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

Predicting Substance Transport in Water Distribution Networks with Symbolic Machine Learning

Researchers developed a novel approach using evolutionary polynomial regression (EPR) to predict substance transport and decay, focusing on chlorine, in water distribution networks (WDNs). By employing symbolic machine learning, the study generated interpretable models, offering accurate estimations of substance concentrations at network nodes and facilitating real-time monitoring and control of water quality, with implications for system design and optimization in drinking water infrastructures.

16 Feb 2024

Creative Competition: AI Outshines Humans in Divergent Thinking Tasks

Researchers compared the creative capabilities of humans and ChatGPT on verbal divergent thinking tasks, revealing that the AI model consistently outperformed humans in generating original and detailed responses across various prompts. This study challenges the notion of creativity as solely human and underscores the potential of AI to inspire and assist in creative endeavors across diverse domains.

15 Feb 2024

Low-Carbon Transformation in Resource-Based Cities by Integrating ChatGPT and ABC Algorithms

Researchers propose a novel approach utilizing ChatGPT and artificial bee colony (ABC) algorithms to advance low-carbon transformation in resource-based cities. Their study demonstrates significant improvements in energy efficiency, carbon emissions reduction, and traffic congestion alleviation, highlighting the potential of these methods in promoting green development and sustainable urban planning.

15 Feb 2024

Landslide Prediction with Full Seismic Waveform Analysis Using Deep Learning

This study presents a novel approach to landslide prediction by incorporating full seismic waveform data into a deep learning model. By leveraging a modified transformer neural network and synthetic waveforms from the 2015 Gorkha earthquake in Nepal, the researchers demonstrated significant improvements over traditional models that rely solely on scalar intensity parameters. Their findings highlight the importance of considering waveform characteristics and spatial distribution for more accurate landslide risk assessment during earthquakes, offering valuable insights for disaster risk reduction efforts.

14 Feb 2024

Improved Analysis of Highway Construction Safety Incidents Using AI

This study in the journal Applied Sciences utilizes large language models (LLMs) and artificial intelligence (AI) to analyze textual narratives from the Occupational Safety and Health Administration (OSHA) severe injury reports (SIR) database related to highway construction accidents. By employing LLMs such as GPT-3.5, along with natural language processing (NLP) techniques and clustering algorithms, the researchers identified major accident causes and types, providing valuable insights for improving accident prevention and intervention strategies in the industry.

14 Feb 2024

AI-Driven Virtual Rehabilitation: Revolutionizing Home-Based Care

"npj Digital Medicine" presents a scoping review on AI applications in home-based virtual rehabilitation (VRehab), showing its effectiveness in stroke, cardiac, and orthopedic rehabilitation. AI-driven VRehab offers personalized feedback, enhances patient outcomes, and overcomes barriers to traditional rehabilitation, heralding a new era in accessible and efficient healthcare delivery. Further research is needed to standardize evaluation methods and ensure privacy while maximizing the potential of AI in personalized rehabilitation programs.

12 Feb 2024

STA-LSTM: Enhancing Vehicle Trajectory Prediction in Connected Environments

Scientific Reports presents the STA-LSTM model, integrating spatial-temporal attention mechanisms for precise vehicle trajectory prediction in connected environments. Outperforming baseline models, STA-LSTM accurately captures dynamic interactions and uncertainty, offering multi-modal predictions crucial for collision avoidance and traffic optimization in intelligent transportation systems and autonomous driving scenarios. Future enhancements could address complex scenarios like intersections and integrate additional factors for comprehensive predictive capabilities.

12 Feb 2024

Transformative Potential of Large Language Models in Chemistry

"Nature Machine Intelligence" presents research showcasing the adaptability of Large Language Models (LLMs), particularly GPT-3, in solving diverse chemistry and materials science tasks. By fine-tuning on small datasets, GPT-3 demonstrates superior performance compared to conventional machine learning methods, offering a paradigm shift in predictive chemistry and materials science with implications for model generalization and inverse design capabilities.

12 Feb 2024

Automating Fruit Harvesting: A Deep Learning Approach

Researchers from the UK, Ethiopia, and India have developed an innovative robotic harvesting system that employs deep learning and computer vision techniques to recognize and grasp fruits. Tested in both indoor and outdoor environments, the system showcased promising accuracy and efficiency, offering a potential solution to the labor-intensive task of fruit harvesting in agriculture. With its adaptability to various fruit types and environments, this system holds promise for enhancing productivity and quality in fruit harvesting operations, paving the way for precision agriculture advancements.

9 Feb 2024

Deep Learning-based Human Activity Recognition with Wireless Body Area Sensor Networks

Researchers from Egypt introduce a groundbreaking system for Human Activity Recognition (HAR) using Wireless Body Area Sensor Networks (WBANs) and Deep Learning. Their innovative approach, combining feature extraction techniques and Convolutional Neural Networks (CNNs), achieves exceptional accuracy in identifying various activities, promising transformative applications in healthcare, sports, and elderly care.

9 Feb 2024