Papers by Adnan Mohsin Abdulazeez
The Reinforcement learning (RL) algorithms solve a wide range of problems we faced. The topic of ... more The Reinforcement learning (RL) algorithms solve a wide range of problems we faced. The topic of RL has achieved a new, complete standard of public opinion. High difficulty in large-scale real-world implementations is the effective use of large data sets previously obtained in augmented learning algorithms. Q-learning (QL), by learning a conservative Q function that allows a policy to be below the predicted value of the Q function, is introduced by us, which aims to circumvent these restrictions. We revealed technical reinforcement learning in this study. In principle, we demonstrate that QL creates a lower relation to current policy importance and that this can be correlated with guarantees of political learning theoretical change. In reality, QL strengthens the benchmark objective with a simple, standardized Q value which, in addition to existing Q-learning and essential applications, is quickly applied. The findings indicate that all algorithms are needed to learn how to play suc...
Medical text classification has a significant impact on disease diagnosis, medical research, and ... more Medical text classification has a significant impact on disease diagnosis, medical research, and the automatic development of disease ontology, acquiring knowledge of clinical results recorded in the medical literature. Hence, medical text classification is challenging because it contains terminologies that describe medical concepts and terminologies. Furthermore, the medical data mostly does not follow natural language grammar; it has inadequate grammatical sentences. The techniques used for text classification give different results comparing to medical text classifications, as extracting text and training sets are different. One of the most significant text classification models in general and medical text classification specifically is CNN-based models. In this paper, many papers on medical text classification have been reviewed, and the details of each article, such as algorithms, or approaches used, databases, classification techniques, and outcomes obtained, are evaluated and...
Gene expression profiles could be generated in large quantities by utilizing microarray technique... more Gene expression profiles could be generated in large quantities by utilizing microarray techniques. Currently, the task of diagnosing diseases relies on gene expression data. One of the techniques which helps in this task is by utilizing deep learning algorithms. Such algorithms are effective in the identification and classification of informative genes. These genes may subsequently be used in predicting testing samples’ classes. In cancer identification, the microarray data typically possesses minimal samples number with a huge feature collection size which are hailing from gene expression data. Lately, applications of deep learning algorithms are gaining much attention to solve various challenges in artificial intelligence field. In the present study, we investigated a deep learning algorithm based on the convolutional neural network (CNN), for classification of microarray data. In comparison to similar techniques such as Vector Machine Recursive Feature Elimination and improved Random Forest (mSVM-RFE-iRF and varSeIRF), CNN showed that not all the data have superior performance. Most of experimental results on cancer datasets indicated that CNN is superior in terms of accuracy and minimizing gene in classifying cancer comparing with hybrid mSVM-RFE-iRF.
Nowadays, many businesses and organizations have begun to collect data on their future and curren... more Nowadays, many businesses and organizations have begun to collect data on their future and current customers to evaluate churning rate and prevent the loss of potential customers while also keeping the current customers and making them happy. The challenging part, however, is not gathering the data, rather, it arises when these data are processed, and consumers are segmented based on the information collected. This paper aims to investigate the potentials of Data Mining in identifying potential churners from a business and more especially focusing on the Telecom industry. Many experiments are carried out, and various classification algorithms are tested to assess their impact and capability in predicting the potential churners, as this is a crucial information for businesses to keep their customers happy and subscribed to their services.
Data Mining is the process of finding knowledge through the processing of massive amounts of data... more Data Mining is the process of finding knowledge through the processing of massive amounts of data from different viewpoints and combining them into valuable information; data mining has been a crucial part in various aspects of human life. It is used to recognize the covered up patterns in a huge amount of data. Classification methods are supervised learning methods that categorize the data item into known categories. Creating classification models from an input dataset is one of the most beneficial techniques in data mining; these methods typically create models that are used to forecast future patterns in data. This work has been done to assess the effectiveness of different classifiers algorithms such as Support Vector Machine (SVM), Naïve Bayes (NB), J48, and Neural Network (NN), these algorithms were applied on several datasets to determine the performance of the algorithm. All techniques were used with 10-fold cross-validation in the machine learning platform WEKA. According to the study’s findings, no algorithm has consistently performed best for each dataset.
Asian Journal of Research in Computer Science, May 4, 2021
This work was carried out in collaboration among all authors. Author KIT prepared a detailed revi... more This work was carried out in collaboration among all authors. Author KIT prepared a detailed review of previous works related to analyzing soil data based on data mining classification algorithms. More so, analysis and discussion of the study have been managed by all authors. All authors read and approved the final manuscript.
Multimedia Tools and Applications
Traitement du Signal
One of the most effective social aspects of the human face is its attractiveness. Automatic facia... more One of the most effective social aspects of the human face is its attractiveness. Automatic facial beauty prediction (FBP) is an emerging research area that has gained much interest recently. However, identifying the significant facial traits and attributes that can contribute to the process of beauty attractiveness estimation is one of the main challenges in this research area. Furthermore, learning the beauty pattern from a relatively small, imbalanced dataset is another concern that needs to be addressed. This research proposes an ensemblebased regression model that integrates judgments made by three various DCNNs, each with a different structure representation. The proposed method efficiently predicts the beauty score by leveraging the strengths of each network as a complementary data source, and it draws attention to the most important beauty-related face features through the Gradientweighted Class Activation Mapping (Grad-CAM). The findings are promising, demonstrating the efficiency of fusing the decision of multiple predictors of the proposed ensemble DCNNs regression models that is significantly consistent with the ground truth of the employed datasets (SCUT-FBP, SCUT-FBP5500, and ME Beauty). Moreover, it can assist in comprehending the relationship between facial characteristics and the impression of attractiveness.
Traitement du Signal
Finger vein biometrics is one of the most promising ways to identify a person because it can prov... more Finger vein biometrics is one of the most promising ways to identify a person because it can provide uniqueness, protection against forgery, and bioassay. Due to the limitations of the imaging environments, however, the finger vein images that are taken can quickly become low-contrast, blurry, and very noisy. Therefore, more robust and relevant feature extraction from the finger vein images is still open research that should be addressed. In this paper, we propose a new technique of deep learning that is based on the attention mechanisms for human finger vein image identification and recognition and is called deep regional learning. Our proposed model relies on an unsupervised learning method that depends on optimized K-Means clustering for localized finger vein mask generation. The generated binary mask is used to build our attention learning model by making the deep learning structure focus on the region-of-interest (ROI) learning instead of learning the whole feature domain. This...
IEEE Access
Metaheuristic algorithms are becoming powerful methods for solving continuous global optimization... more Metaheuristic algorithms are becoming powerful methods for solving continuous global optimization and engineering problems due to their flexible implementation on the given problem. Most of these algorithms draw their inspiration from the collective intelligence and hunting behavior of animals in nature. This paper proposes a novel metaheuristic algorithm called the Giant Trevally Optimizer (GTO). In nature, giant trevally feeds on many animals, including fish, cephalopods, and seabirds (sooty terns). In this work, the unique strategies of giant trevally when hunting seabirds are mathematically modeled and are divided into three main steps. In the first step, the foraging movement patterns of giant trevallies are simulated. In the second step, the giant trevallies choose the appropriate area in terms of food where they can hunt for prey. In the last step, the trevally starts to chase the seabird (prey). When the prey is close enough to the trevally, the trevally jumps out of the water and attacks the prey in the air or even snatches the prey from the water surface. The performance of GTO is compared against state-of-the-art metaheuristics for global optimization on a set of forty benchmark functions with different characteristics and five complex engineering problems. The comparative study, scalability analysis, statistical analysis based on the Wilcoxon rank sum test, and the findings suggest that the proposed GTO is an efficient optimizer for global optimization.
Asian Journal of Research in Computer Science
Multi-label classification is the process of specifying more than one class label for each instan... more Multi-label classification is the process of specifying more than one class label for each instance. The high-dimensional data in various multi-label classification tasks have a direct impact on reducing the efficiency of traditional multi-label classifiers. To tackle this problem, feature selection is used as an effective approach to retain relevant features and eliminating redundant ones to reduce dimensionality. Multi-label classification has a wide range of real-world applications such as image classification, emotion analysis, text mining and bioinformatics. Moreover, in recent years researchers have focused on applying swarm intelligence methods in selecting prominent features of multi-label data. After reviewing various researches, it seems there are no researches that provide a review of swarm intelligence-based methods for multi-label feature selection. Thus, in this study, a comprehensive review of different swarm intelligence and evolutionary computing methods of feature se...
2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA)
Abstract: Deep Learning (DL) has rapidly become a methodology of choice for analyzing medical ima... more Abstract: Deep Learning (DL) has rapidly become a methodology of choice for analyzing medical images and increasingly attracts researchers’ attention in the medical research community. Breast cancer is a common disease among women throughout the world. The medical images and especially Breast Ultrasound (BUS) images are of poor quality, low contrast, and ambiguous. To avoid misdiagnosis, a Computer-Aided Diagnosis (CAD) system has been created for the diagnosis of breast cancer. This study discusses a variety of ultrasonic image segmentation approaches, with an emphasis on several methods developed in the recent four years. As a result, breast ultrasound image segmentation remains a difficult and demanding problem because of several ultrasound aberrations, including strong speckle noise, preprocessing, classification, feature extraction, and segmentation technique to find the accuracy. Lastly, this study outlines the current trends and issues in breast ultrasound images diagnosis, segmentation, and classifications. This review may be useful for both clinicians and researchers who utilize CAD systems for early breast cancer detection.
Computación y Sistemas
The importance of the plant for the human being and the environment led to deeply been studied an... more The importance of the plant for the human being and the environment led to deeply been studied and classified in detail. The advancement of the technology is the main factor in finding many ways for plant identification process. Some kind of initial intelligence systems in order to identify plant, followed by many theories and concepts using methods like; Moment Invariant (MI), Zernike Moments (ZM) and Polar Fourier Transform (PFT), and technologies for classification like; Neural Network (NN), K-Nearest Neighbor Classifier (KNN) and Support Vector Machine (SVM), were used by many researchers through past years. In this paper is Centroid-Radii (C-R) combined with geometric features of the leaves, in order to cover most of shape feature of the leaves, color moments and Grey-Level Co-occurrence Matrix (GLCM) to improve the accuracy of the system identification. in addition to the above features, Veins also involved in the method been used plus Principal Component Analysis (PCA), which is used to convert features into orthogonal features and the results were inputted to the classifiers that used Probabilistic Neural Network (PNN). Two datasets have been used for test, first dataset is created especially for this work and collected from 24 kinds of plants and second dataset is called Flavia which contains 32 kinds. The results were clearly improved to identify the plants. the maximum accuracy reached up to %98.50 when using the first data set and 98.16% for the second dataset.
Transfer learning and deep learning approaches have been utilised in several real-world applicati... more Transfer learning and deep learning approaches have been utilised in several real-world applications and hierarchical systems for pattern recognition and classification tasks. However, in few of the real-world machine learning situations, this presumption does not sustain since there are instances where training data is costly or tough to gather and there is continually a necessity to produce high-performance learners competent with more easily attained data from diverse fields. The objective of this review is to determine more abstract qualities at the greater levels of the representation, by utilising deep learning to detach the variables in the outcomes, formally outline transfer learning, provide information on present solutions, and appraise applications employed in diverse facets of transfer learning and deep learning. This can be attained by rigorous literature exploration and discussion on all presently accessible techniques and prospective research studies on transfer learn...
Indonesian Journal of Electrical Engineering and Computer Science, 2022
The developing of deep learning systems that used for chronic diseases diagnosing is challenge. F... more The developing of deep learning systems that used for chronic diseases diagnosing is challenge. Furthermore, the localization and identification of objects like white blood cells (WBCs) in leukemia without preprocessing or traditional hand segmentation of cells is a challenging matter due to irregular and distorted of nucleus. This paper proposed a system for computer-aided detection depend completely on deep learning with three models computer-aided detection (CAD3) to detect and classify three types of WBC which is fundamentals of leukemia diagnosing. The system used modified you only look once (YOLO v2) algorithm and convolutional neural network (CNN). The proposed system trained and evaluated on dataset created and prepared specially for the addressed problem without any traditional segmentation or preprocessing on microscopic images. The study proved that dividing of addressed problem into sub-problems will achieve better performance and accuracy. Furthermore, the results show ...
Indonesian Journal of Electrical Engineering and Computer Science, 2021
This paper presents a watermarking scheme for grayscale images, in which lifting wavelet transfor... more This paper presents a watermarking scheme for grayscale images, in which lifting wavelet transform and singular value decomposition are exploited based on multi-objective artificial bee colony optimization to produce a robust watermarking method. Furthermore, for increasing security encryption of the watermark is done prior to the embedding operation. In the proposed scheme, the actual image is altered to four sub-band over three levels of lifting wavelet transform then the singular value of the watermark image is embedded to the singular value of LH sub-band of the transformed original image. In the embedding operation, multiple scaling factors are utilized on behalf of the single scaling element to get the maximum probable robustness without changing watermark lucidity. Multi-objective artificial bee colony optimization is utilized for the determination of the optimal values for multiple scaling components, which are examined against various types of attacks. For making the propos...
Asian Journal of Research in Computer Science, 2021
One of the main factors that assist to increase the growth of any country is Agriculture. The det... more One of the main factors that assist to increase the growth of any country is Agriculture. The detection of diseases from plant leaf images is one of the most important fields of agricultural research. To identify disease factors, this field requires a reliable prediction approach. Data Mining (DM) is the process of analyzing data from different aspects and summarizing it into valuable information. It helps users to categorize and identify relationships between data from various dimensions. As there are many plants on the farm, detecting and classifying the diseases of each plant on the farm is extremely difficult for the human eye. And diagnosing each plant is very critical since these diseases may spread. DM classification is an important method that has a wide range of applications. It classifies each item in a set of data into one of a set of predefined classes. In this paper, a comparison of different DM classification methods such as Naive Bayes, Decision trees, SVM, and Random...
2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS), 2021
Machine learning algorithms have been used in many fields, like economics, medicine, etc. Educati... more Machine learning algorithms have been used in many fields, like economics, medicine, etc. Education data mining is one of the areas concerned with exploring patterns of data in an educational environment. One of the most important uses is to predict students' performance to improve the existing educational situation. It can be considered as one of the data mining sciences. The ability to predict in advance in many areas has many benefits. In the case of learning, it enables us to know students' levels in advance and identify students who need special attention. This paper proposes using the algorithm (GBDT) which is a machine learning technology used for regression, classification, and ranking tasks, and is part of the Boosting method family to predict university students' performance in final exams. It compares the proposed system's performance with selected machine learning algorithms (Support vector machine, Logistic Regression, Naive Bayes, Gradient Boosted Trees).
2019 International Conference on Advanced Science and Engineering (ICOASE), 2019
One of the main causes of increased mortality among women is breast cancer. The ultrasound scan i... more One of the main causes of increased mortality among women is breast cancer. The ultrasound scan is the most widely used method for diagnosing geological disease i.e. breast cancer. The first step for identifying the abnormality of the breast cancer (malignant from benign), is the extraction of the region of interest (ROI). In order to achieve this, a new approach to breast ROI extraction is proposed for the purpose of reducing false positive cases (FP). The proposed model was built based on the local pixel information and neural network. It includes two stages namely, training and testing. In the training stage, a trained model was built by extracting the number of batches from both ROI and background. The testing stage involved scanning the image with a fixed size window to detect the ROI from the background. Afterwards, a distance transform was used to identify the ROI and remove non-ROI. Experiments were conducted on the on-data set with 250 ultrasound images (150 benign and 100 malignant) the preliminary results show that the proposed method achieves a success rate of about 95.4% for breast contour extraction. The performance of the proposed solution also has been compared with the existing solutions that have been used to segment different types of images.
Semi-supervised learning is the class of machine learning that deals with the use of supervised a... more Semi-supervised learning is the class of machine learning that deals with the use of supervised and unsupervised learning to implement the learning process. Conceptually placed between labelled and unlabeled data. In certain cases, it enables the large numbers of unlabeled data required to be utilized in comparison with usually limited collections of labeled data. In standard classification methods in machine learning, only a labeled collection is used to train the classifier. In addition, labelled instances are difficult to acquire since they necessitate the assistance of annotators, who serve in an occupation that is identified by their label. A complete audit without a supervisor is fairly easy to do, but nevertheless represents a significant risk to the enterprise, as there have been few chances to safely experiment with it so far. By utilizing a large number of unsupervised inputs along with the supervised inputs, the semisupervised learning solves this issue, to create a good ...
Uploads
Papers by Adnan Mohsin Abdulazeez