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

Impact of Virtual Influencers: A Study on Customer-Brand Engagement

This research delves into the realm of virtual influencers on Instagram, scrutinizing 33 profiles to assess their impact on customer-brand engagement. Contrary to previous notions, the study reveals that non-branded virtual influencers outshine their branded counterparts in engaging customers. Additionally, it categorizes virtual influencers based on their marketing intentions and character narratives, offering insights into effective influencer selection for brands aiming to bolster engagement.

6 Feb 2024

Using AI to Enhance Security Automation

This article delves into the vital role of artificial intelligence (AI) in enhancing security automation, specifically within Security Orchestration, Automation, and Response (SOAR) solutions. It explores the necessity of SOAR in modern cyber defense, highlighting the challenges faced by Security Operations Center (SOC) teams. The article emphasizes the importance of AI and machine learning (ML) in addressing these challenges, detailing their applications in various SOAR platforms, and concludes with recent developments showcasing the evolving landscape of AI-driven cyber defense.

5 Feb 2024

The Transformative Role of NLP in Education

This article explores the pivotal role of Natural Language Processing (NLP) in education, emphasizing its applications such as personalized learning, question answering and construction, and automated assessment. Recent developments include a pilot chatbot for programming assistance, showcasing NLP's potential in revolutionizing education, though challenges like data scarcity and bias need to be addressed for full exploitation.

5 Feb 2024

Handheld Time-Share-Driven Robot with Expandable Degrees of Freedom

Researchers unveil a groundbreaking handheld time-share-driven robot inspired by biological muscle movements, addressing limitations in enhancing controllable degrees of freedom (DOFs) in robots. The innovative design, using a single motor and shape memory alloy wires, offers expandable DOFs, demonstrating exceptional maneuverability and flexibility in transluminal examinations and surgeries, promising advancements in robotics for various applications, particularly in challenging and confined spaces.

2 Feb 2024

A Federated Learning Approach for Passenger Demand Forecasting in Smart Cities

Researchers present a groundbreaking Federated Learning (FL) model for passenger demand forecasting in Smart Cities, focusing on the context of Autonomous Taxis (ATs). The FL approach ensures data privacy by allowing ATs in different regions to collaboratively enhance their demand forecasting models without directly sharing sensitive passenger information. The proposed model outperforms traditional methods, showcasing superior accuracy while addressing privacy concerns in the era of smart and autonomous transportation systems.

1 Feb 2024

MFWD Dataset for High-Quality Weed Species Analysis in Maize and Sorghum Fields

Researchers introduce MFWD, a meticulously curated dataset capturing the growth of 28 weed species in maize and sorghum fields. This dataset, essential for computer vision in weed management, features high-resolution images, semantic and instance segmentation masks, and demonstrates promising results in multi-species classification, showcasing its potential for advancing automated weed detection and sustainable agriculture practices.

31 Jan 2024

Comparison of Computer Vision Approaches and Machine Learning Integration in Cardiac Kinematics Analysis

This research explores the performance of three computer vision approaches—CONTRACTIONWAVE, MUSCLEMOTION, and ViKiE—for evaluating contraction kinematics in cardioids and ventricular isolated single cells. The study leverages machine learning algorithms to assess the prediction performance of training datasets generated from each approach, demonstrating ViKiE's higher sensitivity and the overall effectiveness of machine learning in refining cardiac motion analysis.

31 Jan 2024

Empowering Robotic Vision with Artificial Intelligence

This article explores the transformative impact of Artificial Intelligence (AI) on robotic vision systems, turning robots into insightful observers. Various AI techniques, including Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Restricted Boltzmann Machine (RBM), and Recurrent Neural Networks (RNNs), are discussed, highlighting their applications in object detection, grasping and manipulation, scene representation, and spatiotemporal vision.

30 Jan 2024

Role of AI in Production Planning

This article explores the pivotal role of Artificial Intelligence (AI) and Machine Learning (ML) in transforming Production Planning and Control (PPC) within the Industry 4.0 framework. Highlighting the significance of AI/ML techniques in dynamic scheduling, performance evaluation, and process automation, it delves into recent developments showcasing a fuzzy logic-based decision-making system for closed-loop supply chain management, emphasizing the potential and challenges of integrating AI/ML in optimizing manufacturing systems.

30 Jan 2024

T-Max-Avg Pooling for CNNs Unleashes Adaptive Feature Extraction

Researchers present a groundbreaking T-Max-Avg pooling layer for convolutional neural networks (CNNs), introducing adaptability in pooling operations. This innovative approach, demonstrated on benchmark datasets and transfer learning models, outperforms traditional pooling methods, showcasing its potential to enhance feature extraction and classification accuracy in diverse applications within the field of computer vision.

26 Jan 2024