Anjishnu Saw
UnderGrad AI/ML Researcher & Electronic Systems Student
Research Interests:
About Me
Driven by curiosity and fueled by resilience, I'm on a mission to advance AI/ML research while pursuing my passion for Electronic Systems at IIT Madras.
My Journey
I'm Anjishnu, an emerging computer science researcher with a strong interdisciplinary foundation in Electronics Systems (BS - IIT Madras) and Core Computer Science (B.Tech - KIIT University). Passionate about applying Machine Learning and Deep Learning in real-world applications, I’ve already published several peer-reviewed research papers, including a recent one in Springer LNSS, and presented internationally — solo — in Malaysia. My academic record includes a consistent 9.5+ SGPA, showcasing both technical depth and relentless work ethic.
At the intersection of AI, computational biology, and systems engineering, I’m building impactful projects such as multimodal classifiers on platforms like Kaggle, and deploying real-time analytics tools on Vercel. With over 30 repositories on GitHub, a growing presence on Google Scholar, and certifications from respected institutions across the globe, I’m steadily aligning my career for my research future.
But beyond code and theory, I’m driven by a deeper purpose — to break generational ceilings and give back. I strongly believe in authentic collaboration, emotional intelligence, and lifting others through innovation and mentorship. I don’t just build software — I build systems that reflect compassion, intelligence, and long-term vision. Let’s connect if you’re passionate about AI for good, research-led innovation, or just curious about how technology, humility, and grit can build the future.
Core Values
Research Excellence
Committed to pushing the boundaries of AI/ML research with rigorous methodology and innovative approaches.
Academic Resilience
Overcoming challenges in my academic journey has strengthened my determination to contribute meaningfully to science.
Collaborative Spirit
Believing in the power of interdisciplinary collaboration to solve complex real-world problems.
Innovation Focus
Passionate about translating theoretical research into practical solutions that benefit society.
Programming Languages & Technologies
Python
Primary language for AI/ML research and data analysis
C
Advanced applications in graphics and embedded systems
C++
System programming and performance-critical applications
JavaScript
Website development and modelling for research interactions
Java
Object-oriented programming and interactive developments
Notable Mentors
Research Publications
Exploring the intersection of AI/ML and Electronic Systems through rigorous research and innovation.
Anjishnu Saw - Research Publications & Academic Papers
Machine Learning, AI, Digital IC Testing, Stock Market Prediction Research
Comprehensive research portfolio by Anjishnu Saw covering Machine Learning, Artificial Intelligence, Genetic Algorithms, CPU Scheduling Optimization, Digital IC Testing, Stock Market Prediction using LSTM and ARIMA, Lung Cancer Detection, Asteroid Classification, and Net Asset Value Forecasting. Published in prestigious conferences including IEEE, Springer, and Wiley.
A hybrid approach for Efficient CPU Scheduling: Implementation of ML in Genetic Algorithm
Authors: Anjishnu Saw M/s Krutika Verma Dr. Minakhi Rout Prof. Dr. Suresh Chandra Satapathy
Venue: SCI - Springer 7th International Conference on Smart Computing and Informatics
Year: 2025
Keywords: hybrid hybrid research hybrid machine learning approach approach research approach machine learning for for research for machine learning efficient efficient research efficient machine learning cpu cpu research cpu machine learning scheduling scheduling research scheduling machine learning implementation implementation research implementation machine learning genetic genetic research genetic machine learning algorithm algorithm research algorithm machine learning machine learning machine learning research machine learning paper machine learning publication genetic algorithm genetic algorithm research genetic algorithm paper genetic algorithm publication cpu scheduling cpu scheduling research cpu scheduling paper cpu scheduling publication Anjishnu Saw Anjishnu Saw research Anjishnu Saw publications Krutika Verma Krutika Verma research Krutika Verma publications Minakhi Rout Minakhi Rout research Minakhi Rout publications Dr. Suresh Chandra Satapathy Dr. Suresh Chandra Satapathy research Dr. Suresh Chandra Satapathy publications sci sci conference sci publication springer springer conference springer publication 7th 7th conference 7th publication international international conference international publication conference conference conference conference publication smart smart conference smart publication computing computing conference computing publication and and conference and publication informatics informatics conference informatics publication SCI SCI conference SCI 2025 machine learning machine learning genetic algorithm machine learning optimization optimization research optimization machine learning 2025 research 2025 publication published research peer reviewed
Abstract: This research explores a competitive way of integrating Machine Learning(ML) techniques to Genetic-Algorithm(GA) for efficient CPU scheduling. The proposed method incorporates ML-predicted best-fit functions into the GA framework....
A Novel Method for Testing Digital ICs
Authors: Anjishnu Saw Dr. Arjyadhara Pradhan Dr. Minakhi Rout
Venue: ICIDeA - IEEE International Conference on Industrial Electronics: Developments & Applications
Year: 2025
Keywords: novel novel research novel machine learning method method research method machine learning for for research for machine learning testing testing research testing machine learning digital digital research digital machine learning ics ics research ics machine learning digital ic digital ic research digital ic paper digital ic publication cloud database cloud database research cloud database paper cloud database publication Anjishnu Saw Anjishnu Saw research Anjishnu Saw publications Arjyadhara Pradhan Arjyadhara Pradhan research Arjyadhara Pradhan publications Minakhi Rout Minakhi Rout research Minakhi Rout publications icidea icidea conference icidea publication ieee ieee conference ieee publication international international conference international publication conference conference conference conference publication industrial industrial conference industrial publication electronics: electronics: conference electronics: publication developments developments conference developments publication applications applications conference applications publication IEEE IEEE conference IEEE 2025 digital ics digital ics research digital ics machine learning cloud database integration cloud database integration research cloud database integration machine learning cmos technology cmos technology research cmos technology machine learning 2025 research 2025 publication published research peer reviewed
Abstract: This paper presents A novel and simple method to check these ICs in the form of Cloud Database Integration. The development of such an IC tester has been discussed in detail, highlighting all the aspects ...
A fusion approach to enhance the prediction of Stock prices
Authors: Anjishnu Saw Dr. Minakhi Rout Dr. Sarita Tripathy
Venue: ICISSC - Springer 4th International Conference on INTELLIGENT SYSTEMS & SUSTAINABLE COMPUTING
Year: 2024
Keywords: fusion fusion research fusion machine learning approach approach research approach machine learning enhance enhance research enhance machine learning the the research the machine learning prediction prediction research prediction machine learning stock stock research stock machine learning prices prices research prices machine learning lstm lstm research lstm paper lstm publication arima arima research arima paper arima publication prediction paper prediction publication stock market stock market research stock market paper stock market publication Anjishnu Saw Anjishnu Saw research Anjishnu Saw publications Minakhi Rout Minakhi Rout research Minakhi Rout publications Sarita Tripathy Sarita Tripathy research Sarita Tripathy publications icissc icissc conference icissc publication springer springer conference springer publication 4th 4th conference 4th publication international international conference international publication conference conference conference conference publication intelligent intelligent conference intelligent publication systems systems conference systems publication sustainable sustainable conference sustainable publication computing computing conference computing publication ICISSC ICISSC conference ICISSC 2024 INTELLIGENT INTELLIGENT conference INTELLIGENT 2024 SYSTEMS SYSTEMS conference SYSTEMS 2024 SUSTAINABLE SUSTAINABLE conference SUSTAINABLE 2024 COMPUTING COMPUTING conference COMPUTING 2024 stock market machine learning lstm machine learning arima machine learning hybrid modelling hybrid modelling research hybrid modelling machine learning 2024 research 2024 publication
Abstract: This paper discusses an innovative method to predict the stock market prices using lesser data and extracting the maximum output by utilizing a hybrid modelling strategy of Long-Short-Term-Memory (LSTM) and Auto Regressive-Integrated-Moving-Average (ARIMA) to capture the data trends and hence predict the prices ...
Assessment of the Recurrent RBF Long‐Range Forecasting Model for Estimating Net Asset Value
Authors: Dr. Minakhi Rout Anjishnu Saw AK Jena AK Parida
Venue: Adaptive Artificial Intelligence: Fundamentals, Challenges, and Applications
Year: 2026
Keywords: assessment assessment research assessment machine learning the the research the machine learning recurrent recurrent research recurrent machine learning rbf rbf research rbf machine learning long long research long machine learning range range research range machine learning forecasting forecasting research forecasting machine learning model model research model machine learning for for research for machine learning estimating estimating research estimating machine learning net net research net machine learning asset asset research asset machine learning value value research value machine learning prediction prediction research prediction paper prediction publication forecasting paper forecasting publication net asset value net asset value research net asset value paper net asset value publication rbf paper rbf publication Minakhi Rout Minakhi Rout research Minakhi Rout publications Anjishnu Saw Anjishnu Saw research Anjishnu Saw publications AK Jena AK Jena research AK Jena publications AK Parida AK Parida research AK Parida publications adaptive adaptive conference adaptive publication artificial artificial conference artificial publication intelligence: intelligence: conference intelligence: publication fundamentals, fundamentals, conference fundamentals, publication challenges, challenges, conference challenges, publication and and conference and publication applications applications conference applications publication recurrent rbf recurrent rbf research recurrent rbf machine learning net asset value machine learning long range forecasting long range forecasting research long range forecasting machine learning flann flann research flann machine learning 2026 research 2026 publication published research peer reviewed
Abstract: Few studies have been published on long-term net asset value forecasting, according to a review of the literature on the topic. However, to create an appropriate capital management plan, such longer interval forecasting of net asset data is required. The forecasting models now in use, which rely on soft computing, perform quite poorly in terms of predictions. Considering this, a brand-new soft computing model is created and applied to forecast net asset value up to 30 days in advance. When compared to the prediction performance of four different soft computing models, the simulation results using real-world data demonstrate outstanding performance.
Lung Cancer Prediction Using Machine Leaning Techniques
Authors: Aadi Poddar Mahendra Kumar Gourisaria Monika Padhi Anjishnu Saw Saurabh Bilgaiyan Tanvir Habib Sardar
Venue: Third International Conference on Networks, Multimedia and Information Technology (NMITCON)
Year: 2025
Keywords: lung lung research lung machine learning cancer cancer research cancer machine learning prediction prediction research prediction machine learning using using research using machine learning machine machine research machine machine learning leaning leaning research leaning machine learning techniques techniques research techniques machine learning machine learning machine learning research machine learning paper machine learning publication prediction paper prediction publication regression regression research regression paper regression publication detection detection research detection paper detection publication lung cancer lung cancer research lung cancer paper lung cancer publication Aadi Poddar Aadi Poddar research Aadi Poddar publications Mahendra Kumar Gourisaria Mahendra Kumar Gourisaria research Mahendra Kumar Gourisaria publications Monika Padhi Monika Padhi research Monika Padhi publications Anjishnu Saw Anjishnu Saw research Anjishnu Saw publications Saurabh Bilgaiyan Saurabh Bilgaiyan research Saurabh Bilgaiyan publications Tanvir Habib Sardar Tanvir Habib Sardar research Tanvir Habib Sardar publications third third conference third publication international international conference international publication conference conference conference conference publication networks, networks, conference networks, publication multimedia multimedia conference multimedia publication and and conference and publication information information conference information publication technology technology conference technology publication (nmitcon) (nmitcon) conference (nmitcon) publication NMITCON NMITCON conference NMITCON 2025 lung cancer detection lung cancer detection research lung cancer detection machine learning machine learning machine learning predictive modeling predictive modeling research predictive modeling machine learning regression analysis regression analysis research regression analysis machine learning 2025 research 2025 publication published research
Abstract: The most commonly diagnosed cancers in the world are Lung cancer. Beforehand prediction, detection and diagnosis of lung cancer has become essential. This paper draws light to the lung cancer predictions and detection using common life habits and attributes. Predictions are based on different parameters like gender and age of the person and their habits like smoking and alcohol consumption. The effectiveness of lung cancer prediction system helps the doctor and patients to know their lung cancer risk with minimal cost. The prediction helps people to take appropriate actions based on their lung cancer risk status. This paper involves various machine learning regression models like Lasso Regression, Linear Regression, Ridge Regression, Decision Tree and Random Forests. In this paper all the above models are compared, and it is found that Random Forest Regressor outperforms the best results in MSE …
Classification and Comparative Analysis of Hazardous Asteroids Using Machine Learning
Authors: Aarav Srivastava Mahendra Kumar Gourisaria Anjishnu Saw Dayal Kumar Behara Sonal Jain Tanvir Habib Sardar
Venue: Third International Conference on Networks, Multimedia and Information Technology (NMITCON)
Year: 2025
Keywords: classification classification research classification machine learning and and research and machine learning comparative comparative research comparative machine learning analysis analysis research analysis machine learning hazardous hazardous research hazardous machine learning asteroids asteroids research asteroids machine learning using using research using machine learning machine machine research machine machine learning learning learning research learning machine learning machine learning machine learning research machine learning paper machine learning publication classification paper classification publication regression regression research regression paper regression publication analysis paper analysis publication asteroid asteroid research asteroid paper asteroid publication Aarav Srivastava Aarav Srivastava research Aarav Srivastava publications Mahendra Kumar Gourisaria Mahendra Kumar Gourisaria research Mahendra Kumar Gourisaria publications Anjishnu Saw Anjishnu Saw research Anjishnu Saw publications Dayal Kumar Behara Dayal Kumar Behara research Dayal Kumar Behara publications Sonal Jain Sonal Jain research Sonal Jain publications Tanvir Habib Sardar Tanvir Habib Sardar research Tanvir Habib Sardar publications third third conference third publication international international conference international publication conference conference conference conference publication networks, networks, conference networks, publication multimedia multimedia conference multimedia publication and conference and publication information information conference information publication technology technology conference technology publication (nmitcon) (nmitcon) conference (nmitcon) publication NMITCON NMITCON conference NMITCON 2025 potentially hazardous asteroids (pha) potentially hazardous asteroids (pha) research potentially hazardous asteroids (pha) machine learning near earth asteroids (nea) near earth asteroids (nea) research near earth asteroids (nea) machine learning predictive modeling predictive modeling research predictive modeling machine learning 2025 research
Abstract: Asteroids are small, rocky celestial objects that continuously orbit the Sun. They can be hazardous if classified as Near-Earth Objects (NEOs). Machine learning models can assist in predicting the nature of asteroids and classify them as potentially hazardous or not. The aim of this study is to optimize the identification process of Potentially Hazardous Asteroids (PHAs) and Near-Earth Asteroids (NEAs). In this work, we have implemented several classification and regression models and validated their performance. We implemented Random Forest, Naive Bayes, K-Nearest Neighbours and Logistic Regression classifiers. For regression tasks, we implemented Linear Regression, Ridge Regression and Lasso Regression. We used performance metrics such as Accuracy, Precision, Recall, and F1 Score, and regression evaluation metrics including RMSE and R2 Score, to assess model performance. Results show that …
This paper discusses an innovative method to predict the stock market prices using lesser data and extracting the maximum output by utilizing a hybrid modelling strategy of Long-Short-Term-Memory (LSTM) and Auto Regressive-Integrated-Moving-Average (ARIMA) to capture the data trends and hence predict the prices ...
Few studies have been published on long-term net asset value forecasting, according to a review of the literature on the topic. However, to create an appropriate capital management plan, such longer interval forecasting of net asset data is required. The forecasting models now in use, which rely on soft computing, perform quite poorly in terms of predictions. Considering this, a brand-new soft computing model is created and applied to forecast net asset value up to 30 days in advance. When compared to the prediction performance of four different soft computing models, the simulation results using real-world data demonstrate outstanding performance.
The most commonly diagnosed cancers in the world are Lung cancer. Beforehand prediction, detection and diagnosis of lung cancer has become essential. This paper draws light to the lung cancer predictions and detection using common life habits and attributes. Predictions are based on different parameters like gender and age of the person and their habits like smoking and alcohol consumption. The effectiveness of lung cancer prediction system helps the doctor and patients to know their lung cancer risk with minimal cost. The prediction helps people to take appropriate actions based on their lung cancer risk status. This paper involves various machine learning regression models like Lasso Regression, Linear Regression, Ridge Regression, Decision Tree and Random Forests. In this paper all the above models are compared, and it is found that Random Forest Regressor outperforms the best results in MSE …
Asteroids are small, rocky celestial objects that continuously orbit the Sun. They can be hazardous if classified as Near-Earth Objects (NEOs). Machine learning models can assist in predicting the nature of asteroids and classify them as potentially hazardous or not. The aim of this study is to optimize the identification process of Potentially Hazardous Asteroids (PHAs) and Near-Earth Asteroids (NEAs). In this work, we have implemented several classification and regression models and validated their performance. We implemented Random Forest, Naive Bayes, K-Nearest Neighbours and Logistic Regression classifiers. For regression tasks, we implemented Linear Regression, Ridge Regression and Lasso Regression. We used performance metrics such as Accuracy, Precision, Recall, and F1 Score, and regression evaluation metrics including RMSE and R2 Score, to assess model performance. Results show that …
More publications and preprints coming soon.
Research by Anjishnu Saw KIIT University Machine Learning Genetic Algorithm CPU Scheduling Digital IC Testing Cloud Database LSTM ARIMA Stock Market Prediction Lung Cancer Detection Asteroid Classification Net Asset Value Forecasting IEEE Conference Springer Publication ICIDeA ICISSC NMITCON Research Scholar India AI ML Deep Learning Neural Networks Optimization Algorithms Predictive Modeling Time Series Analysis CMOS Technology Medical AI Space Technology Financial Forecasting Academic Publications Research Papers Conference Proceedings Journal Articles Peer Reviewed Research
Conferences & Talks
Sharing research insights and connecting with the global academic community through international conferences and speaking engagements.
"A hybrid approach for Efficient CPU Scheduling: Implementation of ML in Genetic Algorithm"
Solo international presentation discussing the implementation and usage of ML-Algorithm for efficient CPU Scheduling.
Key Highlights:
- First physical International Conference as UG researcher
- Audience of researchers and industry professionals
- Featured in Springer LNSS
"A Novel Method for Testing Digital ICs"
Presented research on applying simple cloud integration techniques for CMOS Digital ICs verification
Key Highlights:
- Live demonstration
- Networking with industry leaders, and researchers
"A fusion approach to enhance the prediction of Stock prices"
Showcased research on using hybrid modelling techniques for accurate prediction of stock prices in Indian Market.
Key Highlights:
- Featured in Springer SIST
Projects Hub
Innovative projects spanning AI/ML research, hardware design, and practical applications. Each project represents a step forward in bridging theory and practice.
Full-stack stock market analysis and portfolio management app for Indian markets (NSE).
Interactive ML visualizations, explanations, and quizzes for learning algorithms.
Academic Journey
Pursuing excellence in Computer Engineering and Electronic Systems at IIT Madras while building a strong foundation in AI/ML research and maintaining consistent academic performance.
2025
Semester 4
Highlights:
- Physical Paper presentation in Kuala Lumpur, Malaysia
2024
Semester 3
Highlights:
- Extended work on usage of GANs for early stage Lung Cancer Detection
2023
Semester 2
Highlights:
- Physical paper presentation in Hyderabad, India
2023
Semester 1
Highlights:
- First Conference Paper
Major Coursework
Professional Certifications
Deep Learning Specialization
Zero To Mastery Academy
Python 101 for Data Science
IBM
Indian Institute of Remote Sensing
IIRS,ISRO
Achievements & Awards
Recognition for academic excellence, research contributions, and professional achievements throughout my journey in AI/ML and Electronic Systems.
Thought Space
Sharing insights, research findings, and personal reflections on AI/ML, electronic systems, and the future of technology. Join me in exploring the frontiers of innovation in artificial intelligence, machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, automation, data science, and algorithm optimization.
Let's Connect
I'm always excited to discuss research opportunities, collaborate on innovative projects, or simply connect with fellow researchers and technology enthusiasts. Let's build the future together!
Get in Touch
Whether you're interested in research collaboration, have questions about my work, or want to discuss opportunities in AI/ML and electronic systems, I'd love to hear from you.
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