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.

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Articles from Samudrapom

Enhancing Safety in Human-Robot Collaboration: DL-Enhanced Digital Twin Framework for Manufacturing

Researchers propose an intelligent Digital Twin framework enhanced with deep learning to detect and classify human operators and robots in human-robot collaborative manufacturing. The framework improves reliability and safety by enabling autonomous decision-making and maintaining a safe distance between humans and robots, offering a promising solution for advanced manufacturing systems.

18 Jul 2023

SayPlan: Scaling Up LLM-Based Task Planning for Robotics Using 3D Scene Graphs

Researchers propose SayPlan, a scalable approach for large-scale task planning in robotics using large language models (LLMs) grounded in three-dimensional scene graphs (3DSGs). The approach demonstrates high success rates in finding task-relevant subgraphs, reduces input tokens required for representation, and ensures near-perfect executability. While limitations exist, such as graph reasoning constraints and static object assumptions, the study paves the way for improved LLM-based planning in expansive environments.

16 Jul 2023

Advancing Breast Cancer Diagnosis: Harnessing the Fast Learning Network Algorithm

Researchers demonstrate the efficacy of a Fast Learning Network (FLN) algorithm-based classifier for diagnosing breast cancer. The FLN algorithm achieves high accuracy, precision, recall, F-measure, and specificity in breast cancer detection using the Wisconsin Diagnostic Breast Cancer (WDBC) and Wisconsin Breast Cancer Database (WBCD). This study highlights the potential of FLN as a reliable breast cancer diagnosing classifier, although further optimization and exploration of breast cancer stages are needed for future research.

15 Jul 2023

CAT-ViL: A Transformer-Based Approach for Surgical Visual Question Localized Answering

Researchers propose a novel Transformer model with CoAttention gated vision language (CAT-ViL) embedding for surgical visual question localized answering (VQLA) tasks. The model effectively fuses multimodal features and provides localized answers, demonstrating its potential for real-world applications in surgical training and understanding.

13 Jul 2023

Revolutionizing Artificial Neural Networks: Self-Powered In-Sensor ANNs with Molecular Ferroelectric Photomemristors

Researchers have achieved a breakthrough in the development of artificial neural networks (ANNs) by implementing self-powered in-sensor ANNs using molecular ferroelectric (MF)-based photomemristor arrays. These innovative devices enable real-time optical signal recognition and acquisition while minimizing power consumption.

12 Jul 2023

The Future of Large Language Models in Healthcare: Balancing Potential Benefits and Risks

This article discusses the need for regulatory oversight of large language models (LLMs)/generative artificial intelligence (AI) in healthcare. LLMs can be implemented in healthcare settings to summarize research papers, obtain insurance pre-authorization, and facilitate clinical documentation. LLMs can also improve research equity and scientific writing, improve personalized learning in medical education, streamline the healthcare workflow, work as a chatbot to answer patient queries and address their concerns, and assist physicians to diagnose conditions based on laboratory results and medical records.

11 Jul 2023

Powering Superconductivity with AI: Unlocking Potential and Overcoming Challenges

This article reviews the transformative impact of artificial intelligence (AI) techniques such as deep learning and machine learning in the field of superconductivity. From condition monitoring and design optimization to intelligent modeling and estimation, AI offers innovative solutions to overcome challenges, accelerate commercialization, and unlock new opportunities in the realm of superconducting technologies and materials.

10 Jul 2023

Revolutionizing Last-Mile Logistics: A Sustainable Approach Integrating Drones and Trucks

A groundbreaking mathematical model, the FSTSP-DR-MP, has been proposed to transform last-mile logistics into a more sustainable and efficient process. With the surge in online shopping and the subsequent rise in carbon emissions, this innovative approach integrates both delivery and return services using a combination of trucks and drones. The model optimizes routes, considering multiple payloads and customers, to minimize service time.

9 Jul 2023

Demystifying AI: A Comprehensive Overview of eXplainable AI (XAI) for Trustworthy Decision-Making

Demystifying AI: A comprehensive overview of eXplainable AI (XAI) provides a thorough analysis of current trends, research, and concerns in the field, shedding light on the inner workings of AI models for trustworthy decision-making. The review covers various aspects of XAI, including data explainability, model explainability, post-hoc explainability, assessment of explanations, and available XAI research software tools. It highlights the importance of understanding and validating AI systems to ensure transparency, fairness, and accountability in their deployment