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

Advanced Algorithm Enhances Fault Detection in Power Networks

Researchers developed the successive particle swarm genetic algorithm (SPSO-GA) to improve fault location accuracy in complex distribution networks with distributed generation. Tested on an IEEE 33-node network, SPSO-GA outperformed existing algorithms in fault detection accuracy, convergence speed, and stability.

24 May 2024

Predicting Life Satisfaction with Machine Learning

Researchers utilized machine learning algorithms to predict life satisfaction with high accuracy (93.80%) using data from a Danish government survey. By identifying 27 key questions and employing models such as KNN, SVM, and Bayesian networks, the study highlighted the significant impact of health conditions on life satisfaction and made the best predictive model publicly available.

23 May 2024

AI-Driven Green Power Trading Boosts China's Energy Market

This study introduced a novel green power certificate trading system in China, utilizing Q-learning, smart contracts, and multi-agent Nash strategies to enhance efficiency and collaboration. The innovative framework increased trading prices by 20% and transaction success rates by 30-fold, significantly advancing China's renewable energy goals and supporting carbon neutrality targets.

22 May 2024

Augmented Reality in Sports: Enhancing Fan Engagement

This article explores the transformative impact of augmented reality (AR) on fan interaction in sports, highlighting how AR enhances immersion and engagement. By overlaying virtual data on the real world, AR provides fans with real-time statistics, player profiles, and interactive elements, thereby deepening their involvement and excitement during live sports events.

21 May 2024

Machine Learning Enhances Plasma Plume Analysis in PLD

Researchers in Nature explore the application of deep learning to analyze plasma plume dynamics in pulsed laser deposition (PLD). Using ICCD image sequences, a (2 + 1)D convolutional neural network correlates plume behavior with deposition conditions, enabling real-time monitoring and predictive insights for optimizing thin film growth.

21 May 2024

Optimizing Perovskites for Solar Cells with AI and DFT

Researchers combined density functional theory (DFT) with machine learning (ML) to screen 41,400 metal halide perovskites (MHPs), identifying 10 promising candidates with improved stability and optoelectronic properties. Highlighting CsGe0.3125Sn0.6875I3 and CsGe0.0625Pb0.3125Sn0.625Br3, this study offers a new framework for optimizing perovskites for solar cells.

19 May 2024

Advanced Kiwifruit Soft Rot Detection with Deep Learning

Researchers presented a novel dual-branch selective attention capsule network (DBSACaps) for detecting kiwifruit soft rot using hyperspectral images. This approach, detailed in Nature, separates spectral and spatial feature extraction, then fuses them with an attention mechanism, achieving a remarkable 97.08% accuracy.

17 May 2024

AI Boosts Manufacturing Particle Detection

A novel framework combining deep learning and preprocessing algorithms significantly improved particle detection in manufacturing, addressing challenges posed by heterogeneous backgrounds. The framework, validated through extensive experimentation, enhanced in-situ process monitoring, offering robust, real-time solutions for diverse industrial applications.

16 May 2024

Silent Speech Interface Using Graphene-Based Textile Strain Sensors and AI

Researchers introduced a groundbreaking silent speech interface (SSI) leveraging few-layer graphene (FLG) strain sensing technology and AI-based self-adaptation. Embedded into a biocompatible smart choker, the sensor achieved high accuracy and computational efficiency, revolutionizing communication in challenging environments.

15 May 2024

Smart Traffic Surveillance: Faster R-CNN for Vehicle Segmentation

Researchers introduced a novel deep learning approach based on faster R-CNN for segmenting vehicles in traffic videos, addressing challenges like occlusions and varying traffic densities. Through adaptive background modeling and topological active nets, the method achieved superior segmentation accuracy, showcasing its potential to enhance real-world traffic surveillance and management systems.

14 May 2024