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

Simplifying Whole-Slide Image Analysis with SliDL: A Deep Learning Toolbox

The article introduces SliDL, a powerful Python library designed to simplify and streamline the analysis of high-resolution whole-slide images (WSIs) in digital pathology. With deep learning at its core, SliDL addresses challenges in managing image annotations, handling artifacts, and evaluating model performance. From automatic tissue detection to comprehensive model evaluation, SliDL bridges the gap between conventional image analysis and the intricate world of WSI analysis.

11 Aug 2023

Revolutionizing Multi-USV Cooperative Hunting with Innovative Path Planning

Researchers explore a groundbreaking approach to enhance multi-unmanned surface vessel (USV) cooperation in maritime operations. By integrating the A* algorithm with path smoothing techniques and a biomimetic collaborative hunting strategy, the research optimizes path planning and target tracking in obstacle-rich environments. This innovative approach has the potential to reshape maritime operations, leading to safer, more efficient, and successful missions in complex scenarios.

11 Aug 2023

MedMine: Revolutionizing Healthcare with Language Models for Medication Mining

Researchers unveil the MedMine initiative, a pioneering effort that harnesses the power of pre-trained language models like Med7 and Clinical-XLM-RoBERTa for medication mining in clinical texts. By systematically evaluating these models and addressing challenges, the initiative lays the groundwork for a transformative shift in healthcare practices, promising accurate medication extraction, improved patient care, and advancements in medical research.

10 Aug 2023

Deep Learning Safeguards: Strengthening Cybersecurity in Industry 5.0

This article introduces cutting-edge deep learning techniques as a solution to combat evolving web-based attacks in the context of Industry 5.0. By merging human expertise and advanced models, the study proposes a comprehensive approach to fortify cybersecurity, ensuring a safer and more resilient future for transformative technologies.

10 Aug 2023

Enhancing Maritime Safety: Integrating Human Thinking into Intelligent Ship Collision Avoidance

Researchers present a pioneering methodology that incorporates human thinking experiences into intelligent ship collision avoidance models. By emulating both fast and slow thinking patterns, this approach improves interpretability, efficiency, and adaptability, fostering enhanced collaboration between humans and intelligent systems for safer and more efficient maritime navigation.

9 Aug 2023

Nbias: Pioneering Fairness in Natural Language Processing through Comprehensive Bias Detection

Recent advancements in Natural Language Processing (NLP) have revolutionized various fields, yet concerns about embedded biases have raised ethical and fairness issues. To combat this challenge, a team of researchers presents Nbias, an innovative framework introduced in an arXiv* article. Nbias detects and mitigates biases in textual data, addressing explicit and implicit biases that can perpetuate stereotypes and inequalities.

8 Aug 2023

What are Convolutional Neural Networks?

Explore the history, architecture, and diverse applications of CNNs, the transformative force behind the AI revolution. From image classification to object detection, semantic segmentation, style transfer, and even natural language processing, CNNs have reshaped technology, opening the door to innovative solutions in healthcare, autonomous systems, creative arts, and more.

7 Aug 2023

Deep Learning for Enhanced Network Intrusion Detection: Smaller Features, Greater Accuracy

This cutting-edge research explores a novel deep learning approach for network intrusion detection using a smaller feature vector. Achieving higher accuracy and reduced computational complexity, this method offers significant advancements in cybersecurity defense against evolving threats.

6 Aug 2023

Leveraging Transfer Learning for Intelligent 6G Networks

Researchers discuss the integration of artificial intelligence (AI) and networking in 6G networks to achieve efficient connectivity and distributed intelligence. It explores the use of Transfer Learning (TL) algorithms in 6G wireless networks, demonstrating their potential in optimizing learning processes for resource-constrained IoT devices and various IoT paradigms such as Vehicular IoT, Satellite IoT, and Industrial IoT. The study emphasizes the importance of optimizing TL factors like layer selection and training data size for effective TL solutions in 6G technology's distributed intelligence networks.

5 Aug 2023

FERN: Advancing Earthquake Rate Forecasting with Neural Encoder-Decoder Models

Researchers introduce FERN, a neural encoder-decoder model designed to revolutionize earthquake rate forecasting. By overcoming the limitations of traditional models like ETAS, FERN leverages the power of artificial intelligence and deep learning algorithms to provide more accurate and flexible earthquake predictions. With its ability to incorporate diverse geophysical data and offer improved short-term forecasts, FERN holds promise for enhancing seismic risk management and ensuring safer communities in earthquake-prone regions.

3 Aug 2023