Computer
Adnan T. Kareem; Hasanen S. Abdullah; Ahmed T. Sadiq
Abstract
argumentation has become an attraction recently, because it is widely used in decision-making, at 1994 Dung invented a new argumentation model, called Argumentation Framework AF. This system investigates assaults of arguments, and it also works away on attributes, this model is designed to take care ...
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argumentation has become an attraction recently, because it is widely used in decision-making, at 1994 Dung invented a new argumentation model, called Argumentation Framework AF. This system investigates assaults of arguments, and it also works away on attributes, this model is designed to take care of argument attacks among them, without paying attention to how the sentences are formulated or arranged, and identify the supporting and supporting arguments. It is also possible for a group of experts, to evaluate arguments to resolve the debate about the current problem, by determining the extent to which a particular argument affects the other by attacking it, This framework was a comprehensive new system, called the gaming argumentation framework (GAF), It helps make decisions about the current problems, through making Claims and Attack Determinations (CAD) to arguments, and after that, putting the result of those CAD to game theory, with 2 players for the purpose of achieving final results, which are helpful for decision-makers, in making decisions concerning current problems. The present paper gives a proposed system, using the GAF to build the dynamic model based on the gaming argumentation framework (DGAF); it as works as the GAF by adding the feedback to suit all possible conditions, and by making a companion between them, the argumentation and the game theory. Since the foreign exchange market depends on changing conditions, it was a case study.
Israa N. Mahmood; Hasanen Abdullah
Abstract
Lung cancer is one of the most fatal cancers in the world for both genders. It has a high mortality rate compared to other types of cancer. Early detection can save lives and enhance the treatment process. As a result, the demand for approaches to detect cancer at an early stage is growing. In this paper, ...
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Lung cancer is one of the most fatal cancers in the world for both genders. It has a high mortality rate compared to other types of cancer. Early detection can save lives and enhance the treatment process. As a result, the demand for approaches to detect cancer at an early stage is growing. In this paper, an Artificial Neural Network (ANN) model is developed to identify the level of having lung cancer based on environmental, diagnostic, and statistical factors. The features that highly affect the risk level of lung cancer were identified. The model's performance was assessed using a variety of criteria, including accuracy, precision, recall, and f-measure. Experimental results show that the model attains a high accuracy rate of 91.79% and risk factors like obesity, alcohol use, genetic risk, and coughing of blood can lead to lung cancer.
Computer
Ashraf Tahseen Ali; Hasanen Abdullah; Mohammed Natiq Fadhil
Abstract
Biometrics signs are the most important factor in the human recognition field and considered an effective technique for person authentication systems. Voice recognition is a popular method to use due to its ease of implementation and acceptable effectiveness. This research paper will introduce a speaker ...
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Biometrics signs are the most important factor in the human recognition field and considered an effective technique for person authentication systems. Voice recognition is a popular method to use due to its ease of implementation and acceptable effectiveness. This research paper will introduce a speaker recognition system that consists of preprocessing techniques to eliminate noise and make the sound smoother. For the feature extraction stage, the method Mel Frequency Cepstral Coefficient (MFCC) is used, and in the second step, the four features (FF) Mean, Standard Division, Zero-Cross and Amplitude, which added to (MFCC) to improve the results. For data representation, vector quantization has been used. The evaluation method (k-fold cross-validation) has been used. Supervised machine learning (SML) is proposed using Quadratic Discriminant Analysis (QDA) classification algorithms. And the results obtained by the algorithm (QDA) varied between 98 percent and 98.43 percent, depending on the way of features extraction that was used. These results are satisfactory and reliable.
Ashraf Tahseen Ali; Hasanen Abdullah; Mohammed Natiq Fadhil
Abstract
Finger veins are different from other biometric signs; it is a special characteristic of the human body. The challenge for an imposter to explore and comprehend it, since the veins are below the skin, it is impossible to tell which one is, and which one stands out because the person has more than one ...
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Finger veins are different from other biometric signs; it is a special characteristic of the human body. The challenge for an imposter to explore and comprehend it, since the veins are below the skin, it is impossible to tell which one is, and which one stands out because the person has more than one finger to examine. Impostor recognition based on applying three machine-learning methods will be presented in this article, and then there is a discussion at preprocessing, Linear Discriminant Analysis (LDA) for feature extraction, and k fold cross-validation as an evaluation method. These measures were carried out on two different datasets, which are the Shandong University Machine Learning and Applications - Homologous Multi-modal Traits (SDUMLA-HMT) Dataset and the University of Twente Finger Veins (UTFV) dataset. The classifier with the best results was Support Vector Machine (SVM) and Linear Regression (LR) had the lowest classifier accuracy.
Ashraf Tahseen Ali; Hasanen Abdullah; Mohammed Natiq Fadhil
Abstract
As compared to other conventional biometrics systems, voice is a unique and important metric, where it is used in many vital fields as the security and communication domains that do not need to be expensive to achieve. The purpose of this article is to see how machine learning (ML) algorithms perform ...
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As compared to other conventional biometrics systems, voice is a unique and important metric, where it is used in many vital fields as the security and communication domains that do not need to be expensive to achieve. The purpose of this article is to see how machine learning (ML) algorithms perform for speaker Authentication to recognize impostors. To boost the audios usable in real environments, it was suggested the preprocessing of audio, like noise decreasing and voiced improving. Mel Frequency Cepstral Coefficients (MFCC) and the four features (Amplitude, Zero Crossing, Mean, and Standard Division) are extracted for all audio metrics, straight beside their differentials and accelerations. Then, Vector Quantization (VQ) is applied to these files. The algorithms were prepared and examined on two datasets, by applying k-fold cross-validation. The preparation for testing and comparing the three (ML) approaches are as follows: Support Vector Machine (SVM), One Rule (One-R), Linear Regression (LR). The result of the (SVM) algorithm average accuracy of 96.33 percent was superior.
