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

Enhancing Biomedical Named Entity Recognition with Dictionary-Based Matching Graph Network

Researchers present a novel approach, the Dictionary-Based Matching Graph Network (DBGN), for Biomedical Named Entity Recognition (BioNER). By incorporating biomedical dictionaries and utilizing BiLSTM and BioBERT encoders, DBGN outperforms existing models across various biomedical datasets, demonstrating significant advancements in entity recognition with improved efficiency.

14 Dec 2023

Enhancing IoT Security: Evaluating Tree-Based Machine Learning Algorithms for Botnet Detection

This article delves into bolstering Internet of Things (IoT) security, specifically countering botnet attacks that jeopardize IoT ecosystems. Employing tree-based algorithms, including Decision Trees, Random Forest, and boosting techniques, the researchers conduct a thorough empirical analysis, highlighting Random Forest's standout multi-class classification accuracy and superior computational efficiency.

13 Dec 2023

Unraveling the Influence of Depth Movements on 3D Tracking Performance in Virtual Reality

This article delves into the impact of required depth movements on three-dimensional (3D) tracking performance in manual tasks, scrutinizing binocular and monocular visions. The study's findings uncover the nuanced role of depth information in 3D tracking behaviors, particularly in a virtual reality (VR) environment. The research contributes valuable insights for designing effective motor skill training programs by comprehensively examining how depth movements, vision conditions, and task complexities interplay in shaping tracking performance in immersive environments.

12 Dec 2023

Applications of AI in Network Security

This article explores the intersection of Artificial Intelligence (AI) and network security, emphasizing the transformative role of AI in addressing evolving cyber threats. It highlights applications such as threat detection, automated response mechanisms, and predictive analysis. Ethical considerations, responsible AI usage, integration challenges, and the looming threat of adversarial attacks are discussed.

12 Dec 2023

Innovative Sandfly Identification Using Wing Interferential Patterns and Deep Learning

The article presents a groundbreaking approach for identifying sandflies, crucial vectors for various pathogens, using Wing Interferential Patterns (WIPs) and deep learning. Traditional methods are laborious, and this non-invasive technique offers efficient sandfly taxonomy, especially under field conditions. The study demonstrates exceptional accuracy in taxonomic classification at various levels, showcasing the potential of WIPs and deep learning for advancing entomological surveys in medical vector identification.

10 Dec 2023

Public Preferences for Protective Features in AI Decision-Making: Insights from the Adult Danish Population

This research, published in PLOS One, investigates the protective feature preferences of the adult Danish population in various AI decision-making scenarios. With a focus on both public and commercial sectors, the study explores the nuanced interplay of demographic factors, societal expectations, and trust in shaping preferences for features such as AI knowledge, human responsibility, non-discrimination, human explainability, and system performance.

8 Dec 2023

Autonomous Welding Advancement: YOLOv5 Algorithm and RealSense Depth Camera Integration

Researchers developed a cutting-edge robot welding guidance system, integrating an enhanced YOLOv5 algorithm with a RealSense Depth Camera. Overcoming limitations of traditional sensors, the system enables precise weld groove detection, enhancing welding robot autonomy in complex industrial environments. The experiment showcased superior accuracy, reaching 90.8% mean average precision, and real-time performance at 20 FPS, marking a significant stride in welding automation and precision.

7 Dec 2023

Unveiling Bias in Base Language Models: A Comprehensive Assessment and Mitigation Study

Meta researchers address the crucial need for fair generative large language models (LLMs), focusing on base models. The study introduces novel definitions of bias, employing a demographic parity benchmark, and expands evaluation metrics and datasets. Results reveal varying toxicity and bias rates, highlighting the importance of intersectional demographics, prompting, and ongoing dataset evolution for effective mitigation strategies in language models.

7 Dec 2023

AI and ML Analysis of Elite Junior Female Tennis Players: Predicting Tournament Outcomes and Career Trajectories

Employing AI and ML, this study analyzed elite junior female tennis players' game statistics to predict tournament outcomes and understand career trajectories. While accurately forecasting junior tournament results, predicting future careers faced challenges, emphasizing the role of non-game factors and junior tournament participation in shaping successful careers. The study recommends refining models, emphasizing serve improvement, and supporting young talents through international tournaments for a nuanced understanding of tennis dynamics and enhanced training programs.

6 Dec 2023

Efficient Monotonic Multihead Attention (EMMA) for Real-time Speech-to-Text Translation

Researchers introduced EMMA, a model designed for simultaneous speech-to-text translation, addressing challenges in numerical stability, alignment shaping, and fine-tuning. EMMA showcased state-of-the-art performance, emphasizing quality and latency in bilingual and multilingual setups. The model's adaptation of transformer-based monotonic attention proved crucial for achieving real-time, context-aware speech translation in diverse linguistic scenarios.

6 Dec 2023

Soham Nandi

Articles from Soham

Enhancing Biomedical Named Entity Recognition with Dictionary-Based Matching Graph Network

Enhancing IoT Security: Evaluating Tree-Based Machine Learning Algorithms for Botnet Detection

Unraveling the Influence of Depth Movements on 3D Tracking Performance in Virtual Reality

Applications of AI in Network Security

Innovative Sandfly Identification Using Wing Interferential Patterns and Deep Learning

Public Preferences for Protective Features in AI Decision-Making: Insights from the Adult Danish Population

Autonomous Welding Advancement: YOLOv5 Algorithm and RealSense Depth Camera Integration

Unveiling Bias in Base Language Models: A Comprehensive Assessment and Mitigation Study

AI and ML Analysis of Elite Junior Female Tennis Players: Predicting Tournament Outcomes and Career Trajectories

Efficient Monotonic Multihead Attention (EMMA) for Real-time Speech-to-Text Translation

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