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

Machine Learning Models Predict Arsenic Contamination

A comparative study showed that random forest models outperformed traditional linear regression in predicting arsenic contamination in groundwater. The research highlighted the importance of hydro-chemical and geological factors in managing contamination risks, especially in Asia.

27 Aug 2024

Neural Networks Adhere to Pre-specified Dynamics

The gFTP algorithm constructs binary recurrent neural networks with user-defined dynamics by adjusting non-realizable graphs and solving linear problems. This innovative approach enhances the understanding and robustness of neural dynamics, offering new insights into network behavior and structure.

26 Aug 2024

LLMs Navigate Game Theory with Strategic Decision-Making

A study explored the strategic behavior of large language models (LLMs) like GPT-3.5, GPT-4, and LLaMa-2 in game-theoretic scenarios. Findings revealed distinct decision-making patterns, with LLMs balancing game structures and contextual framing in nuanced ways.

23 Aug 2024

Exploring Consciousness in Large Language Models with AI

A study explored the potential for consciousness in large language models (LLMs), analyzing the conditions under which they might achieve consciousness. The research mapped theoretical landscapes, emphasizing the need for further data to substantiate any conclusions on artificial consciousness.

22 Aug 2024

ML Discovers 2D Materials with Unique Thermal Expansion

Researchers used machine learning and symbolic regression to identify 2D materials with diverse thermal expansion properties, including ZTE and extreme expansion cases. The study provided critical insights for designing materials with tailored thermal properties for advanced applications.

21 Aug 2024

Using Machine Learning to Predict Water Quality

Machine learning techniques, such as KNN and XGBoost, have significantly improved water quality predictions by analyzing complex data. These models enhance early detection of contamination, ensuring safer water management practices and better public health outcomes.

20 Aug 2024

Hybrid Detection Mitigates DDoS Attacks in SDNs

Researchers combined entropy-based detection with machine learning clustering to effectively identify and mitigate DDoS attacks in software-defined networks. The approach demonstrated superior accuracy and robustness, providing a more resilient defense against sophisticated threats

19 Aug 2024

YOLO Enhances Pothole Detection for the Visually Impaired

Researchers developed a deep learning model using the YOLOv5 algorithm to detect potholes in real-time, assisting visually impaired individuals. The model, integrated into a mobile app, achieved 82.7% accuracy, offering auditory or haptic feedback to enhance user safety.

18 Aug 2024

Neural Networks Map Forest Tree Species

Researchers used convolutional neural networks and Sentinel-2 satellite imagery to classify tree species in Austrian forests. By integrating mixed species classes and spatial autocorrelation analysis, they improved the accuracy and reliability of large-scale tree species mapping, despite challenges with mixed pixels.

16 Aug 2024

Reinforcement Learning Simulates Climbing Plant Growth

Researchers used reinforcement learning (RL) to model the growth and structural efficiency of climbing plants. The "Searcher-Shoot" environment accurately mimicked plant behavior, demonstrating how plants optimize their mass and radius distribution to manage stress and support growth.

14 Aug 2024