukh - 1NH20AI019; Gowardhan Reddy V - 1NH20AI032; V Hashith 1NH20AI111Automatic Speech Recognition (ASR) technology has revolutionized the way to capture and transcribe spoken words, enabling effective documentation and analysis of verbal communication. Despite significant advances, existing ASR systems for office meetings face persistent problems such as limited speaker diarization, insufficient punctuation recovery, and variable accuracy across languages. These limitations often result in transcripts that are difficult to follow and less useful for detailed meeting records. This project aims to develop an improved ASR system tailored specifically for office meeting environments. By utilizing advanced deep learning models and integrating features such as speaker diarization and punctuation recoveryItem ProductGuard: Empowering Informed Choices for Safe Living(2024) ADITYA S MANAKAR -1NH20AI005; ARSHAD PASHA - 1NH20AI009; JIYA ANN ELIAS - 1NH20AI037; KARTIKEY TIWARI - 1NH20AI044In a landscape where consumer decisions significantly influence personal health and wellbeing, the necessity for informed choices regarding product safety is paramount. This project introduces ProductGuard, a mobile application designed to empower users in making conscientious and informed decisions about skincare and personal care products. The project integrates four core components aimed at assisting users in evaluating product safety, comparing ingredients, providing personalized recommendations based on skin type, and offering expert guidance through a chatbot interface. The first component employs advanced image analysis technology utilizing a dataset extracted from the Environmental Working Group (EWG) website, enabling users to capture product ingredient images and receive detailed safety analyses.Item Anemia Detection Using Machine Learning(2024) A Chetu Chandhan - 1NH20AI004; K Lingeswar Balaji - 1NH20AI040; K Datta Ram Vivek - 1NH20AI046; P Sanjay reddy - 1NH20AI143This study investigates the feasibility of using eye datasets for predictive modeling. Three machine learning algorithms, decision tree, random forest, and XGBoost, were employed to classify individuals with and without anemia. A comprehensive dataset of eye images was obtained and preprocessed to capture key characteristics like color variations, textural patterns, and structural details. These features were then fed into the respective algorithms to construct predictive models. Evaluation metrics, including accuracy, revealed promising performance from all three models in identifying anemia based on eye imagery. Notably, the XGBoost algorithm achieved the highest accuracy, followed by random forest and decision tree. These findings suggest that eye images hold significant potential as a non-invasive and cost-effective tool for early anemia detection. The developed machine learning models utilizing decision tree, random forest, and XGBoost offer a promising avenue for further research and development in this area.Item A Full-Fledged communicatin platform with Enhanced Accessibility and Functionality(2024) Adhithya B N - 1NH20AI003; Chrish vinson Kunnankada (1NH20AI022); Pranit Prakash Prabhu - (1NH20AI078)Item Flashback.ai: A Study on Digital Second Brain(2024) Mr. Syam Dev R S - 1NH20AI001; Abhishek Narsepalli Venkata sai - 1NH20AI002; Atman Mishra - 1NH20AI010In an era defined by information abundance, the human capacit to capture, retian, and recall essential insights faces unprecedented challenges. The sheer volume of data encountered during online interactions and meetings often overwhelms our cognitive abilities, leading to the loss of valuable knowledge. Recognizing this critical gap, the Flashback.ai Project emerges as a revolutionary solution designed to bridge the divide between human memory limitation and expansive digital landscape.