Intel® Student Ambassador Profiles
Current Ambassadors
Americas Sales & Marketing Organization (ASMO)
Aashik Mathew Prosper, University of Illinois Chicago
Aashik's project is an innovative API for Intel that harnesses his expertise in data analytics and machine learning to streamline computational workflows and enhance resource efficiency. In addition, he has made significant strides in public safety and urban planning through his work on the Crimes in Chicago project. This project employed sophisticated machine learning algorithms and advanced data visualization techniques to analyze crime patterns in Chicago, offering actionable insights that could transform law enforcement strategies and community safety initiatives.
Aiden Miah, Trent University
Aiden's GuPiTEER project is an innovative platform designed to streamline the use of Generative Pretrained Transformers (GPT) by providing a centralized access point to various models. This platform significantly enhances the user experience by allowing the selection of previous chats for context in new prompts, ensuring continuity and relevance in interactions. With a focus on accuracy and efficiency, GuPiTEER serves as an invaluable tool for professionals, students, and developers.
Alper Şahıstan, University of Utah
Alper is adapting ZFP, a compressed format library for representing multidimensional floating-point and integer arrays, to work with SYCL* through the Intel® DPC++ Compatibility Tool and other Intel profiling tools. ZFP's capacity to employ serial and parallel (OpenMP and CUDA*) compression techniques makes it an indispensable asset for applications that require efficient data exchange with storage systems. In this context, Alper uses the Intel DPC++ Compatibility Tool to facilitate the transition from CUDA to SYCL, and enhance and refine the implementation as needed. His plans involve exploring the oneAPI rendering capabilities for scientific volume visualization for ZFP compressed arrays.
Anant Rastogi, Arizona State University
Anant's Estate Edge uses satellite imagery and machine learning to forecast real estate trends by analyzing progressive images for notable landscape changes. This innovative method provides users with deeper insights into market dynamics, aiding in informed decisions for their next home purchase. Estate Edge empowers users to identify areas experiencing significant growth, offering a strategic advantage in the real estate market. Whether residential or commercial, Estate Edge ensures users can capitalize on emerging opportunities and maximize returns.
Anass El Hallaoui, Laval University
Anass's TrustFile is a decentralized document storage application that uses blockchain technology. TrustFile empowers users to securely upload and retrieve their documents, ensuring the documents' accessibility and security. By using blockchain's immutable and transparent nature, TrustFile mitigates the risk of document loss.
Animikh Aich, Boston University
Animikh takes advantage of the power of OpenVINO™ toolkit and Intel® toolkits to fine-tune and advance machine learning algorithms. Notably, he has successfully applied these tools in a prior endeavor, achieving face blurring with results achieving 60 FPS inference on 10th generation Intel® Core™ i5 processors using OpenVINO toolkit. Presently, Animikh is immersed in the realm of multimodal learning. His goal is to construct a streamlined mini Large Language and Vision Assistant (LLaVA) through the potent technique of knowledge distillation, finely tuned with oneAPI for optimal performance. This innovative approach not only ensures seamless compatibility across various hardware platforms but also significantly accelerates the inference process.
Clarence Lin, Columbia University
Clarence is improving a facial recognition project to boost the safety and security of classrooms and schools worldwide. The project maps labeled faces to an attendance sheet with respective timestamps using face encodings that are created with just one image. Using OpenCV, the program can support a built-in or USB webcam for live feed detection to update attendance systems.
Diego Abad, Florida A&M University and Florida State University (FAMU-FSU) College of Engineering
Diego's research project involves creating a basic neural network using C++, and using CUDA to modify the original C++ code into a CUDA format. He is using the Intel® DPC++ Compatibility Tool to translate the CUDA code into DPC++, and then modify the translated file to ensure it compiles while using the time library to measure the time it takes to complete the task.
Drake Du, Harvard University
Drake uses oneAPI technologies to build, optimize, and scale his programming projects. He uses the state-of-the-art machine learning algorithms contained in Intel® oneAPI Data Analytics Library (oneDAL) to help develop a web application that helps university students to more effortlessly assess and appraise the inclusivity of student organizations on campus. Outside of serving as an Intel® Student Ambassador, Drake is involved with Harvard Tech for Social Good, the Harvard-MIT Mathematics Tournament (HMMT), and the Harvard Computer Society.
Ebenezer Daniel, DePaul University
Ebenezer's project focuses on using Allen's Mice Brain Data to study the sagittal and coronal data of genes in the brain stem that regulate orofacial behaviors. The dataset includes 20,000 genes, and their corresponding correlation tables contain 400 million entries. The intent is to identify patterns and reveal hidden relationships among them for multiple structures in the brain stem. This will help us take a crucial step toward uncovering the complex neural circuitry associated with orofacial behaviors. To achieve this task, we plan to use oneAPI to accelerate the correlation table calculations on CPUs and GPUs.
Hadrien Gayap, University of Moncton
Hadrien's project focuses on lung cancer diagnosis using deep learning. His project uses deep learning to improve the accuracy and sensitivity of early detection of lung cancer. According to a systematic review of conference papers and scientific journals published in 2022, deep learning algorithms have become a superior way to automatically diagnose disease.
Hamed Barzamini, Northern Illinois University
Hamed's project focuses on implementing requirement engineering for AI-enabled Software (RE-AIS), a framework that uses humans' semantic knowledge of domain concepts to establish a benchmark for evaluating the quality of randomly collected training datasets in AIS. For this project, Hamed uses AI Tools to take advantage of the capabilities of Intel® oneAPI Deep Neural Network Library (oneDNN), oneDAL, and other Intel technologies. By incorporating these tools, he enhances the performance and accuracy of machine learning models in autonomous driving software systems.
Harshith Senthilkumaran, University of California, Los Angeles (UCLA)
Harshith's LangUR project is an LLM-based system that helps users learn languages on the go. It studies a user's content consumption pattern and suggests further content while translating portions of the content into a language that the user wants to learn based on their skill level.
Isaac Fung, University of British Columbia
Isaac's AffiniDock (AD) is a tool designed to enable researchers to easily visualize interactions between ligands and receptors, and accurately calculate the affinity score of these interactions. AD aims to streamline drug discovery processes by providing a user-friendly interface, detailed visualizations, and precise affinity scoring.
Jiaqi Wang, University of Washington
Jiaqi is developing a platform for AI project deployment that helps to integrate and optimize model usage and examination. He uses Intel toolkits to investigate AI modeling and to address cross-architecture compatibility. Also, he will be using Intel® Distribution of OpenVINO™ toolkit and Intel® Tiber™ Developer Cloud to help manage deployment.
Joshua Almonte, Northern Virginia Community College
Joshua's project, Doctor Hoo, is an AI healthcare chatbot application that helps University of Virginia students take care of their health quicker. Using Mistral 7B and the OpenAI API, Doctor Hoo gathers information from all University of Virginia (UVA) Health web pages within milliseconds and detects keywords using content to deliver appropriate responses. Instead of going through multiple redirects or wordy pages, Doctor Hoo provides an appointment form or a price estimate within milliseconds.
Kieran Llarena, University of Washington
Kieran is working on My YouTube Companion, an AI assistant that seamlessly integrates into YouTube* via a Google Chrome* extension. The assistant provides real-time explanations of the content that you're watching.
Marc Karimi, University of California, Berkeley
Marc's project is a Google Chrome extension that uses the OpenAI GPT-3* API to notify users in real time how their data is used on the website they're on. The extension checks if a user is on a website, uses its URL to scrape necessary information about the website, and then issues a prompt that returns an in-depth report on what the website does with the user's information.
Migara Amarasinghe, Florida A&M University and Florida State University (FAMU-FSU) College of Engineering
Migara focuses on investigating parallel architectures for AI algorithms. To conduct hardware performance analyses, he uses a diverse range of heterogeneous computer systems that consist of high-end GPUs and CPUs with single and multicore processing capabilities. To optimize the scalability of AI algorithms, Migara uses CUDA and high-level programming models such as SYCL. He is using the Intel DPC++ Compatibility Tool to migrate code from CUDA to SYCL for the Intel® oneAPI DPC++ Compiler and the Intel® HPC Toolkit to maximize the performance of Intel processors.
Mohan Gopi Gadde, Northern Illinois University
Mohan integrates Intel OSPRay into the ParaView virtual reality (VR) interface, through a plug-in that enables immersive exploration of datasets in virtual reality. He uses the InsituBloodFlow simulation, which couples Palabos and LAMMPS to simulate the flow of blood cells within plasma, as a test case for demonstrating the benefits of Intel OSPRay for in situ rendering. Mohan's goal is to enhance the visual quality and interactivity of the blood flow simulation in VR, as well as to showcase the capabilities of oneAPI for scientific visualization applications.
Nicholas Synovic, Loyola University Chicago
Nicholas develops new and improves existing computer vision models to run on low-powered systems. He uses AI Tools to develop and test low-powered machine learning models. This involves developing power-efficient and performant pipelines to prepare, process, and render data on devices. Nicholas focuses on developing computer vision models and finding solutions that can enable other model classes to run efficiently.
Nitin Dantu, Northeastern University
Nitin uses AI Tools to build and fine-tune classical machine learning and deep learning algorithms. He works with machine learning on large-scale data for a variety of domains, including healthcare, finance, real estate, agricultural technology (agro tech), and autonomous vehicles. Using Intel GPUs, Nitin creates robust, and scalable machine learning pipelines that are suitable for production in real time.
Om Rajpal, Georgia Institute of Technology
Om's project uses YOLOv8* to achieve accurate and efficient object detection and classification on recycling household items.
Owen McGrath, Illinois Institute of Technology
Owen explores the oneAPI programming framework, specifically focusing on DPC++ and oneTBB, and their suitability and performance for very fined-grained parallelism, where each task may consist of only a few instructions. He runs benchmarks on Intel® oneAPI Threading Building Blocks (oneTBB) and compares the results to other parallel programming solutions.
Reagan Austin, University of Tennessee, Knoxville
Sunrise is a rendering service of the National Parks Service. It uses machine learning to predict concentrations of different species in the Smoky Mountains. This data is rendered on a top-scale 3D model of the Earth using the Intel® OSPRay ray tracing library.
Rohan Sethi, Loyola University Chicago
Rohan is focused on optimizing computer vision models to run on low-power edge devices. He uses the AI support in the AI Tools to implement and test the efficiency of data preprocessing pipelines on such devices. Rohan plans on developing and comparing performance of generative and discriminative machine learning pipelines to assess optimal solutions for healthcare and nonhealthcare computer vision applications.
Rohit Vuppala, Oklahoma State University
Rohit implements deep learning models to predict wind fields in complex urban spaces. He uses oneDNN for training machine learning-based surrogate models. Rohit also focuses on developing numerical solvers to generate computational fluid dynamics training data using MPI and SYCL for multiprocessor parallelism and offloading on HPCs.
Safiullah Saif, San Jose State University
Safiullah project is a progressive web app that preserves cherished family recipes and fosters connections through culinary heritage. Inspired during a hackathon challenge, Safiullah plans to integrate OpenAI Whisper* and Fetch.ai* LLM APIs for enhanced functionality. This app documents and celebrates cultural recipes through a user-friendly interface, addressing challenges of memory and transmission across generations while emphasizing community and cultural celebration.
Sage Lyon, University of Massachusetts Lowell
Sage is performing interoperability testing and hardening of the latest hardware technologies and open source software. He is focused on cloud computing, 5G networking, and virtualized radio access network (vRAN) use cases. For performance benchmarking, Sage uses the oneAPI-based open RAN reference architecture, FlexRAN.
Sam Duong, Brown University
Sam is developing a smart bike lock that you can unlock with your phone. The lock uses machine learning to detect when someone is trying to break into the lock. The machine learning model is being developed to take input from multiple sensors (GPS, piezo vibration sensor, inertial measurement unit [IMU]) in an embedded system to take up as little memory as possible and use the least amount of computing power to preserve the battery.
Sarah Huang, University of Tennessee, Knoxville
Sarah's Researchable Archives for Interactive Visualizations (RAIV) is a browser extension and server combination for capturing interactivity in web-based visualizations. The browser extension allows you to specify which interactions in the visualization to capture. It takes a screenshot of the visualization after each captured interaction and sends the screenshots to the server to be prepared for playback. The server encodes the screenshots taken by the browser extension into an MP4 video file that simulates the original visualization's interactivity without any of the original source code or data.
Sinda Besrour, University of Moncton
Sinda's bird species classification is a deep learning project based on oneAPI and TensorFlow*. The data includes a total of 1,856 audio records split into 40 bird species. She implemented the project on the Intel Tiber Developer Cloud and created a conda* environment that inherits from the already existing TensorFlow* environment.
Taufeq Razakh, University of Southern California
As a member of the Collaboratory for Advanced Computing and Simulations (CACS) group, Taufeq leads performance optimization of nonadiabatic quantum molecular dynamics (NAQMD) and neural-network quantum molecular dynamics (NNQMD) simulation engines for Intel® architectures. His operations are on HPC processors and accelerators from Intel, which results in Taufeq keeping up with the most recent advancements in oneAPI to extract the best performance during his application development cycles.
Timothy Gao, University of California, Berkeley
Timothy harnesses the unique power of AI to build novel and impactful solutions to socially relevant problems at scale. In his winning hackathon project, Cancer360°, he and his team used machine learning models trained on Intel Tiber Developer Cloud to create a novel, multimodal approach to detecting and predicting lung cancer. He led his team in building a convolutional neural network (CNN) and an XGBoost classifier using TensorFlow, Keras*, and scikit-learn*. The resources predict lung cancer likelihood with over 92% accuracy based on lung computed tomography (CT) scans, patient data, and risk factors.
Utkarsh Singh, Stony Brook University
Utkarsh's project, Put Me In, is a personal coach on your arm. It's adaptable to multiple sports, giving you professional guidance at your fingertips. His smart sleeve, driven by cutting-edge machine learning, assesses your movements across sports (like basketball and weightlifting), offering personalized insights for improvement. But it doesn't stop there, it also tracks your progress, ensuring safer and more effective workouts.
Utsab Khakurel, Howard University
Using predictive models, Utsab’s project uses England's Premier League dataset from seasons 2021/2022 and 2022/2023 to assess and validate the optimal model for predicting match winners. The data undergoes a comprehensive preprocessing phase, encompassing tasks such as data cleaning, addressing missing values, substituting absent numerical values with medians, categorization, label encoding, normalization, and employing SMOTE for target label balancing to ensure equitable predictions. Utsab uses k-nearest neighbor (KNN), Naïve Bayes, and decision tree classifiers to train and make predictions. Intel® Extension for Scikit-learn* is used to enhance model accuracy and performance.
Xiao Zhang, University of Toronto
Xiao's project focuses on creating a model to predict RNA molecule structures and their chemical mapping profiles. He uses a combination of CNNs and Transformer*-based models in this project. The aim is to develop a tool that enhances the understanding of RNA folding processes, which is essential for the scientific community's efforts in researching genetic diseases, designing mRNA vaccines, and exploring new biological insights.
Xiaoxian Wang, University of British Columbia
Xiaoxian's project is a venture in the financial technology (fintech) sector, developing a machine learning-driven quantitative trading strategy by taking advantage of OpenVINO toolkit and oneAPI. Through analyzing extensive historical financial data, the project predicts market trends with high accuracy, surpassing traditional heuristic-based methods. With the combination of econometrics and machine learning, the model makes data-driven, rational investment decisions, enhancing profitability while reducing risk. This approach, coupled with rigorous testing, positions Xiaoxian's project at the forefront of integrating AI into financial trading, potentially revolutionizing investment strategies.
Yojan Chitkara, University of Texas at Austin
Yojan Chitkara focuses on accelerating linear algebra workloads on servers with Intel CPUs and GPUs.
Zohreh Aghababaeyan, University Of Ottawa
Zohreh's project addresses a critical challenge in autonomous driving from Tesla* self-driving cars: Selecting the most informative test cases from large volumes of daily unlabeled image data, often over 4 gigabytes per car. Her approach involves an automated tool that sifts through this data to find the most challenging and informative images for testing. This is crucial for reducing the cost and effort of labeling and ensuring that self-driving models are accurate and reliable. Using the AI Tools, Zohreh's project improves the model's performance and efficiency.
Europe, Middle East, and Africa (EMEA)
Aftab Ahmed, Sukkur IBA University
Aftab focuses on using AI techniques to build solutions for computer vision and digital image processing. He is working on a project that uses oneDNN, machine learning, deep learning techniques, and the Modified National Institute of Standards and Technology (MNIST) dataset. He is also part of multiple tech student communities on campus, and is a tech blogger and community builder.
Ahmed Abdelhady, Egypt Japan University of Science and Technology
Ahmed's El SOUQ is a web app that uses a mix of Python, Streamlit*, and Facebook* Prophet for forecasting stock prices. The app emphasizes the role of social media and news as it analyzes company financial statements, historical data, and market sentiment using technologies like pandas, NumPy, and Plotly* to assist in data visualization, while the Yahoo! Finance* API facilitates data integration, requests handle web scraping, enhancing the app's ability to predict stock trends and offer business news insights.
Clare Cordeiro, University of the Witwatersrand
Clare is developing an innovative pothole detection system to enhance road safety in Africa. This initiative not only focuses on improving vehicle navigation and safety but also contributes to the broader goal of infrastructure maintenance and optimization. Her project uses oneDNN and TensorFlow to create an advanced machine learning framework that's tailored to the detection and analysis of road surface irregularities. By integrating algorithms with real-time data acquisition from diverse sensors, Clare creates an accurate and efficient system capable of identifying potential road hazards.
Daniel Obare Nyakundi, University of Nairobi
Daniel's project harnesses the power of machine learning to forecast customer behavior using existing metrics such as the cost of plane ticket purchases and purchase frequency. Additionally, he developed sophisticated machine learning models that detect customer fraud within bank transaction data. By delving into these datasets, his goal is to decipher customer purchasing patterns and enhance financial security by identifying and preventing fraudulent activities.
Daudi Wampamba, University of Nottingham
Daudi's project centers on the development of a parallel algorithm using oneAPI to find approximate solutions to the Quadratic Unconstrained Binary Optimization (QUBO), an NP-Hard minimization problem. His project compares this novel solution with existing quantum and classical approaches to the same problem, fostering a deeper understanding of their respective merits and limitations.
Devesh Seethi, Northern Illinois University
Devesh builds multimodal frameworks on Intel Tiber Developer Cloud using data from inertial sensors, vision, and audio to solve real-world problems to improve public health. He is creating a framework to track activity levels and behavioral patterns in Alzheimer patients using person re-identification, point of interest detection, motion tracking, and monocular depth estimation in the Intel® Distribution of OpenVINO™ toolkit.
Fatih Şengül, Sivas University of Science and Technology
Fatih uses AI techniques to detect cyberattacks for intrusion detection systems (IDS). To do this, he uses oneDNN, Intel® Optimization for TensorFlow*, AI Tools, and Intel Tiber Developer Cloud. Fatih uses computer vision and digital image processing techniques using the Intel Distribution of OpenVINO toolkit and OpenCV to detect surface cracks on roads and architectural constructions.
Guan Yan Lye, University of Nottingham (Malaysia)
The Generic Circuit Level Tool for evaluation of Nano-Crossbar Memory using Memristors is a software tool designed to help researchers and engineers in the field of memristor technology. It is a circuit-level tool that automates the process of generating the memristor array circuit in a spice environment, making it easier and faster for researchers to design and simulate memristor circuits. The tool also allows researchers to easily modify and optimize the simulation circuit parameters, such as the size of the memristor array circuit or the type of memristor used.
Harvey Johnson, University of Nottingham
Harvey is using oneAPI to implement an efficient and timely data preprocessing pipeline for AI. This system uses GPU and accelerator resources to compress and decompress hundreds of millions of data samples in seconds. This process simplifies loading large datasets for Gavin AI to allow for training on larger datasets without I/O or storage bottlenecks.
Iancecil Njoroge, Strathmore University
Iancecil aims to develop a simple VR game using oneAPI, a software development environment for optimizing applications on multiple hardware platforms. The game will be optimized to run on both CPUs and GPUs, using the Intel Rendering Toolkit to enable specific rendering of images. His project will provide valuable resources and documentation for other developers interested in using oneAPI for VR game development.
Joshua Shiells, University of Kent, United Kingdom
Joshua is using oneAPI to take advantage of GPUs as accelerators for his AI projects and research. He is working on "Gavin," which is a transformer chatbot trained on the Reddit* Pushshift dataset. He is also focused on building a machine learning library using oneAPI to easily support multiple applications on both GPU and CPU compute. He used oneAPI to simplify the amount of code needed to handle many different devices or accelerators.
Kaan Olgu, University of Bristol
Kaan is using oneAPI, specifically SYCL, to delve into the realm of random memory accesses on FPGAs, with a specific focus on optimizing the Breadth-First Search (BFS) algorithm. The goal of his research is to uncover strategies for enhancing the efficiency of random memory accesses in BFS implementations. By using the power of oneAPI's SYCL, he seeks to unlock new insights and techniques that can significantly improve memory handling in FPGA-based BFS computations. This endeavor holds promise for advancing the performance capabilities of BFS applications across a range of domains.
Lauria Rubega, Maastricht University
Lauria's project delves into strategy games, exploring both human-human and human-computer interactions through object-oriented programming. The focus involves creating a sophisticated artificial player by incorporating a game data structure and a smart evaluation function, ensuring adaptability and learning capabilities. This endeavor aligns with rigorous software engineering principles across its three phases. Inspiration is drawn from Lauria's use of machine learning to analyze geoscientific datasets, discovering novel correlations for mineral exploration, and geophysical data inversion using AI Tools, reflecting the project's innovative and analytical approach.
Martin Muchai, University of Nairobi
Martin's project focuses on developing a oneAPI-powered AI chatbot that can effectively handle customer queries, provide personalized recommendations, and enhance the overall customer experience using oneAPI libraries. The proposed solution will use oneAPI's extensive array of libraries and tools to develop an AI chatbot that can understand natural language inputs and provide relevant responses.
Mohammed Bangie, University of the Witwatersrand
Mohammed's project focuses on recommending crops to plant based on soil attributes. He uses machine learning in oneDAL and algorithms like SVM, KNN, and decision trees. Aimed at improving agricultural practices, this integration enhanced model performance and accuracy, achieving up to 98.64% accuracy. oneDAL was key in optimizing running the model and boosting its efficiency by 15%.
Muhammad Hanzaila Maqsood, Technical University Munich
Muhammad uses deep learning to train the agent in Python by using multiple libraries and Intel® oneAPI Math Kernel Library (oneMKL) for mathematical functions like PyTorch* and TensorFlow. He also uses oneDNN to increase GPU performance while using the deep learning neural network in PyTorch.
Nadav Schneider, Ben-Gurion University
Nadav researches and develops AI technologies focusing on computer vision and large-scale optimization domains. While studying for a master's degree, he is supervised by Dr. Gal Oren and Dr. Yuval Pinter, who are researching how to automatically convert a serial code into a distributed parallelism MPI code using generative language models.
Peizhao Qiu, University of Edinburgh
Peizhao's project uses a oneAPI framework to simulate Lidar operation to aid laser design and operation.
Peter Dike, Lucerne University of Applied Science and Arts (HSLU)
Peter develops AI-driven computer vision tools to improve healthcare outcomes. His work focuses on applying machine learning to medical imaging for the early detection and diagnosis of diseases, with the goal of enhancing accuracy and accessibility in healthcare diagnostics.
Sanober Ahmed, Birla Institute of Technology and Science, Dubai Campus
Sanober's project involves parallelizing the matrix multiplication operation across different devices. This code initializes two matrices (A and B) with random values, performs matrix multiplication using DPC++, and prints the input matrices along with the result.
Tal Kadosh, Ben-Gurion University
Tal uses the latest advancements in the field of large language models related to code to develop a method for automatically generating and inserting OpenMP pragma into serial code. By exploiting different code representations, Tal's method improves the accuracy of OpenMP parallelization detection and can potentially accelerate the implementation of computationally intensive tasks. This method can be incorporated into the Intel® Advisor tool.
Victor Olet, Curtin University
Victor integrates HPC, AI, and machine learning techniques with quantum computational chemistry to enhance the speed and accuracy of molecular simulations. In particular, Victor's goal is to predict chemical properties based on the properties of known chemical datasets. Victor also explores using quantum computing algorithms to conduct quantum chemistry simulations for enhanced chemical accuracy and computation speeds. He hopes to promote HPC and AI techniques to science students in high school and college.
Youssef Faqir, Complutense University of Madrid
Youssef uses Intel toolkits to develop portable algorithms without losing performance for multiarchitecture devices such as CPUs and GPUs. For that work, he developed the Non-Negative Matrix Factorization (NMF) algorithm in DPC++ (the oneAPI implementation of SYCL) and OpenMP, and the K-means algorithm focused to run over multivendor accelerators. Youssef plans to extend these works to FPGAs and compare the differences among architectures.
Yuri Winche Achermann, RWTH Aachen University
Yuri works on a predictive maintenance system for the manufacturing industry. In particular, he checks metal-cutting tool wear during each cycle of machining. He does this by using computer vision based on a deep learning algorithm that segments the wear of the tool image to approve (or not) an extended use for a tool's lifetime optimization. At the same time, the system keeps the data and shows dashboards of tool wearing for engineering insights.
Zhibo Li, University of Edinburgh
Zhibo is working on a high-performance declarative data collection system based on oneAPI. He developed a front end for the data collection using C++ metaprogramming. Based on the front end, he is integrating concurrent data structures from oneTBB as back ends and applying DPC++ (SYCL) to parallelize the program, and to port it to multiple platforms.
Asia Pacific and Japan (APJ)
Aagaman Bhattarai, Kathmandu University
Aagaman works on a facial recognition public transportation system to enable fast and smart commutes. This transportation system will help Nepal's transit system by integrating data-driven, efficient buses with seamless boarding, facial authentication, and cashless payments.
Aaron Masuba, BRAC University
Aaron is developing the Applied Machine Learning and Industrial IoT (AMLIIoT) Architecture. This is a state-of-the-art system control suite based on intelligent edge electronic devices and sensor fusion for machine learning applications in industrial processes, automation, and diagnostics. He uses Intel® FPGA high-performance computing (HPC) applications to accelerate and scale operations. Aaron hopes to solve some of the world's most pressing challenges such as access to efficient, sustainable carbon-neutral energy and other control system applications where optimization is required through the oneAPI and Applied Machine Learning and Industrial IoT (AMLIIoT) architecture.
Aasima Thanzeem, Sona College of Technology
Aasima's project focuses on pneumonia detection using a dataset of chest X-ray images. The work involved feature extraction using OpenCV and the construction of a CNN model. This model incorporates essential layers—including Conv2D, BatchNormalization, MaxPooling2D, Flatten, Dense, and Dropout—to enhance its learning capabilities.
Adhithiyan R, Sona College of Technology
Adhithiyan's project focuses on Human Activity Recognition (HAR) by taking advantage of the UCF101 dataset. HAR is achieved using OpenCV to extract features, followed by training using ConvLSTM3D. The trained model was tested using TensorFlow* and OpenVINO toolkit intermediate representation (IR) models.
Aditya Krishna, Sri Krishna College of Technology
Aditya is implementing a drug classification system using the random forest algorithm and optimizing it with oneAPI. This classification system for drugs based on their attributes achieves an accuracy of approximately 96.6%. This approach has potential in drug discovery, predicting drug interactions, and personalized medicine.
Ahamed Thaiyub, KPR Institute of Engineering and Technology
Ahamed takes advantage of oneAPI technology to predict employee attrition, aiding organizations in reducing turnover costs. By analyzing historical data and performing feature engineering, he identifies key attrition indicators. The project encompasses data prep, feature engineering, model selection, training, optimization with Intel® tools, and evaluation. Ahamed's work showcases the oneAPI role in data-driven human resources (HR) solutions for recruitment and retention.
Aishwarya R, Sri Sivasubramaniya Nadar (SSN) College of Engineering
Ashwarya's project showcases integrated cutting-edge technology to enhance the performance and efficiency of a two-stage machine learning model to precisely predict flight delay. He further optimized the project by incorporating oneAPI. The integration of oneAPI, particularly Intel® Extension for Scikit-learn*, has yielded remarkable results. Harnessing the power of optimized machine learning libraries from Intel resulted in a significant runtime reduction, enhancing the overall speed and efficiency of the system.
Akshay Ramakrishnan, Sastra University
Akshay is focused on building powerful and precise machine learning systems for bioengineering applications and optimized healthcare systems using AI Tools from Intel. His project will use the capabilities of oneDNN, Intel® oneAPI Data Analytics Library (oneDAL), and other Intel technologies to enhance the performance and accuracy of machine learning models in healthcare.
Amrithesh Kakkoth, Government Model Engineering College
Amrithesh uses oneAPI to optimize and accelerate his fire-detection model. By taking advantage of oneAPI's unified programming model, he can efficiently use the CPU, GPU, and other accelerators in a heterogeneous computing environment. He achieves better performance and scalability for his fire-detection application, which ultimately improves its accuracy and real-time responsiveness.
Aneerban Saha, Manipal University Jaipur
Aneerban uses sentimental analysis and machine learning to develop an AI voice bot that listens to feelings that a user shares and then gives advice. He uses AI Tools from Intel for this project. His goal is to minimize problems such as depression, anxiety, and mental health issues.
Arun GK, Christ University
Using machine learning, Arun's project aims to detect edible mushrooms based on their physical characteristics such as cap shape, cap color, gill size, spore print color, and habitat. Models such as logistic regression, decision trees, random forest, support vector machine (SVM), and XGBoost were used to build prediction models, and their accuracy was compared. A heat map was plotted to analyze the correlations between different features, and the relationships between other characteristics and edibility were explored. Arun used oneAPI and oneDAL for model optimization.
Aryan Kaushik, Krishna Institute of Engineering and Technology (KIET) Group of Institutions
Aryan's model predicts whether women fall into a higher risk category for polycystic ovary syndrome (PCOS) by analyzing characteristics such as age and hormone levels. This information can help women to be proactive with potential preventative measures or gain awareness about individual risk factors to make informed decisions.
Ashish Madhup, Gurukul Kangri University
Ashish's project focuses on predicting the results of England's Premier League soccer matches. In this project, he implemented three different classification algorithms, KNN, Naïve Bayes, and decision trees to predict the outcomes of England's Premier League matches. Ashish's goal is to determine which classifier yields the most accurate predictions by analyzing historical data and using various features such as team statistics, player performance, and match circumstances.
Balasuriya R, Amrita Vishwa Vidyapeetham Coimbatore
Balasuriya's project analyzes network traffic data and detects anomalies within it. Network traffic data, which includes information about communication between devices or systems over a network, can be vast and complex. Detecting unusual patterns or behaviors within this data is crucial for network security, system optimization, and monitoring performance.
Barath S, Chennai Institute of Technology
Barath's gender and age detection project aims to accurately detect gender and age from facial images using deep learning techniques. Despite recent advancements, variations in facial features, lighting, and expressions pose challenges. The goal is to develop a robust model capable of demographic analysis for applications in marketing, healthcare, and security.
Bhaskar Trivedi, Madan Mohan Malaviya University of Technology
Bhaskar uses machine learning techniques to build applications for users in finance and healthcare. He uses AI Tools for a project that connects stock market traders. The project provides them with a no-code interface to predict the stock prices in real time and, through HPC, make better decisions.
Brindha S, Sona College of Technology
Brindha's project combines the OpenVINO toolkit with automatic speech recognition (ASR) and grammar correction. The project optimizes models, processes audio into mel spectrograms for precise ASR, and uses OpenVINO for efficient inference. The project seamlessly transitions to grammar correction, showcasing the OpenVINO role in refining transcribed text quality. This highlights how OpenVINO empowers ASR and grammar correction, ensuring accurate speech recognition while handling complexities in audio data and linguistic refinements within the oneAPI ecosystem.
D. Gopalakrishnan, Excel Engineering College
D. Gopalakrishnan is developing an NLP-based emotion recognition system that is used to understand accurate emotion of users. He used oneDNN, which provides optimized implementations of deep learning building blocks for deep learning applications and accelerate the speed of the training.
Deepak Joshi, Bharati Vidyapeeth's College of Engineering, Delhi
Deepak improves and optimizes existing machine learning models and develops crucial preprocessing pipelines to make them work efficiently and accurately on low-specification computers. He uses Intel toolkits (powered by oneAPI) to implement new machine learning models and extract meaningful data from huge datasets.
To hasten the process of training and predicting large data, Deepak uses oneAPI to take advantage of GPUs and CPUs as accelerators for his AI and natural language processing (NLP) projects.
Dev Aryan Khanna, Guru Gobind Singh Indraprastha University
Dev Aryan Khanna develops assistive smart glasses for blind people by taking advantage of the power of Intel technologies. The project uses AI Tools, oneDAL, and other Intel technologies to optimize object recognition, text identification, and navigation assistance that empower blind people with improved independence and accessibility.
Dhanushkumar R, St. Joseph's College Of Engineering
Dhanushkumar's AI model predicts the likelihood of a student dropping out based on key factors such as attendance, grades, and engagement. Powered by the robust scikit-learn-intelex package, the system uses advanced machine learning algorithms to analyze vast amounts of data, providing educators with timely alerts. The technology stack that drives this project also relies on the scikit-learn-intelex package, which is seamlessly integrated with scikit-learn. This powerful combination enables accelerated and optimized machine learning processes, enhancing the efficiency of this predictive model.
Dharanidharan N, Jansons Institute of Technology
Dharanidharan's project focuses on revolutionizing the prediction of genetic disorders using Intel toolkits, powered by oneAPI. By harnessing the immense computational power and advanced tools provided by Intel toolkits and oneDNN, he aims to deliver highly accurate predictions for a diverse range of genetic disorders. The successful implementation of the genetic disorder predictive model holds immense potential in transforming the healthcare industry and advancing genetic research.
Durai Saravanak Kumar G, Jansons Institute of Technology
Bitcoin* price prediction is a challenging task due to its extreme volatility and complex market dynamics. Forecasting involves analyzing historical data, market sentiment, and fundamental factors. For this project, Durai uses various methods including technical analysis, machine learning models, and sentiment analysis. For scaling, he used the data preprocessing feature in oneDAL. Durai used Keras to build and train deep learning models, analyze historical data, and make future price forecasts. Using TensorFlow, Durai trained the model, and with matplotlib he made a visual representation for the predicted trained model.
Fernando Schettini, SENAI CIMATEC
Fernando's project creates informative and interactive Jupyter Notebook scripts to provide an accessible platform for learning the fundamentals of perceptron in a neural network using Intel® Distribution for Python*. Additionally, the project covers essential concepts related to neural networks, search algorithms, HPC, and simulations.
Gagan Agarwal, Kalinga Institute of Industrial Technology
Gagan uses oneAPI to optimize quantum circuit simulation for composite quantum systems. He plans to use the Intel® HPC Toolkit to unlock quantum computing for heterogeneous devices that would otherwise be restricted to high-performance machines. It will help users to better understand the quantum behavior, optimizing the simulation with AI Tools.
Gangesh Basker, SASTRA University
Gangesh applies machine learning methods to develop models in computer vision to improve driver and passenger safety in autonomous vehicles. His project uses the capabilities of oneDNN and other Intel technologies to enhance the performance and accuracy of machine learning models in driver and passenger safety. This system reduces the number of accidents on the road by detecting the driver's drowsiness, warning them using an alarm, and assisting the driver using the front-end application.
Gomathi Ramachandran, Chennai Institute of Technology
Gomathi's Telecom Churn Prediction model uses advanced machine learning techniques such as logistic regression, Naïve Bayes, and KNN to analyze customer data and accurately forecast the likelihood of churn in telecom services. This model empowers telecom companies to proactively identify at-risk customers and implement targeted retention strategies, such as personalized offers and proactive customer support. By providing valuable insights into churn drivers, the model optimizes services and enhances overall customer satisfaction, revolutionizing customer relationship management in the telecom industry.
Hare Chakraborthy, Chennai Institute of Technology
Hare's machine learning project predicts the likelihood of a heart attack based on a set of clinical attributes and patient information. The integration of oneAPI and sklearnex into the project facilitated the optimization of machine larning tasks, resulting in improved performance and efficiency.
Harish Raaghav DV, KGiSL
Harish's project is focused on classifying drugs based on patient characteristics such as age, sex, blood pressure, cholesterol level, and sodium-to-potassium ratio (Na_to_K). He employs various machine learning algorithms and techniques to achieve accurate drug classification.
Harshit Saini, Gautam Buddha University
Harshit developed an automatic labeling tool that streamlines the process of image annotation. This tool employs OpenCV for the user interface of the annotator and uses PyTorch*, which benefits from several back-end libraries from the AI Tools. Its primary objective is to automate and expedite the labor-intensive task of image labeling. This back end from Intel enhances the tool's performance, making it highly adaptable to various hardware configurations. Harshit's vision includes further integration with the Intel Distribution for Python, creating a stand-alone Linux* version of the tool to bolster its capabilities for image annotation efficiency.
Hitesh Joshi, Bharati Vidyapeeth's College of Engineering
Hitesh created a machine learning project with Intel Extension for TensorFlow that identifies various medicinal plants found in his local environment. This model is not yet deployed on a mobile application.
Jenina Angelin D, St. Joseph's College of Engineering
Jenina leads a pioneering project that uses oneAPI technologies for computer network data analysis. Focused on optimizing data processes, she develops algorithms for interpreting network data including traffic patterns and security events. By using oneAPI, Jenina accelerates large-scale dataset analysis for swift decision-making. Key components involve algorithm development, scalability, and seamless integration with the oneAPI ecosystem. This work has promising advancements in network data analysis, benefiting administrators and cybersecurity professionals. The integration of oneAPI ensures heightened efficiency, accuracy, and scalability in handling complex network data challenges.
Jeyasundar R, KPR Institute of Engineering and Technology
Predict chronic kidney disease using the Random Forest algorithm and Intel® toolkit. This project was a demonstration of how the code works in Google* Colab with oneAPI.
Joel John Joseph, Christ University
Joel's project uses machine learning algorithms to provide customized crop recommendations to farmers based on their soil data. The project used oneDAL to optimize the performance of the machine learning models used in the application. This platform enabled faster computation times, improved accuracy, and enhanced efficiency, resulting in more accurate and reliable crop recommendations. The application used various machine learning models such as SVM, logistic regression, random forest (RF), and XGBoost to generate crop recommendations for farmers.
Kalyana Sundaram M, Amrita Vishwa Vidhyapeetam
Kalyana's project addresses improving low-resolution images by offering a user-friendly interface that takes input images and uses a pretrained OpenVINO model to improve the quality, resulting in stunning high-resolution versions. By using the capabilities of Gradio, OpenVINO, and OpenCV, Kalyana made an intuitive web application that allows users to upload images, and then the application uses a model to perform image upscaling. The uploaded images undergo processing using the enhanced deep super-resolution (EDSR) model, which is designed to enhance image quality.
Kamesh Ragupathi, Jansons Institute of Technology
Kamesh's project is focused on building a time-efficient and approximate stock predictions for Google* stocks with a recurrent neural network (RNN) using the long short-term memory (LSTM) Keras model. The Intel Distribution of OpenVINO toolkit enables these models to be trained more efficiently, and results in faster training times. He uses the ADAM optimization algorithm and optimizes a mean squared error (MSE) loss function.
Kavirajar Balakrishnan, Chennai Institute of Technology
Kavirajar's project seeks to simplify the complexity of finding the most important articles. The project provides article summaries that are based on natural language queries, accompanied by a recommender system for additional reading. The project's performance saw significant improvement through the integration of Intel® tools such as the Intel® Extension for Scikit-learn*.
Keerthana K M, Manipal Academy of Higher Education
Keerthan's project detects anomalies in time series over a million rows dataset. She needs oneAPI to perform operations over this large dataset for better computation and high performance.
Keerthi Parthipan, Anna University Regional Campus Madurai
Keerthi's project addresses and provides a comprehensive machine learning and deep learning solution to common agricultural problems. By using the potential of CNN, the project achieves disease identification in tomato leaves using image analysis and offers insights by visualizing trends in local tomato markets. The project uses Intel Optimization for TensorFlow to accelerate computation and enhance accuracy.
Kenichi Hayakawa, Costa Rica Institute of Technology
Kenichi's project uses oneAPI toolkits and Intel FPGA development tools to analyze where hardware and software performance bottlenecks lie when running computationally intensive workloads on multicore architectures. His goal is to gain insights into the hardware and software aspects that limit performance, and then design and implement efficient hardware-accelerated solutions for tasks such as cryptography, image processing, and algorithm acceleration, while ensuring that the performance improvements achieved do not compromise portability.
Krishnan Manushresth, Chennai Institute of Technology
Krishnan is working on Eduquery, a Generative AI (GenAI) application powered by Llama 2. Teachers can upload study materials in PDF, Microsoft Excel*, and other formats. Students can submit subject-related queries and get more subject-oriented answers.
Kunjal Jethwani, ATLAS SkillTech University
Kunjal's goal is improved education using machine learning and deep learning models to extract valuable insights from data. By employing advanced visualizations, statistical tests, and innovative modeling techniques, she unravels patterns that influence student performance and engagement to provide a more personalized and effective learning experience for students.
Lakshay Taneja, Shoolini University
Lakshay develops new and improves existing machine learning models and processes to make models work in a production environment. Lakshay focuses on developing machine learning algorithms to extract meaningful information from large amounts of datasets. He uses Intel toolkits to implement new machine learning models and improve the existing datasets.
Manas Marwah, Bharati Vidyapeeth's College of Engineering
Manas develops new ways to visualize and understand complex patterns in data. His work includes the Intel Rendering Toolkit, including ray-surface hit testing, volumetric space iteration, and image denoise. He also uses the Intel Rendering Toolkit with Intel® OSPRay to create an interactive visualization application.
Manikanta Bukapindi, Dayananda Sagar College Of Engineering
Manikanta uses AI Tools and machine learning to develop an AI system that detects real-world objects like pedestrians, cars, and obstacles, program autonomous driving, and then detect violations. His goal is to minimize problems caused due to violations and make it easier for autonomous driving systems to make faster and better decisions in real-world scenarios.
Mayurdhvajsinh Jadeja, Marwadi University
Mayurdhvaj is developing a sign-language-to-text-and-speech converter for individuals who are deaf and mute. This innovative project uses advanced technologies, including real-time gesture recognition through the MediaPipe library, oneAPI libraries like oneDNN and Intel Distribution of OpenVINO toolkit, and NLP and speech-synthesis techniques. The system accurately detects and interprets sign language gestures, and then converts them into text and synthesized speech. By improving communication between deaf and mute individuals, Mayurdhvaj's project aims to enhance inclusivity and accessibility, benefiting millions in the deaf and mute community.
Md. Jannatul Nayem, Mymensingh Engineering College
The cerx-ML project strives to innovate within rocketry experiments, focusing on sounding rockets by applying machine learning methods. With the support of Intel extensions and oneAPI-optimized libraries, the project contributes to cost reduction and performance enhancement. By predicting engine characteristics such as detonation probability and maximum thrust, cerx-ML streamlines experimentation processes. With clear instructions and a commitment to efficiency, it aspires to empower researchers in conducting cost-effective and reliable rocket tests.
Melbin Martin, Christ University
Melbin's smart garbage segregation project aims to use AI and machine learning to efficiently and effectively sort waste into different categories, such as plastic and glass, using oneDNN. Image classification for recycling refers to the use of machine learning algorithms to automatically classify images of waste materials into their respective categories. This process involves training a model using a large dataset of labeled images, and then using this model to predict the category of new, unlabeled images.
Miguel Graça, INESC-ID
Miguel develops machine learning methods for epistasis detection, a bioinformatics application that aims at identifying associations between Single Nucleotide Polymorphisms and complex diseases. To this end, he is using the Intel-optimized versions of TensorFlow, as well as OpenVINO, to deploy enhanced transformer models for epistasis on a wide variety of AI accelerators.
Muhammad Faeez Shabbir, The Islamia University of Bahawalpur
Muhammad's Sentify project uses oneAPI to perform sentiment analysis on natural language text. The project involves processing input text through various NLP techniques, such as tokenization, stemming, and part-of-speech tagging to extract meaningful information about the text. The goal of the project is to provide a reliable and accurate way to analyze the sentiment of large volumes of text data, which can be used in a wide range of applications such as social media monitoring, customer feedback analysis, and market research.
Project
Navabhaarathi Asokan, Amrita Vishwa Vidhyapeetham, Coimbatore
Navabhaarathi's project focuses on anomaly detection in computer networks using autoencoder neural networks. By training on Wireshark-captured data packets, the autoencoder learns normal network traffic patterns. During testing, anomalies are identified through higher reconstruction errors, indicating deviations from learned norms. This approach offers efficient real-time detection of network intrusions and abnormalities, enhancing network security and stability.
Nirranjana Rajasekar, Sri Sivasubramaniya Nadar College of Engineering
Nirranjana's project develops and evaluates rainfall prediction models for three distinct districts in Tamil Nadu: Erode, Dindigul, and Karur. Using historical rainfall data, meteorological variables, and machine learning techniques, this project provides accurate and timely rainfall forecasts. The work represents a groundbreaking integration of cutting-edge technology, aimed at enhancing the performance and efficiency of the rainfall prediction model for these regions. Comprising a sophisticated ensemble of machine learning techniques, Nirranjana has further elevated the project's capabilities by incorporating Intel Extension for Scikit-learn, specifically tailored for Intel hardware through oneAPI.
Nishank Satish, Dayananda Sagar College of Engineering
Nishank focuses on applying machine learning methods to tackle real-world problems by using computer vision. He is working on a traffic management system that addresses traffic issues, which include emergency vehicle detection, dynamic traffic signaling, and violation detection. The dynamic traffic signaling and emergency vehicle detection models use AI Tools. The OpenVINO Toolkit is being used to enhance object detection models.
Nishanth P, Chennai Institute Of Technology
Nishanth develops a stock market prediction system using yfinance in Python. Fetch historical data, preprocess the data, train machine learning models, and generate predictions.
Orlando Mota, SENAI CIMATEC
Orlando's project uses oneAPI, which is pivotal for studying the DPC++ programming language and its application in scientific computing. He uses reverse time migration (RTM) as a proof-of-concept to assess DPC++ performance and flexibility. oneAPI also helps evaluate the DPC++ ability to handle basic data structures and algorithms. Its tools assist in generating precise documentation at each development stage.
Padmakumar RP, PSNA College of Engineering & Technology
Padmakumar's project focuses on developing an efficient object-detection deep learning model for autonomous vehicles with a strong focus on using oneAPI libraries. Using AI Tools, he optimizes the model to achieve exceptional performance in real-time processing. He achieves low latency and enhanced training and inference speed by incorporating the Intel® Extension for PyTorch* and using Intel® Neural Compressor libraries.
Prajwal Kumar, Maharshi Dayanand University
Prajwal uses oneDNN and Intel Optimization for TensorFlow to optimize various charging stations for electronic vehicles (EV). His project involves developing a new EV charging network to eliminate inconveniences due to a small charging infrastructure and long charging times—the primary obstacles to mobility decarbonization.
Praveen Kumar, KGISL
Praveen developed a resume screening model using oneDAL library to classify resumes into predefined categories. After importing libraries, he performed exploratory data analysis (EDA) and cleaned data with text preprocessing. Using the power of oneDAL, he built and evaluated nine models, including k-nearest neighbor, linear support vector, Stochastic Gradient Descent, and more. Using Term Frequency-Inverse Document Frequency (TF-IDF) for text transformation, he achieved high accuracy. Stochastic Gradient Descent emerged as the top-performing model, attaining 100% accuracy. The oneDAL library significantly enhanced performance and efficiency in this project.
Raghul Senthilkumar, Amrita Vishwa Vidyapeetham
Raghul is building a real-time face recognition system using oneDNN and the AI Tools. His current focus is on building an automated attendance system that can be used in different scenarios. Raghul tests different models using the AI Tools to provide fast and efficient feedback to the application. He also uses various oneAPI models to improve the accuracy and reliability of machine learning models in healthcare.
Ragul R, Sona College of Technology
Ragul leads a project that compares YOLOv7* native and oneAPI libraries for real-time pothole detection, aiming to strike a balance between processing power and accuracy. Using PyTorch, he achieved a twofold increase in processing power with oneAPI libraries, notably the OpenVINO toolkit, that optimized model deployment across Intel hardware, simplifying YOLOv7 integration. The OpenVINO synergy with the Neural Network Compression Framework (NNCF) post-quantized models enhances efficiency.
Raison Sabu, Christ University
Raison's project aims at using AI and machine learning to efficiently sort waste into different categories using oneDNN. This process involves training a model using a large dataset of labeled images and then using this model to predict the category of new, unlabeled images. The goal of image classification for recycling is to improve the efficiency and accuracy of recycling processes by automating the sorting of materials, reducing human error, and increasing the amount of recyclable materials that can be recovered.
Rayan Rasheed, University of Engineering and Technology, Lahore
Rayan's project harnesses the potential of cutting-edge machine learning tools to provide timely and insightful assessments of mental well-being. He uses the high-performance capabilities of oneDNN for deep neural network tasks, enhancing the accuracy and efficiency of real-time mental health assessments.
Rick Mondal, Narula Institute of Technology
Rick uses oneAPI in his project to accelerate the training and inference of large language models (LLMs). LLMs are computationally expensive to train and deploy, so it is important to use efficient hardware and software tools. oneAPI allows him to target a variety of hardware architectures, including CPUs, GPUs, and FPGAs, with a single code base.
Rittik Chandra Das Turjy, Daffodil International University
Rittik focuses on Bangladesh's vital agriculture sector and how farmers struggle with limited funds for crop health monitoring, which leads to disease infestation. He developed an AI and machine learning solution that diagnoses crop health from a photo. This tool enables farmers to assess crop conditions easily with a single click.
Santhosh Mamidisetti, Amrita Vishwa Vidyapeetham
Santhosh's machine learning project shows that early diagnosis of breast cancer can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. The correct diagnosis of breast cancer and classifying patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex breast cancer datasets, machine learning is widely recognized as the methodology of choice in breast cancer pattern classification and forecast modeling.
Sarvesh Shashikumar, Amrita Vishwa Vidyapeetham
Sarvesh's Gradio app uses uploaded videos to determine if an auto accident occurred. The model behind the application is based on the ResNet*-50 architecture and has undergone several optimization processes to ensure swift and accurate detections. oneAPI optimizes a ResNet-50 deep learning model. Intel Extension for PyTorch enhances model performance after the initial training of five epochs, with optimization extending for an additional 15 epochs.
Shivaram Velayutham, Indian Institute of Technology, Madras
Shivaram's project implements a bitonic sorting algorithm using oneAPI and explores its optimization possibilities for efficient running on modern heterogeneous computing architectures. The bitonic sorting algorithm is particularly well suited for parallel processing on shared-memory systems, making it an ideal candidate for acceleration on GPUs, FPGAs, and other accelerators.
Shubham Luharuka, RV Institute of Technology and Management*
Shubham is developing an optimized and efficient task-oriented deep neural network to enhance the quality of videos. He uses Intel Optimization for TensorFlow and oneAPI to target the FPGA circuit for its real-time implementation. To reduce the training time of the model, he uses the Intel Tiber Developer Cloud. This implementation can be used in areas such as medical simulation, image processing, material engineering, video processing, computational materials science, optical flow estimation, computational electromagnetics, and more.
Sri Hari R V, Chennai Instititue of Technology
Sri's WhatsApp* chat-analysis project delves into the intricacies of human interaction and gleans valuable insights from the conversations. By using data science techniques, this project aims to uncover patterns and trends in communication, providing users with a deeper understanding of their interactions.
Sritha Kalvagadda, National Institute of Technology, Calicut
Sritha's project applies CNN to animal recognition. Her project explores using computer vision to enhance the understanding of various animal species for the betterment of wildlife studies.
Srivatsan K B, Chennai Instiute of Technology
Srivatsan's Black Friday sales analysis unveils fascinating insights into consumer behavior and market trends. By delving into the intricacies of Black Friday sales, businesses gain a competitive edge, optimizing their operations to meet evolving consumer demands. Ultimately, robust analysis empowers retailers to enhance customer experiences, maximize revenue, and stay ahead in the dynamic retail landscape.
Subash Palvel, Jansons Institute of Technology
Subash works on music generation using oneDNN, drawing attention to the exciting fusion of music and AI. He describes how the AI music generator (powered by oneDNN) transforms the traditional music composition process. By training LSTM neural networks on an extensive MIDI dataset, the project produces personalized soundtracks that can elevate any production scenario. The technology automates the composition process, ensures efficiency, and unleashes creativity.
Subhadip Saha, JIS College of Engineering
Subhadip's project focuses on using the power of machine learning and sentimental analysis to develop an AI bot that can understand and respond to a user's emotions. He plans to use AI Tools for this purpose. His objective is to alleviate prevalent issues such as depression, anxiety, and mental health concerns.
Subhranil Paul, Techno India University
Subhranil's Text-to-SQL Generator is a user-friendly data exploration tool that uses Gemini Pro, a powerful language model to translate natural language prompts into dynamic SQL queries. Integrated with SQLite, it efficiently retrieves and displays data through an intuitive Streamlit interface. Designed for users of all SQL proficiency levels, it democratizes data exploration by simplifying complex queries. The project enhances accessibility, efficiency, and interactivity in database interaction. He used the following technologies: Python, Gemini Pro, SQLite, and Streamlit.
Tejshree Sasikumar, Sri Sivasubramaniya Nadar College of Engineering
Tejshree focuses on predicting heart diseases and making a positive impact on healthcare. The implementation of effective data-driven systems for heart disease prediction can significantly enhance the overall research and prevention efforts. By harnessing the power of data, he can better understand the factors contributing to heart diseases, thereby improving our ability to prevent and treat them.
Thejas Elandassery, Indian Institute of Technology Madras
The goal of Thejas' Vision AI project is to help the blind navigate their life easily. He developed the hardware project using Raspberry Pi* and an Android* app.
Tushar Suman, Poornima Group of Institutions
Tushar's project aims at drawing astrological charts using algorithms and calculations from Vedic math in DPC++. Using the OpenVINO toolkit, interpreting astrological charts would be faster, more accurate, and more user friendly by displaying the charts in a 3D view.
Utsav Mehta, Pandit Deendayal Energy University
Utsav is focused on automating and innovating firmware using AI. He is currently working on a project to create an automated trash ecosystem using machine learning and oneDNN.
Vedansh Jaiswal, Shri Ramdeobaba College of Engineering and Management
Vedansh is working on a machine learning project that uses facial expression recognition to determine a user's mood and then suggests various song playlists based on that mood. His project works by obtaining a live video feed from a webcam and running it through the model to forecast emotion. He uses oneDAL for a better user experience and to make it possible for blind people to listen to music that suits their mood.
Vignesh Nagavel, VSB Engineering College, Karur
Vignesh is developing a hybrid model with recurrent neural networks to estimate river flow through hydropower plants from pluviometer and satellite precipitation data. For this work, Vignesh uses oneDNN, Intel Optimization for TensorFlow, and the Intel Tiber Developer Cloud.
Vishal Gupta, Guru Gobind Singh Indraprastha University
Vishal developed a machine learning method for proactive healthcare, specializing in early detection of heart disease and diabetes. Employing SVM and Logistic Regression models, his project enables timely intervention and preventive measures. Through data preprocessing and model validation using Python and scikit-learn, Vishal ensures robustness and accuracy.
Vishal V, Sona College of Technology
Vishal's project uses SYCL to classify pneumonia in medical image processing. SYCL takes advantage of the power of heterogeneous hardware architectures, such as CPUs, GPUs, and FPGAs. With SYCL, he optimized classification algorithms for parallel processing, enabling faster and more accurate diagnoses.
Yash Zanwar, Vellore Institute of Technology
Yash's project demonstrates real-time pose estimation using the PoseNet model and the ml5.js library. It detects key body points from a webcam feed, allowing for creative overlays of images. This work showcases the potential of pose estimation technology in interactive applications, art installations, and augmented reality experiences. By accurately tracking body poses, this project opens doors to various production applications, including fitness tracking, virtual try-on, and interactive gaming.
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