Aryaman Pattnayak

Bachelor of Technology - BTech, Computer Science

AI/ML Specialist, Data science, Data Analytics

Aryaman Pattnayak is a Tech writer based in Bhubaneswar, India. His academic background is in Computer Science and Engineering. Aryaman is passionate about leveraging technology for innovation and has a keen interest in Artificial Intelligence, Machine Learning, and Data Science.

Aryaman’s work encompasses several topics, including "FinGPT" and "Financial Fraud Detection," reflecting his dedication to practical AI solutions. Outside of his professional pursuits, Aryaman finds joy in sharing insights through Medium and exploring the convergence of technology and finance. In his free time, he enjoys various hobbies, including reading and listening to music.

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Articles from Aryaman

Graph of Thoughts: Revolutionizing Large Language Models' Reasoning

Researchers introduce the Graph of Thoughts (GoT), a pioneering framework that enhances the reasoning abilities of large language models (LLMs) like GPT-3 and GPT-4. Unlike traditional linear or tree-based prompting, GoT leverages associative graphs to enable flexible and powerful thought transformations, significantly improving LLM performance on complex tasks.

23 Aug 2023

AI-Powered Product Recommendations: Transforming Retail Experiences

The evolution of AI is revolutionizing retail by transforming basic recommendation engines into sophisticated, personalized systems. These AI-driven recommendation engines provide tailored product suggestions based on data-driven algorithms, enhancing customer engagement and business revenue.

21 Aug 2023

Tackling Digital Ageism in AI: A Call to Action

A review published in Humanities and Social Sciences Communications highlights the pressing issue of age-related bias in AI systems, termed digital ageism. The study reveals the extent of age bias in AI data, deployment, and societal impact, emphasizing the need for collaborative efforts to mitigate this bias and ensure equitable AI for all age groups.

21 Aug 2023

AI in Sentiment Analysis - Unveiling the Power of Textual Emotions

AI-powered sentiment analysis unlocks insights from textual expressions, influencing decisions across domains. This comprehensive exploration delves into its foundations, advanced AI techniques, real-world applications, challenges, and the promising future of empathetic decision-making through sentiment analysis.

19 Aug 2023

Bio-Inspired Hierarchical Control Empowers Humanoid Robots

Researchers introduce a hierarchical generative model that mimics the deep temporal hierarchy of human motor control, allowing humanoid robots to autonomously perform complex motor tasks. This approach combines high-level planning, mid-level limb controllers, and low-level joint servo control, enabling versatile and resilient coordination of actions.

19 Aug 2023

Revolutionizing Urban Tree Mapping: AI and Remote Sensing in Action

The article highlights a recent study that showcases the transformative potential of combining artificial intelligence (AI) and remote sensing data sources for automated large-scale mapping of urban street trees. By leveraging geographic imagery and deep learning algorithms, the study demonstrates an efficient and scalable approach to overcome the challenges of conventional field-based surveys.

16 Aug 2023

Transforming Construction and Communities: AI and ML for Sustainable Development

Researchers examine the multifaceted applications of artificial intelligence (AI) and machine learning (ML) in revolutionizing construction processes and fostering sustainable communities. Covering the entire architecture, engineering, construction, and operations (AECO) domain, the study categorizes and explores existing and emerging roles of AI and ML in indoor and outdoor sustainability enhancements, construction lifecycles, and innovative integration with blockchain, digital twins, and robotics.

15 Aug 2023

TinyML Sensors for Non-Destructive Shelf Life Prediction in Fresh Dates

Researchers introduce a novel approach using TinyML sensors and models to estimate the shelf life of fresh dates non-destructively. The study develops a lightweight TinyML system combining a miniature NIR spectral sensor and an Arduino microcontroller for on-device inference. This edge computing approach enables real-time prediction of date shelf life, eliminating the need for continuous cloud connectivity.

13 Aug 2023

Real-Time Assessment of Driver Drowsiness using PPG Signals: A Novel End-to-End Approach

This study presents an innovative pipeline for continuous real-time assessment of driver drowsiness levels using photoplethysmography (PPG) signals. The approach involves customized PPG sensors embedded in the steering wheel, coupled with a tailored deep neural network architecture for accurate drowsiness classification. Previous methods using ECG signals were susceptible to motion artifacts and complex preprocessing.

9 Aug 2023

Advancements in Unmanned Surface Vehicle Path Planning: A Comprehensive Review

Researchers provide an in-depth analysis of cutting-edge path planning algorithms for unmanned surface vehicles (USVs). As USVs gain prominence in maritime applications, including transport, monitoring, and defense, path planning becomes vital for autonomous navigation. The review covers global and local path planning methods, hazard avoidance techniques, and multi-USV cluster coordination.

8 Aug 2023