Samudrapom Dam

Bachelor of Technology: Electronics and Communication Engineering Maulana Abul Kalam Azad University of Technology

Material Science, Advanced Technologies, Metals, Clean Technologies

Samudrapom Dam is a freelance scientific and business writer based in Kolkata, India. He has been writing articles related to business and scientific topics for more than one and a half years. He has extensive experience in writing about advanced technologies, information technology, machinery, metals and metal products, clean technologies, finance and banking, automotive, household products, and the aerospace industry. He is passionate about the latest developments in advanced technologies, the ways these developments can be implemented in a real-world situation, and how these developments can positively impact common people.

Samudrapom also writes about the significance of different technologies for modern businesses, such as how the implementation of technology can improve a business process or streamline the entire business operation of an enterprise. He completed his Bachelor of Technology in Electronics and Communication Engineering in 2018 from Maulana Abul Kalam Azad University of Technology (MAKAUT). He is an avid reader and enjoys playing chess.

See less

Articles from Samudrapom

Predicting Customer Churn Using AI

This article emphasizes the growing significance of artificial intelligence (AI), particularly machine learning and deep learning, in predicting customer churn for businesses. It discusses the importance of customer churn prediction, the evolving role of AI, various ML algorithms applied in research, the emergence of deep learning approaches, and recent developments like a novel AI-based customer churn prediction model. The integration of AI in this domain not only aids in retaining customers but also contributes to informed decision-making for sustainable business growth.

16 Jan 2024

Efficient Gearbox Fault Diagnosis: A Leap in Precision

In this article, researchers unveil a cutting-edge gearbox fault diagnosis method. Leveraging transfer learning and a lightweight channel attention mechanism, the proposed EfficientNetV2-LECA model showcases superior accuracy, achieving over 99% classification accuracy in both gear and bearing samples. The study signifies a pivotal leap in intelligent fault diagnosis for mechanical equipment, addressing challenges posed by limited samples and varying working conditions.

12 Jan 2024

Optimizing Control for Continuum Robots: A PSO-based Breakthrough

Researchers unveil a pioneering approach to model and control continuum robots using particle swarm optimization (PSO). Introducing a dynamic model based on the Euler–Lagrange formulation, the study employs PSO-optimized controllers, including fuzzy logic and proportional integral derivative, showcasing significant advancements in precision, stability, and adaptability for these flexible robotic systems. This breakthrough bears substantial implications for fields relying on continuum robots, enhancing their accuracy and responsiveness in intricate tasks.

11 Jan 2024

High-Throughput Soft Microrobot Fabrication with Programmable Anisotropy

Researchers pioneer a versatile technique, integrating droplet-based microfluidics and simulation-guided design, for mass-producing soft microrobots with programmable magnetic and structural anisotropy. This breakthrough enables precise control over collective behaviors, offering potential applications in biomedical functions such as drug delivery and biosensing within complex physiological environments.

10 Jan 2024

Revolutionizing Ergonomics in Agriculture Using AI

Researchers propose an AI-powered posture classification system, employing MoveNet and machine learning, to address ergonomic challenges faced by agricultural workers. The study demonstrates the feasibility of leveraging AI for precise posture detection, offering potential advancements in safety practices and worker health within the demanding agricultural sector.

10 Jan 2024

What is K-Nearest Neighbors (KNN)?

This comprehensive exploration delves into the intricacies of K-nearest neighbors (KNN), an instance-based learning algorithm widely employed for classification and regression tasks. From its foundational principles to recent advancements like DkNN and QkNN, this article navigates through various aspects of KNN, shedding light on its effectiveness, variants, and recent developments, including an optimized breast cancer detection model.

9 Jan 2024

Enhancing Cyber Threat Intelligence with AI

This article explores the critical role of Cyber Threat Intelligence (CTI) in cybersecurity, emphasizing the escalating complexity of cyber threats. It delves into the fundamental importance of artificial intelligence (AI), particularly machine learning (ML), in fortifying CTI measures. The applications of AI in CTI are discussed, showcasing its potential in automating data processing, structuring unstructured data, risk assessment, and active sensor deployment.

9 Jan 2024

Debates, Controversies, and Knowledge Gaps in Automation, AI, and Robotics

The article emphasizes the pivotal role of Human Factors and Ergonomics (HFE) in addressing challenges and debates surrounding trust in automation, ethical considerations, user interface design, human-AI collaboration, and the psychological and behavioral aspects of human-robot interaction. Understanding knowledge gaps and ongoing debates is crucial for shaping the future development of HFE in the context of emerging technologies.

5 Jan 2024

Quantum Machine Learning's Potential Impact on Science

This article explores the rising significance of Quantum Machine Learning (QML) in reshaping the scientific landscape. With attention from tech giants like IBM and Google, QML combines quantum computing and machine learning, holding promise despite challenges. The article highlights ongoing studies, the application landscape, challenges such as quantum-classical data fusion, and the potential of quantum sensing techniques, urging a balanced focus on experimentation over solely relying on theoretical quantum speed-up claims.

4 Jan 2024

An AI-based Approach for Constructing Custom Thermodynamics

Researchers present an AI platform, Stochastic OnsagerNet (S-OnsagerNet), that autonomously learns clear thermodynamic descriptions of intricate non-equilibrium systems from microscopic trajectory observations. This innovative approach, rooted in the generalized Onsager principle, enables the interpretation of complex phenomena, showcasing its effectiveness in understanding polymer stretching dynamics and demonstrating potential applications in diverse dissipative processes like glassy systems and protein folding.

3 Jan 2024