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.

Articles from Ashutosh

Transforming Food Supply Chains with Digital Twin Technology and Real-Time Strategies

Researchers have introduced a pioneering methodology utilizing digital twin technology to revolutionize real-time planning, monitoring, and control within medium-scale food processing companies. Published in Technological Forecasting and Social Change, the study highlights how this innovative approach can optimize food supply chains, enhance resource allocation, mitigate risks, and improve customer satisfaction.

28 Aug 2023

Revolutionizing Deep Learning Energy Measurement with FECoM

‌Researchers have introduced the Fine-grained Energy Consumption Meter (FECoM) framework to tackle the energy consumption challenges of Deep Learning (DL) models. This novel approach provides precise method-level energy measurement, offering a granular view of energy consumption and enabling energy-efficient development practices in various domains.

27 Aug 2023

Lens Flare Removal Breakthrough: Enhancing Object Detection in Autonomous Driving

Researchers have introduced a groundbreaking solution, the Class Attention Map-Based Flare Removal Network (CAM-FRN), to tackle the challenge of lens flare artifacts in autonomous driving scenarios. This innovative approach leverages computer vision and artificial intelligence technologies to accurately detect and remove lens flare, significantly improving object detection and semantic segmentation accuracy.

27 Aug 2023

Navigating User Intent Modeling for AI-Driven Conversations

Researchers delve into the intricacies of user intent modeling in conversational recommender systems, revealing symbiotic relationships between models and features. Through systematic literature reviews and real-world case studies, they present a structured decision model that emphasizes practical adaptability and promotes collaboration, equipping academics and practitioners to innovate in the realm of AI-driven conversations.

21 Aug 2023

Enhancing Aquaculture Water Quality Prediction with PID-RENet

Researchers combine deep neural networks (DNN) with a PID-RENet (Proportional-Integral-Derivative Residual Elimination Network) to improve time-series water quality predictions in aquaculture. The PID-RENet approach effectively corrects DNN predictions using PID control principles, leading to more accurate forecasts for crucial water quality parameters.

19 Aug 2023

TE-VS: Wearable Marvel for Motion Monitoring and Energy Harvesting

A groundbreaking innovation, the TE-VS combines triboelectrification and electromagnetic power generation to revolutionize wearables. With machine learning integration and applications in healthcare and sustainable energy, the TE-VS promises accurate motion monitoring and energy harvesting, shaping a brighter future for technology and well-being.

17 Aug 2023

ARGUS: Pioneering the Evolution of Intelligent AR Assistants

Researchers explore the transformative potential of ARGUS, a visual analytics tool designed to enhance the development and refinement of intelligent augmented reality (AR) assistants. By offering real-time monitoring, retrospective analysis, and comprehensive visualization, ARGUS empowers developers to understand user behavior, AI model performance, and physical environment interactions, revolutionizing the precision and effectiveness of AR assistance across diverse domains.

16 Aug 2023

Enhancing Mathematical Problem Solving: GPT-4's Collaboration with Plugins

Researchers investigate the potential of combining GPT-4 with plugins like Wolfram Alpha and Code Interpreter for solving complex mathematical and scientific problems. The study explores how this collaborative approach amplifies AI's capabilities in problem-solving, showcasing strengths and challenges in handling diverse problem scenarios. While GPT-4 and plugins exhibit promise, the study highlights the importance of refining their interaction and addressing limitations to fully harness the potential of AI-powered problem-solving.

15 Aug 2023

Transforming Traffic Management: Cooperative DRL and Shapley Value Rewards for Congestion Reduction

Researchers propose the synergy of cooperative Deep Reinforcement Learning (DRL) and the Shapley value reward system to revolutionize traffic signal management. This approach leverages intelligent agents representing intersections that collaborate through communication and information sharing, optimizing traffic flow.

13 Aug 2023

Unmasking Fake News: Transformer Models Illuminate Indonesian Language Detection

Researchers explored the effectiveness of transformer models like BERT, ALBERT, and RoBERTa for detecting fake news in Indonesian language datasets. These models demonstrated accuracy and efficiency in addressing the challenge of identifying false information, highlighting their potential for future improvements and their importance in combating the spread of fake news.

13 Aug 2023