Computer
Zainab Hashim; Hanaa Mohsin; Ahmed Alkhayyat
Abstract
Handwritten signature identification is a process that determines an individual’s true identity by analyzing their signature. This is an important task in various applications such as financial transactions, legal document verification, and biometric systems. Various techniques have been developed ...
Read More ...
Handwritten signature identification is a process that determines an individual’s true identity by analyzing their signature. This is an important task in various applications such as financial transactions, legal document verification, and biometric systems. Various techniques have been developed for signature identification, including feature-based methods and machine learning-based methods. However, verifying handwritten signatures in digital transactions and remote document authentication is still challenging. The inherent variety in people’s signatures, which may occur due to factors such as mood, exhaustion, or even the writing tool used, contributes to the problem. Furthermore, the proliferation of sophisticated forgery methods, such as freehand mimicking and sophisticated picture manipulation, necessitates the development of reliable and precise tools for identifying authentic signatures from fake ones.The present paper suggests a method for identifying signatures based on integrating static (off-line) handwritten signature data. This is done by fusing three types of signature features: Linear Discriminant Analysis (LDA) as appearance-based features, Fast Fourier Transform (FFT) as frequency- features, and Gray-Level Co-occurrence Matrix (GLCM) as texture-features. Then, these fused features are inputted into four types of machine learning algorithms: Naive Bayes, K-Nearest Neighbor, Decision Tree, and AdaBoost classifiers, to identify each person and to find the most robust algorithm in terms of accuracy and precision and recall. For experiments, we have used two famous datasets: SigComp2011 and CEDAR. After training datasets, the highest accuracy achieved was 100% on the CEDAR dataset and 94.43% on the SigComp2011 dataset using a Naive Bayes classifier.
Communication
Mohammad Abd Abbas; Bilal Ghazal Ghazal; Ahmad ghandour Ghandour
Abstract
Online payment methods for e-commerce and many websites in various fields have increased significantly. Therefore, credit card frauds are easy targets, and their rate is on the rise which poses a major problem for online payments. The basic concept is to examine consumers' purchasing histories to extrapolate ...
Read More ...
Online payment methods for e-commerce and many websites in various fields have increased significantly. Therefore, credit card frauds are easy targets, and their rate is on the rise which poses a major problem for online payments. The basic concept is to examine consumers' purchasing histories to extrapolate their typical behavior patterns, classify cardholders into different groups, and then attempt to detect credit card fraud. Credit card fraud detection based on a machine learning model uses a combination of supervised and unsupervised learning techniques such as Random Forest, Decision Tree, Logistic Regression, and Extreme Gradient Boost. We used the synthetic minority oversampling (SMOTE) technique to balance the dataset. The model is trained on a large set of data related to credit card transactions and uses features such as transaction amount, transaction location, and time of day to identify patterns and anomalies in the data that indicate fraudulent activity. Our goal is to build a model based on machine learning technology that detects and analyzes online shopping fraud Detecting fraud in credit card systems is crucial to protecting consumers from financial losses and maintaining the integrity of the financial ecosystem after collecting Creditcard.csv data. With the help of several algorithms, including Random Forest (RF) algorithm accuracy reached 99%, Logistic Regression (LR) algorithm accuracy reached 97%, and Decision Tree (DT) algorithm accuracy reached 99% Researchers provide a comprehensive method for identifying fraud in credit card transactions Precision Recall F1-score. The proposed system includes four main steps: pre-processing, classification using the algorithm, and checking whether the transaction is fraudulent or not.
Computer
Mohammed E. Seno; Ban N. Dhannoon; Omer K. Jasim Mohammad
Abstract
Cloud computing is an evolving and high-demand research field at theforefront of technological advancements. It aims to provide software resources andoperates based on service-oriented delivery. Within the infrastructure as a service (IaaS)framework, the cloud offers end customers access to crucial infrastructure ...
Read More ...
Cloud computing is an evolving and high-demand research field at theforefront of technological advancements. It aims to provide software resources andoperates based on service-oriented delivery. Within the infrastructure as a service (IaaS)framework, the cloud offers end customers access to crucial infrastructure resources,including CPU, bandwidth, and memory. When a cloud system fails to deliver asexpected, it is referred to as an event, signifying a deviation from the anticipated service.To meet their service-level agreement (SLA) obligations, cloud service providers (CSPs)must ensure continuous access to fault-tolerant, on-demand resources for their clients,particularly during outages. Consequently, finding the most efficient ways to accomplishtasks while considering the rapid depletion of resources has become an urgent concern.Researchers are actively working to develop optimal strategies tailored to the cloudenvironment. Machine learning plays a critical role in these endeavors, serving as a keycomponent in various cloud computing platforms. This study presents a comprehensiveliterature review of current research papers that employ machine learning algorithms topropose strategies for optimizing cloud computing environments. Additionally, the surveyprovides authors with invaluable resources by extensively exploring a diverse range ofmachine learning techniques and their applications in the field of cloud computing. Byexamining these areas, researchers aim to enhance their understanding of efficientresource allocation and scheduling, addressing the challenges posed by resource scarcitywhile meeting SLA obligations.
Computer
Mohammed Majid Msallam
Abstract
In recent years, safe lockers have been spread in public places to secure valuable belongings. The people are concerned about losing safe locker keys or use of the spare key by others and will remain worried about their things. To solve the forenamed matter, this paper proposed a system that depends ...
Read More ...
In recent years, safe lockers have been spread in public places to secure valuable belongings. The people are concerned about losing safe locker keys or use of the spare key by others and will remain worried about their things. To solve the forenamed matter, this paper proposed a system that depends on biometrics to secure valuable things and can minimize their concerns and worries. The proposed system consists of two major parts, software, and hardware. In the hardware part, A microcontroller with a camera and an electronic lock will be used to securely open or close the door of the safe locker. In the software part, the images that are pictured by the camera will be prepared by an image processing algorithm and then Support Vector Machines (SVM) will be trained in the images of a person. The images and information of the person will be saved until the belongings come out and after that deletes everything to prepare for another one.
Computer
Afrah Salman Dawood; Zena Mohammed Faris
Abstract
Recently, Deep Learning (DL) has accomplished enormous prosperity in various areas, like natural language processing (NLP), image processing, different medical issues and computer vision. Both Machine Learning (ML) and DL as compared to traditional methods, can learn and make better and enhanced use ...
Read More ...
Recently, Deep Learning (DL) has accomplished enormous prosperity in various areas, like natural language processing (NLP), image processing, different medical issues and computer vision. Both Machine Learning (ML) and DL as compared to traditional methods, can learn and make better and enhanced use of datasets for feature extraction. This paper is divided into three parts. The first part introduces a detailed information about different characteristics and learning types in terms of learning problems, hybrid learning problems, statistical inference and learning techniques; besides to an exhausted historical background about feature learning and DL. The second part is about the major architectures of DL with mathematical equations and clarified examples. These architectures include Autoencoders (AEs), Generative Adversarial Networks (GANs), Deep Belief Networks (DBNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Recursive Neural Networks. The third part of this work represents an overview with detailed explanation about different applications and use-cases. Finally, the fourth part is about hardware/ software tools used with DL.
Computer
Maryam Raad Shihab; Rana Fareed Ghani; Athraa Jasim Mohammed
Abstract
The traffic surveillance system is a type of intelligent system of traffic control. Traffic control provides solutions to most problems faced by people. It helps to monitor, detect traffic congestion and traffic accidents. As science evolved, it became possible to control traffic using video surveillance. ...
Read More ...
The traffic surveillance system is a type of intelligent system of traffic control. Traffic control provides solutions to most problems faced by people. It helps to monitor, detect traffic congestion and traffic accidents. As science evolved, it became possible to control traffic using video surveillance. Video surveillance is the most economical option that does not involve high costs or changes in infrastructure. Vehicle detection is one of the main parts of the traffic surveillance system. In this paper, vehicles will be detected using two different artificial intelligence methods (the YOLO method and the HAAR cascade classifier method). The first one is smarter than the second method, and both of them contain machine learning. The first processing step will read the video. Then vehicle detection algorithms are applied using two different ways. The comparison between them depends on the results to find the most effective and applicable vehicle detection method. After implementing the two methods, results were obtained using YOLO, that the accuracy is 91.31% and the error rate is 8.69%, in time 10 sec. As for using the XML (HAAR cascade classifier method) method, the accuracy is 86.9%, the quality is 86.9%, completeness is 90.9%, and the error rate is 13%, in time 17 sec. Thus, we conclude that the YOLO method has better results than the second method.
Computer
Shaymaa Taha Ahmed; Suhad Malallah Kadhem
Abstract
Alzheimer’s disease (AD) is caused by multiple variables. Alzheimer's disease development and progression are influenced by genetic variants. The molecular pathways causing Alzheimer's disease are still poorly understood. In Alzheimer's disease research, determining an effective and reliable diagnosis ...
Read More ...
Alzheimer’s disease (AD) is caused by multiple variables. Alzheimer's disease development and progression are influenced by genetic variants. The molecular pathways causing Alzheimer's disease are still poorly understood. In Alzheimer's disease research, determining an effective and reliable diagnosis remains a major difficulty, particularly in the early stages (i.e., Moderate Cognitive Impairment (MCI)). Researchers and technologists working in the fields of machine learning and data mining can help improve the situation, early AD diagnosis but face a hurdle when it comes to high- dimensional data processing. By reducing irrelevant and redundant data from microarray gene expression data, the technique of feature selection can save computing time, improve learning accuracy, and encourage a deeper effect on the learning system or data. The feature selection strategy described in this article reduces data noise well. In particular, Pearson's correlation coefficient is used to assess data redundancy. The efficacy of these features is assessed using the Support Vector Machine (SVM) classification approach. The proposed approach has an accuracy of up to 91.1 %. As a result, newly established approaches for early diagnosis of Alzheimer's disease(AD) are being improved.
Computer
Heba Mohammed Fadhil; Mohammed Najm Abdullah; Mohammed Issam Younis
Abstract
Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques ...
Read More ...
Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Trees (DT), K- nearest neighbor (KNN), and Logistic Regression (LR), have been used to identify the parameters that allow for effective price estimation. These approaches were tested on a data set of an extensive Indian airline network. When it came to estimating flight prices, the results demonstrate that the Decision tree method is the best conceivable Algorithm for predicting the price of a flight in our particular situation with 89% accuracy. The SGD method had the lowest accuracy, which was 38 %, while the accuracies of the KNN, NB, ADA, and LR algorithms were 69 %, 45 %, and 43 %, respectively. This study's presented methodologies will allow airline firms to predict flight prices more accurately, enhance air travel, and eliminate delay dispersion.
