2024-25
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Item Platform epoch(NHCE, 2024) AMOGH.S.KUMAR-1NH21CS280 AARAMBH JAISWAL -1NH21CS283 T SAI SREE CHARAN -1NH21AI109 VIBHA JOSHI -1NH21AI117Children with Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) often face unique developmental challenges that demand tailored interventions and educational support. This project, "Platform Epoch” seeks to bridge the gap between advanced diagnostic tools and personalized learning systems. By leveraging the power of digital identity and learning analytics, the platform provides an integrated solution to support early detection, informed decision-making, and customized educational pathways for children with special needs. At the core of the platform lies a machine learning model that computes an autism risk score (A_Score) by analyzing behavioral and demographic data collected through structured input forms. The model employs advanced neural network architectures to classify risk levels with precision, ensuring timely and accurate diagnostics. Complementing this functionality is a dynamic learning ecosystem that uses learning analytics to track progress, identify strengths and weaknesses, and provide actionable recommendations for parents and educators. By offering a robust backend powered by Flask APIs and secure data handling mechanisms, the platform ensures real-time processing and reliability. The platform is designed with accessibility and inclusivity in mind, emphasizing features like intuitive dashboards, responsive interfaces, and multilingual support. A unique digital identity system links parent and child accounts, facilitating personalized interactions while ensuring data privacy. Additionally, the system incorporates adaptive learning pathways tailored to the child’s cognitive and behavioral profiles, enabling gradual improvement and sustained engagement. Visualization tools such as Matplotlib and Plotly enhance user experience by presenting insights in an interactive and comprehensible manner.Item Design and Fabrication of Smart Wheelchair-Cum-Bed Equipped with Health Monitoring System(NHCE, 2025) SURESH A 1NH22ME412 BHAGATH MANJUNATH S 1NH22ME402 SWASTITIRTHA DASH 1NH21AI108 SAI KRISHNA KM 1NH22AI409This project focuses on the design and development of a smart Wheelchair-Cum-Bed. capable of seamlesaly transforming into either a wheelchair or a bed at the press of a button. The system incorporates a rack-and-pinion mechanism for smooth transition between modes, ensuring ease of use for individuals with mobility challenges. Additionally, the wheelchair can be moved from one location to another with a simple button press. Integrated health monitoring features, including sensors for heart rate, pulse, humidity, and ECG, provide real-time health data to enhance the user's well-being. This innovative design aims to offer both mobility assistance and healthcare support, improving comfort, independence, and affordability for usersItem Plant disease detection and diagnosis(NHCE, 2025) CHANDANA KS 1NH22CS404 MAHARATHI GN 1NH22CS410 NITHISH SOMANI 1NH21AI100 ABHILASH G M 1NH22AI400The "Plant Disease Detection and Diagnosis using Deep Learning" project is designed to provide an innovative and effective solution to ansist farmers in identifying and diagnosing plant diseases in real-time. The system leverages TensorFlow Lite, a lightweight framework optimized for mobile and edge devices, to deploy a deep learning model trained on a dataset of 1.5k images. These images represent seven plant disease classes, including rust, mold, blight, rot, powdery mildew, scab, and spot. By using computer vision techniques combined with Mediapipe for real-time detection, the system can analyze plant images captured from mobile devices or cameras, offering instant feedback to farmers regarding the presence of disease. The deep learning model has been specifically designed to run efficiently on resource-constrained devices, ensuring that farmers can easily deploy the solution in the field. The backend of the system is powered by Flask, while the frontend imerface is built using HTML, CSS, and JavaScript to provide a simple, user-friendly platform. The web interface allows farmers to upload images of their plants, where the system analyzes the data, diagnoses the disease, and recommends appropriate remedies. This real-time diagno sis helps farmers take immediate corrective actions to prevent the spread of diseases, reducing potential crop losses and improving productivity. In addition to diagnosis, one of the key features of this system is its ability to support multiple languages. Using the Google Translate API, the system can translate disease diagnosis and remedial advice into several regional languages, including Kannada, Hindi, Malayalam, Telugu, and Tamil. This multilingual feature ensures that the system is accessible to farmers in different linguistic regions, empowering them to understand the diagnosis and suggested remedies in a language they are familiar with, thereby bridging communication gaps in agriculture.Item Adaptable Arms: innovations in flexible robotic manipulation(NHCE, 2025) SURESH R 1NH22A1410 VARUN MS 1NH22A1411 SANGAMESH POLICE PATIL 1NH22ME411 SUDEEP 1NH21ME073The problem definition of flexible robotic arm movement in the agriculture field centres around the need for automation solutions that can handle the diverse, unpredictable, and often delicate tasks involved in modern farming. Agriculture presents unique challenges that traditional rigid robotic arms struggle to address due to the variability of the environment, crop types, terrain conditions, and the need for precision in handling perishable goods. Flexible robotic arms offer a promising solution to these challenges, but their design and deployment come with several complex problems that need to be solved.Item Real-time fire and smoke detection and retardation(NHCE, 2025) HEMANTH KUMAR REDDY Ρ 1ΝΗ21Α1127 RAHUL D 1NH21A1128 MANU M 1NH21EC090 NANDEESH GOWDA B 1NH21EC102Fire and smoke detection systems play a vital role in ensuring safety and reducing losses caused by fire-related incidents. However, conventional systems that rely on heat and smoke sensors are often susceptible to false alarms and may fail to detect fires at their earliest stages. This paper presents a modern, cost-effective, and intelligent approach to fire and smoke detection, utilizing the ESP32-CAM microcontroller combined with a Convolutional Neural Network (CNN). The ESP32-CAM, a compact and affordable lot-enabled microcontroller with an integrated camera, serves as a reliable platform for real-time image capture and processing. When paired with CNNs, a cutting-edge deep learning architecture for image recognition, the system provides a robust and highly accurate solution for detecting fire and smoke. By integrating lot and Al technologies, the proposed system bridges the gap between affordability and advanced functionality, ensuring timely and precise alerts The system architecture comprises three core components: the ESP32-CAM for image acquisition, the CNN for image-based analysis, and a communication interface for transmitting alerts. The ESP32-CAM continuously captures images or video streams, which are then preprocessed to enhance detection accuracy. Preprocessing techniques, such as resizing, normalization, and noise reduction, prepare the visual inputs for the CNN. The trained CNN identifies critical features in the data to detect fire and smoke reliably. At the heart of the system lies the CNN, optimized specifically for deployment on resource-constrained devices. Its lightweight design ensures compatibility with the ESP32-CAM's limited computational capabilities while maintaining high accuracy. Optimization techniques like model pruning, quantization, and transfer learning help reduce computational demands without compromising performance.Item Crop Sense: An Integrated WebApp for Crop Health Monitoring and Disease Detection(NHCE, 2025) NARJIT LEISHANGTHEM 1NH21AI064 MOHAMMED AFFAN 1NH21AI056 ADRIAN MATHEW ALOYSIUS 1NH21CS011 DAVE PINTO 1NH21CS065The increasing demand for sustainable agriculture necessitates innovative solutions to enhance crop health monitoring and disease detection. This project integrates advanced technologies, including satellite imagery, loT sensors, machine learning, and cloud computing, to address the limitations of traditional farming practices. The proposed system leverages high-resolution satellite data and real-time IoT sensor inputs to monitor environmental conditions and detect early signs of crop diseases. Machine learning models, such as Convolutional Neural Networks (CNNs) and Random Forest classifiers, process these datasets to provide accurate diagnostics and actionable insights. A user-friendly web-based interface ensures that farmers can access real-time data and receive tailored recommendations for efficient resource management. This system offers several key benefits, including improved disease detection accuracy, reduced reliance on chemical inputs, and enhanced agricultural productivity. By enabling precision farming, the system not only optimises resource utilisation but also promotes environmental sustainability. The project represents a significant step towards addressing the challenges of modern agriculture, contributing to food security and the adoption of smart farming practices globally.Item Levraging data analytics for fetal health monitoring(NHCE, 2025) CETHIVEN REDOΥ 1ΝΗ21AI129, PNUTHAN SAI 1NH21AI075The Fetal and Maternal Health Monitoring System integrates real-time data analytics, machine learning (ML), and multi-source data aggregation to improve prenatal care. Traditional methods like cardiotocography (CTG) often fall short in accuracy, real-time monitoring, and predictive capabilities, which can result in missed diagnoses or unnecessary interventions. This system addresses these issues by providing continuous, non-invasive monitoring of fetal heart rate (FHR) and maternal health metrics, enabling early detection of complications such as fetal distress, hypoxia, and preeclampsia. Key goals include enhancing predictive accuracy with ML models, integrating maternal and environmental data, and offering actionable insights through an intuitive user interface. Real-time monitoring and predictive analytics enable timely interventions, improving outcomes. The system uses technologies like convolutional neural networks (CNNs), decision trees, and advanced visualization tools, advancing anomaly detection and personalized care. Its scalable architecture supports integration with electronic health records (EHRs) and existing healthcare infrastructure, ensuring adaptability. Prioritizing data security and compliance with healthcare regulations, this approach fosters a proactive, personalized, and data-driven healthcare ecosystem.Item Implementation of IoT-based irrigation system for Climate resilient farming(NHCE, 2025) Paruchuri Uday 1NH21AI134 M Babu Dhanush Kumar 1NH21AI144 Bhanu Dhushyanth S 1NH21CS233 Mahendra Reddy Y 1NH21CS269Implementation of IoT-based Smart irrigation System for Climate Resilient Farming is pivotal for enhancing agricultural productivity and sustainability, particularly in the face of climate change. This project implements an intelligent farming system using an Arduino Uno microcontroller integrated with various sensors, including a DHT11 for temperature and humidity, a pH sensor for soil acidity measurement, an LDR sensor for light intensity detection, a soil moisture sensor, and a gas sensor for monitoring air quality. The collected data is analyzed using machine learning algorithms to provide crop recommendations tailored to the specific environmental conditions. In scenarios where soil moisture levels fall below the optimal threshold, an automated pump system is activated to irrigate the soil, ensuring efficient water usage and improved crop health. This approach not only enhances agricultural productivity but also promotes resource conservation, supporting farmers in making informed decisions and fostering resilience against climate variability.Item Speakeasy: a real-time speech translation system that Simplifies cross- language communication(NHCE, 2025) JASHWANTH S 1NH21A1089 VIGNESH A 1NH21A1118 VISWANANTH KOLLI 1NH21EC189 SELVAMOURIYAND 1NH21EC143The Real-Time Indian Languages Translation Project is an innovative initiative aimed at addressing the linguistic diversity of India by enabling seamless communication across various Indian languages. India, with its complex linguistic landscape, presents significant challenges in cross-linguistic communication, particularly in sectors such as education, healthcare, business, and government. By leveraging advanced technologies in speech recognition, natural language processing (NLP), and machine translation (MT), this project provides real-time translations, aiming to break down communication barriers and promote inclusivity, understanding, and collaboration across diverse linguistic groups. Challenges of Linguistic Diversity in India India is home to over 22 officially recognized languages and hundreds of regional dialects, with each state often having its own unique linguistic and cultural identity. The sheer volume of languages and dialects creates considerable barriers to communication, especially in contexts like education, healthcare, and public administration. In multilingual classrooms, students may struggle to grasp concepts taught in languages they don't speak, which can lead to educational disparities. Similarly, in healthcare, patients from rural areas may face difficulties explaining their symptoms to healthcare professionals who speak a different language, resulting in potential misdiagnoses. Additionally, language can create significant obstacles in government services, legal proceedings, and business transactions, where people often encounter difficulties in accessing crucial services due to language barriers. To address these issues, the Real-Time Indian Languages Translation Project aims to create a system that enables real-time translation between various Indian languages, ensuring effective communication across linguistic divides. This will provide a solution that promotes inclusivity and improves access to services, education, and healthcare for people from diverse linguistic backgrounds.Item Polyglot cam(NHCE, 2025) RAYNA HALLEY R 1NH21A1085 SHOAILUDDIN 1ΝΗ21A1096 ROSHAN RANJIT 1NH21IS125 SYED ARFA HUSSAINI 1NH2115116The Polyglot Cam project is an ambitious initiative that seeks to transform the way indi-viduals access and understand written information in foreign languages. By leveraging ad-vanced technologies, this web application will enable users to point their device's camera at any text-be it in a book, a street sign, product packaging, or a menu-and instantly translate it into a desired language with remarkable accuracy and efficiency. Polyglot Cam integrates state-of-the-art Optical Character Recognition (OCR) models, such as Tesseract, for precise text recognition, OpenCV for advanced image processing, and the Google Translate API for seamless language translation. The application is meticulously designed to address the challenges that arise in real-world scenarios, such as poor lighting, skewed or distorted text, complex backgrounds, and di-verse fonts and text styles. Through robust image preprocessing techniques, it ensures op-timal text recognition performance even under challenging environmental conditions. By focusing on adaptability and reliability, the application caters to a broad range of use cases, empowering travelers, language enthusiasts, students, and professionals to navigate multi-lingual environments effortlessly. Polyglot Cam emphasizes accessibility through its intuitive user interface, allowing users to interact seamlessly regardless of their technical expertise. The application will also fea-ture real-time overlays, customizable settings, and an optional audio output for translated text, further enhancing its usability and appeal. By delivering translations quickly and ef-fectively, it eliminates the barriers that written language can create, fostering communica-tion and understanding in both personal and professional contexts. This project is a profound demonstration of how artificial intelligence (AI) and advanced computing technologies can be harnessed to break down linguistic barriers and create a more inclusive world. Polyglot Cam goes beyond being a mere utility, it serves as a pow-erful tool for cross-cultural connection, enabling individuals to engage with new languages and environments confidently. By addressing a fundamental human need-understanding written language-it sets the stage for future advancements in Al-driven translation solu-tions, contributing to a more accessible and interconnected global community.Item Exploratory Analysis of Heart Attack and Breast Cancer Early Stage Prediction c(NHCE, 2025) Yashwanth Pavan Kumar K INH21AI124 P Sree Dinesh Reddy 1NH21AI143 Neha Singh 1NH21IS099 M Shivani Kashyap 1NH21IS083Cardiovascular events such as heart attacks and breast cancer rank among the foremost health issues worldwide, substantially influencing mortality statistics and presenting significant challenges for healthcare systems. These ailments are interconnected by the shared opportunity for better prognoses through timely detection and proactive measures. The present research emphasizes the utilization of data-informed strategies to improve comprehension and forecasting of these conditions during their initial phases. This study is set out to find significant patterns, associations, and predictors that may help in earlier detection based on clinical and lifestyle data relating to the risk of heart attacks and tumor- related factors of interest for breast cancer. Heart attacks are mainly caused by cardiovascular conditions; various factors such as age, cholesterol levels, blood pressure, diabetes mellitus, smoking, and a lack of exercise affect them. These parameters often have cross-references, leading to complex configurations, which are not often recognizable with standard diagnostic methods. Analogously, breast cancer is among the leading causes of cancer-related deaths in females, and involves intricate relations between tumor characteristics (size, texture, and margins) with demographic or genetic predispositions. The prompt identification of such factors is essential, because early diagnosis significantly increases the effectiveness of treatment outcomes as well as survival for the patients. This study uses an exploratory data analysis framework to analyze datasets that include clinical and demographic information for both conditions. EDA is crucial in understanding the data structure, recognizing patterns, and detecting potential anomalies. Through statistical summaries and graphical representations such as histograms, scatterplots, and heatmaps, this research reveals vital insights into the relationships among variables. For instance, in the case of forecasting heart attacks, variables such as high levels of cholesterol, obesity, and inactive lifestyle prove to have strong correlations with increased risk. In much the same way, data involving breast cancer highlight the importance of certain tumor features and demographic features in predicting cancer.Item Leveraging data analytics for fetal heart monitoring(NHCE, 2025) RITA SHALINI RAJ K 1NH21AI087 S VINAY KUMAR INH21AI092 KASIS ALI KHAN 1NH21CS126 BHIMANAGOUDA PATIL INH21CS048The Fetal and Maternal Health Monitoring System integrates real-time data analytics, machine learning (ML), and multi-source data aggregation to improve prenatal care. Traditional methods like cardiotocography (CTG) often fall short in accuracy, real-time monitoring, and predictive capabilities, which can result in missed diagnoses or unnecessary interventions. This system addresses these issues by providing continuous, non-invasive monitoring of fetal heart rate (FHR) and maternal health metrics, enabling early detection of complications such as fetal distress, hypoxia, and preeclampsia. Key goals include enhancing predictive accuracy with ML models, integrating maternal and environmental data, and offering actionable insights through an intuitive user interface. Real-time monitoring and predictive analytics enable timely interventions, improving outcomes. The system uses technologies like convolutional neural networks (CNNs), decision trees, and advanced visualization tools, advancing anomaly detection and personalized care. Its scalable architecture supports integration with electronic health records (EHRs) and existing healthcare infrastructure, ensuring adaptability. Prioritizing data security and compliance with healthcare regulations, this approach fosters a proactive, personalized, and data-driven healthcare ecosystem.Item Smart Earthquake Detection with ESP32 and MPU6050: Real- Time Monitoring and Email Alerts(2025) RAKSHITHE 1NH21AI147 RUTHWIK SIDDHARTHA 1NH21EC132 SUMESH R 1NH21EC157 VSHREYAS REDDY 1NH21EC167Earthquakes zre among the worst natural phenomena which damage properties take lives and disturb people. The harm caused by seismic activity can be reduced through their early danaction and surveillance. In principle, the need for such technologies exists all over the world but the resources available to such systems are limited in many developing countries, since maditional earthquake detection systems tend to be expensive. Nevertheless, the emerging technology has provided new opportunities to develop modern approaches and low costs microcontrollers and sensors based systems for reliable and effective detection of earthquakes The Internet of Things (IoT) has significantly changad various fields by allowing for real-time data collection, analysis, and communication bersem devices. In the context of earthquake monitoring. IoT facilitates the use of mnerconnected sensor networks that continuously track ground movement and sand data for analysis. These systems can deliver valuable real-time insights into seismic activities, alerting designated recipients when on sarthquake occurs The proposed sarthquake detection system is built around the ESP32 microcontroller. This microcontroller is recognized for its impressitie performance and energy efficiency, featuring dual-core processors along with integrated Wi-Fi and Bluetooth capabilities, which makes it ideal for IoT applications. Its small size and cost-effectiveness facilitate widespread use without imposing significant financial burdans. The ESP32 is capable of efficiently processing data from sensors, analyzing seismic activity, and communicating with cloud platforma for additional processing and alertItem Optimizing urban mobility a comprehensive public transport monitoring system(NHCE, 2025) CHETHAN SP 1NH21A1023 PAVANAKUMAR 1NH21CS176 SUSHRUTH MS 1NH21A1107 PRAJWALS 1NH21CS182Urban mobility is a crucial sepect of modern cities, necessitating efficient and reliabla public transport systems to cater to the growing whan population and alleviate traffic congestion This project, titled "Optimizing Urban Mobility: A Comprehensive Public Transport Monitoring System, aneks to enhance the performance and use experience of public transport services through an integrated matiring approach The system employs mal-time data collection from GPS devices installed on public transport vehicles, coupled with passenger feedback to evaluate service quality. Advanced data analytics and machine learning techniques are utilized to analyze the collected data, providing valuable insights ima delays, route efficiency, and passenger satisfaction The system architecture compenses a robust buckend developed using Python and Flack/Django, with SQL. Line for data storage and management. Visualization tools such as 113 ja and Tableau are employed to create дупатьс, запactive dathboards that present ab time performance metrics and analytics. Thuse dashboards smable transport authorities to monitor public transport operations effectively and make data-driven decisions to optimize routes, reduce delays, and enhance overall service quality The expected outcomes of this project include improved mate efficiency, minimized delays, and meressed passenger satisfaction, ultimately leading to a more reliable and user-friendly public transport system. Additionally, the system aims to promote sustainable urban transport by encouraging highet public transport usage, thereby reducing traffic congestiver and environmental impact. This project addresses exacting inefficiencies in public transport systems and sans the stage for future imovations in urban mobility, contributing to the development of amarter, more sustainable citiesItem Neuro-Voice(NHCE, 2025) KOYAL CHADRESH 1NH21AI045 THARANISH K M 1NH21AI137 AKSHAY GOWDAS 1NH21EC012 RAGHAVA REDDY N 1NH21EC125This paper provides an in-depth review of EEG-based thought-to-text decoding systems, focusing on key methodologies and the challenges in decoding brain activity into meaningful output, such as text or speech . The use of deep learn i ng models, particularly Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, has shown significant potential in addressing many of the limitations associated with traditional EEG signal processing methods. CNNs, with their ability to capture spatial featLlres in EEG data, and LSTMs, which handle sequential data and time dependencies, have demonstrated improvements in decoding accuracy . However, despite these advancements, several challenges remain. Issues such as high computational costs, the need for extensive datasets, subject-specific variations in EEG patterns, and real-time performance constraints continue to hinder the widespread adoption of these systems . These problems also affect the generalizability of models across different individuals, limiting their practical applications. The review also explores potential solutions to these challenges. Techniques such as advanced preprocessing methods, including artifact removal, wavelet transforms, and adaptive filtering, can help improve the quality of raw EEG signals, making them more suitable for analysis by deep learning models. Additionally, optimizing deep learning models through approaches like transfer learning, data augmentation, and model compression can improve scalability and real-time performance .Item MedPredict(NHCE, 2025) EESHA NAVEEN 1NH21AI031 MUSAB 1NH21AI131 SIDDHARTH KULKARNI 1NH21IS151 VINAY S 1NH21IS177Many of the existing machine learning models for health care analysis are concentrating on one disease per analysis. Like one analysis if for diabetes analysis, one for cancer analysis, one for skin diseases like that. There is no common system where one analysis can perform more than one disease prediction. In this article proposing a system which used to predict multiple diseases by using Flask API. In this article used to analyse Diabetes analysis, Diabetes Retinopathy analysis, Heart disease and breast cancer analysis. Later other diseases like skin diseases, fever analysis and many more diseases can be included. To implement multiple disease analysis used machine learning algorithms, tensorflow and Flask API. Python pickling is used to save the model behaviour and python unpickling is used to load the pickle file whenever required. The importance of this article analysis in while analysing the diseases all the parameters which causes the disease is included so it possible to detect the maximum effects which the disease will cause. For example for diabetes analysis in many existing systems considered few parameters like age, sex, bmi, insulin, glucose, blood pressure, diabetes pedigree function, pregnancies, considered in addition to age, sex, bmi, insulin, glucose, blood pressure, diabetes pedigree function, pregnancies included serum creatinine, potassium, GlasgowComaScale, heart rate/pulse Rate, respiration rate, body temperature, low density lipoprotein (LDL), high density lipoprotein (HDL), TG (Triglycerides). Final models behaviour will be saved as python pickle file. Flask API is designed. When user accessing this API, the user has to send the parameters of the disease along with disease name. Flask API will invoke the corresponding model and returns the status of the patient. The importance of this analysis to analyse the maximum diseases, so that to monitor the patient's condition and warn the patients in advance to decrease mortality ratio.Item Intelligent toll collection system(NHCE, 2025) RESHAB VASANTH 11H214100 SRAVAN OE INH23A1101 SHRUJAN KR 1H2105228This Project presents an enhanced Intelligent Toll Collection System (ITCS) integrating RFID and ANPR technologies to address traffic congestion and operational inefficiencies in conventional toll collection. We propose a novel multi-stage image preprocessing pipeline combined with adaptive thresholding techniques, achieving a 95.8% plate detection rate and mean confidence score of 0.85. Our implementation demonstrates significant performance improvements with average processing times of 0.8 seconds per vehicle and sustained accuracy rates of 92% in daylight conditions. The system maintains robust performance across varying environmental conditions through an optimized bilateral filtering approach (11, 17, 17) and specialized morphological operations. Experimental results show substantial reductions in processing time and enhanced accuracy compared to traditional methods, while maintaining scalability for high-traffic deployments. This implementation advances the field of intelligent transportation systems, providing a foundation for future smart city infrastructure development.Item Revolutionizing skull classification: leveraging Digital forensics and deep learning in physical Anthropology(NHCE, 2025) VASANTH KUMAR S 1NH21AI116 ABHILASH REDDY B 1NH21AI146 C G PRASANTH REDDY 1NH21EC036 C ANJAN REDDY 1NH21EC039This study explores the integration of digital forensics and deep learning to revolutionize skull classification within physical anthropology. It employs advanced image processing techniques and convolutional neural networks (CNNs) for accurate identification and classification of skulls, particularly based on the presence or absence of the mandible. The research process begins with preprocessing skull images, including resizing, noise removal, and contrast enhancement, to ensure high-quality inputs. Feature extraction techniques such as Gray-Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT), Gabor filters, and Segmentation-based Fractal Texture Analysis (SFTA) are used to capture intricate textural and structural details of the skulls. The study compares traditional methods utilizing Support Vector Machines (SVMs) with the proposed CNN-based approach, demonstrating the latter's superior performance with significantly enhanced accuracy metrics. Data augmentation techniques like rotation, flipping, and scaling further bolster model robustness and mitigate overfitting. Evaluation metrics, including accuracy, precision, recall, and F1 score, validate the methodology's effectiveness, achieving classification accuracies exceeding 99%. This innovative framework supports anthropologists, forensic scientists, and archaeologists in efficient management, analysis, and preservation of anthropological collections. Its application extends to museum collections, forensic investigations, archaeological research, and medical studies. The findings emphasize the transformative potential of integrating machine learning and digital forensics in anthropological research, offering a robust tool for advancing our understanding of human evolution and cultural heritageItem Voice command systems for drivers(NHCE, 2025) M SAI VAMSI 1NH21AI135 LIKHITESWAR REDDY 1NH21AI114 VINAY K 1NH21EC182 G RAHUL S 1NH21EC126This project introduces a Bluetooth-enabled, voice command system tailored for drivers, allowing hands-free control over essential vehicle functions, particularly beneficial for tasks such as reversing. Utilizing Bluetooth technology, the system seamlessly connects a smartphone or other Bluetooth-enabled device to the vehicle’s control module, enabling users to issue voice commands in any language to perform specific tasks. The system’s architecture includes a microphone for capturing voice input, a speech processing unit for command recognition, and a control unit that interfaces with the vehicle's motor mechanisms. By employing natural language processing techniques, the system ensures accurate recognition of diverse languages and dialects, enhancing accessibility for a broad range of users. Designed with safety and convenience in mind, this innovative solution minimizes physical interaction with vehicle controls, promoting a more intuitive and safer driving experience. The proposed system’s effectiveness and accuracy have been evaluated through extensive testing in varied acoustic environments, demonstrating its potential to improve driving accessibility and usability. This project explores the technical challenges faced, the solutions implemented, and the broader implications for future vehicle automation and smart mobility technologies.Item Nano-Robot for Pest Control and Soil Health Monitoring(NHCE, 2025) Guru Ari Haran M 1NH21A1037 Joebin Peter S 1NH21AI041 Nilabja Mitra 1NH21ME049 Roopesh Kumar CK 1NHME058The future of agriculture lies in precision—a move away from blanket approaches to crop management toward practices that cater to the specific needs of each field. In this quest for efficiency and sustainability, Nano-Robot for Pest Detection and Soil Health Monitoring emerges as a groundbreaking initiative, leveraging the power of nanotechnology and artificial intelligence to redefine how we approach farming challenges. This project is not just about innovation; it’s about creating practical solutions for real-world agricultural issues, from pest infestations to soil degradation. Nanotechnology offers unprecedented tools to monitor and manage soil and crop health. With the help of nanoscale sensors, materials, and devices, farmers can now gather critical data in real time, including soil moisture levels, pH, nutrient availability, and microbial activity. This level of precision transforms decision-making, allowing irrigation, fertilization, and pest control to be tailored to the unique conditions of each plot of land. Unlike traditional methods, which often rely on sporadic sampling and visual inspections, nanotechnology ensures continuous monitoring and immediate feedback, leading to smarter, more sustainable farming practices. At the heart of this project lies the integration of nanotechnology with advanced AI systems. The Nano-Robot for Pest Detection and Soil Health Monitoring combines a capacitive soil moisture sensor and a high-resolution camera with a Raspberry Pi 4, creating a compact yet powerful prototype. The system doesn’t stop at data collection—it processes this information in real time, thanks to deep learning models. These models analyze soil health by interpreting sensor data and identify pests by processing images captured by the camera. Together, these components form a seamless system that empowers farmers to intervene precisely and proactively, reducing waste and boosting productivity. The choice of nanomaterials, such as carbon nanotubes and graphene, amplifies the capabilities of the Nano-Robot. These materials, with their exceptional sensitivity and efficiency, enable the sensors to detect even the slightest changes in soil conditions.