Project Reports

Permanent URI for this community

http://172.16.1.132:4000/handle/123456789/6263

Browse

Now showing 1 - 20 of 392

A 2 Step Random Colour Grid Authentication
(2016-07-22T09:09:13Z) Roopa, M; Prathika, L; Swarna, A; Purushothaman, G R
Security in the computer is largely supported by passwords for authentication process. Lack of security has become a major concern, given the prevalence of attackers, hackers, crackers, scammers and spammers Alphanumeric passwords still remain as the most common Authentication method. This conventional authentication method has been shown to be susceptible security threats such as phishing attack, brute force attack, dictionary attack, spyware attack etc. To overcome the vulnerabilities of traditional methods, a numerous graphical password authentication systems have been designed as a possible security enhancement. These graphical passwords are usually seen as complex and time consuming. Furthermore, the existing graphical passwords are susceptible to spyware and shoulder surfing attacks. In this report we propose this 2 step random color grid graphical password scheme to abolish the above mentioned well known security threats. Considering the drawbacks of the existing graphical password systems, we have proposed a robust graphical password scheme, which is highly adaptable for traditional desktop systems, smart phones and other web applications
3 Strike Dashboard
(2018-09-01T10:40:46Z) Rosy, Guha
Healthcare professionals must use a computerized automated tool to keep a record of the number of SRs which have not received any updates within 7 days. Currently Cerner follows a manual process to achieve 3 Strike Rule which becomes cumbersome when the queue size crosses 25+. Associates might have to keep an eye on every SR to follow 3 strike rule which would then qualify for SR Closure on 8th day. The development of dashboard will help associate to keep automatic count on the number of SRs which has not received any response in 7 working days. Also, associates need not have to manually go and check every SR for next follow-up as dashboard itself will keep count and display a grid(notification) for every SR. Even if the associate doesn’t open the tool, this dashboard will provide an extra leverage by sending the same notification to his/her mail.This will always keep the associate aware on what would be the action item that is required from his/her end without any manual calculation and ensure that the risk of inconsistencies and inaccuracy is minimized. The aim of this project is to create a dashboard to notify associates of Cerner Healthcare India Pvt. Ltd. about the SR (Service Requests) which has not received any response from client for continuous 7 working days. Moreover, it will also display an immediate report with respect to the pending updates for each SR. The achievement of this goal will let the engineers save a huge amount of time instead of doing it manually.
Adaptive Monitoring in Microkernel OS
(2017-08-18T11:49:22Z) Priyadarshini, D; Sridevi, S; Sai Bhargavi, P
Operating Systems (OSs) are perhaps the most complex and critical part of the software stack in modern systems: they are made up of millions of lines of code (LoCs), provide fundamental services to user programs, and are tightly coupled with hardware resources. However, complexity raises concerns about dependability, since it is challenging to detect and fix software faults (bugs) in such a complex system within time-to-market constraints, as demonstrated by several studies on OSs failures occurring after release. Therefore, significant research efforts have been spent on dependable architectures for OSs, in order to mitigate the issue of software faults in the OS. (Microkernel) OS have a modular based architecture that can potentially be used for developing more dependable systems.
Adventures of thePixel Knight
(NHCE, 2024) ARYAN B1NH21IS025, CHETHAN D1NH21IS043, DARENA PEARL S A1NH21CS063, AFIYA YOUSUF1NH21CS012
The Adventures of the Pixel Knight is a fun 2D platformer game that uses Unity 2D and C# The game comes with a pixelated knight hero, giving the experience a retro feel that's full of dynamic challenges, combat mechanics, and strategic exploration. Designed to be accessible and scalable, the game utilizes WebGL technology, allowing players to enjoy the game right in their browsers without needing any additional downloads. This feature ensures a wider reach and enhances the overall gaming experience. This involves control of the knight to fight with enemies, avoid obstacles, and collect coins and potions. Coins are added to the player's score and, upon completing the game, it is a high score. The WebGL version features a local leaderboard system showing high scores for multiple players to encourage friendly competition and play repeatedly. The health system, yet another strategic addition, is such that the player is obliged to make wise decisions navigating the perfectly designed levels. Technical implementation emphasizes smooth and responsive controls through efficient C# scripting. Movement logic is optimized to prevent character sliding; this allows for smooth movement. Combat mechanics add complexity to the game, so the player needs to be as precise and strategic as possible in defeating enemy knights and bosses. The WebGL feature increases accessibility since leaderboard functionality may be displayed in real-time for visually appealing tracking of scores and encouragement of competitive play.
Agri-Smart 2.0
(NHCE, 2024) AKASH S CHERIAN1NH21IS012 ASHIMA PRASAD 1NH21IS029 Nagaprasad H S1NH21ME048 Tharun B 1NH21ME077
The field of agriculture, which has been providing the foundation for human existence and economic security, mcreasingly faces a host of daunting problems, such as inefficient utilization of water, labor deficiencies, and inability to notice crop diseases in their very early stages. These concerns call for innovative solutions toward maintaining agricultural productivity while reducing resource utilization. Agri-Smart 2.0 is a web-based smart imigation and crop monitoring system that seems to answer these challenges comprehensively and through advanced automation Technologies, wireless control mechanisms, and artificial intelligence for enhancing the optimization of farming practice. This project is a step forward for the digital transformation of agriculture with the aim of promoting productivity and resource efficiency.
AGRICULTURAL INTELLIGENCE DECISION SYSTEM
(2019-06-25T12:26:57Z) SURAJ SHET H
In the recent years, the huge volume of real time data in the agricultural sector and its need for an efficient and effective processing, stimulate the use of novel technologies and platform to acquire, store, process, analyze and visualize large data sets for future predictions and decision making. Big Data is an evolving term given to a wide area of data-intensive technologies in which the datasets are extremely large that dealing with them become more challenging than how it was. Before, Due to the critical challenges facing the agriculture sector farmers feel more forced to adopt intensive farming practices and sustainable agricultural ones, in order to increase both economic and environmental costs. Bringing data mining technologies into agriculture presents a significant challenge; at the same time, this technology contributes effectively in many countries’ economic and social development. In this work, we will study environmental data provided by precision agriculture information technologies, which represents a crucial source of data in need of being wisely managed and analyzed with appropriate methods and tools in order to extract the meaningful information. i
Agripulse - Revolutioning Agriculture through Wireless Sensor Networks and Machine Learning
(NHCE, 2023) Nikhil Kamate 1NH20IS094 Pinaki Sankar Matruprasad 1NH20IS109 Om Gawade 1NH21IS409 Vamsi Krishna Sai1NH20IS088
The introduction of cutting-edge, intelligent medical equipment into the virtual doctor system is orchestrating a remarkable transformation within community healthcare. Research in the healthcare sector has unveiled promising developments in extending human lifespan. However, obtaining healthcare services in remote areas remains a formidable challenge as patients often have to endure long journeys for treatment
AI BASED DIAGNOSTIC SYSTEMS FOR HEART ANOMOLY DETECTION USING CMR and ECG
(NHCE, 2024) ANIKET HERLE1NH21CS027 , ARPEETA SONOLI1NH21CS031 , SRUSTI HIREGOUDAR 1NH21IS158, Ananya K G 1NH21IS014
This project focuses on developing an Al-based diagnostic system for heart anomaly detection, integrating cutting-edge deep learning methodologies for both classification and segmentation tasks using Electrocardiogram (ECG) and Cardiac Magnetic Resonance (CMR) imaging data. By leveraging publicly available datasets, including ECG data from Mendeley and CMR images from the ACDC dataset, the system aims to enhance the precision and efficiency of cardiac diagnostics. The system comprises two key components: ECG Image Classification and CMR Image Segmentation. For ECG classification, convolutional neural networks (CNNs) were employed to detect and classify anomalies in ECG images into five categories: normal, myocardial infarction, dilated cardiomyopathy, hypertrophic cardiomyopathy, and abnormal right ventricle. The classification model incorporates advanced preprocessing techniques, including normalization and denoising, to ensure high-quality inputs for training. The model's output is a probability distribution across the categories, optimized using categorical cross-entropy loss and evaluated with standard metrics like accuracy and F1-score.
AI Based Virtual Patient Assistant Chatbot
(NHCE, 2024) Abhay G 1NH21CS003 Abishek J1NH21CS004 Bharath J1NH21IS037 BHARATWAJ 1NH21IS080
This work introduces an Al-based virtual patient assistant chatbot powered by the LLaMA (Large Language Model Meta Al) architecture, designed to enhance healthcare services by offering efficient, real-time medical assistance. The growing demand for Al-driven healthcare solutions highlights the need for tools that can offer timely, accurate responses with minimal computational overhead. LLAMA, developed by Meta Al, addresses this requirement by providing a highly scalable, low-latency solution, making it ideal for use in medical diagnostics, especially where speed and accuracy are paramount. The virtual assistant engages users through natural language conversations, collecting essential patient information such as name, age, and medical history. This personalized data allows the chatbot to provide tailored responses. The system processes symptoms provided by users in real-time, cross-referencing them with a comprehensive medical database to generate potential diagnoses and treatment recommendations. The use of LLaMA's fine-tuned capabilities ensures that the chatbot offers contextually relevant and medically accurate suggestions, making it a reliable tool for symptom analysis and healthcare advice.
AI Powered Automated Assessment and Evaluation System
(NHCE, 2024) HARSH AHUJA1NH21CS092 NIKHILA SUVEDA MALLA1NH21IS101 PENTELA DINESH TRILOK1NH21IS112 Siddharth pradeep1NH21CS231
The Automated Evaluation and Assessment System (AEAS) is an innovative technology designed to modernize evaluation processes in educational institutions. By combining dynamic and static analysis methodologies, AEAS incorporates advanced techniques such as abstract syntax tree analysis, plagiarism detection, and adaptive learning models to give a comprehensive assessment platform. The system is based around three basic interfaces -Administrator, Instructor, and Student - interconnected by a scalable and flexible web server architecture. This modular design provides smooth integration across multiple academic environments while keeping flexibility to accommodate varied evaluation criteria. Key features of AEAS include real-time plagiarism detection, tailored learning pathways depending on students' CGPA, and automated grading of assignments and examinations. These functions aim to reduce manual tasks for instructors, provide fast and actionable feedback to students, and enhance the overall quality of education delivery.
“AI-BASED FARMER SOCIAL MEDIA “
(NHCE, 2025) MANOJ KUMAR S PATIL 1NH23IS410 SHASHANK H U 1NH23IS415 RAVI KUMAR B 1NH23IS413 TEJAS D 1NH23IS416
The Farmer Social Media web-based application is designed to empower farmers by providing a digital platform that enables them to sell agricultural products directly to customers and merchants, thereby eliminating middlemen and ensuring fair pricing. The system allows each registered farmer to create their own store, manage product listings, and engage in online bidding. Alongside product selling, the platform also delivers realtime information related to market prices, government schemes, agricultural news, and farming suggestions. It supports secure login access with unique IDs for farmers, customers, and merchants, ensuring privacy and data security. The application promotes transparency in transactions, encourages quality production, and facilitates communication among all stakeholders in the agricultural ecosystem. By providing a user-friendly and multilingual interface, the system is especially beneficial for rural users with limited digital experience. Ultimately, this solution contributes to improving farmer livelihoods, increasing digital participation, and modernizing agricultural marketing in a sustainable way.
“AI-POWERED DETECTION OF BODY FLUID MARKERS FOR EARLY WARNING OF LIFESTYLE DISEASES”
(NHCE, 2025) DARSHAN D G -1NH22IS032 LOHITH GOWDA C P -1NH22IS080 MITHUN RAJ URS T V -1NH22IS086 RANGANATHA N -1NH22IS124
The machine-learning reinforced web app at the core of this project is presented as a tool to uncover potential health risks and thus to raise the awareness of them, using body fluid biomarkers as a basis. The model takes the main clinical parameters such as BMI, Total Cholesterol, HDL, LDL, Triglycerides, Creatinine, BUN and other metabolic markers provided by the users and then processes them through the trained predictive models, which are used to infer the likelihood of the user getting sick from a range of conditions like diabetes, hypercholesterolemia, hypertriglyceridemia, kidney dysfunction, cardiovascular stress, and metabolic syndrome. The backend of the application combines supervised learning techniques applied on real medical datasets to find out the highly complex interactions between biomarkers and disease risk, thus producing a risk score along with a rationale of which biomarkers were the main ones influencing the result. Besides the numerical estimations, the solution provides users with general lifestyle improvements, preventive measures, and professional guidance that leads to the healthcare professionals' consultation for more thorough examination
AIR POLLUTION METER
(2019-06-25T10:10:13Z) NIKITA CHOUDHARY
Air pollution occurs when gases, dust particles, fumes (or smoke) or odor are introduced into the atmosphere in a way that makes it harmful to humans, animals and plant. Air pollution threatens the health of humans and other living beings in our planet. It creates smog and acid rain, causes cancer and respiratory diseases, reduces the ozone layer atmosphere and contributes to global warming. In this industrial age, air pollution cannot be eliminated completely, but steps can be taken to reduce it. The government has developed, and continues to develop, guidelines for air quality and ordinances to restrict emissions in an effort to control air pollution. On an individual level, we can reduce our contribution to the pollution problem by carpooling or using public transportation. Additionally, buying energy-efficient light bulbs and appliances or otherwise reducing our electricity use will reduce the pollutants released in the production of electricity, which creates the majority of industrial air pollution
“AN ACADEMIC NAVIGATION AND INFORMATION COLLECTOR FOR PERSONALIZED LEARNING “
(NHCE, 2025) ADITYA P V 1NH22IS007 AKSHAJ GUPTA 1NH22IS011 B KHYATHI SUMASRI 1NH22IS026 DEEPIKA M 1NH22IS037
In the evolving landscape of higher education, students often struggle to manage multiple academic tasks due to the fragmented nature of digital learning tools. PlanIC is an artificial intelligence-supported study aid platform designed to simplify, centralize, and enhance the management of student learning. The system integrates diverse academic functionalities into a single intelligent framework—featuring a smart study planner with built-in reminders, a performance monitoring dashboard for maintaining academic records, and AI-driven modules such as note summarization, question generation, and an academic chatbot for contextual assistance.
Android Based Application to Detect Photholes and Uneven Roads
(NHCE, 2023) Shilpa S 1NH18IS106, Sonali Preetha 1NHIS109, Shripriya J 1NH18IS133
Android Based Application to Detect Potholes and Uneven Roads
Android Based Smartphone as A Hearing Aid
(2020-09-29T10:38:06Z) M S Veeresh, Prasad; Vinod Kumar, Y S; Ajay, H
Suffering from hearing loss can really be frustrating and in most situations, it can ruin one’s life. Depending on the severity if the case, hearing impairment can be treated in most usual cases. But for hearing loss due to inner ear damage, hearing aids is the viable solution. With the help of technology, some smartphones are now capable of helping those who are having problems with hearing. If you are someone or knows someone who suffers from hearing impairment, then, keep on reading. The project android-based Smartphone as hearing aid that is associate android application that's helpful for Hearing-loss individuals. Affected by hearingloss may very be frustrating, and in several things, it may ruin one’s life. Counting on the severity of the case, impairment might be treated in most usual cases. However, for deafness caused due to receptor harm, hearing aids is the viable resolution. With the help of technology, some smartphones are presently capable of serving those who area unit having issues with hearing.
Arduino Based Maverick Weather Station
(2020-09-29T12:07:01Z) ADITYA, SHARDA; AFIA, KULSUM; AMOGH P, SHANKAR; SIDDHARTH, CHAUHAN
Weather is the state of the atmosphere, describing for example the degree to which it is hot or cold, wet or dry, calm or stormy, clear or cloudy. Most weather phenomena occur in the lowest level of the atmosphere, the troposphere, just below the stratosphere. Weather refers to day-to-day temperature and precipitation activity, whereas climate is the term for the averaging of atmospheric conditions over longer periods of time. Weather forecasting is the application of science and technology to predict the state of the atmosphere for a given location. Human beings have attempted to predict the weather informally for millennium and formally since the nineteenth century. Weather forecasts are made by collecting quantitative data about the current state of the atmosphere on a given place and using scientific understanding of atmospheric processes to project how the atmosphere will evolve on that place. Weather is driven by air pressure, temperature and moisture differences between one place and another. These pressure and temperature differences can occur due to the sun angle at any particular spot, which varies by latitude. The atmosphere is a chaotic system, so small changes to one part of the system can grow to have large effects on the system as a whole. This makes it difficult to accurately predict weather more than a few days in advance, though weather forecasters are continually working to extend this limit through the scientific study of weather – meteorology. It is theoretically impossible to make useful day-to-day predictions more than three weeks ahead, imposing an upper limit to potential for improved prediction skill. Once an all-human endeavor based mainly upon changes in barometric pressure, current weather conditions, and sky condition, weather forecasting now relies on computer-based models that take many atmospheric factors into account. Human input is still required to pick the best possible forecast model to base the forecast upon, which involves pattern recognition skills, tele-connections, knowledge of model performance, and knowledge of model biases.
Arduino Based Personal Weather Station
(2019-06-25T07:53:31Z) Divyashree DR
Weather forecasting is the application of science and technology to predict the state the atmosphere for a given location. Human beings have attempted to predict the weather informally for millennium and formally since the nineteenth century. Weather forecasts are made by collecting quantitative data about the current state of the atmosphere on a given place and using scientific understanding of atmospheric processes to project how the atmosphere will evolve on that place. Weather is driven by air pressure (temperature and moisture) differences between one place and another. These pressure and temperature differences can occur due to the sun angle at any particular spot, which varies by latitude from the tropics. The atmosphere is a chaotic system, so small changes to one part of the system can grow to have large effects on the system as a whole. This makes it difficult to accurately predict weather more than a few days in advance, though weather forecasters are continually working to extend this limit through the scientific study of weather, meteorology. It is theoretically impossible to make useful day-to- day predictions more than about two weeks ahead, imposing an upper limit to potential for improved prediction skill.
Arduino-Based Air Quality Monitoring Robot with ML Analysis
(NHCE, 2023) SantoshReddy1NH20IS148 Yuktha S1NH20IS186 Darshini R1NH20IS040 Monisha R1NH20IS124
As everyone is aware, many dangerous gasses are emitted from automobiles and industry, which can lead to a range of health issues such as lung cancer, asthma, and a range of heart-related complications, or even lead to premature death if the ambient pollution is severe
Association Rule Based Product Recommendation Using Big Data
(2017-08-18T09:56:17Z) Pavithra, K; Sumanth, Reddy M
Recommender systems are integral part of any ecommerce store in order to sustain and compete with other growing businesses. There are various recommendation techniques which are used to appropriately recommend a product to the active user. The recommendation techniques like content-based recommendation system, collaborative recommendation system, context aware recommendation system, knowledge based recommendation have their own limitations which could be overcome by using hybrid systems. Also, these existing systems alone are enough to recommend products by analysing huge amount of data in the databases of the respective large retailer stores. The recommendation system has to analyse large amount of data to provide better recommendation and such important issue can be addressed using Hadoop ecosystem. In this paper, a recommendation system for product based on Hadoop framework is proposed. The proposed system recommend products to the user depending on the products present in the user cart. First, it uses framework to import the product transactions. Furthermore, the Apriori for finding frequent itemsets and Association rules are implemented in Hadoop and processing the data using MapReduce.

Browse

Browsing Project Reports by Title

Results Per Page

Sort Options