Muhammad Osama

BTech(MechEng)

Mechanical Engineering with specialization in AI & Robotics

Muhammad Osama is a full-time data analytics consultant and freelance technical writer based in Delhi, India. He specializes in transforming complex technical concepts into accessible content. He has a Bachelor of Technology in Mechanical Engineering with specialization in AI & Robotics from Galgotias University, India, and he has extensive experience in technical content writing, data science and analytics, and artificial intelligence. Muhammad has worked on various research projects related to data analytics, machine learning, and deep learning for industries such as retail, medical, finance, agriculture, and Ed-Tech. He is enthusiastic and passionate about Artificial Intelligence-related research. Outside of work, he enjoys learning about new developments across all the fields related to science, technology, engineering, mathematics, and statistics.

Articles from Muhammad

Machine Learning Revolutionizes Plant Disease Detection

A recent article in "Artificial Intelligence in Agriculture" reviewed machine learning (ML) techniques for detecting plant diseases in apple, cassava, cotton, and potato crops. The study highlighted the superior accuracy of convolutional neural networks (CNNs) and emphasized ML's potential to enhance crop yield and quality, despite challenges related to data quality and ethical considerations.

22 May 2024

AI Unveils Key Turbulence Structures

Researchers used eXplainable AI (XAI) to identify critical coherent structures in wall-bounded turbulence, improving predictions of flow states. This novel approach, applicable to complex and high Reynolds number flows, enhances understanding and control of turbulent phenomena in engineering and natural systems.

21 May 2024

Deep Learning Enhances Robot Obstacle Avoidance in Power Plants

Researchers developed a deep learning and particle swarm optimization (PSO) based system to enhance obstacle recognition and avoidance for inspection robots in power plants. This system, featuring a convolutional recurrent neural network (CRNN) for obstacle recognition and an artificial potential field method (APFM) based PSO algorithm for path planning, significantly improves accuracy and efficiency.

19 May 2024

Crack the Code: Advanced Method Enhances Train Rivet Inspection

This study proposes an innovative method for detecting cracks in train rivets using fluorescent magnetic particle detection (FMPFD) and instance segmentation, achieving high accuracy and recall. By enhancing the YOLOv5 algorithm and developing a single coil non-contact magnetization device, the researchers achieved significant improvements in crack detection.

17 May 2024

Machine Learning Tames Chaos on Edge Devices

This study highlights the use of a next-generation reservoir computing (NG-RC) algorithm to control chaotic dynamics with high sensitivity and efficiency. Implemented on an FPGA, the NG-RC-based controller outperformed conventional chaos control techniques by stabilizing various unstable states and trajectories with minimal power consumption, suggesting promising applications in diverse fields like autonomous systems and biological control.

16 May 2024

JARVIS-Leaderboard: Advancing Materials Design Benchmarking

The article introduces JARVIS-Leaderboard, an open-source platform facilitating materials design benchmarking across various categories like AI, electronic structure, force-field, quantum computation, and experiments. Integrated with NIST-JARVIS infrastructure, it offers a dynamic framework for comparing methods and datasets, fostering reproducibility and collaboration in materials science research.

15 May 2024

Novel Metrics for Assessing Prediction Difficulty in Neural Networks

Researchers introduced innovative metrics to gauge the prediction difficulty of individual cases within datasets using neural networks (NN). These metrics, designed for fully connected feedforward NNs, offer a unique approach to understanding data complexity by considering factors like model complexity, double modeling, and predictive uncertainty.

14 May 2024

Advancing Sports Performance Using Motion Capture Technology

In a recent article published in Sensors, researchers conducted a thorough review of motion capture technology (MCT) in sports, comparing and evaluating various systems including cinematography capture, electromagnetic capture, computer vision capture, and multimodal capture.

13 May 2024

Sustainable Smart Glasses with Text Mining, QFD and TRIZ

This article introduces an innovative methodology combining quality function deployment (QFD), text mining, and the theory of inventive problem solving (TRIZ) for sustainable product design, demonstrated through the design of smart glasses for augmented reality (AR) technology.

10 May 2024

AI and ML in Volatility Forecasting: Trends and Future Directions

Researchers explore the application of AI and ML in volatility forecasting, revealing their promise in improving accuracy and informing financial decisions. The review underscores the need for further exploration in explainable AI, uncertainty quantification, and alternative data sources to advance forecasting capabilities.

9 May 2024