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

Agricultural Digital Twins for Precision Farming

Researchers demonstrate the transformative potential of agricultural digital twins (DTs) using mandarins as a model crop, showcasing how data-driven decisions at the individual plant level can enhance precision farming, optimize resource allocation, and improve fruit quality, ultimately leading to a paradigm shift in agriculture towards individualized farming practices.

28 Feb 2024

A Web Mining and ML Approach to Enhance Learning Object Reusability

Researchers propose a novel approach combining web mining and machine learning (ML) techniques to classify learning objects effectively in e-learning systems, aiming to maximize their reusability. By employing advanced ML algorithms and web mining methods, the study demonstrates significant improvements in resource discovery and knowledge dissemination, ultimately enhancing the efficiency of e-learning environments.

27 Feb 2024

The Impact of AI on Construction

AI is revolutionizing the construction industry, addressing challenges such as labor shortages and safety concerns while improving efficiency and reducing costs. From waste management to predictive modeling, AI applications abound, yet risks such as privacy violations and job displacement must be mitigated for successful adoption. This article explores the multifaceted role of AI in construction, emphasizing its transformative potential alongside the need for careful consideration of associated risks and challenges.

26 Feb 2024

Optimizing Solar-Powered Hybrid Systems with Grasshopper Optimization Algorithm

Researchers propose an energy management scheme (EMS) using a rule-based grasshopper optimization algorithm (RB-GOA) to efficiently manage solar-powered battery-ultracapacitor (UC) hybrid systems, addressing output fluctuations and maximizing energy extraction. The RB-GOA EMS outperforms other swarm intelligence techniques in minimizing oscillations, reducing power surges, and ensuring stable output across varying load demands, demonstrating its effectiveness in optimizing solar energy utilization.

25 Feb 2024

ML-based Prediction of Punching Shear Capacity in Ultra-High-Performance Concrete

Researchers investigated the feasibility of using machine learning (ML) models to predict the punching shear capacity of post-tensioned ultra-high-performance concrete (UHPC) flat slabs. By proposing correction factors based on finite element method-artificial intelligence (FEM-AI/ML) techniques, they extended the validity of punching shear capacity provisions in design codes like EC2 and ACI-318 to include PT-UHPC flat slabs.

23 Feb 2024

AutoGPT+P: Integrating Affordance-Based Scene Representation with LLMs for Robotic Task Planning

Researchers proposed AutoGPT+P, a novel system for robotic task planning that merges an affordance-based scene representation with large language models (LLMs). By combining visual data-based scene affordance extraction with LLMs' capabilities, AutoGPT+P enables robots to understand natural language commands, perceive their environment, and generate plans to fulfill user requests, even in scenarios where required objects are absent.

22 Feb 2024

A Novel Approach for Edge Unlearning in Graph Neural Networks

Researchers introduced Unlink to Unlearn (UtU), a novel method designed to simplify edge unlearning in graph neural networks (GNNs) and address the issue of over-forgetting. UtU facilitates unlearning by unlinking forget edges from the original graph structure, effectively preserving privacy protection capabilities and link prediction accuracy while minimizing computational overhead. Experimental evaluation across real-world datasets demonstrated UtU's efficacy in unlearning, maintaining high accuracy in downstream tasks, and effectively preventing over-forgetting, making it a practical and lightweight solution for edge unlearning in GNNs.

21 Feb 2024

Automated Dimension Reduction in Archaeometry Using Autoencoder Neural Networks

The article discusses the application of autoencoder neural networks in archaeometry, specifically in reducing the dimensions of X-ray fluorescence spectra for analyzing cultural heritage objects. Researchers utilized autoencoders to compress data and extract essential features, facilitating efficient analysis of elemental composition in painted materials. Results demonstrated the effectiveness of this approach in attributing paintings to different creation periods based on pigment composition, highlighting its potential for automating and enhancing archaeological analyses.

20 Feb 2024

Enhancing E-Commerce with AI Virtual Assistants

This article examines the role of AI virtual assistants in revolutionizing e-commerce by providing personalized shopping experiences, explores factors influencing their usage, and discusses recent developments. AI-powered virtual assistants leverage NLP and ML to engage in real conversations, offer product recommendations, and facilitate transactions, enhancing user engagement and satisfaction. Factors influencing their usage, such as perceived usefulness and ease of use, are identified through empirical studies, while recent innovations, including inclusive designs for visually impaired users, highlight the continuous evolution of AI virtual assistants in e-commerce.

20 Feb 2024

Harnessing Machine Learning for Story Generation

This article explores the application of machine learning (ML) in story generation, covering script learning, story completion, and story generation. Various ML techniques, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and transformers, are utilized to predict and generate story events and sentences. While ML shows promise in automating storytelling, challenges such as maintaining coherence and addressing biases remain, highlighting the need for further advancements in the field.

19 Feb 2024