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

Simple Algorithm Boosts Brain-Machine Interface Success

Researchers showcase a breakthrough in brain-machine interfaces (BMIs) by introducing a bio-feedback approach using a straightforward group weight algorithm for 2D cursor control. Rhesus monkeys exhibit a 70% improvement in trial success rates over a week, highlighting the paradigm's effectiveness in leveraging user learning without complex parameter estimation, opening avenues for user-friendly BMI designs.

22 Jan 2024

ScabyNet: Image Processing and Deep Learning Application for Potato Tuber Evaluation

Researchers unveil ScabyNet, a groundbreaking tool utilizing image processing and deep learning to accurately assess potato tuber morphology and detect common scab (CS) severity. With user-friendly interfaces, ScabyNet overcomes limitations of previous methods, offering a comprehensive solution for precise, automated, and efficient phenotyping with applications in potato breeding and quality assessment, heralding a significant advancement in agricultural research.

19 Jan 2024

Neural Network-Based Automated Measurement System for Eyelid Assessment

In a groundbreaking article, researchers unveil an automated eyelid measurement system employing neural network (NN) technology. This innovative system showcases high accuracy and efficiency, providing precise measurements of critical parameters and effective detection of eyelid abnormalities, demonstrating its potential for transformative applications in clinical settings.

18 Jan 2024

Real-Time Bidding in Recommendation Systems with Contextual-Bandit Framework

Researchers introduce BART, a novel contextual-bandit framework, addressing bid price optimization and ranking in recommendation systems. Through reinforcement learning and deep neural networks, BART achieves remarkable improvements in user engagement, demonstrated in online experiments on Facebook services, filling a crucial gap in the literature and advancing the field of recommendation systems.

17 Jan 2024

IF-HUPM Algorithm Offers Interpretable Insights in Fuzzy Medical Databases

Researchers introduce the Improved Fuzzy High-Utility Pattern Mining (IF-HUPM) algorithm, a groundbreaking approach for computerized medical decision-making. By addressing interpretability challenges in existing High-Utility Pattern Mining (HUPM) algorithms, IF-HUPM incorporates fuzzy preprocessing, achieving efficient and interpretable results for multidimensional medical data. The algorithm demonstrates superior performance, providing a promising avenue for intelligent decision-making in healthcare.

16 Jan 2024

SBeA Unveils Versatile Few-Shot Learning Framework to Enhance Animal Behavior Studies

Researchers introduce the Social Behavior Atlas (SBeA), a pioneering computational framework for studying animal social behavior. Leveraging few-shot learning, 3D pose estimation, and identity recognition, SBeA overcomes data limitations, addresses occlusion challenges, and unveils previously unnoticed social behavior phenotypes across various species, showcasing its potential as a transformative tool in the field.

15 Jan 2024

Unraveling Tropical Weather Secrets: CNNs Illuminate Madden-Julian Oscillation Predictability

Researchers harness Convolutional Neural Networks (CNNs) to enhance the predictability of the Madden-Julian Oscillation (MJO), a critical tropical weather pattern. Leveraging a 1200-year simulation and explainable AI methods, the study identifies moisture dynamics, particularly precipitable water anomalies, as key predictors, pushing the forecasting skill to approximately 25 days and offering insights into improving weather and climate predictions.

15 Jan 2024

CrisisViT: Transforming Emergency Response with Image Classification

Researchers present CrisisViT, a novel transformer-based model designed for automatic image classification in crisis response scenarios. Leveraging in-domain learning with the Incidents1M crisis image dataset, CrisisViT outperforms conventional models, offering enhanced accuracy in disaster type, image relevance, humanitarian category, and damage severity classification. This innovation provides an efficient solution for crisis responders, enabling rapid image analysis through smartphones and social media, thereby aiding timely decision-making during emergencies.

12 Jan 2024

Enhancing Wind Speed Prediction for Sustainable Energy Using Neural Networks

Researchers introduce an advanced wind speed prediction model using a refined Hilbert–Huang transform (HHT) with complementary ensemble empirical mode decomposition (CEEMD). Leveraging a dynamic neural network, this model significantly improves accuracy in wind speed time series modeling, addressing the challenges posed by the unpredictable nature of wind speeds. The optimized HHT-NAR model demonstrates superior performance in wind-rich and wind-limited areas, contributing to the effective scheduling and control of wind farms and promoting the stability of power systems for sustainable wind energy utilization.

11 Jan 2024

Machine Learning Enhances Additive Manufacturing: Predicting Laser Absorption in Real Time

Researchers leverage synchrotron X-ray imaging and machine learning models, including deep convolutional neural networks (ConvNets) and semantic segmentation, to predict laser absorptance and segment vapor depressions in metal additive manufacturing. The end-to-end and modular approaches showcase efficient and interpretable solutions, offering potential for real-time monitoring and decision-making in industrial processes.

10 Jan 2024