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

RVTALL: Advancing Speech Recognition with Multimodal Dataset

Researchers unveil RVTALL, a groundbreaking multimodal dataset for contactless speech recognition. Integrating data from UWB and mmWave radars, depth cameras, lasers, and audio-visual sources, the dataset aids in exploring non-invasive speech analysis. The study demonstrates applications in silent speech recognition, speech enhancement, analysis, and synthesis, though it acknowledges limitations in sample size and diversity. The dataset stands as a robust tool for advancing research in speech-related technologies.

19 Dec 2023

Lightweight Detection of Sugarcane Stem Nodes: G-YOLOv5s-SS

Researchers present G-YOLOv5s-SS, a novel lightweight architecture based on YOLOv5 for efficient detection of sugarcane stem nodes. Achieving high accuracy (97.6% AP) with reduced model size, parameters, and FLOPs, this algorithm holds promise for advancing mechanized sugarcane cultivation, addressing challenges in seed cutting efficiency and offering potential applications in broader agricultural tasks.

18 Dec 2023

Spatial Variation-Dependent Verification for Enhanced Handwriting Identification using Artificial Intelligence

Researchers from Nanjing University of Science and Technology present a novel scheme, Spatial Variation-Dependent Verification (SVV), utilizing convolutional neural networks and textural features for handwriting identification and verification. The scheme outperforms existing methods, achieving 95.587% accuracy, providing a robust solution for secure handwriting recognition and authentication in diverse applications, including security, forensics, banking, education, and healthcare.

15 Dec 2023

SZU-EmoDage: A Novel AI-Synthesized Facial Dataset for Cross-Cultural Emotion and Age Studies

Researchers from China introduce the SZU-EmoDage dataset, a pioneering facial dataset crafted with StyleGAN, featuring Chinese individuals of diverse ages and expressions. This innovative dataset, validated for authenticity by human raters, surpasses existing ones, offering applications in cross-cultural emotion studies and advancements in facial perception technology. The study emphasizes the dataset's value in exploring cognitive processes, detecting disorders, and enhancing technologies like face recognition and animation.

14 Dec 2023

Audiobox: Advancing Controllable Audio Generation with Unified Models

Researchers from Meta present Audiobox, a novel model integrating flow-matching techniques for controllable and versatile audio generation. Audiobox demonstrates unprecedented controllability across various audio modalities, such as speech and sound, addressing limitations in existing generative models. The proposed Joint-CLAP evaluation metric correlates strongly with human judgment, showcasing Audiobox's potential for transformative applications in podcasting, movies, ads, and audiobooks.

13 Dec 2023

Enhanced Workflow for High-Resolution Lithology Logs Using Multiple ML Algorithms

This article presents a novel workflow for generating high-resolution lithology logs from conventional well logs, addressing challenges in multiclass imbalanced data classification. The enhanced weighted average ensemble approach, incorporating error-correcting output code (ECOC) and cost-sensitive learning (CSL) techniques, outperforms traditional machine learning algorithms.

12 Dec 2023

Novel Method for Realistic Evaluation of ML Models in Software Bug Assignments

Researchers detail a groundbreaking approach for creating realistic train-and-test datasets to evaluate machine learning models in software bug assignments. The novel method, based on time dependencies, addresses limitations in existing techniques, ensuring more reliable assessments in real-world scenarios. The proposed method offers potential applications in telecommunication, software quality prediction, and maintenance, contributing to the development of bug-free software applications.

10 Dec 2023

Exploring Unique Feature Memorization in Deep Neural Networks for Image Classification

This research explores Unique Feature Memorization (UFM) in deep neural networks (DNNs) trained for image classification tasks, where networks memorize specific features occurring only once in a single sample. The study introduces methods, including the M score, to measure and identify UFM, highlighting its privacy implications and potential risks for model robustness. The findings emphasize the need for mitigation strategies to address UFM and enhance the privacy and generalization of DNNs, especially in fields like medical imaging and computer vision.

8 Dec 2023

Metamodeling Neuronal Networks: Power Spectra Estimation and Parameter Inference

Utilizing deep Gaussian process regression (DGPR) and masked autoregressive flow (MAF) metamodeling techniques, researchers approximate simulations of neuronal networks, specifically a two-population recurrent spiking network model. The study demonstrates the effectiveness of metamodels in estimating power spectra of population spiking activities and local field potentials (LFPs), offering a computationally efficient method for understanding network dynamics and inferring parameter distributions.

6 Dec 2023

AI Fortification: Safeguarding IoT Systems Through Comprehensive Algorithmic Approaches

This research delves into the synergy of Artificial Intelligence (AI) and Internet of Things (IoT) security. The study evaluates and compares various AI algorithms, including machine learning (ML) and deep learning (DL), for classifying and detecting IoT attacks. It introduces a novel taxonomy of AI methodologies for IoT security and identifies LSTM as the top-performing algorithm, emphasizing its potential applications in diverse fields.

5 Dec 2023