Soham Nandi

Computer Science Engineering, specializing in Artificial Intelligence and Machine learning

Soham Nandi is a technical writer based in Memari, India. His academic background is in Computer Science Engineering, specializing in Artificial Intelligence and Machine learning. He has extensive experience in Data Analytics, Machine Learning, and Python. He has worked on group projects that required the implementation of Computer Vision, Image Classification, and App Development.

Soham's passion for AI and Machine Learning propels him toward his goal of becoming a distinguished Machine Learning Engineer and leveraging his knowledge to help the less fortunate. His projects include the Rice grain classifier and Handwriting-based Personality Detector, and he is certified in Data Analytics and Data Science.

Drawing inspiration from writers ranging from Satyajit Ray to Mark Manson, he has his own science fiction stories and blog. He also creates content on personal development and mental health through his Instagram handle. Beyond the screen and code, Soham's world is filled with music. With the ability to play the guitar, ukulele, and drums, he rocks to the tunes of the metal and rock genres, using music as a creative outlet, planning to start a band of his own.

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Articles from Soham

Integration of AI into Game Physics

Recent developments showcase AI's integration into game physics, revolutionizing gaming experiences. From predictive simulation to reinforcement learning, diverse applications enhance game mechanics, design, and adaptability. These advancements reshape gameplay dynamics, improve player engagement, and pave the way for more immersive virtual environments, heralding a new era in interactive entertainment.

13 Feb 2024

AIoT's Impact on Sustainable Development: A Bibliometric Analysis

Analyzing 9,182 documents from 1989 to 2022, this study unveils the burgeoning role of Artificial Intelligence of Things (AIoT) in realizing Sustainable Development Goals (SDGs). With a focus on interdisciplinary collaboration, global trends, and thematic evolution, it emphasizes the dynamic synergy between AI, IoT, and sustainability, guiding future endeavors in leveraging technology for global well-being.

12 Feb 2024

Robust Visual Object Tracking with Language-driven Resamplable Continuous Representation

Researchers propose a novel defense mechanism, Language-driven Resamplable Continuous Representation (LRR), to enhance the robustness of visual object trackers against adversarial attacks. By leveraging semantic text guidance and constructing a spatial-temporal implicit representation (STIR), LRR ensures appearance consistency and semantic alignment, achieving high accuracy on both clean and adversarial data. Extensive experiments demonstrate LRR's superiority over existing defenses, highlighting its potential for improving the security of autonomous systems.

9 Feb 2024

YOLOv5s-ngn: Advancing Air-to-Air UAV Detection with Enhanced Object Detection Model

This research presents YOLOv5s-ngn, a novel approach for air-to-air UAV detection addressing challenges in collision avoidance. Enhanced with lightweight feature extraction and fusion modules, alongside the EIoU loss function, YOLOv5s-ngn showcases superior accuracy and real-time performance, marking a significant advancement in vision-based target detection for unmanned aerial vehicles.

8 Feb 2024

Advancing Connectivity Analysis in Cubic Fuzzy Graphs for Disaster Management: A Study on Tsunami Threat Assessment

Researchers introduce novel concepts of partial cubic edges in cubic fuzzy graphs (CFGs), enhancing connectivity analysis for disaster management. The study explores connectivity index bounds, average connectivity, and node classification, offering insights into network dynamics. Application to tsunami threat assessment demonstrates practical utility, highlighting the significance of CFGs in modeling complex systems and informing decision-making in disaster preparedness and mitigation.

7 Feb 2024

MaTE: Macroscopic Traffic Estimator for Enhanced Traffic Flow and Travel Time Prediction

MaTE, a novel traffic estimator, utilizes macroscopic flow theory, logit-based stochastic traffic assignment, and neural networks to accurately predict traffic flow and travel time in areas with limited sensor coverage. Demonstrating superiority over existing models, MaTE offers interpretability and scalability, bridging the gap between data-driven and model-based methodologies in transportation planning.

7 Feb 2024

Enhancing Student Support Systems Using AI

Explore the transformative impact of Artificial Intelligence (AI) on education, focusing on personalized learning assistance, the role of chatbots and virtual assistants, and the revolutionary advancements in Natural Language Processing (NLP). The article delves into the potential drawbacks and challenges, emphasizing the importance of addressing bias, emotional intelligence limitations, privacy concerns, and the need to strike a balance between technological assistance and fostering students' independent learning capabilities.

5 Feb 2024

Revolutionizing Reinforcement Learning: Function Encoders for Seamless Zero-Shot Transfer

Researchers present the Function Encoder (FE), a novel algorithm enabling zero-shot transfer in reinforcement learning (RL) by representing functions as learned basis functions. The FE demonstrates superior data efficiency, asymptotic performance, and training stability across various RL domains, showcasing its potential as a versatile and efficient solution for adaptive policies in diverse sequential decision-making scenarios.

5 Feb 2024

AudioSeal: Unveiling a Sonic Sentry for AI-Generated Speech Detection

AudioSeal, an avant-garde audio watermarking technique, takes center stage in an arXiv article, presenting a localized detection strategy for AI-generated speech. With its generator/detector architecture, unique perceptual loss, and multi-bit watermarking, AudioSeal achieves state-of-the-art performance, demonstrating unparalleled robustness, speed, and efficiency in real-time applications.

2 Feb 2024

DeepONet's Leap in Real-time Prediction Empowers Nuclear Digital Twins

Researchers explored the integration of Deep Neural Operator Network (DeepONet) as a robust surrogate modeling method for digital twin (DT) technology in nuclear energy systems. DeepONet's unique architecture, trained with various operational conditions, showcased unparalleled accuracy and speed, positioning it as a promising algorithm for real-time predictions in complex particle transport problems.

1 Feb 2024