Ashutosh Roy

MTech in Control Systems

Data Scientist, Business Analytics, Cloud Application Development

Ashutosh Roy has an MTech in Control Systems from IIEST Shibpur. He holds a keen interest in the field of smart instrumentation and has actively participated in the International Conferences on Smart Instrumentation. During his academic journey, Ashutosh undertook a significant research project focused on smart nonlinear controller design. His work involved utilizing advanced techniques such as backstepping and adaptive neural networks. By combining these methods, he aimed to develop intelligent control systems capable of efficiently adapting to non-linear dynamics.

Currently residing in Kolkata, India, Ashutosh is working with a startup where he is actively involved in the field of data science. His primary responsibilities include exploring and developing various products utilizing machine learning and artificial intelligence. This role allows him to leverage his expertise in data science and apply it to real-world scenarios, contributing to the development of innovative solutions.

Outside of his professional pursuits, Ashutosh has a passion for traveling and exploring new cultures. He finds joy in immersing himself in different environments and experiencing diverse cuisines. Additionally, he has a strong affinity for music and frequently attends live gigs when possible. He is also an avid sports enthusiast with a particular love for football and golf. He actively engages in running and walking to maintain an active and healthy lifestyle. With a curious and adventurous spirit, Ashutosh constantly seeks new opportunities for personal and intellectual growth.

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Articles from Ashutosh

Enhancing Smart Cities with Dual-Branch Residual Networks for Urban Sound Classification

Researchers from China Jiliang University and Hangzhou Aihua Intelligent Technology Co., Ltd. propose a novel approach using dual-branch residual networks to enhance urban environmental sound classification in smart cities. By accurately identifying and classifying various sounds, this advanced system offers valuable insights for city management, security, environmental monitoring, traffic management, and urban planning, leading to more livable and sustainable urban environments.

2 Aug 2023

Green AI: Transforming Energy Forecasting for a Sustainable Future

Researchers delve into the world of Green AI, a promising technology that combines artificial intelligence with sustainability practices to address energy forecasting and management challenges. The article explores applications in green energy load forecasting, power consumption prediction, and electricity price forecasting, highlighting the potential of Green AI to optimize energy distribution, promote renewable energy sources, and foster a greener and more sustainable future.

2 Aug 2023

AI Revolutionizes Ocular Imaging to Tackle SANS in Space Exploration

This review explores how Artificial Intelligence (AI), particularly Generative Adversarial Networks (GANs) and Supervised Learning, revolutionizes ocular imaging in space, offering new insights into Spaceflight Associated Neuro-Ocular Syndrome (SANS), a condition affecting astronauts' eyes during long-duration space missions.

1 Aug 2023

Advancements in Fault-Tolerant Control for Underwater Robotics

Researchers explore the advancements in Fault-Tolerant Control (FTC) technology for Autonomous Underwater Vehicles (AUVs) in a review, enhancing their reliability and robustness for underwater missions, marine exploration, environmental monitoring, and offshore industries.

1 Aug 2023

What is a Decision Tree?

Decision trees are interpretable and versatile models that recursively split data based on features for accurate predictions in various domains. Boosting and bagging techniques enhance their predictive power, while regularization mitigates overfitting, and handling imbalanced datasets improves performance.

30 Jul 2023

Ensemble Learning: Combining Models for Better Predictions

Ensemble learning is a machine learning technique that involves combining multiple models to achieve more accurate results than individual models. It operates on the idea that diverse models can collectively outperform single models. Popular methods include bagging, boosting, stacking, and the random subspace method. Ensemble learning has found applications in medical diagnosis, fraud detection, sentiment analysis, and more, making it a powerful tool in improving predictive accuracy.

29 Jul 2023

What is Random Forest?

Random Forest, a breakthrough machine learning algorithm, offers unparalleled accuracy and robustness in predictive analysis and land cover classification. Its ability to handle complex data and mitigate overfitting has made it a game-changer in various industries, but responsible exploration of its potential is essential to ensure ethical and equitable applications.

27 Jul 2023

What is Unsupervised Machine Learning?

Unsupervised machine learning, a crucial branch of AI, focuses on finding patterns and structures in unlabeled data, enabling data exploration and understanding of complex systems. Techniques like Principal Components Analysis, clustering, and dimensionality reduction play vital roles in diverse applications, including genetics, recommender systems, and anomaly detection, making unsupervised learning a powerful tool for knowledge extraction and pattern discovery.

26 Jul 2023

Unlocking Creativity: Human-AI Collaboration for Generating Visual Metaphors

Researchers propose a new task of generating visual metaphors from linguistic metaphors using a collaboration between Large Language Models (LLMs) and Diffusion Models. They create a high-quality dataset containing 6,476 visual metaphors for 1,540 linguistic metaphors and their associated visual elaborations using a human-AI collaboration framework.

26 Jul 2023

AI-Powered ChatGPT: Revolutionizing Chemistry Education and Problem Solving

The research investigates the conceptual difficulties faced by ChatGPT, an AI-powered tool, in comprehending and responding to chemistry problems related to Introduction to Material Science. The study highlights the limitations of ChatGPT's text-based capabilities and proposes the use of converters that can transform text into graphical representations to overcome these limitations.

26 Jul 2023