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

Dynamic Accommodation and Vergence Measurement: A Purkinje Reflections and Machine Learning Approach

This paper introduces an innovative method for real-time measurement of dynamic accommodation and vergence in the human eye, utilizing Purkinje reflections and machine learning. The technique offers high accuracy, addressing limitations in current measurement methods and holds significant potential for applications in vision diagnostics, 3D displays, and customized virtual reality headsets. The study's comprehensive analysis showcases the efficacy of the proposed approach, emphasizing its simplicity, accuracy, and adaptability for diverse ophthalmic and technological settings.

12 Dec 2023

How is AI Transforming Risk Assessment?

This article delves into the transformative role of Artificial Intelligence (AI) in risk assessment across criminal justice, financial access, healthcare, and insurance. It explores how AI rectifies human biases in criminal recidivism predictions, reshapes credit risk assessment for inclusive lending, improves healthcare outcomes through personalized diagnostics, and aligns insurance premiums with actual driving behavior.

11 Dec 2023

AI-Optimized Safety Monitoring in Urban Construction

This article introduces an AI-based solution for real-time detection of safety helmets and face masks on municipal construction sites. The enhanced YOLOv5s model, leveraging ShuffleNetv2 and ECA mechanisms, demonstrates a 4.3% increase in mean Average Precision with significant resource savings. The study emphasizes the potential of AI-powered systems to improve worker safety, reduce accidents, and enhance efficiency in urban construction projects.

10 Dec 2023

Displacement Effects of Automation: Urban vs. Non-Urban Perspectives

This article delves into the unequal impacts of automation on urban and non-urban labor markets. Analyzing Italian data, it reveals that automation universally displaces workers, challenging assumptions about urban immunity. The study emphasizes the need for ongoing research to understand the evolving landscape of automation, its impact on workforce composition, and the potential for exacerbating inequalities between privileged urban occupations and excluded low-skill workers.

10 Dec 2023

Elevating Pedestrian Safety in Autonomous Vehicles: A Kernel-Attentive Approach

This study introduces a sophisticated pedestrian detection algorithm enhancing the lightweight YOLOV5 model for autonomous vehicles. Integrating extensive kernel attention mechanisms, lightweight coordinate attention, and adaptive loss tuning, the algorithm tackles challenges like occlusion and positioning inaccuracies. Experimental results show a noticeable accuracy boost, especially for partially obstructed pedestrians, offering promising advancements for safer interactions between vehicles and pedestrians in complex urban environments.

5 Dec 2023

AI-Driven Robotic Labs: Revolutionizing Material Discovery

Researchers, leveraging DeepMind's GNoME, showcase AI's potential in accelerating the discovery of functional materials. The synergy of advanced graph networks and autonomous lab robots, exemplified at Lawrence Berkeley National Lab, yields 381,000 viable materials for energy solutions. The paradigm shift combines AI's scalability with adaptive experimentation, promising groundbreaking advances in materials science, energy, and sustainability.

3 Dec 2023

Automated Forensic Sex Determination from Skull Morphology Using CT and AI

A groundbreaking study from Kyoto Prefectural University of Medicine introduces an advanced AI system leveraging deep neural networks and CT scans to objectively and accurately determine the biological sex of deceased individuals based on skull morphology. Outperforming human experts, this innovative approach promises to enhance forensic identification accuracy, addressing challenges in reliability and objectivity within traditional methods.

3 Dec 2023

Machine Learning Advancements in Sprinkler System Analysis for Precision Irrigation

This paper demonstrates the efficacy of advanced machine learning techniques in accurately estimating crucial water distribution uniformity metrics for efficient sprinkler system analysis, design, and evaluation. The study explores the intersection of hydraulic parameters, meteorological influences, and machine learning models to optimize sprinkler uniformity, providing valuable insights for precision irrigation management.

1 Dec 2023

Non-Invasive Microscopy in Complex Environments Using Overlapping Speckle Correlation Algorithm

A groundbreaking computational imaging technique named "speckle kinetography" is introduced in this study. Developed by a team from Nanjing University of Aeronautics and Astronautics, this method enables non-invasive, high-resolution imaging in complex turbid environments, surpassing previous limitations and offering a transformative approach to microscopy.

30 Nov 2023

Autonomous Programmable Graph Learning for Efficient Heterogeneous Computing

Researchers propose a groundbreaking framework, PGL, for autonomous and programmable graph representation learning (PGL) in heterogeneous computing systems. Focused on optimizing program execution, especially in applications like autonomous vehicles and machine vision, PGL leverages machine learning to dynamically map software computations onto CPUs and GPUs.

30 Nov 2023