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

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

Decoding Healthcare: Evaluating Advanced Language Models in Clinical Decision-Making

Researchers scrutinize the clinical prowess of cutting-edge language models like GPT-3.5 and GPT-4 alongside Google search, shedding light on their diagnostic and therapeutic capabilities across various medical scenarios. While GPT-4 emerges as a frontrunner, particularly in diagnosing common ailments, challenges persist, emphasizing the imperative for ongoing advancements and regulatory vigilance in integrating artificial intelligence into healthcare.

15 Mar 2024

Advancements in Image-Based Crop Yield Calculation

Researchers delve into the evolving landscape of crop-yield prediction, leveraging remote sensing and visible light image processing technologies. By dissecting methodologies, technical nuances, and AI-driven solutions, the article illuminates pathways to precision agriculture, aiming to optimize yield estimation and revolutionize agricultural practices.

14 Mar 2024

Beneath the Waves: Advancing Underwater Image Restoration with DIMN

Researchers introduce DIMN, a novel Dual Information Modulation Network designed for accurate Underwater Image Restoration (UIR). By integrating spatial-aware attention blocks and multi-scale structural transformer blocks, DIMN outperforms existing methods in correcting color deviations, recovering details, and enhancing sharpness and contrast in underwater images. This groundbreaking technology promises to revolutionize underwater visualization, offering unprecedented clarity and detail in exploring the depths of our oceans.

14 Mar 2024

Insights into the Ethical Terrain of AI-Driven Political Microtargeting

This study explores the ethical dimensions of employing AI, particularly ChatGPT, for political microtargeting, offering insights into its effectiveness and ethical dilemmas. Through empirical investigations, it unveils the persuasive potency of personalized political ads tailored to individuals' personality traits, prompting discussions on regulatory frameworks to mitigate potential misuse.

13 Mar 2024

AI-Powered Privacy Revolution: Coordinated Data Sharing in the Digital Era

Researchers proposed coordinated data sharing within a collective as a solution to address the pressing issue of privacy loss in the digital age. Leveraging decentralized AI, the study demonstrated that this approach not only recovered privacy for individuals but also significantly reduced costs for service providers. By shifting the focus from personal to collective privacy, coordinated data sharing offers a transformative path forward, emphasizing transparency, user-friendly policies, and a delicate balance between privacy preservation and data-sharing needs in our evolving society.

12 Mar 2024

The Role of AI in Animal Farming and Monitoring

The integration of AI into animal farming marks a pivotal shift from traditional methods to data-driven decision-making. From monitoring livestock health to optimizing feeding programs, AI's impact on agriculture promises improved productivity and sustainability while meeting ethical standards and consumer expectations.

11 Mar 2024

Synergies of Progress: AI, Religious Freedom, and Economic Growth

Through a PVAR model, researchers explored the intricate relationship between AI progress, religious freedom, and economic growth across 26 countries. Their findings highlighted a significant positive correlation, showcasing the escalating influence of these factors on future economic landscapes and emphasizing the importance of traditional drivers like labor and capital. The study underscores policy implications for prioritizing AI integration and fostering religious freedom, while also suggesting avenues for future research to deepen our understanding of these complex dynamics.

11 Mar 2024

Reliable, Adaptable, and Attributable Language Models with Retrieval

Researchers advocate for retrieval-augmented language models (LMs) as superior to traditional parametric LMs due to improved reliability, adaptability, and verifiability. While acknowledging challenges hindering widespread adoption, they propose a roadmap focusing on nuanced retriever-LM interactions, infrastructure improvements, and interdisciplinary collaboration to unleash the full potential of retrieval-augmented LMs beyond conventional knowledge-centric tasks.

8 Mar 2024