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

LLMs Develop Reality Understanding through Language

MIT researchers demonstrated that large language models (LLMs) could develop an understanding of reality through internal simulations without direct physical experience. This breakthrough in AI suggests LLMs' potential for complex problem-solving across robotics and natural language processing.

27 Aug 2024

Deep Learning Transforms Solar Cell Design

DeepAcceptor, a deep learning framework, accelerates the discovery of high-performance non-fullerene acceptors for organic solar cells, enhancing efficiency and sustainability. This method significantly reduces time and costs associated with traditional material development, paving the way for advanced green energy solutions.

26 Aug 2024

NN Framework Secures Robot Stability with Lyapunov Control

This research introduces a framework for verifying Lyapunov-stable neural network controllers, advancing robot safety in dynamic, sensor-driven environments.

23 Aug 2024

Evaluating Tea Quality with Computer Vision

Researchers utilized computer vision and machine learning to develop an objective method for evaluating the color quality of needle-shaped green tea. The study showed that the DT-Adaboost model accurately assessed tea quality, offering a reliable and efficient alternative to traditional sensory analysis.

22 Aug 2024

ML Predicts CO2 Corrosion Rates in Oil Industry

Researchers applied machine learning to predict CO2 corrosion rates and severity in the oil and gas industry. The random forest model outperformed others, offering accurate predictions that could enhance material selection, maintenance, and corrosion management strategies.

21 Aug 2024

Deep Learning Secures IoT with Federated Learning

Researchers introduced a federated learning-based intrusion detection system for IoT networks, improving security and data privacy. The system outperformed traditional models by reducing false positives and safeguarding data, marking a significant advancement in IoT security technology.

19 Aug 2024

ML-based Biochar Optimization for Heavy Metal Remediation

This study explores machine learning models to predict biochar’s effectiveness in immobilizing heavy metals in soil-plant systems. The findings emphasize the importance of soil and biochar properties, offering insights to enhance remediation strategies in contaminated soils.

18 Aug 2024

AI Improves Air Pollution Prediction Accuracy

A recent review highlights the superiority of machine learning methods over traditional statistical models in predicting air pollution levels. ML techniques, particularly tree-based algorithms, offer enhanced accuracy in modeling pollutants like NO₂, UFPs, and black carbon, crucial for health impact assessments.

16 Aug 2024

AI Boosts Advanced SQL Injection Detection

The study introduces SqliGPT, an LLM-driven tool designed to enhance SQL injection detection. By leveraging contextual understanding and defense-bypassing capabilities, SqliGPT outperforms traditional scanners, providing a more effective solution for web application security against SQLI threats.

14 Aug 2024

LLMs Detect UI Design Flaws Accurately

Researchers introduced UISGPT, a large language model system that identifies UI design smells by analyzing guidelines and input designs. The system significantly improves design flaw detection and generates clear, actionable reports, setting a new standard in UI design evaluation.

13 Aug 2024