2025-26 (Autonomous)

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Advanced Traffic Monitoring & Speed Regulation using YOLOVII
(New Horizon College of Engineering, 2025) Alwin Jose-1NH22CE002, Prathyek K- 1NH22CE018
This work proposes an end-to-end real-time traffic monitoring and over-speed detection system with deep learning and computer vision to improve road safety and automate the process of traffic enforcement. Traditional speed detection methods—such as radar guns, fixed speed cameras, and manual surveillance—suffer from scalability, high costs, and limited coverage. In response, the proposed system will present a fully automated, vision based framework that integrates high-accuracy vehicle detection, speed estimation, license plate recognition, and automated alert delivery using modern AI techniques. At its core, the system employs the YOLOv11 object detection architecture, Paddle OCR for optical character recognition, and edge-compatible processing workflows for real-time performance. The system's architecture design surrounds four major modules: real-time vehicle detection, speed estimation through pixel displacement, license plate recognition, and alert notification. Initial steps incorporate an Ultralytics version of the YOLOv11 model-an effective, deep-learning-based convolutional neural network-to identify the presence of vehicles in video streams, either live or recorded. With high confidence, the model distinguishes between objects of interest such as cars, trucks, motorcycles, and buses in each frame. It detects at a rate of 30 frames per second, ensuring applicability for deployment in real-time applications.
Smart Edu note - A retrival augmented platform for personalized note taking, revision, and carrer guidance
(New Horizon College of Engineering, 2025) Kashish Upadhyay-1NH22CE019, Tarun Kumar Pradhan, 1NH22CE060- Shah Suraj Kumar, 1NH22CE049, Shreya S S- 1NH22CE052
In today's fast-growing digital world, students, teachers, and professionals heavily depend on written information for studying, teaching, research, planning, and decision- making. Notes from classes, research references, meeting summaries, project documentation, and study materials often span a large number of devices, apps, and storage platforms. Although technology has replaced traditional notebooks with digital tools, most existing note-taking applications function only as storage platforms; they enable the user to write, save, and search text but do not enable deeper understanding, summarization, or intelligent interaction with the stored content. This results in a number of common challenges: information overload, difficulty revising large collections of notes, poor retention of key points, and wasted time searching for important concepts buried in long documents. The SmartEduNotes project was designed to solve these modern problems through the transformation of simple digital notes into a smart, interactive, and personalized learning system. Instead of acting like a passive notebook, SmartEduNotes behaves more like an intelligent study partner. Using advanced models of AI together with Retrieval- Augmented Generation and modern full-stack development technologies, it guarantees a powerful yet user-friendly and secure platform that supports active learning, revision, and research. SmartEduNotes provides more than the ability to store information: users can interact with their notes through natural language—almost as if speaking to a tutor. It can automatically summarize large topics, extract important points, generate quizzes for self-testing, create flashcards, and answer questions directly from the user’s own notes. Using a retrieval system, the AI scans over the stored documents, identifies the most relevant sections to base its answers on, and generates them. That means accuracy, personalization, and pure relevance to what the user has provided, without generic or unrelated information. This is a highly reliable learning environment, where AI enhances understanding, strengthens memory, and gives meaningful and context-aware
Blockchain based voting system
(New Horizon College of Engineering, 2025) Goutham KrishnaA -1NH22CE012, Rohith S G-1NH22CE042, Sanjan Manjunath-1NH22CE045, Baladitya Godavarthi-1NH22CE005
This project presents a secure and transparent Blockchain-Based Voting System integrated with Geo-Fencing, designed to address the critical challenges of modern electoral processes. Traditional paper-based and electronic voting systems suffer from problems such as lack of transparency, centralized control, vote tampering, limited auditability, and vulnerabilities in both physical and digital infrastructures. Internet-based voting systems, while convenient, face additional threats including cyberattacks, DDoS disruptions, weak authentication, and absence of geographical verification. These constraints highlight the need for a technologically advanced, tamper-proof, and jurisdiction-compliant voting mechanism. The proposed system leverages blockchain technology to ensure immutability, decentralization, transparency, and end-to-end verifiability of votes. Every vote is recorded as a cryptographically secured transaction on a decentralized ledger, eliminating the risk of unauthorized modification. The use of smart contracts ensures automatic enforcement of election rules, prevents duplicate voting, validates voter eligibility, and supports real-time tallying without manual intervention. By eliminating the need for a central authority, the system enhances trust and provides a fully auditable election trail. To strengthen jurisdictional compliance, a Geo-Fencing module is incorporated that validates whether the voter is physically within the authorized constituency or polling boundary before vote submission. The system captures real-time GPS coordinates with ±10-meter accuracy, verifies location using the point-in-polygon algorithm, and generates a signed location token as proof. This ensures that all votes originate from approved locations, preventing cross-border or unauthorized remote voting, and aligning with legal election requirements.
Medical diagnosis prediction using Machine Learning
(New Horizon College of Engineering, 2026) P Thejaswini 1NH22CE033 Miruthula S 1NH22CE029 CH L Sriram 1NH22CE008
Clinical knowledge, manual symptom interpretation, and laborious laboratory tests have historically been the mainstays of medical diagnosis. Recent developments in machine learning (ML) have made it possible to create intelligent diagnostic systems that can quickly and accurately analyze vast amounts of medical data. The goal of this work is to develop and assess a machine learning-based diagnostic framework that helps medical practitioners identify diseases early and accurately. In addition to investigating feature-engineering techniques that enhance predictive performance, the main goal is to use supervised learning techniques to classify patient conditions using structured clinical datasets. Starting with data collection and preprocessing, the suggested system integrates multiple stages. The most important factors influencing diagnostic accuracy are found using feature selection algorithms like mutual-information analysis and Recursive Feature Elimination (RFE). Several machine learning algorithms, such as Logistic Regression, Random Forests, Support Vector Machines, and Gradient Boosting, are used in the construction of the diagnostic model. The best classifier for the chosen disease category can be found through comparative analysis. To guarantee generalizability and avoid overfitting, stratified data splitting and cross-validation are used during model training. The model gives each patient a risk score in place of binary classification, allowing clinical workflows to prioritize patients. By anonymizing patient data and implementing secure data-handling procedures, emphasis has been placed on protecting data privacy and adhering to healthcare standards. Overall, this study shows how machine learning can improve medical diagnosis by increasing accuracy, decreasing manual labour, and facilitating early disease detection. A complete ML-based diagnostic pipeline from preprocessing to deployment, comparative assessment of various algorithms, interpretability mechanisms for clinician trust, and risk-based diagnostic outputs that support medical decision-making are among the work's notable contributions. The results demonstrate how incorporating machine learning into healthcare environments can greatly improve patient outcomes and strengthen diagnostic support systems.
AI Based disease detection system
(New Horizon College of Engineering, 2026) Ashvin Sam Suraj M 1NH22CE065 K Aman Kumar 1NH22CE016 Sharanya Kattishetti 1NH22EC149 Sanjana Shaji Nair 1NH22EC146
Timely and accurate identification of plant diseases is critical for improving crop yields and ensuring food security. Manual inspection methods are often laborious, time-consuming, and may require specialized agronomic expertise that is not always accessible, particularly to small-scale farmers. This project, titled "AI-BEDS (AI Based Early Diagnosis System)," addresses these limitations by developing an innovative software-based system. The system utilizes deep learning and computer vision techniques to automatically detect and identify various plant leaf diseases directly from digital images. The core of AI-BEDS is a robust Convolutional Neural Network (CNN) model, trained on a comprehensive dataset of plant leaf images. This model is integrated into a user-friendly mobile or web application. This application allows farmers and agricultural workers to easily capture new images of plant leaves using their mobile devices or upload existing images. Upon submission, the system processes the image through the AI model and provides prompt diagnostic feedback. This feedback includes the identified plant species, the name of the detected disease (if any), and a confidence score indicating the reliability of the prediction. A significant aspect of the AI-BEDS project is its design for versatility and accessibility. While the primary interface is through mobile and desktop platforms to ensure broad reach, the system architecture also supports optional deployment on a Raspberry Pi. This feature enables the system to function as an offline, field-deployable diagnostic tool, extending its utility to rural or remote areas where internet connectivity may be unreliable or unavailable. The methodology for this project encompasses several key stages: the collection and meticulous preprocessing of a large and diverse dataset of plant leaf images (e.g., from public repositories like PlantVillage, potentially augmented with custom data); the training and optimization of a suitable deep learning classification model (such as MobileNetV2 or EfficientNet, chosen for their balance of accuracy and efficiency) using frameworks like TensorFlow/Keras; the conversion of the trained model to a lightweight format (e.g., TensorFlow Lite) for efficient deployment on edge devices, including mobile phones and the Raspberry Pi; and the development of an intuitive and responsive user interface for both the mobile/web application and the optional Raspberry Pi setup. Furthermore, the system is designed to include functionality for logging diagnostic results, which can help track disease prevalence over time, and for suggesting general, evidence-based treatment guidelines to assist farmers in making informed decisions. The anticipated outcomes of the AI-BEDS project are a highly accurate, fast, and reliable mobile or web application capable of identifying common plant diseases from leaf images. The optional offline capability provided by Raspberry Pi deployment will further II enhance its practical value in diverse agricultural settings. From a societal perspective, AI-BEDS aims to empower farmers with an accessible, low-cost tool
Healings Hands: Yoga Mudra Detection and Correction using AI/ML
(New Horizon College of Engineering, 2026) Nivedita Niran 1NH22CE032 Pranjali Sinha 1NH22CE037 Preeyana Shree 1NH22CE038 Thoppe Sridhar Srivarshine 1NH22CE062
For generations, people have practiced yoga, which has many advantages for the body and the mind. It emphasizes developing inner awareness, strength, flexibility, and balance. The use of hand gestures known as mudras is an important but frequently disregarded aspect of yoga practice. These mudras are important for strengthening mental attention, facilitating meditation, and guiding the body's energy. But doing things correctly calls for direction and accuracy. Many people who practice yoga at home find it difficult to execute mudras well enough to reap the full benefits. To help users recognize and improve their hand motions during practice, this project presents a real-time yoga mudra detection system. The technology can identify mudras and give instant feedback on the user's performance using a standard webcam and sophisticated technologies. Improving the quality of self-practice is the main goal, particularly for those without access to regular yoga instruction. This approach aims to increase the accessibility, interactivity, and efficacy of mindful practice by fusing the knowledge of traditional yoga with the capabilities of contemporary technology.
Breast Cancer Classification aided by Homomorphic Encryption
(New Horizon College of Engineering, 2026) M. Preeti 1NH22CE023 ShakthiPriya 1NH22CE050 Gauri B Nair 1NH22CE011 Ishmita Menon 1NH22CE014
wealthcare has sundergime a major change in technology awww the last ten years, with the mam factors being rapid aduancemem Armficial Intelligence (Al), Machine Learning (MU, data analytics, and secure computational methods. Theur new technologies have changed the face of medicine by introduung automated analysis, predictive modelling, and des support systems that nela comcians in thagnoung donirses with improved apuracy and efficiency M impact related issues were numimoun, the one on breast cancer detection was very entical because of the disease's orevalence, mortality rate, and importance of the early stage diagnosis, Breast cancer is a complicated medical condition that results from the abromal development of cells in the breast area, farly detection & a great advantage in treatment success, but accurate diagnosis a must which can only be achieved bo analysing varied types of clinical data such as manunography images, biopsy reports, Tumor measurements, hormonal recesitor status, and patient medical histories The datasets aned in the case are extremely sensitive since they contain personal denshable health informator that a protested under very stric globul privacy regulations. The digitization of medical records along with the demand for collaborative diagnonte soort has brought to light the issues related to the secure handling and processing of sach data Medical datasets used for first canem lemon typically came of Tumur characteristics jeg margin terture Tenging fratures (ng, density masi shape) Demapphic dietalli leg age, family history) Pathology.
Zero Trust Web Application FireWall using blockchain
(New Horizon College of Engineering, 2026) Srinivas Nagalingam 1NH22CE054 SP Darshan 1NH22CE043 Melvin Vikas 1NH22CE028 Akshay 1NH22CE001
The rapid growth of remote work and the Internet of Things (IoT) has, in the recent digital world, led to the disappearance of traditional network perimeters and, as a result, the users are more and more exposed to highly sophisticated web attacks. In case that a user connects to a sensitive application through an untrusted public network, the user is exposed to significant risks due to the possible occurrence of such exploits as SQL Injection (SQLi) and Cross-Site Scripting (XSS). In general, security solutions like static Web Application Firewalls (WAFs) are not able to identify recently emerged attack patterns and have a serious architectural problem, i.e., they depend on centralized logging databases that attackers can modify or delete to hide their actions. In order to fix these security issues, this work develops a mobile Zero Trust Web Application Firewall (WAF) that can be positioned at the network edge by means of a Raspberry Pi. The device, by application of the "Zero Trust" model—requiring a very thorough check of each data packet—acts as an automatic safety gateway. It uses a Machine Learning-based detection engine, which implements a Logistic Regression algorithm together with TF-IDF vectorization, to perform the analysis of the HTTP request payloads on-the-fly. This in turn enables the device to make the right decision between the normal traffic and the sophisticated attack traffic with a very high degree of accuracy. Besides that, to enhance the level of data security, the system adopts Blockchain technology for impervious auditing. Each prevented threat is encrypted, signed, and logged through a Solidity Smart Contract on a private Ethereum blockchain. This results in a distributed, intervention-free record of security incidents, which is the ultimate proof of the attempted attacks. The envisaged system is an excellent example of the successful convergence of the Edge Computing, Artificial Intelligence, and Blockchain technologies
Third Eye - Real Time Object & Threat Detection System
(New Horizon College of Engineering, 2026) Kovuruharish 1NH22CE021 Venkatesh patil 1NH22CE064 Farhan S 1NH23CE401 Sanjay BM 1NH22CE047
Third Eye - Real Time Object & Threat Detection System
AI-Powered Real-Time Safety Monitoring and Hazard Detection System for Industrial Workspace
(New Horizon College of Engineering, 2026) Madhuri Chillarge 1NH22CE025 Syeda Suhasana 1NH22CE059 K J Mohit 1NH22CE017 D Navyashri 1NH22CE009
An innovative web-based platform designed to increase safety in industrial and construction settings is the GuardianEye Safety Al Monitoring System. It operates directly within a standard web browser and employs real-time Al recognition, monitoring, and immediate waming to spot risks and make sure employees abide by safety regulations, it becomes simple to use in both small and large businesses because it doesn't require complicated installation or hefty gear The three fundamental tenets of the system are modularity, accessibility, and efficiency The major control point of its hub-and-spoke architecture is its main dashboard (Index.html). Three distinct Al-powered modules are available to users via this dashboard: Vehicle Speed Monitoring, Fall Detection, and PPE Compliance Monitoring. Since each module is built as a stand-alone web application, changes or enhancements can be made without impacting the system as a whole. GuardianEye is scalable, adaptable, and appropriate for long-term deployment thanks to its modular design. The Göögle Gemini Vision API is used by the PPE Compliance Module to verify if employees are donning safety vests and helmets. When infractions happen, the technology automatically monitors live footage and generates alerts TensorFlow.js MoveNet is used by the Fall Detection Module to monitor human body motions and apot unusual postures or unexpected falls In order to minimise accidents caused by overspeed, the Speed Detection Module uses TensorFlow.js COCO-SSD to identify vehicles and determine their speed. Every module uses the same pipeline, which includes utilising the MediaDevices API to record live video, running Al models locally or via cloud services, analysing the findings, and using the Canvas and Web auditory APIs to create real-time warnings with visual and auditory signals. For convenience, localStorage is used to save user settings such as theme mode and sensitivity levels. All things considered, GuardianiEye is a strong and clever safety platform. It enhances situational awareness, lowers human error, and fortifies industry-wide workplace safety regulations by fusing cutting-edge Al technologies
SkillMatch: ML-Based Career Path Predictor for School Students
(New Horizon College of Engineering, 2026) Dikshyaa Satapathy -1NH22CE010, Heema sharma -1NH22CE013, Swati shastri -1NH22CE057, Madhu shivanand hosakoti-1NH22CE024
Career guidance, as a discipline, has been heavily influenced by changes in the tech world and the use of data analytics over the last few years. Students who are about to finish their secondary education and enter the world of either higher studies or jobs, usually face a lot of confusions about which way to choose. This problem is even more notable in the educational systems like India ones, where the decision made at the 10th-grade level determine streams (e.g., Science, Commerce, or Arts), choices at the 12th-grade level point to undergraduate fields, and degree-level assessments lead to job roles. The Career Prediction System, as elaborated in this document, is basically a machine learning solution that can effectively work towards reducing intricacies by taking into account a variety of inputs by the user such as academic performance,personal interests, skills, and behavioral traits, hence providing the most appropriate suggestions.
Design and development of Refreshable Braille Display
(New Horizon College of Engineering, 2026) Ayushi Kumari 1NH22CE004 Saloni Khemka 1NH22CE044 Harshit Rana 1NH22ME025 Mukesh Kumar Pandey 1NH22ME039
Although the world has made remarkable progress in digital accessibility, the visually impaired population continues to face major educational and informational barriers due to the lack of affordable reading devices. According to global studies, less than 10 percent of visually impaired people are Braille literate, and one of the main reasons is the high cost and limited availability of Braille materials and displays. In countries such as India, where the visually impaired population exceeds eight million, only a fraction of educational institutions can afford to install Braille reading systems. Current commercial refreshable Braille displays rely heavily on complex and costly piezoelectric actuators. These actuators provide accurate and reliable tactile movement, but each dot requires an individual piezo crystal, driver circuit, and mechanical support. The overall cost increases exponentially with the number of dots or Braille cells. Additionally, the requirement for specialized manufacturing and imported materials further increases cost and reduces availability. Existing devices also consume relatively high power and are not easily repairable. Many are designed for fixed setups rather than portable use, limiting flexibility for students and professionals who need lightweight, mobile solutions. Furthermore, most systems are proprietary, meaning they cannot be modified or improved easily by researchers or users. This restricts open innovation in the field of assistive technology.
Climate Adaptive IoT Drip Irrigation System
(New Horizon College of Engineering, 2026) Sankalpa Kashyap 1NH22CE048 Mrinank Kumar Saini 1NH22CE030 Jaydeep Mukharjee 1NH22EC066 Kiran Ghosh 1NH22EC076
Agriculture today faces growing challenges due to erratic weather conditions, water scarcity, and inefficient irrigation methods. Most conventional systems follow fixed irrigation schedules or manual operation, which often results in over-irrigation, increased labour, and suboptimal crop performance. To overcome these limitations, our project—Climate-Adaptive IoT Drip Irrigation System—aims to design a real-time, intelligent irrigation solution that adapts based on soil conditions and weather forecasts. The system integrates soil moisture sensors to continuously monitor field conditions. A microcontroller-based control unit processes this data and makes irrigation decisions dynamically. Instead of using ESP32, an alternative microcontroller suited for stable connectivity and modular expansion has been implemented to support multiple sensor inputs and control components. To avoid unnecessary irrigation, the system fetches real-time weather forecasts from the OpenWeatherMap API, delaying irrigation when rain is expected. For data communication, we use the MQTT (Message Queuing Telemetry Transport) protocol—a lightweight and efficient IoT protocol that allows real-time transmission of sensor data and control commands. The microcontroller publishes the data to a cloud server, where it is stored and displayed through a custom web dashboard. The dashboard provides users with access to live soil conditions, system status, and irrigation history, along with manual override features for better user control. This is an interdisciplinary project, with contributions from both Electronics and Communication Engineering (ECE) and Computer Engineering (CE) domains. The ECE team handled sensor calibration, hardware interfacing, and relay-based control of solenoid valves for drip irrigation. Meanwhile, the CE team focused on backend development, API integration, cloud database setup, and dashboard design, ensuring smooth real-time monitoring and user interaction.
Network Packet Sniffer and Traffic Analyzer for Real-time Data Monitoring
(New Horizon College of Engineering, 2026) Ravikumar A 1NH22CE041 Rahul Patil 1NH22CE040 Sooraj Naik 1NH23CE404 Srushti Navale 1NH22CE056
The domain of this project belongs to Computer Networks, specifically focusing on Network Traffic Monitoring, Packet-Level Analysis, and Network Security Operations [1]. In today’s hyper-connected environment, computer networks serve as the backbone for communication between devices, services, cloud platforms, and enterprise applications [2]. Every interaction whether browsing a website, streaming a video, sending an email, or accessing a database involves countless packets moving seamlessly across the network [3]. Understanding these packets is essential for ensuring that the network remains efficient, secure, and reliable [3]. As modern networks continue to scale due to cloud adoption, IoT expansion, and distributed systems, the volume and variety of data transmitted have grown enormously [4]. This creates new challenges for administrators who must maintain visibility into what is happening within the network at any given moment [5]. Packet analysis helps bridge this gap by offering a microscopic view of network behavior, allowing engineers to see exactly how data is formed, transmitted, and interpreted across devices [5]. Such insight is crucial not only for performance monitoring but also for identifying misconfigurations, failures, and malicious activity [6]. Network security is another critical aspect of this domain [7]. Cyber threats such as spoofing, sniffing, malware distribution, unauthorized access, and data exfiltration often manifest as unusual or abnormal network traffic [7]. By examining packet patterns and protocol behavior, these threats can be detected early before they disrupt systems or compromise sensitive information [8]. Traditional firewall logs and system alerts are often insufficient to capture low-level anomalies, making packet-level inspection an essential layer of definitions in modern cybersecurity frameworks [9].
FairCourt: A System to Evaluate Fairness in Legal Judgments
(New Horizon College of Engineering, 2026) Pranava Gopavaram 1NH22CE036 Priyanka S Reddy 1NH22CE039 Keerthana N 1NH22CE020 Bhavishya Likitha Kondaveeti 1NH22CE007
The Indian judiciary, though constitutionally mandated to uphold justice and equality, faces critical challenges that undermine its effectiveness and credibility. These include inconsistent judicial interpretations, prolonged delays due to case backlogs, and growing concerns over biases related to caste, religion, gender, and socioeconomic status. Additionally, the lack of transparency in judicial appointments and perceived instances of corruption have contributed to a decline in public trust. To address these systemic issues, there is a pressing need for a data-driven, transparent, and scalable solution that can objectively evaluate the fairness of court judgments. FairCourt is an Al-powered framework designed to assess judicial fairness using advanced Natural Language Processing (NLP), fairness metrics, and explainable Al. The system architecture consists of six core modules: data acquisition from public legal platforms such as Indian Kanoon, NJDG, and eCourts, preprocessing to clean and structure unstructured legal texts, semantic analysis using transformer-based models like BERT and LegalBERT; fairness assessment through metrics like statistical parity and equality of opportunity; explainability using tools such as SHAP and LIME to provide interpretable justifications, and a dashboard to visualize fairness trends across judges, regions, and case types. By identifying patterns of bias and inconsistency in judgments, FairCourt aims to promote judicial accountability, improve public trust, and support policy reforms. Aligned with Sustainable Development Goal 16, the project advances the rule of law, reduces bias and corruption, and supports the development of transparent and inclusive judicial institutions
Emotion Recognition with deep learning
(New Horizon College of Engineering, 2026) Shashank TJ 1NH23CE403 Niskarsh Naulakha 1NH22CE031 Naveen Kumar 1NH23CE402 Sanjay A 1NH22CE046
Emotion recognition using deep learning has emerged as a pivotal area of research, leveraging advanced neural network architectures to analyse and interpret human emotions from various modalities, including speech, text, and facial expressions. This mini project explores the integration of deep learning techniques in emotion recognition systems, focusing on their effectiveness, challenges, and applications. The project begins with a comprehensive literature review, highlighting the evolution of emotion recognition technologies and the role of deep learning in enhancing accuracy and efficiency. Traditional methods often relied on handcrafted features and shallow learning algorithms, which limited their performance. In contrast, deep learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have demonstrated superior capabilities in automatically extracting relevant features from raw data, thus improving recognition rates. A significant portion of the project is dedicated to the implementation of a deep learning model for emotion recognition. The chosen model is trained on a diverse dataset comprising audio, visual, and textual inputs, allowing for a multimodal approach. Data preprocessing techniques, such as normalization and augmentation, are employed to enhance the robustness of the model. The training process involves optimizing hyperparameters and employing techniques like dropout and batch normalization to prevent overfitting. The evaluation of the model's performance is conducted using various metrics, including accuracy, precision, recall, and F1-score. The results indicate that the deep learning model outperforms traditional methods, achieving high accuracy in recognizing emotions such as happiness, sadness, anger, and surprise. Additionally, the project discusses the importance of dataset diversity and quality in training effective emotion recognition systems.
Last food first hope
(New Horizon College of Engineering, 2026) P Sai Hithesh 1NH22CE034 Shivam Kumar 1NH22CE051 Tushar Gourav 1NH22CE063 L Satwika 1NH22CE022
In a world where food scarcity coexists with widespread food wastage, there is an urgent need for sustainable and technology-driven interventions. “Last Food First Hope” is an innovative digital platform designed to bridge the gap between surplus food providers—such as restaurants, caterers, and individuals—and nearby non-governmental organizations (NGOs) serving underprivileged communities. Leveraging geolocation, real-time cloud updates, and role-based access control, the platform enables donors to easily register excess food and connect with the nearest NGOs for timely redistribution. Features like food expiry alerts, donation tracking, feedback systems, and Firebase-based real-time database integration ensure safe, efficient, and transparent operations. By minimizing food waste and maximizing its value through smart redistribution, this project not only addresses hunger but also promotes social responsibility and environmental sustainability in urban communities. The core idea of this platform is to serve as a digital bridge between restaurants, individuals, and food-based businesses with excess food, and NGOs that work towards feeding the underprivileged. The platform allows users to register as either donors or NGOs, and uses geolocation services to identify the nearest potential recipients for a given food donation. This minimizes delays in transportation, ensuring timely and safe delivery of food while maintaining its quality and edibility. Overall, “Last Food First Hope” demonstrates how the thoughtful application of modern technologies such as real-time databases, cloud computing, and geolocation services can create meaningful social impact. The platform is not only a step toward zero food waste but also a hope-driven initiative that channels excess food into a lifeline for many. With further scalability, this system can be extended to regional and national levels, contributing significantly to food security and community welfare.
HelpMate: A Community-Driven Platform for Real-Time Assistance Coordination
(New Horizon College of Engineering, 2026) Anvith Kumar 1NH22CE003 Jeshta B D 1NH22CE015 Manoj R 1NH22CE026 Syed Tayyab 1NH22CE058
HelpMate is an all-inclusive digital aid platform that intelligently manages requests and coordinates based on location to connect those who need help with community volunteers. Individuals with mobility issues, the visually impaired, or those requiring daily assistance are often unable to obtain timely human help when automated solutions fail them in complicated real-life situations. With multi-priority classification, skill-based volunteer matching, and full lifecycle tracking, the platform features a highly advanced request orchestration system. Its main functions are: dynamic and user-friendly request creation; a volunteer search based on geolocation; status updates in real-time; emergency escalation procedures; and a detailed community impact dashboard powered by a robust service-oriented architecture. Some of the most significant features are: an intelligent request categorization system with filtering options; a priority system consisting of four levels ranging from routine assistance to critical emergencies; a volunteer search radius along with skill matching; the full cycle of a request from the writing to the execution stage; feedback integration; and a set of community engagement metrics. The platform is designed to provide both immediate assistance and support scheduled in advance while at the same time ensuring secure user authentication and transparent volunteer coordination. Being aligned with the U.N. Sustainable Development Goals 10 (Reduced Inequalities) and 11 (Sustainable Cities and Communities), HelpMate is a promoter of individual freedom as well as community integration through the facilitation of communication links between people in need of help and the ones who can offer support.

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