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

Introducing CNSABC Algorithm for Enhanced Convergence and Solution Quality

Researchers unveil the Chaotic and Neighborhood Search-based Artificial Bee Colony (CNSABC) algorithm, a groundbreaking variant addressing limitations in traditional Artificial Bee Colony (ABC) for optimization problems. Demonstrating superior convergence speed and solution quality, CNSABC surpasses other algorithms in extensive experiments, showcasing its potential for practical problem-solving, particularly in complex engineering optimization scenarios.

24 Nov 2023

Enhancing Dissolved Oxygen Prediction in Rivers Using Metaheuristic Algorithms and Neural Networks

Researchers introduced and evaluated four metaheuristic algorithms—teaching–learning-based optimization, sine cosine algorithm, water cycle algorithm, and electromagnetic field optimization—integrated with a multi-layer perceptron neural network for predicting dissolved oxygen concentration in the Klamath River. These algorithms optimized computational variables, improving DO prediction accuracy in water quality assessment.

23 Nov 2023

Enhancing AR Glasses Adaptability in Aviation: A Multi-Modal IoT Approach

Researchers introduce a pioneering framework leveraging IoT and wearable technology to enhance the adaptability of AR glasses in the aviation industry. The multi-modal data processing system, employing kernel theory-based design and machine learning, classifies performance, offering a dynamic and adaptive approach for tailored AR information provision.

22 Nov 2023

UAV-Based Individual Chinese Cabbage Weight Prediction Using Multi-Temporal Data

This study presents an innovative method for predicting individual Chinese cabbage harvest weight using unmanned aerial vehicles (UAVs) and multi-temporal features. By automating plant detection with an object detection algorithm and leveraging various UAV data sources, the study achieves accurate and early predictions, addressing limitations in existing methods and offering valuable insights for precision agriculture and crop management.

21 Nov 2023

Lite3D: Real-Time Anomalous Behavior Recognition for Marine Life Conservation

This article in Nature features a groundbreaking approach for monitoring marine life behavior using Lite3D, a lightweight deep learning model. The real-time anomalous behavior recognition system, focusing on cobia and tilapia, outperforms traditional and AI-based methods, offering precision, speed, and efficiency. Lite3D's application in marine conservation holds promise for monitoring and protecting underwater ecosystems impacted by global warming and pollution.

19 Nov 2023

Evaluating User Perceptions of Generative AI Advice on Challenges

This study examined how people perceive advice from generative AI, exemplified by ChatGPT, on societal and personal challenges. The research, involving 3308 participants, revealed that while AI advisors were perceived as less competent when their identity was transparent, positive experiences mitigated this aversion, highlighting the potential value of clear and understandable AI recommendations for addressing real-world challenges.

19 Nov 2023

Addressing Discrepancies in Scope 3 Emissions Reporting and Improving Predictive Accuracy Using Machine Learning

This study delves into the discrepancies within Scope 3 emissions data reported by major providers like Bloomberg, Refinitiv Eikon, and ISS. Analyzing divergence, data composition, and predictive accuracy through statistical and machine learning techniques, the research unveils substantial inconsistencies, incomplete reporting, and predictive challenges, emphasizing the urgent need for standardized disclosure and awareness among investors.

17 Nov 2023

NFT-Based Authentication: Securing IoT Assets in Smart City Infrastructure

Researchers introduce a groundbreaking Non-Fungible Token (NFT)-based authentication structure for IoT assets in smart cities, enhancing security and autonomy. The architecture, leveraging blockchain and Externally Owned Addresses (EOA), provides a cost-effective and accessible solution by digitizing devices without the need for additional hardware upgrades.

17 Nov 2023

Chiral Magnets Empower Adaptive Neuromorphic Computing: Task-Adaptive Physical Reservoir Computers

Researchers introduced a paradigm-shifting approach to neuromorphic computing by showcasing the reconfigurability of physical reservoir computers (PRC). Leveraging the unique properties of chiral magnets, particularly the controlled nucleation of metastable skyrmions through magnetic field manipulation, the research demonstrated on-demand reconfiguration of reservoir properties, paving the way for energy-efficient and task-adaptive computing systems.

16 Nov 2023

A Computational Event-Driven Sensor with In-Sensor Spiking Neural Network for Motion Recognition

This article introduces a transformative computational event-driven vision sensor featuring a WSe2-based photodiode. This sensor directly converts dynamic motion into programmable, sparse spiking signals, overcoming limitations of conventional frame-based sensors. The innovation allows for in-sensor spiking neural network (SNN) formation, enabling real-time motion recognition with potential applications in edge computing vision chips, showcasing remarkable adaptability and efficiency.