Jihad Anwar Qadir

@uor.edu.krd

Departmetn of Software Enginnering, University of Raparin, Rania,46012,Kurdistan REgion, Iraq

Jihad Anwar Qadir


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https://researchid.co/jihadcs

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Signal Processing, Computer Science, Computer Vision and Pattern Recognition
9

Scopus Publications

77

Scholar Citations

5

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • Enhancing Parkinson's Disease Detection by Combining SMOTE and Feature Selection for Improved Machine Learning Classification Using Voice Recordings
    Jihad A. Qadir
    Journal of Voice, 2025
  • CKSD: COMPREHENSIVE KURDISH-SORANI DATABASE
    Jihad Anwar Qadir, Samer Kais Jameel, Wshyar Omar Khudhur, Kamaran H. Manguri
    Informatyka Automatyka Pomiary W Gospodarce I Ochronie Srodowiska, 2025
    Every individual has a specific language with which he/she communicates. Each language has special letters and features distinguishing it from other languages. Ideas, cultures, and sciences are exchanged through some notions of languages, including retrieval, translation, and classification of texts from journals, books, journals, research, and the internet. It is accomplished through database availability. Unfortunately, due to some reasons, Kurdish language databases may be rare or non-existent. In the present study, a Comprehensive Kurdish-Sorani Database (CKSD) is generated, which contains datasets of dates, letters, and common words in the Kurdish language, as well as the documents employed for the extraction of these datasets. Elements of these collections were extracted from the written documents in 27 different fonts. It bestows a comprehensiveness feature to the CKSD database that can be utilized by researchers. In order to determine the extent to which classifiers can categorize such data, these data were utilized in this study. Indeed, this study demonstrated the reliability of this data and its suitability for use in the fields of machine learning and other artificial intelligence applications.
  • Deep Learning for Speaker Recognition: A Comparative Analysis of 1D-CNN and LSTM Models Using Diverse Datasets
    Hiwa Hassanzadeh, Jihad Anwar Qadir, Saman Muhammad Omer, Mohammed Hussein Ahmed, Edris Khezri
    Interdisciplinary Conference on Electrics and Computer Intcec 2024, 2024
    Speaker recognition is a vital component of identity verification and security systems that has made significant progress through the use of deep neural networks. This article examines the comparative performance of two neural network models, namely a one-dimensional convolutional neural network (1D-CNN) and a long short-term memory (LSTM-based) network in identifying individuals based on their recorded voices. The article uses three diverse datasets, including the Raparin Artificial Intelligent Lab (RAIL) dataset, which was created locally, and two public datasets, namely the TIMIT dataset and the Jordan dataset. The results show that the proposed 1D-CNN model consistently outperforms the LSTM model and has a significant accuracy rate, especially in the RAIL dataset, which achieves an accuracy of over 95.22%. This study emphasizes the potential of deep learning algorithms in improving sound-based identity recognition and highlights its effects on system security and communications.
  • Face Identification Using Conditional Generative Adversarial Network
    Samer Kais Jameel, Jafar Majidpour, Abdulbasit K Al-Talabani, Jihad Anwar Qadir
    Computer Journal, 2023
    Most of research studies that have dealt with face corrupted images to the level of being unrecognizable by a human are based on full image reconstruction. In some real scenarios, a single corrupted image might need to be recognized among a limited number of available clean images. This study deals with face identification from artificially corrupted images with various kinds of noises. The work proposes a face identification conditional generative adversarial network (FI-CGAN) model to identify faces based on the CGAN. The proposed models reconstruct the corrupted image based on available clean images to map the corrupted image to a specific label. The classification is made using the nearest neighbor method with peak signal-to-noise ratio, mean squared error and structural similarity index as metrics. The study used the Specs on Faces dataset and achieved a satisfactory performance for face identification.
  • Face Identification System Based on Synthesizing Realistic Image using Edge-Aided GANs
    Jafar Majidpour, Samer Kais Jameel, Jihad Anwar Qadir
    Computer Journal, 2023
    Presently, facial image recognition via a thermal camera is a critical phase in numerous fields. Systems using thermal facial images suffer from numerous problems in face identification. In this paper, a model Edge-Aided Generative Adversarial Network (EA-GAN) is introduced to overcome the difficulties of thermal face identification by synthesizing a visible faces image from the thermal version. To enhance the performance of the Conditional Generative Adversarial Network (CGAN) model for the create realistic face images, the edge information extracted from the thermal image has been used as input, thus lead to improving overall the system's achievement. Moreover, a new model is presented in the present work for face identification by integrating two Convolutional Neural Networks (CNN) to achieve high and rapid accuracy rates. Based on the experiments on the Carl dataset for faces, it is indicated that EA-GAN can synthesize visually comfortable and identity-preserving faces; thus, better performance is achieved in comparison with the state-of-the-art approaches for thermal facial identification.
  • Improved Time-Saving Face Detection in Video
    Jihad Anwar Qadir, Shayan Ihsan Jalal, Samer Kais Jameel, Abdulbasit Al-Talabani
    9th International Engineering Conference on Sustainable Technology and Development Iec 2023, 2023
    Human face detection from video sequences is a difficult problem in computer vision. Face detection is the process of determining the location of a face or faces in each frame of a video. Face detection in real-time videos is an ongoing under-investigated research problem. This paper proposes a new method for face detection in videos that significantly reduces the time required to detect faces. The method targets a Search Area Boundary (SAB) in the next frames that are determined based on the position of the detected faces in the current frame. The model will look for the face in a box that is slightly larger than the SAB from the previous frame. This means that it will not search the entire video frame for the face, so it will take less time to find the face. After a few frames, the method will begin searching the entire frame for new faces. The results show that the proposed method can save up to 73.5% of the time, especially when the SAB is small, with an accuracy degradation of no more than 7% in the best case.
  • Covid-19 detection and overcome the scarcity of chest X-Ray datasets based on transfer learning and GAN model
    Jihad A. Qadir, Samer K. Jameel, Jafar Majidpour
    International Conference on Communication and Information Technology Icict 2021, 2021
    Diseases which affect the respiratory system are considered some of the most dangerous, since defects in the breathing process may lead to death. Currently, the coronavirus is one of the most complex strains of the corona family of diseases for which, as yet, there is no successful vaccine. Therefore, detecting the virus is of the utmost importance to global health. The use of deep learning techniques is considered to be a successful method by which to diagnose such diseases. However, there is a lack of data which models deep learning techniques required for training. In this paper, we suggest a method for data augmentation based on the CGAN model. To synthesize realistic chest X-ray images, we proposed to set edge information of the image to the generator, which is used as supporting information to increase the reality of the generated images. A MobileNet CNN model was used for diagnosing COVID-19. When this suggestion was applied to chest data, the diagnostic results were very satisfactory which the accuracy exceeds 99% in some cases.
  • Isolated Spoken Word Recognition Using One-Dimensional Convolutional Neural Network
    Jihad Anwar Qadir, Abdulbasit K. Al-Talabani, Hiwa A. Aziz
    International Journal of Fuzzy Logic and Intelligent Systems, 2020
    Isolated uttered word recognition has many applications in human–computer interfaces. Feature extraction in speech represents a vital and challenging step for speech-based classification. In this work, we propose a one-dimensional convolutional neural network (CNN) that extracts learned features and classifies them based on a multilayer perceptron. The proposed models are tested on a designed dataset of 119 speakers uttering Kurdish digits (0–9). The results show that both speaker-dependent (average accuracy of 98.5%) and speaker-independent (average accuracy of 97.3%) models achieve convincing results. The analysis of the results shows that 9 of the speakers have a bias characteristic, and their results are outliers compared to the other 110 speakers.
  • Human gait recognition using preprocessing and classification techniques
    Samer Kais Jameel, Jihad Anwar Qadir, Mohammed Hussein Ahmed
    International Journal of Electrical and Computer Engineering, 2020
    Biometric recognition systems have been attracted numerous researchers since they attempt to overcome the problems and factors weakening these systems including problems of obtaining images indeed not appearing the resolution or the object completely. In this work, the object movement reliance was considered to distinguish the human through his/her gait. Some losing features probably weaken the system’s capability in recognizing the people, hence, we propose using all data recorded by the Kinect sensor with no employing the feature extraction methods based on the literature. In these studies, coordinates of 20 points are recorded for each person in various genders and ages, walking with various directions and speeds, creating 8404 constraints. Moreover, pre-processing methods are utilized to measure its influences on the system efficiency through testing on six types of classifiers. Within the proposed approach, a noteworthy recognition rate was obtained reaching 91% without examining the descriptors.

RECENT SCHOLAR PUBLICATIONS

  • Cgr-net: a robust deep learning framework for thyroid cancer classification using conditional GAN and Gaussian-regularized ResNet50
    J A. Qadir
    Chinese Journal of Academic Radiology, 1-13 , 2026
    2026
  • Enhancing Parkinson's Disease Detection by Combining SMOTE and Feature Selection for Improved Machine Learning Classification Using Voice Recordings
    JA Qadir
    Journal of Voice , 2025
    2025
    Citations: 1
  • Enhancing Skin Disease Diagnosis: A Hybrid Approach Combining Vision Transformer and Feature Selection Techniques
    JA Qadir
    Zanin Journal of Science and Engineering 1 (1), 54-71 , 2025
    2025
    Citations: 4
  • CKSD: Comprehensive Kurdish-Sorani database
    JA Qadir, SK Jameel, WO Khudhur, KH Manguri
    2025
    Citations: 1
  • Deep learning for speaker recognition: A comparative analysis of 1D-CNN and LSTM models using diverse datasets
    H Hassanzadeh, JA Qadir, SM Omer, MH Ahmed, E Khezri
    2024 4th Interdisciplinary conference on electrics and computer (INTCEC), 1-8 , 2024
    2024
    Citations: 31
  • Improved Time-Saving Face Detection in Video
    JA Qadir, SI Jalal, SK Jameel, A Al-Talabani
    2023 9th International Engineering Conference on Sustainable Technology and … , 2024
    2024
    Citations: 1
  • Face identification using conditional generative adversarial network
    S Kais Jameel, J Majidpour, AK Al-Talabani, J Anwar Qadir
    The Computer Journal 66 (7), 1687-1697 , 2023
    2023
    Citations: 2
  • Face identification system based on synthesizing realistic image using edge-aided GANs
    J Majidpour, S Kais Jameel, J Anwar Qadir
    The Computer Journal 66 (1), 61-69 , 2023
    2023
    Citations: 8
  • Covid-19 detection and overcome the scarcity of chest X-Ray datasets based on transfer learning and GAN model
    JA Qadir, SK Jameel, J Majidpour
    2021 International Conference on Communication & Information Technology … , 2021
    2021
    Citations: 6
  • Isolated Spoken Word Recognition Using One-Dimensional Convolutional Neural Network
    JA Qadir, AK Al-Talabani, HA Aziz
    International Journal of Fuzzy Logic and Intelligent Systems 20 (4), 272-277 , 2020
    2020
    Citations: 9
  • Human gait recognition using preprocessing and classification techniques
    SK Jameel, JA Qadir, MH Ahmed
    International Journal of Electrical and Computer Engineering (IJECE) 10 (3 … , 2020
    2020
    Citations: 5
  • HOG-BASED HUMAN VISUAL DETECTION AND TRACKING
    JA QADIR, AH ABDULHAFIZ
    EURO ASIA 5th. INTERNATIONAL CONGRESS ON APPLIED SCIENCES, p.568 - p.581 , 2019
    2019
  • Uttered Kurdish digit recognition system
    SM Omer, JA Qadir, ZK Abdul
    Journal of University of Raparin 6 (No 2 (2019)), 78-85 , 2019
    2019
    Citations: 9
  • Human detection and tracking in video Sequence
    JA QADIR
    2016

MOST CITED SCHOLAR PUBLICATIONS

  • Deep learning for speaker recognition: A comparative analysis of 1D-CNN and LSTM models using diverse datasets
    H Hassanzadeh, JA Qadir, SM Omer, MH Ahmed, E Khezri
    2024 4th Interdisciplinary conference on electrics and computer (INTCEC), 1-8 , 2024
    2024
    Citations: 31
  • Isolated Spoken Word Recognition Using One-Dimensional Convolutional Neural Network
    JA Qadir, AK Al-Talabani, HA Aziz
    International Journal of Fuzzy Logic and Intelligent Systems 20 (4), 272-277 , 2020
    2020
    Citations: 9
  • Uttered Kurdish digit recognition system
    SM Omer, JA Qadir, ZK Abdul
    Journal of University of Raparin 6 (No 2 (2019)), 78-85 , 2019
    2019
    Citations: 9
  • Face identification system based on synthesizing realistic image using edge-aided GANs
    J Majidpour, S Kais Jameel, J Anwar Qadir
    The Computer Journal 66 (1), 61-69 , 2023
    2023
    Citations: 8
  • Covid-19 detection and overcome the scarcity of chest X-Ray datasets based on transfer learning and GAN model
    JA Qadir, SK Jameel, J Majidpour
    2021 International Conference on Communication & Information Technology … , 2021
    2021
    Citations: 6
  • Human gait recognition using preprocessing and classification techniques
    SK Jameel, JA Qadir, MH Ahmed
    International Journal of Electrical and Computer Engineering (IJECE) 10 (3 … , 2020
    2020
    Citations: 5
  • Enhancing Skin Disease Diagnosis: A Hybrid Approach Combining Vision Transformer and Feature Selection Techniques
    JA Qadir
    Zanin Journal of Science and Engineering 1 (1), 54-71 , 2025
    2025
    Citations: 4
  • Face identification using conditional generative adversarial network
    S Kais Jameel, J Majidpour, AK Al-Talabani, J Anwar Qadir
    The Computer Journal 66 (7), 1687-1697 , 2023
    2023
    Citations: 2
  • Enhancing Parkinson's Disease Detection by Combining SMOTE and Feature Selection for Improved Machine Learning Classification Using Voice Recordings
    JA Qadir
    Journal of Voice , 2025
    2025
    Citations: 1
  • CKSD: Comprehensive Kurdish-Sorani database
    JA Qadir, SK Jameel, WO Khudhur, KH Manguri
    2025
    Citations: 1
  • Improved Time-Saving Face Detection in Video
    JA Qadir, SI Jalal, SK Jameel, A Al-Talabani
    2023 9th International Engineering Conference on Sustainable Technology and … , 2024
    2024
    Citations: 1
  • Cgr-net: a robust deep learning framework for thyroid cancer classification using conditional GAN and Gaussian-regularized ResNet50
    J A. Qadir
    Chinese Journal of Academic Radiology, 1-13 , 2026
    2026
  • HOG-BASED HUMAN VISUAL DETECTION AND TRACKING
    JA QADIR, AH ABDULHAFIZ
    EURO ASIA 5th. INTERNATIONAL CONGRESS ON APPLIED SCIENCES, p.568 - p.581 , 2019
    2019
  • Human detection and tracking in video Sequence
    JA QADIR
    2016