Firas Husham Almukhtar
@cue.edu.krd
Catholic University in Erbil
Catholic University in Erbil
RESEARCH INTERESTS
machine Learning and image processing
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Md. Abdullah Al Noman, Zhai LI, Firas Husham Almukhtar, Md. Faishal Rahaman, Batyrkhan Omarov, Samrat Ray, Shahajan Miah, and Chengping Wang
Institute of Advanced Engineering and Science
<span lang="EN-US">Automatic lane detection for driver assistance is a significant component in developing advanced driver assistance systems and high-level application frameworks since it contributes to driver and pedestrian safety on roads and highways. However, due to several limitations that lane detection systems must rectify, such as the uncertainties of lane patterns, perspective consequences, limited visibility of lane lines, dark spots, complex background, illuminance, and light reflections, it remains a challenging task. The proposed method employs vision-based technologies to determine the lane boundary lines. We devised a system for correctly identifying lane lines on a homogeneous road surface. Lane line detection relies heavily on the gradient and hue lightness saturation (HLS) thresholding which detects the lane line in binary images. The lanes are shown, and a sliding window searching method is used to estimate the color lane. The proposed system achieved 96% accuracy in detecting lane lines on the different roads, and its performance was assessed using data from several road image databases under various illumination circumstances.</span>
Shahab Wahhab Kareem, Firas Husham Almukhtar, Ari Taha Guron, and Hasanin Mohammed Salman
IEEE
The mortality rate has grown in recent years because of an increase in the frequency of encephalon tumors in each age group. In medical imaging, tumors are hard to see because of their complicated structure and noise, which makes it complex and time-consuming for specialists to find them. It is essential to find and pinpoint the tumor’s location at an early stage, so this is very important. Medical scans can be used to look for and predict cancerous spots at different levels. These scans can be combined with segmentation and relegation methods to help doctors make an early diagnosis, which can save a lot of time. Physical tumor identification has become a challenging and time-consuming process for medical practitioners due to the intricate structure of tumors and the involution of noise in magnetic resonance (MR) imaging data. As a result, detecting and pinpointing the site of cancer at an early stage is critical. Medical scans can be used with segmentation and relegation procedures to deliver an accurate diagnosis at an early stage in cancer tumor locations at various levels. This research offers a system based on machine learning for segmenting and classifying MRI images for brain tumor identification. As a side note, several machine learning algorithms, such as Naïve Bayes, Decision Tree, Nearest Neighbours, Random Forest, and SVM, have been explicitly used for automatically segmenting and labelling MRI scans of the brain to help in the detection of malignant growths, image segmentation, feature extraction, and classification.
Hasanin Mohammed Salman and Firas Husham Almukhtar
International Association of Online Engineering (IAOE)
Recently, tablet-based devices have become significantly more utilized platforms for electronic medical record (EMR) systems. EMR is the digital counterpart of the medical doctor’s office paper charts. EMR systems contain the medical and treatment histories of the patients in a unified practice. Nevertheless, statistics indicate that a considerable percentage of medical doctors are elderly, aged 60 and above. As using mobile handheld devices (including tablets) poses a well-recognized usability challenge for elderly users, the user interface (UI) usability of tablet-based EMR systems must be thoroughly assessed, considering the needs of elderly medical doctors. Accordingly, our objective is to address this need. Three expert evaluators implemented the heuristic evaluation (HE) approach to evaluate the UI usability of a commercial EMR system that is a tablet-based platform. Applying the HE approach helped identify usability problems that elderly medical doctors might encounter when utilizing a tablet-based EMR UI. In total, eight usability problems contributed to the seven heuristic violations discovered.
Firas H. Almukhtar
Informa UK Limited
Mohammad Gouse Galety, Firas Husham Almukhtar, Rebaz Jamal Maaroof, and Fanar Fareed Hanna Rofoo
Springer Nature Singapore
Firas Husham Almukhtar, Asmaa Abbas Ajwad, Amna Shibib Kamil, Refed Adnan Jaleel, Raya Adil Kamil, and Sarah Jalal Mosa
MDPI AG
Recently, pattern recognition in audio signal processing using electroencephalography (EEG) has attracted significant attention. Changes in eye cases (open or closed) are reflected in distinct patterns in EEG data, gathered across a range of cases and actions. Therefore, the accuracy of extracting other information from these signals depends significantly on the prediction of the eye case during the acquisition of EEG signals. In this paper, we use deep learning vector quantization (DLVQ), and feedforward artificial neural network (F-FANN) techniques to recognize the case of the eye. The DLVQ is superior to traditional VQ in classification issues due to its ability to learn a code-constrained codebook. On initialization by the k-means VQ approach, the DLVQ shows very promising performance when tested on an EEG-audio information retrieval task, while F-FANN classifies EEG-audio signals of eye state as open or closed. The DLVQ model achieves higher classification accuracy, higher F score, precision, and recall, as well as superior classification abilities as compared to the F-FANN.
Bilal S.A. Alhayani, Nagham Hamid, Firas Husham Almukhtar, Omar A. Alkawak, Hemant B. Mahajan, Ameer Sardar Kwekha-Rashid, Haci İlhan, Haydar Abdulameer Marhoon, Husam Jasim Mohammed, Ibrahim Zeghaiton Chaloob,et al.
Elsevier BV
Firas Husham Almukhtar
International University of Sarajevo
An in-depth fake video uses an Artificial Intelligent (AI), AI programming, and a Personal computer (PC) mix to create a deep fake video of the action. Deep-faking can also be used to represent images and sounds. We provide insights into our reviews in this document. We're showing our dataset to start. At this point, we present the subtleties and reproductively of exploratory settings to evaluate the discovered effects finally. It is no surprise to find deep fake videos, which only monitor a tiny section of the video (e.g., the target face appears quickly on the video; hence the time is limited). We remove our system's fixed duration's persistent effects as each video contributes to the preparation, approval, and testing sections to reflect this. The edge groups are isolated from each video successively (without outline skips). The entire pipeline is ready to be finished when the approval stage is ten years old. Convolutional Neural Network (CNN) was the best and most reliable of the classification systems. Fake videos typically use low-quality pictures to mask faults or insist that the general public regard camera defects as unexplainable phenomena. 'This is a common trope with Unidentified Flying Object (UFO) videos: ghostly orbs are lenses; snakes are compression artifacts on one's face. In this study, we have implemented a sophisticated, knowledgeable method to recognize false images. Our test results using various monitored videos have shown that we can reliably predict whether videos are monitored through with simple co-evolutionary Long Short-Term Memory (LSTM) structure.
Muhaned Al‐Hashimi, Shymaa Mohammed Jameel, Firas Husham Almukhtar, Musaddak Maher Abdul Zahra, and Refed Adnan Jaleel
IET Networks Institution of Engineering and Technology (IET)
N. V. L. M Krishna Munagala, V. Saravanan, Firas Husham Almukhtar, Naveed Jhamat, Nadeem Kafi, and Samiullah Khan
Hindawi Limited
Autism Spectrum Disorder (ASD) is a complicated collection of neurodevelopmental illnesses characterized by a variety of developmental defects. It is a binary classification system that cannot cope with reality. Furthermore, ASD, data label noise, high dimension, and data distribution imbalance have all hampered the existing classification algorithms. As a result, a new ASD was proposed. This strategy employs label distribution learning (LDL) to deal with label noise and uses support vector regression (SVR) to deal with sample imbalance. The experimental results show that the proposed method balances the effects of majority and minority classes on outcomes. It can effectively deal with imbalanced data in ASD diagnosis, and it can help with ASD diagnosis. This study presents a cost-sensitive approach to correct sample imbalance and uses a support vector regression (SVR)-based method to remove label noise. The label distribution learning approach overcomes high-dimensional feature classification issues by mapping samples to the feature space and then diagnosing multiclass ASD. This technique outperforms previous methods in terms of classification performance and accuracy, as well as resolving the issue of unbalanced data in ASD diagnosis.
dina Mikhail, Firas Al-Mukhtar, and Shahab Kareem
EpiSmart Science Vector Ltd
Objective: Cancer is one of the horrendous diseases. Classifying cancer is founded on identifying cancer-causing mutations in gene sequences. Although genetic analysis can predict certain types of cancer, there is currently no effective method for predicting cancers. Therefore, the purpose of this paper is to predict the cancer types and to find a data mining technique that uses two different machine learning algorithms for classifying cancer. Moreover, earlier detection of the mutated tumor protein P53 gene can predict treatment and gene therapy techniques. Methods: (UMD-2010) the Universal Mutation Database is used to diagnose mutations in genes. The challenge, however, is that the database very basic. Besides, it is an excel format database. Due to its limitations, the data base cannot be used to classify cancer. In addition, bioinformatics techniques such as pairwise alignment and BLAST are used, followed by machine learning algorithms that use neural network algorithms to classify cancer based on malignant mutations in the TP53 gene, by selecting (12) out of (53) database fields for the TP53 gene database in the second stage. It should be noted that the (UMDCell-line2010) database does not have one of these twelve fields (Field of gene locus). Result: As a Utilizing MLP and SVM for training and testing a set number of fields, the Machin learning methods were found to be an effective way to classify cancers. Where the Relative Absolute Error for MLP and SVM is 83.6005 % ,65.6605 %, the accuracy is 90 %, 93.7% respectively. Conclusion: Following the learning and testing stages, the mean absolute error (MAE), used to measure the errors was found in the SVM less than the (MAE) in MLP algorithm. we can conclude that using SVM is considered better than the MLP algorithm because the accuracy in SVM better than the accuracy of MLP.
Mohammad Gouse Galety, Firas Husham Almukhtar, Rebaz Jamal Maaroof, Fanar Rofoo, and S. Arun
IEEE
Face Recognition and Detection encompasses an ocean of study and development involving picture analysis and algorithm-based comprehension, sometimes known as computer vision. Attendance is a right that no one can reject, and to support this right, many efforts and studies are being conducted around the world. A Deep Convolutional Neural Network (CNN) using the OpenCV model has been suggested for marking Attendance in this work. Convolutional Neural Network is employed to gain the unique features of the faces based on the distance. A wide variety of parameters influence the training of a Convolutional Neural Network (CNN) based classifier. These aspects include assembling an appropriate dataset, choosing a suitable Convolutional Neural Network (CNN), processing the dataset, and choosing training parameters to get the required classification results. The current publication compiles state-of-the-art research that used dataset preparation and artificial augmentation before training. Accuracy rates are achieved using the proposed model.
Anthony Jesus Bustamante Suarez, Barjinder Singh, Firas Husham Almukhtar, Rajnish Kler, Sonali Vyas, and Karthikeyan Kaliyaperumal
Hindawi Limited
Agricultural producers and enterprises face a dizzying array of decisions every day, and the many factors that influence them are incredibly complex. Agricultural planning relies heavily on accurately calculating the yields of the various crops that will be used. If you want realistic and successful solutions, data mining is an essential component. Researchers in this study are looking for ways to evaluate agricultural data and extract valuable information from the results in order to increase agricultural output. Use of the CART and random forest algorithms is a data mining technique that may be used to various datasets. It is possible to recognise the effects of various climatic and other factors on agricultural output using the MATLAB software and data mining methods, and a potential strategy is highlighted.
Asmita Mahajan, Nonita Sharma, Firas Husham Almukhtar, Monika Mangla, Krishna Pal Sharma, and Rajneesh Rani
Inderscience Publishers
Firas H. Al-Mukhtar, Nawzad Mahmoodd and S. Kareem
The Search Engine has a critical role in presenting the correct pages to the user because of the availability of a huge number of websites, Search Engines such as Google use the Page Ranking Algorithm to rate web pages according to the nature of their content and their existence on the world wide web. SEO can be characterized as methodology used to elevate site keeping in mind the end goal to have a high rank i.e., top outcome. In this paper the authors present the most search engine optimization like (Google, Bing, MSN, Yahoo, etc.), and compare by the performance of the search engine optimization. The authors also present the benefits, limitation, challenges, and the search engine optimization application in business.