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

AI-driven Innovation in Hydroponics

Hydroponics, a method of growing plants using nutrient-rich solutions instead of soil, ensures efficient water and nutrient use, accelerates growth, and enables controlled environment cultivation. Integrating artificial intelligence (AI) into hydroponics enhances plant care and productivity through advanced monitoring and automation.

23 Jul 2024

Enhancing Emotion Detection with Multimodal LLMs

A study published in Future Internet explored the use of multimodal large language models (MLLMs) for emotion recognition from videos. The researchers combined visual and acoustic data to test MLLMs in a zero-shot learning setting, finding that MLLMs excelled in recognizing emotions with intensity deviations, though they did not outperform state-of-the-art models on the Hume-Reaction benchmark.

23 Jul 2024

Enhancing Well Log Data Prediction in the Drava Basin

Researchers validated predictive regression algorithms for filling missing geophysical logging data in the Drava Super Basin, focusing on Gola Field. They found that LSTM neural networks and tree-based algorithms excelled in predicting missing well log data, while unsupervised learning effectively identified lithological patterns, enhancing subsurface characterization and understanding.

21 Jul 2024

Deep Learning Predicts Lake Water Levels with Precision

Researchers found that deep learning models significantly outperformed ANN and ARIMA models in predicting water levels in Lakes St. Clair and Ontario, offering enhanced accuracy for resource management.

19 Jul 2024

Machine Learning Boosts Uranium Gamma-Ray Logging

Researchers highlighted the efficacy of machine learning (ML) in improving uranium spectral gamma-ray logging, particularly using backpropagation (BP) neural networks. Addressing challenges like low statistical efficacy and spectral drift, their study demonstrated that ML models, especially BP, significantly enhance the accuracy and stability of uranium quantification in high-speed logging, outperforming traditional methods.

18 Jul 2024

LLMs Enhance Robotic Anomaly Detection

Researchers presented a two-stage framework utilizing large language models (LLMs) for detecting and addressing anomalies in robotic systems. The fast anomaly classifier operates in an LLM embedding space, while a slower reasoning system ensures safe, trustworthy operation of dynamic robots, mitigating computational costs and enhancing control frameworks.

17 Jul 2024

How Can AI Help in Deep Sea Exploration and Mining?

Artificial Intelligence (AI) is poised to revolutionize deep-sea exploration and mining by enhancing data accuracy, operational efficiency, and environmental sustainability. This article explores AI's role in addressing the high costs, environmental concerns, and operational challenges of deep-sea activities.

16 Jul 2024

Counterfactual Tasks Reveal Limits of Language Models

Researchers evaluated recent language models (LMs) on counterfactual task variants to test their abstract reasoning and generalizability. The study found that while LMs like GPT-4 and PaLM-2 showed some task generalization, their performance significantly degraded under counterfactual conditions, indicating reliance on narrow, non-transferable procedures.

16 Jul 2024

Fuzzy Logic Redefines AI Fairness

Researchers have proposed a novel framework using basic fuzzy logic to redefine group fairness in AI, separating it from social context and uncertainty. This framework translates complex fairness definitions into more accessible terms, allowing for continuous truth values based on stakeholder opinions and enhancing practical application and interpretability in diverse contexts.

15 Jul 2024

Enhancing Education with AI-Generated QA Pairs

In an article published in Computers and Education: Artificial Intelligence, researchers explored various methods for generating question-answer (QA) pairs using pre-trained large language models (LLMs) in higher education. They assessed pipeline, joint, and multi-task approaches across three datasets through automated metrics, teacher evaluations, and real-world educational settings.

12 Jul 2024