Preferred Language
Articles
/
ahb0j4oBVTCNdQwC_J-J
A Hybrid Deep Learning Construct for Detecting Keratoconus From Corneal Maps
...Show More Authors

Scopus Clarivate Crossref
View Publication
Publication Date
Wed Dec 01 2021
Journal Name
Computers & Electrical Engineering
Utilizing different types of deep learning models for classification of series arc in photovoltaics systems
...Show More Authors

View Publication
Crossref (14)
Crossref
Publication Date
Fri Jun 08 2018
Journal Name
Advances In Intelligent Systems And Computing
Improve Memory for Alzheimer Patient by Employing Mind Wave on Virtual Reality with Deep Learning
...Show More Authors

View Publication
Scopus (1)
Crossref (2)
Scopus Clarivate Crossref
Publication Date
Fri Dec 01 2023
Journal Name
Al-khwarizmi Engineering Journal
An Overview of Audio-Visual Source Separation Using Deep Learning
...Show More Authors

    In this article, the research presents a general overview of deep learning-based AVSS (audio-visual source separation) systems. AVSS has achieved exceptional results in a number of areas, including decreasing noise levels, boosting speech recognition, and improving audio quality. The advantages and disadvantages of each deep learning model are discussed throughout the research as it reviews various current experiments on AVSS. The TCD TIMIT dataset (which contains top-notch audio and video recordings created especially for speech recognition tasks) and the Voxceleb dataset (a sizable collection of brief audio-visual clips with human speech) are just a couple of the useful datasets summarized in the paper that can be used to test A

... Show More
View Publication Preview PDF
Scopus (2)
Crossref (1)
Scopus Crossref
Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
peridos for transversal coincidence maps on compact manifolds with a given cohomology
...Show More Authors

View Publication Preview PDF
Publication Date
Fri Apr 14 2023
Journal Name
Journal Of Big Data
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
...Show More Authors
Abstract<p>Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for</p> ... Show More
View Publication Preview PDF
Scopus (534)
Crossref (527)
Scopus Clarivate Crossref
Publication Date
Thu Nov 03 2022
Journal Name
Sensors
A Novel Application of Deep Learning (Convolutional Neural Network) for Traumatic Spinal Cord Injury Classification Using Automatically Learned Features of EMG Signal
...Show More Authors

In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesi

... Show More
View Publication
Scopus (9)
Crossref (10)
Scopus Clarivate Crossref
Publication Date
Sat Aug 01 2015
Journal Name
Modern Applied Science
A New Method for Detecting Cerebral Tissues Abnormality in Magnetic Resonance Images
...Show More Authors

We propose a new method for detecting the abnormality in cerebral tissues present within Magnetic Resonance Images (MRI). Present classifier is comprised of cerebral tissue extraction, image division into angular and distance span vectors, acquirement of four features for each portion and classification to ascertain the abnormality location. The threshold value and region of interest are discerned using operator input and Otsu algorithm. Novel brain slices image division is introduced via angular and distance span vectors of sizes 24˚ with 15 pixels. Rotation invariance of the angular span vector is determined. An automatic image categorization into normal and abnormal brain tissues is performed using Support Vector Machine (SVM). St

... Show More
Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Oil spill classification based on satellite image using deep learning techniques
...Show More Authors

 An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification

... Show More
View Publication Preview PDF
Scopus (7)
Crossref (6)
Scopus Crossref
Publication Date
Mon Apr 07 2025
Journal Name
Al-nahrain Journal For Engineering Sciences
Navigating the Challenges and Opportunities of Tiny Deep Learning and Tiny Machine Learning in Lung Cancer Identification
...Show More Authors

Lung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens. Distinguishing the size, shape, and location of lymphatic nodes can identify the spread of the disease around these nodes. Thus, identifying lung cancer at the early stage is remarkably helpful for doctors. Lung cancer can be diagnosed successfully by expert doctors; however, their limited experience may lead to misdiagnosis and cause medical issues in patients. In the line of computer-assisted systems, many methods and strategies can be used to predict the cancer malignancy level that plays a significant role to provide precise abnormality detectio

... Show More
View Publication
Scopus Crossref
Publication Date
Sat Jul 22 2023
Journal Name
Journal Of Engineering
Hybrid Controller for a Single Flexible Link Manipulator
...Show More Authors

In this study, the dynamic modeling and step input tracking control of single flexible link is studied. The Lagrange-assumed modes approach is applied to get the dynamic model of a planner single link manipulator. A Step input tracking controller is suggested by utilizing the hybrid controller approach to overcome the problem of vibration of tip position through motion which is a characteristic of the flexible link system. The first controller is a modified version of the proportional-derivative (PD) rigid controller to track the hub position while sliding mode (SM) control is used for vibration damping. Also, a second controller (a fuzzy logic based proportional-integral plus derivative (PI+D) control scheme) is developed for both vibra

... Show More
View Publication Preview PDF
Crossref