Yazar "Mohammed, Tareq Abed" seçeneğine göre listele
Listeleniyor 1 - 16 / 16
Sayfa Başına Sonuç
Sıralama seçenekleri
Öğe A novel software engineering approach toward using machine learning for improving the efficiency of health systems(Ieee-Inst Electrical Electronics Engineers Inc, 2020) Moreb, Mohammed; Mohammed, Tareq Abed; Bayat, OğuzRecently, machine learning has become a hot research topic. Therefore, this study investigates the interaction between software engineering and machine learning within the context of health systems. We proposed a novel framework for health informatics: the framework and methodology of software engineering for machine learning in health informatics (SEMLHI). The SEMLHI framework includes four modules (software, machine learning, machine learning algorithms, and health informatics data) that organize the tasks in the framework using a SEMLHI methodology, thereby enabling researchers and developers to analyze health informatics software from an engineering perspective and providing developers with a new road map for designing health applications with system functions and software implementations. Our novel approach sheds light on its features and allows users to study and analyze the user requirements and determine both the function of objects related to the system and the machine learning algorithms that must be applied to the dataset. Our dataset used in this research consists of real data and was originally collected from a hospital run by the Palestine government covering the last three years. The SEMLHI methodology includes seven phases: designing, implementing, maintaining and defining workflows; structuring information; ensuring security and privacy; performance testing and evaluation; and releasing the software applications.Öğe Advancing medical imaging: detecting polypharmacy and adverse drug effects with Graph Convolutional Networks (GCN)(2024) Dara, Omer Nabeel; Ibrahim, Abdullahi Abdu; Mohammed, Tareq AbedPolypharmacy involves an individual using many medications at the same time and is a frequent healthcare technique used to treat complex medical disorders. Nevertheless, it also presents substantial risks of negative medication responses and interactions. Identifying and addressing adverse effects caused by polypharmacy is crucial to ensure patient safety and improve healthcare results. This paper introduces a new method using Graph Convolutional Networks (GCN) to identify polypharmacy side effects. Our strategy involves developing a medicine interaction graph in which edges signify drug-drug intuitive predicated on pharmacological properties and hubs symbolize drugs. GCN is a well-suited profound learning procedure for graph-based representations of social information. It can be used to anticipate the probability of medicate unfavorable impacts and to memorize important representations of sedate intuitive. Tests were conducted on a huge dataset of patients' pharmaceutical records commented on with watched medicate unfavorable impacts in arrange to approve our strategy. Execution of the GCN show, which was prepared on a subset of this dataset, was evaluated through a disarray framework. The perplexity network shows the precision with which the show categories occasions. Our discoveries demonstrate empowering advance within the recognizable proof of antagonistic responses related with polypharmaceuticals. For cardiovascular system target drugs, GCN technique achieved an accuracy of 94.12%, precision of 86.56%, F1-Score of 88.56%, AUC of 89.74% and recall of 87.92%. For respiratory system target drugs, GCN technique achieved an accuracy of 93.38%, precision of 85.64%, F1-Score of 89.79%, AUC of 91.85% and recall of 86.35%. And for nervous system target drugs, GCN technique achieved an accuracy of 95.27%, precision of 88.36%, F1-Score of 86.49%, AUC of 88.83% and recall of 84.73%. This research provides a significant contribution to pharmacovigilance by proposing a data-driven method to detect and reduce polypharmacy side effects, thereby increasing patient safety and healthcare decision-making.Öğe Breast sentinel lymph node cancer detection from mammographic images based on quantum wavelet transform and an atrous pyramid convolutional neural network(Hindawi Limited, 2022) Qasim, Mohammed N.; Mohammed, Tareq Abed; Bayat, OğuzThis study proposes an optimal approach to reduce noise in mammographic images and to identify salt-and-pepper, Gaussian, Poisson, and impact noises to determine the exact mass detection operation after these noise reductions. It therefore offers a method for noise reduction operations called quantum wavelet transform filtering and a method for precision mass segmentation called the image morphological operations in mammographic images based on the classification with an atrous pyramid convolutional neural network (APCNN) as a deep learning model. The hybrid approach called a QWT-APCNN is evaluated in terms of criteria compared with previous methods such as peak signal-to-noise ratio (PSNR) and mean-squared error (MSE) in noise reduction and accuracy of detection for mass area recognition. The proposed method presents more performance of noise reduction and segmentation in comparison with state-of-the-art methods. In this paper, we used the APCNN based on the convolutional neural network (CNN) as a new deep learning method, which is able to extract features and perform classification simultaneously, but it is intended as far as possible, empirically for the purpose of this research to be able to determine breast cancer and then identify the exact area of the masses and then classify them according to benign, malignant, and suspicious classes. The obtained results presented that the proposed approach has better performance than others based on some evaluation criteria such as accuracy with 98.57%, sensitivity with 90%, specificity with 85%, and also ROC and AUC with a rate of 86.77.Öğe Designing a solar system that improved the solar system's performance in instances of partial shadowing(Elsevier, 2022) Namaa, Hayder Makkee; Mohammed, Tareq Abed; Ibrahim, Abdullahi AbduThe great and decisive influence of the decision of nature or the natural factors of the earth, especially the strength of the solar radiation emitted by the sun and the temperature, remains an important condition and a factor that must be when talking about the exploitation or use of solar energy. From the beginning of the manufacture of solar cells to this day, the solar cell, which is the smallest component of the solar module or structure, has faced many challenges and difficulties, including design, manufacturing and cost challenges in addition to natural and abnormal factors. Among those challenges are natural and unnatural factors such as partial shading or full shading of the solar cell or the solar cells within the solar structure in the solar system, which directly affects the process of producing solar cells from electrical energy. As a result, these natural and unnatural challenges prevent the optimal and full exploitation of solar energy. Through this research paper, a new strategy was found, which is a simple electronic system to enhance the performance of solar cell production within the solar structure in the solar system, and this strategy includes the establishment of a new existing solar system. On a boosting system linked with the solar structure through which this electronic system can be connected to be an integral part of a solar system, to enhance the solar system's effectiveness in the production of electrical energy. The new solar system works in any conditions (natural and abnormal) and any type of shading (partial or total) for the solar panel or structure, through this new system, The quantity of electrical energy generated by the new solar system is equal to twice the energy of solar cells or arrays solar system within the solar structure under normal circumstances. In addition, the use of solar radiation tracking techniques is not important, as it is the strategic comparison used to determine the maximum power point tracking (MPPT) technology under various circumstances to produce good solar energy in poor lighting conditions by concentrating on the impacts of shadowing on the array of solar photovoltaic panels, a strategy that did not succeed in maintaining the productivity of the solar array from electrical energy in natural conditions, but the proposed model or the new methodological strategy helped to maintain the productivity of the solar cells or solar array from electrical energy in addition to increasing the production of electrical energy under the bad exposure conditions of the solar system. The proposed model is chosen in view of the fact that it may be able to reproduce different ranges of non-standard PV frames standardized by a new solar system with good multidimensional uses. The study may be presented here as a suitable source for future business of power generation (PV).Öğe Efficient hybrid memetic algorithm for multi-objective optimization problems(Ieee, 2017) Mohammed, Tareq Abed; Sahmoud, Shaaban; Bayat, OğuzImportance of multi-objective optimization problems has been rapidly increasing in the artificial intelligence community. This significant is due to the fact that there is high number of real-world applications having optimization problems that include more than one objective function. As has been evident in the last ten years, the evolutionary algorithms are one of the best choices to solve multi-objective optimization problems. In this paper a set of improved hybrid Memetic evolutionary algorithms are proposed to solve multi-objective optimization problems. The proposed algorithms enhance the performance of NSGA-II algorithm by using different search schemes. Merging a simple and efficient search technique to NSGA-II significantly enhances the convergence ability and speed of the algorithm. To assess the performance of proposed algorithms, three multi-objective test problems are used from ZDT set. Our empirical results in this paper show that the proposed algorithms significantly enhance the NSGA-II algorithm performance in both diversity and convergence.Öğe Efficient hybrid multi-objective evolutionary algorithm(Int Journal Computer Science & Network Security-Ijcsns, 2018) Mohammed, Tareq Abed; Bayat, Oğuz; Uçan, Osman NuriIn the artificial intelligence community the multi-objective optimization problem become very common and has been rapidly increasing attention. This significant is due to the fact that there is high number of real-world applications having optimization problems that include more than one objective function. As has been evident in the last ten years, the evolutionary algorithms are one of the best choices to solve multi-objective optimization problems. Although evolutionary algorithms are the most common approach to solve multi-objective optimization problems, there is still many issues and drawbacks that need solving and enhancing. In this paper a set of improved hybrid Memetic evolutionary algorithms are proposed to solve multi-objective optimization problems. The proposed algorithms enhance the performance of NSGA-II algorithm by using different new proposed and simple search schemes. Merging a simple and efficient search technique to NSGA-II significantly enhances the convergence ability and speed of the algorithm. To assess the performance of proposed algorithms, three multi-objective test problems are used from ZDT set. Our empirical results in this paper show that the proposed algorithms significantly enhance the NSGA-II algorithm performance in both diversity and convergence.Öğe Feature reduction based on hybrid efficient weighted gene genetic algorithms with artificial neural network for machine learning problems in the big data(Hindawi Ltd, 2018) Mohammed, Tareq Abed; Alhayali, Shaymaa; Bayat, Oğuz; Uçan, Osman NuriA large amount of data being generated from different sources and the analyzing and extracting of useful information from these data becomes a very complex task. The difficulty of dealing with big data arises from many factors such as the high number of features, existence of lost data, and variety of data. One of the most effective solutions that used to overcome the huge amount of big data is the feature reduction process. In this paper, a set of hybrid and efficient algorithms are proposed to classify the datasets that have large feature size by merging the genetic algorithms with the artificial neural networks. The genetic algorithms are used as a prestep to significantly reduce the feature size of the analyzed data before handling that data using machine learning techniques. Reducing the number of features simplifies the task of classifying the analyzed data and enhances the performance of the machine learning algorithms that are used to extract valuable information from big data. The proposed algorithms use a new gene-weight mechanism that can significantly enhance the performance and decrease the required search time. The proposed algorithms are applied on different datasets to pick the most relative and important features before applying the artificial neural networks algorithm, and the results show that our proposed algorithms can effectively enhance the classifying performance over the tested datasets.Öğe Hybrid efficient genetic algorithm for big data feature selection problems(Springer, 2020) Mohammed, Tareq Abed; Bayat, Oğuz; Uçan, Osman Nuri; Alhayali, ShaymaaDue to the huge amount of data being generating from different sources, the analyzing and extracting of useful information from these data becomes a very complex task. The difficulty of dealing with big data optimization problems comes from many factors such as the high number of features, and the existing of lost data. The feature selection process becomes an important step in many data mining and machine learning algorithms to reduce the dimensionality of the optimization problems and increase the performance of the classification or clustering algorithms. In this paper, a set of hybrid and efficient genetic algorithms are proposed to solve feature selection problem, when the handled data has a large feature size. The proposed algorithms use a new gene-weighted mechanism that can adaptively classify the features into strong relative features, weak or redundant features, and unstable features during the evolution of the algorithm. Based on this classification, the proposed algorithm gives the strong features high priority and the weak features less priority when generating new candidate solutions. In the same time, the proposed algorithm tries to more concentrate on unstable features that sometimes appear and sometimes disappear from the best solutions of the population. The performance of proposed algorithms is investigated by using different datasets and feature selection algorithms. The results show that our proposed algorithms can outperform the other feature selection algorithms and effectively enhance the classification performance over the tested datasets.Öğe Hybrid solution of challenges future problems in the new generation of the artificial intelligence industry used operations research industrial processes(Association for Computing Machinery, 2021) Mohammed, Tareq Abed; Qasim, Mohammed N.; Bayat, OguzKey technologies such as a new generation of industrial systems highly depends on artificial intelligence, and electronic physical systems that can digitize the entire supply chain together with data mining, machine learning, and more. At present, uses of artificial intelligence-based solutions are very important to improve the accuracy and efficiency of production processes. Artificial intelligence (AI) is playing a key role in the fourth industrial revolution, and we see significant improvements in different methods of machine learning. Artificial intelligence is widely used by practitioner engineers to solve various problems. This journal provides an international forum for quick articles that describes the practical application of artificial intelligence in all areas of mechanical engineering. Many researchers cited the development of technology in industrial fields to reduce problems in industry. Both the Operations Research (OR) community and Artificial Intelligence (AI) show that these problems are still interesting. While AI focuses linearly on increasing production and mitigating industry difficulties that may be seen as a revolution in the future. AI techniques offer a richer and more flexible presentation of real problems. The article presents the architecture of the industrial laboratory and the challenges associated with the use of artificial intelligence in industrial processes. © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.Öğe Intelligent database interface techniques using semantic coordination(Institute of Electrical and Electronics Engineers Inc., 2018) Mohammed, Tareq Abed; Alhayli, Shaymaa; Albawi, Saad; Duru, Adil DenizMore and more the use of artificial intelligence and data mining techniques established in many fields to solve the problem of classification. This paper consider most new database applications request smart interface to improve effective collaborations in the middle of database and the clients. The most open interfaces for databases must be clever and ready to comprehend characteristic dialect expressions. The overall aim of this study is to look at the importance of using data mining techniques with artificial intelligence in algorithms and applications. We propose a general design for an intelligent database interface. Furthermore, a genuine usage of such a framework which can be connected to any database. One of the fundamental attributes of this interface is space, freedom, which implies that this interface can be utilized with any database. Another aspect of this framework is that it is easy setup. The intelligent interface utilizes semantic coordinating procedure to change natural language query to Structured Query Language (SQL) by depending lexicon and set of creation guidelines. The lexicon comprises semantics sets for tables and sections. The query model is executed and the outcomes are introduced to the client. This interface was initially tested utilizing Supplier-Parts database by using JAVA and the result proves the efficiency of the proposed method in intelligent database system. © 2018 IEEE.Öğe Intelligent enhancement of organization work flow and work scheduling using machine learning approach tree algorithm(Int Journal Computer Science & Network Security-Ijcsns, 2018) Mohammed, Tareq Abed; Hamodi, Yaser Issam; Yousir, Naeem Th.Decision Tree is one of the most used machine learning language used for analyzing business problems mostly applied to predict and decide the best way to the problems. Mostly it works on existing data and avoid repeating which is done in the past. Finding the best solution for a set of problem it will get the data of the user performance both with worst and best-case scenario. The purpose of this paper is to find the Employee Performance issues related to Educations, health, Government and many other organizations and give the solution to improve their performance and avoid hiring irrelevant employees using Decision Tree Algorithm. The type of information generated from data-sheets and decided with the data processing method. More of the data which contains valuable information can be produced, Industry sector includes important information. The employee performance is calculated to find out. Well, the best way is using a favor Management and processing Staff database.Öğe Neural network behavior analysis based on transfer functions MLP & RB in face recognition(Assoc Computing Machinery, 2018) Mohammed, Tareq Abed; Alazzawi, Abdulbasit; Uçan, Osman Nuri; Bayat, OğuzWe performed multi-layer perceptron neural networks MLPNN and Radial Basis neural networks RBNN. In the MLPNN, we applied three layers (input, Hidden, and output) with sigmoid transfer function. Similarly, we used RBNN. Both classifiers are used after preprocessing operations BIOID data set is used in training and testing phases to test the proposed face recognition system combinations. According to the experimental results, the proposed schemes achieved satisfactory results with high accuracy classificationÖğe A Novel Software Engineering Approach Toward Using Machine Learning for Improving the Efficiency of Health Systems (vol 8, pg 23169, 2020)(Ieee-Inst Electrical Electronics Engineers Inc, 2020) Moreb, Mohammed; Mohammed, Tareq Abed; Bayat, Oguz; Ata, Oguz[No abstract available]Öğe The Statistical Learning Methods in image processing and Facial Recognition(Association for Computing Machinery, 2021) Mohammed, Tareq Abed; Rafeeq, Sarbaz Omar; Bayat, OguzThe aim of this paper is to develop a new approach for The Statistical Learning Methods in image processing and Facial Recognition using the deep learning techniques in python. In the recent years there have been significant advances in face recognition by using deep neural networks. One of the potential next steps is to develop optimized 3D facial recognition. Shifting from 2D to 3D increases complexity of the problem by adding an- other dimension to data, making possible solutions more resource hungry. We will investigate different depth camera based facial recognition techniques and test their performance by deploying them on an embedded processor. We focus on applications for embedded systems and use a small low-resolution time of flight (ToF) camera with our system to keep overall system portable and compact. All faces images are then projected on the feature space ("face space") to find the corresponding coordinators. The face space is composed of "Eigenfaces"or "Fisherfaces"which are actually eigenvectors found after doing a matrix composition - Eigen decomposition. At the heart of Eigenface method is the Principal Component Analysis (PCA) - one of the most popular unsupervised learning algorithms - while Fisherface is a better version of the previous one which makes use of both Principal Component Analysis and Linear Discrimination Analysis (LDA) to get more reliable results. The algorithms were realized by Python in Anaconda. Given initial images in the database, the program can detect and recognize the human faces in the provided pictures before saving them in the database to improve the calculation accuracy in the future. After evaluation, the recognition general results are exported on the screen with details included in the text files. © 2021 ACM.Öğe The prediction of fusion degree of International groups from their Twitter accounts(Ieee, 2017) Abbas, Ahmed K.; Mohammed, Tareq Abed; Bayat, Oğuz; Uçan, Osman Nuri; Bayat, Oğuz; Abbas, Ahmed K.; Mohammed, Tareq AbedRecently, social media has had a tremendous impact on our life and culture. Most of people enter the social media websites every day to do several things which makes them the most popular data sources on the Internet. According to this increasing impact of social media, many research works have done to study these websites, analyze them and predict useful information from them. Twitter is one of most popular and widely used social media network in the world. In this paper, a new efficient algorithm is proposed to solve society problem that does not discussed before which is the fusion degree of international groups on their new countries. During the many wars, political problems and other personal situations, many people changing their places and try to find better life in other countries. Therefore, it becomes very important for those new countries to simplify that task as can as possible and continuously follow the fusion process of those new coming persons in the society. The proposed algorithm in this paper will measure the fusion degree of international groups automatically from their twitter accounts. The proposed algorithm uses some features from the Twitter public information to estimate this degree. As a case study the new algorithm was applied on Arabic people in Turkey.Öğe Understanding of a convolutional neural network(Ieee, 2017) Albawi, Saad; Mohammed, Tareq Abed; Al-Zawi, SaadThe term Deep Learning or Deep Neural Network refers to Artificial Neural Networks (ANN) with multi layers. Over the last few decades, it has been considered to be one of the most powerful tools, and has become very popular in the literature as it is able to handle a huge amount of data. The interest in having deeper hidden layers has recently begun to surpass classical methods performance in different fields; especially in pattern recognition. One of the most popular deep neural networks is the Convolutional Neural Network (CNN). It take this name from mathematical linear operation between matrixes called convolution. CNN have multiple layers; including convolutional layer, non-linearity layer, pooling layer and fully-connected layer. The convolutional and fully-connected layers have parameters but pooling and non-linearity layers don't have parameters. The CNN has an excellent performance in machine learning problems. Specially the applications that deal with image data, such as largest image classification data set (Image Net), computer vision, and in natural language processing (NLP) and the results achieved were very amazing. In this paper we will explain and define all the elements and important issues related to CNN, and how these elements work. In addition, we will also state the parameters that effect CNN efficiency. This paper assumes that the readers have adequate knowledge about both machine learning and artificial neural network.
