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

ML and Molecular Engineering Boosts Halide Perovskite Stability

Researchers combined molecular engineering and machine learning to enhance the stability and performance of halide perovskite materials in aqueous environments. Their study demonstrated that molecular modifications significantly improve the photoelectrochemical stability of perovskite films, revealing a promising system with high photocurrent and stability, paving the way for advanced optoelectronic applications.

5 Jun 2024

Computer Vision for Object Tracking: A Review

A comprehensive review highlights the evolution of object-tracking methods, sensors, and datasets in computer vision, guiding developers in selecting optimal tools for diverse applications.

4 Jun 2024

ChatGPT-4: Unveiling and Influencing AI Personality

Researchers explored whether ChatGPT-4's personality traits can be assessed and influenced by user interactions, aiming to enhance human-computer interaction. Using Big Five and MBTI frameworks, they demonstrated that ChatGPT-4 exhibits measurable personality traits, which can be shifted through targeted prompting, showing potential for personalized AI applications.

3 Jun 2024

AI-Enhanced Generative Design Revolutionizes Static Mixers

Researchers have developed a groundbreaking generative design framework that uses machine learning and fluid dynamics to optimize complex functional structures, significantly improving static mixer performance in industrial applications. By leveraging evolutionary algorithms and computational fluid dynamics, the new approach outperforms traditional designs, offering innovative solutions for chemical, pharmaceutical, and biomedical industries.

31 May 2024

Advancing PD Localization in Power Transformers Using ML and DL

This article in Scientific Reports compares ML and DL methods for localizing PD sources within power transformer tanks using single-sensor electric field measurements. Various techniques including CNN, SVR, SVM, BPNN, KNN, MLP, and XGBoost were evaluated across multiple case studies, demonstrating the CNN model's superior accuracy and robustness.

30 May 2024

Advanced Model Boosts Vessel Trajectory Classification

Researchers introduced biSAMNet, a cutting-edge model integrating word embedding and deep neural networks, for classifying vessel trajectories. Tested in the Taiwan Strait, it significantly outperformed other models, enhancing maritime safety and traffic management.

29 May 2024

Machine Learning Accelerates Magnesium Alloy Design

A recent review in the Journal of Materials Research and Technology explores machine learning's transformative potential in designing and optimizing magnesium (Mg) alloys. By leveraging ML, researchers can efficiently enhance Mg alloy properties, expediting their development and broadening industrial applications.

28 May 2024

Parallel Computing Boosts Feature Model Analysis

Parallel computing techniques enhance automated analysis of feature models, improving efficiency in large-scale product configurations. Researchers employed speculative programming to parallelize QuickXPlain and FastDiag algorithms, significantly speeding up the identification of minimal conflict sets and diagnoses in complex feature models.

27 May 2024

LiDAR and Computer Vision for Collision Prediction in Port Channels

Researchers from China have integrated computer vision (CV) and LiDAR technologies to improve the safety and efficiency of autonomous navigation in port channels. This innovative approach utilizes advanced path-planning and collision prediction algorithms to create a comprehensive perception of the port environment, significantly enhancing navigation safety and reducing collision risks.

24 May 2024

Seismic Damage Assessment Using Machine Learning

This article showcases a machine learning approach using K-nearest neighbors (KNN) and linear regression to assess seismic damage in moment-resisting frame buildings. By training on data generated through a new simulation procedure, researchers achieved accurate predictions of the Park-Ang structural damage index, with KNN demonstrating superior performance.

23 May 2024