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

Unsupervised CycleGAN for Weakly Conductive SEM Samples

Researchers introduced an unsupervised CycleGAN method to enhance SEM images of weakly conductive materials, surpassing traditional techniques. By leveraging unpaired blurred and clear images and introducing an edge loss function, the model effectively removed artifacts and restored crucial material details, promising significant implications for material analysis and image restoration in SEM.

27 Mar 2024

AR and Tourism: Transforming Travel Experiences

AR technology has revolutionized tourism by offering immersive experiences, virtual tours, and interactive explorations of destinations. Particularly relevant post-pandemic, AR provides contactless alternatives, rebuilding consumer confidence, and driving innovation in travel. Collaborative efforts and investments in AR infrastructure are crucial for unlocking its full potential in enhancing tourist engagement and satisfaction.

25 Mar 2024

Deep Learning-based Automation of Cephalometric Landmarking

Researchers proposed a groundbreaking deep learning method for automated cephalometric landmark annotation on 3D facial photographs, achieving precision akin to manual methods. By integrating DiffusionNet models, the approach offers efficiency and reliability, promising advancements in clinical diagnosis, follow-up, and virtual surgical planning across orthodontics and craniofacial surgery fields, despite inherent challenges and the need for further validation and refinement.

25 Mar 2024

Large Language Models could Revolutionize Pest Management in Agriculture

Researchers investigated the potential of large language models (LLMs), including GPT and FLAN series, for generating pest management advice in agriculture. Utilizing GPT-4 for evaluation, the study introduced innovative prompting techniques and demonstrated LLMs' effectiveness, particularly GPT-3.5 and GPT-4, in providing accurate and comprehensive advice. Despite FLAN's limitations, the research highlighted the transformative impact of LLMs on pest management practices, emphasizing the importance of contextual information in guiding model responses.

22 Mar 2024

LLM3: Bridging Symbolic Task Planning and Continuous Motion Generation with LLMs

In their paper submitted to arxiv, researchers introduced LLM3, a groundbreaking Task and Motion Planning (TAMP) framework that utilizes large language models (LLMs) to seamlessly integrate symbolic task planning and continuous motion generation. LLM3 leverages pre-trained LLMs to propose action sequences and generate action parameters iteratively, significantly reducing the need for domain-specific interfaces and manual effort.

21 Mar 2024

Electron Density-Based Host-Guest Binder Optimization

Researchers pioneer a novel approach using machine learning and optimization techniques to generate and optimize host-guest binders, achieving over 98% accuracy in molecular prediction. By harnessing electron density representations and transformer models, this method offers a groundbreaking avenue for accelerated discovery and optimization in host-guest chemistry, heralding advancements in materials science and molecular design.

20 Mar 2024

AI in Academic Assessments: Evaluating GPT-4 in Biomedical Science Exams

This study, published in Nature, delves into the performance of GPT-4, an advanced language model, in graduate-level biomedical science examinations. While showcasing strengths in answering diverse question formats, GPT-4 struggled with figure-based and hand-drawn questions, raising crucial considerations for future academic assessment design amidst the rise of AI technologies.

19 Mar 2024

AI in Art: Exploring the Creative Potential of Machines

The intersection of AI and art reshapes creative landscapes, from generative experiments to platforms like Playform. Ethical concerns and debates, exemplified by the Edmond auction, challenge notions of creativity. Despite challenges, AI expands artistic expression, blurring boundaries between human and machine creativity.

19 Mar 2024

Efficient Epileptic Seizure Prediction and Forecasting Using Machine Learning

Researchers introduce a paradigm shift in epilepsy management with seizure forecasting, offering nuanced risk assessment akin to weather forecasting. By comparing prediction and forecasting methodologies using patient-specific algorithms, the study demonstrates improved sensitivity and patient outcomes, highlighting the potential for more effective seizure warning devices and enhanced quality of life for epilepsy patients.

18 Mar 2024

Deep Learning Reveals Neural Hierarchy in C. elegans Mating

Through deep learning and calcium imaging, researchers elucidated the hierarchical structure of mating behavior in C. elegans males, uncovering distinct behavioral modules and highlighting the influence of serotonergic neurons. This comprehensive analysis provides insights into decision-making within neuromuscular circuits and lays the groundwork for further exploration of reproductive actions in this model organism.

15 Mar 2024