Computer
Zeina Abdullah Humadi; Qusay Fadhel Al-Doori
Abstract
Communication and computing systems have made it easier for the world to transfer data and information from the sender to the recipient at the lowest cost and most efficiency. The transmission process may cause data corruption or error for many reasons, including the environment, the large volume of ...
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Communication and computing systems have made it easier for the world to transfer data and information from the sender to the recipient at the lowest cost and most efficiency. The transmission process may cause data corruption or error for many reasons, including the environment, the large volume of transmitted data, heat, and noise. for these Reasons, There is a need to correct and treat these errors. Individual errors can be easily corrected by hamming code, while burst errors cannot be corrected easily and need a hardware device called the interleaver used to correct the burst error. In this research, the different types of interleaver are studied and compared to find the best interleaver in order to increase the efficiency and accuracy of the systems. The issue is that interleaving takes a long time, which increases the turbo code's overall execution time. Our goal is to create an interleaver that is more sensitive and efficient than other varieties.
Computer
Noor Abdul Khaleq Zghair; Ahmed S. Al-Araji
Abstract
A mobile robot's major purpose is to get to its destination by traveling over an optimum path defined by various parameters such as time, distance, and the robot's safety from any impediments in its path. As a result, the backbone of the autonomous mobile robot is path planning and obstacle avoidance. ...
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A mobile robot's major purpose is to get to its destination by traveling over an optimum path defined by various parameters such as time, distance, and the robot's safety from any impediments in its path. As a result, the backbone of the autonomous mobile robot is path planning and obstacle avoidance. Several algorithms for path planning and obstacle avoidance have been presented by various researchers, each with its own set of benefits and drawbacks. This paper focuses on two parts; the first part finds the short and smooth collision-free path for a mobile robot to navigate in a static environment based on two proposed hybrid algorithms. The first hybrid is between Firefly Algorithm (FA) and Modify Chaotic Particle Swarm Optimization (MCPSO), namely (HFACPSO), while the other hybrid is between Genetic Algorithm (GA) and MCPSO, namely (HGACPSO).The second part suggests an algorithm planner for improving the efficiency of the route-planning algorithm with moving obstacle avoidance by adjusting the velocity or re-planning the path for the mobile robot. To demonstrate the effectiveness of the proposed algorithms in terms of the shortest path length and collision-free, as well as obtaining optimal or near-optimal wheel velocities with the minimum number of iterations. The proposed hybrid (FAMCPSO) algorithm provides enhancement on the path length equal to (0.82%) compared to the firefly algorithm (FA). Moreover, the hybrid (GAMCPSO) algorithm enhancement on the path length equals (0.67%) compared to the genetic algorithm (GA). All methods are simulated in a static and dynamic obstacle environment using MATLAB 2018b.
Computer
Ekhlas Kadhum Hamza; Marwan Alaa Hussein
Abstract
As the Internet of Things (IoT) is growing in popularity globally, which has resulted in a rise in cyber threats, experts are focusing more on its security. The majority of IoT security research to date has concentrated on huge devices, while small IoT devices have received comparably little attention. ...
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As the Internet of Things (IoT) is growing in popularity globally, which has resulted in a rise in cyber threats, experts are focusing more on its security. The majority of IoT security research to date has concentrated on huge devices, while small IoT devices have received comparably little attention. Our primary purpose is, therefore, to research how to ensure the operation of IoT devices that are small. The security gateway is a Security Settings on the Gateway for RaspberryPi built gateway that may link Internet of Things devices to their private network, safeguarding IoT devices from exposure to external networks. In addition, a variety of Security Settings on the Gateway for RaspberryPi security settings are installed, including fiel2ban and a Security Settings on the Gateway for RaspberryPi firewall, in order to avoid brute force and dictionary attacks. This article also studies the communication between Internet of Things (IoT) devices utilizing various secure communications, including Secure Shell (SSH), and analyzes their performance in a variety of circumstances. The gateway's experimental evaluation reveals that the proposed framework can secure tiny IoT devices.
Computer
Raja’a M. Mohammed; Suhad M. Kadhem
Abstract
Sign language (SL) is Non-verbal communication and a way for thedeaf and mute to communicate without words. A deaf and mute person's hands,face, and body shows what they want to say. Since the number of deaf and dumbpeople is increasing, there must be other ways to learn sign language orcommunicate with ...
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Sign language (SL) is Non-verbal communication and a way for thedeaf and mute to communicate without words. A deaf and mute person's hands,face, and body shows what they want to say. Since the number of deaf and dumbpeople is increasing, there must be other ways to learn sign language orcommunicate with deaf and dumb people. One of these ways is using advancedtechnology to produce systems that help the deaf/dumb, such as creatingrecognition and sign language translators. This paper presents an applicationthat works on the computer for machine translation of Iraqi sign language intwo directions from sign language to Arabic language (text/speech) and fromArabic language(text) to Iraqi sign language. The proposed system uses aConvolution Neural Network (CNN) to classify sign language based on itsfeatures to predicate the sign meaning. The sign language to Arabiclanguage(text/speech) part of the proposed system has an accuracy of 99.3% forletters.
Computer
Asmaa Hasan Alrubaie; Maisa'a Abid Ali Khodher; Ahmed Talib Abdulameer
Abstract
Target detection, one of the key functions of computer vision, has grown in importance as a study area over the past two decades and is currently often employed. In a certain video, it seeks to rapidly and precisely detect and locate a huge amount of the objects according to redetermined categories. ...
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Target detection, one of the key functions of computer vision, has grown in importance as a study area over the past two decades and is currently often employed. In a certain video, it seeks to rapidly and precisely detect and locate a huge amount of the objects according to redetermined categories. The two forms of deep learning (DL) algorithms that are used in the model training algorithm are single-stage and 2-stage algorithms of detection. The representative algorithms for every level have been thoroughly discussed in this work. The analysis and comparison of numerous representative algorithms in this subject is after that explained. Last but not least, potential obstacles to target detection are anticipated.
Computer
Suha Mohammed Saleh; Abdulamir A. Karim
Abstract
From big data analytics to computer vision and human-level control, deep learning has been effectively applied to a wide range of complicated challenges. However, these same deep learning advancements have also been used to develop malicious software that threatens individuals' personal data, democratic ...
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From big data analytics to computer vision and human-level control, deep learning has been effectively applied to a wide range of complicated challenges. However, these same deep learning advancements have also been used to develop malicious software that threatens individuals' personal data, democratic processes, and even national security. Apps backed by deep learning have lately appeared, with deepfake being one of the most notable. Deepfake algorithms can create fake images and videos that humans cannot distinguish them from authentic ones. One of the fields that deep learning accomplished major success is face synthesis and animation generation. On the other hand, it can create unethical software called deepfake that presents a severe privacy threat or even a huge security risk that can affect innocent people. This work introduces the most recent algorithms and methods used in deepfake. In addition, it provides a brief explanation of the principles that underpin these technologies and facilitates the development of this field by identifying the challenges and scopes that require further investigation in the future.
Computer
Talah Oday Alani; Ameer Mosa Al-Sadi
Abstract
Software-Defined Network (SDN) is one of the most predominant technologies for networking in the existing and next-generation networks. SDN can conFig. , control, protect, and optimize network resources through software. The fundamental benefit of SDN is enabling the application of dynamic management. ...
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Software-Defined Network (SDN) is one of the most predominant technologies for networking in the existing and next-generation networks. SDN can conFig. , control, protect, and optimize network resources through software. The fundamental benefit of SDN is enabling the application of dynamic management. In addition, the literature shows that partitioning a Software-Defined Wide Area Network (SD-WAN) into several logical networks efficiently will optimize its performance. The main aim of paper is to design an algorithm to slice SD-WAN dynamically into several virtual networks according to the server-clients’ correlation using the Virtual Local Area Network (VLAN). The several virtual networks improve QoS of SD-WAN and reduce its broadcasting domain. The proposed framework consists of two parts. The first part is the management algorithm that finds the best server for each client; then it groups this server with their client in a dedicated logical network. The second part includes creating a VLAN for each logical network in an SD-WAN. The application of the POX controller calculates and maintains the dynamic VLAN, which will be applied by the control plan to slice the topology in the data plan. SD-WAN topology is tested before and after applying VLANs. The results show enhancement in latency by 42.85%, throughput by 4.61%, loss packet by 72% and jitter by 47.86% after applying VLAN. Finally, the greatest gain is reducing the broadcasting ratio by 77.77%.
Computer
Huda M. Rada; Alia Karim Abdul Hassan; Ali H. Al-Timemy
Abstract
Upper limb amputation is a condition that severely limits the amputee’s movement. Patients who have lost the use of one or more of their upper extremities have difficulty performing activities of daily living. To help improve the control of upper limb prosthesis with pattern recognition, non-invasive ...
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Upper limb amputation is a condition that severely limits the amputee’s movement. Patients who have lost the use of one or more of their upper extremities have difficulty performing activities of daily living. To help improve the control of upper limb prosthesis with pattern recognition, non-invasive approaches (EEG and EMG signals) is proposed in this paper and are integrated with machine learning techniques to recognize the upper-limb motions of subjects. EMG and EEG signals are combined, and five features are utilized to classify seven hand movements such as (wrist flexion (WF), outward part of the wrist (WE), hand open (HO), hand close (HC), pronation (PRO), supination (SUP), and rest (RST)). Experiments demonstrate that using mean absolute value (MAV), waveform length (WL), Wilson Amplitude (WAMP), Sine Slope Changes (SSC), and Cardinality features of the proposed algorithm achieves a classification accuracy of 89.6% when classifying seven distinct types of hand and wrist movement.
Computer
Lafta R. Al-Khazraji; Ayad R. Abbas; Abeer S. Jamil
Abstract
Deep Dream (DD) is a new technology that works as a creative image-editing approach by employing the representations of CNN to produce dreams-like images by taking the benefits of both Deep CNN and Inception to build the dream through layer-by-layer implementation. As the days go by, the DD becomes widely ...
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Deep Dream (DD) is a new technology that works as a creative image-editing approach by employing the representations of CNN to produce dreams-like images by taking the benefits of both Deep CNN and Inception to build the dream through layer-by-layer implementation. As the days go by, the DD becomes widely used in the artificial intelligence (AI) fields. This paper is the first systematic review of DD. We focused on the definition, importance, background, and applications of DD. Natural language processing (NLP), images, videos, and audio are the main fields in which DD is applied. We also discussed the main concepts of the DD, like transfer learning and Inception. We addressed the contributions, databases, and techniques that have been used to build the models, the limitations, and evaluation metrics for each one of the included research papers. Finally, some interesting recommendations have been listed to serve the researchers in the future.
Computer
Sabah Abdulazeez Jebur; Khalid A. Hussein; Haider Kadhim Hoomod
Abstract
The use of video surveillance systems has increased due to security concerns and their relatively low cost. Researchers are working to create intelligent Closed Circuit Television (CCTV) cameras that can automatically analyze behavior in real-time to detect anomalous behaviors and prevent dangerous accidents. ...
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The use of video surveillance systems has increased due to security concerns and their relatively low cost. Researchers are working to create intelligent Closed Circuit Television (CCTV) cameras that can automatically analyze behavior in real-time to detect anomalous behaviors and prevent dangerous accidents. Deep Learning (DL) approaches, particularly Convolutional Neural Networks (CNNs), have shown outstanding results in video analysis and anomaly detection. This research paper focused on using Inception-v3 transfer learning approaches to improve the accuracy and efficiency of abnormal behavior detection in video surveillance. The Inceptionv3 network is used to classify keyframes of a video as normal or abnormal behaviors by utilizing both pre-training and fine-tuning transfer learning approaches to extract features from the input data and develop a new classifier. The UCF-Crime dataset is used to train and evaluate the proposed models. The performance of both models was evaluated using accuracy, recall, precision, and F1 score. The fine-tuned model achieved 88.0%, 89.24%, 85.83%, and 87.50% for these measures, respectively. In contrast, the pre-trained model obtained 86.2%, 86.43%, 84.62%, and 85.52%, respectively. These results demonstrate that transfer learning using Inception-v3 architecture can effectively classify normal and abnormal behaviors in videos, and fine-tuning the weights of the layers can further improve the model's performance.
Computer
Haider Saeed Wdhayeh; Raghad Abdulaali Azeez; Athraa Jasim Mohammed
Abstract
In this paper, an algorithm to hide information in an image using QR code technology is presented. QR Code “QUICK RESPONSE CODE” is a two-dimensional array that can include different types of data and was first developed in 1993 for the Japanese Toyota Corporation for the purpose of tracking ...
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In this paper, an algorithm to hide information in an image using QR code technology is presented. QR Code “QUICK RESPONSE CODE” is a two-dimensional array that can include different types of data and was first developed in 1993 for the Japanese Toyota Corporation for the purpose of tracking products through production and marketing. The researchers paid great attention to QR code technology especially in the field of information security. The proposed algorithm in this paper hides the secret text in the image in a random way by generating random positions and using the LSB “Least Significant Bit” method. After that, the random positions are saved in the QR code file, and this is the hiding stage. In the extraction stage, the QR code file is read first to extract the positions where the secret text has been hidden. After that, the secret text is read according to the LSB method. The algorithm was implemented using the C-Sharp programming language and Microsoft Visual Studio 2019 development environment. After conducting experiments on a number of images and extracting results based on the PSNR “Peak signal-to-noise ratio “test method, the results were good and the algorithm is very strong against a brute force attack. This algorithm can be used in building authentication systems.
Computer
Safa S. Abdul-Jabbar; Alaa K. Farhan; Rana F. Ghani
Abstract
Blockchain technology relies on cryptographic techniques that provide various advantages, such as trustworthiness, collaboration, organization, identification, integrity, and transparency. Meanwhile, data analytics refers to the process of utilizing techniques to analyze big data and comprehend the relationships ...
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Blockchain technology relies on cryptographic techniques that provide various advantages, such as trustworthiness, collaboration, organization, identification, integrity, and transparency. Meanwhile, data analytics refers to the process of utilizing techniques to analyze big data and comprehend the relationships between data points to draw meaningful conclusions. The field of data analytics in Blockchain is relatively new, and few studies have been conducted to examine the challenges involved in Blockchain data analytics. This article presents a systematic analysis of how data analytics affects Blockchain performance, with the aim of investigating the current state of Blockchain-based data analytics techniques in research fields and exploring how specific features of this new technology may transform traditional business methods. The primary objectives of this study are to summarize the significant Blockchain techniques used thus far, identify current challenges and barriers in this field, determine the limitations of each paper that could be used for future development, and assess the extent to which Blockchain and data analytics have been effectively used to evaluate performance objectively. Moreover, we aim to identify potential future research paths and suggest new criteria in this burgeoning discipline through our review.
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 ...
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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
Asmaa Ibrahim Hussieen; Abeer Tariq MaoLood; Ekhlas Khalaf Gbash
Abstract
Conventional voting activities are often replaced by electronic voting (EV) in light of the quick expansion of the Internet. For a variety of reasons, various nations have lately switched to EV rather than conventional voting. Different EV systems were presented up to this point. In both practical and ...
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Conventional voting activities are often replaced by electronic voting (EV) in light of the quick expansion of the Internet. For a variety of reasons, various nations have lately switched to EV rather than conventional voting. Different EV systems were presented up to this point. In both practical and theoretical fields, on the other hand, there is no perfect solution. To meet such objectives, the researchers strive for preserving cryptographic primitives when developing high-efficiency e-voting schemes. The concept of fog computing was developed to improve network infrastructure to satisfy the demands of large amounts of data the same time as also increasing the efficiency of the processing power. Also, it has been created as well to address concerns with Cloud computing, like the distribution environment complexity, real-time response, mobility, and IoT application location awareness. The concentration of this study was on a complete review regarding the systems of EVs through various scholars as a platform to detect flaws or problems in the deployment of extremely secure EV systems. In addition, nations having a history of EV system adoption were examined. A concept for future work on establishing a safe EV system depends on problems discovered in numerous works.
Computer
Muna Khalaf; Ban N. Dhannoon
Abstract
Semantic segmentation refers to labeling each pixel in the scene to its belonging object. It is a critical task for many computer vision applications that requires scene understanding because It attempts to mimic human perceptual grouping. Despite the unremitting efforts in this field, it is still a ...
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Semantic segmentation refers to labeling each pixel in the scene to its belonging object. It is a critical task for many computer vision applications that requires scene understanding because It attempts to mimic human perceptual grouping. Despite the unremitting efforts in this field, it is still a challenge and preoccupies of researchers. Semantic segmentation performance improved using deep learning rather than traditional methods. Semantic segmentation based on deep learning models requires capturing local and global context information, where deep learning models usually can extract one of them but is challenging to integrate between them. Deep learning based on attention mechanisms can gather between the capturing of local and glopal information, so it is increasingly employed in semantic segmentation. This paper gives an introductory survey of the rising topic attention mechanisms in semantic segmentation. At first, it will discuss the concept of attention and its integration with semantic segmentation requirements. Then, it will review deep learning based on attention mechanisms in semantic segmentation.
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.
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. ...
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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 ...
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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
Shaymaa Mahmood Naser; Yossra Hussain Ali
Abstract
Cybersecurity systems have been taken into account in modern information systems and methods. This is due to the increase in electronic attacks on storage information in terms of transmission, reception and storage. Therefore, the need to produce such systems in a complete way to prevent their penetration ...
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Cybersecurity systems have been taken into account in modern information systems and methods. This is due to the increase in electronic attacks on storage information in terms of transmission, reception and storage. Therefore, the need to produce such systems in a complete way to prevent their penetration into the network has increased. In addition, artificial intelligence (AI) methods are used in cybersecurity systems as classifiers, attack detectors, and components for predicting threats that surround the network. This requires more information about threats and vulnerabilities to be covered to avoid any errors. In this paper, a systematic review is conducted to cover cybersecurity used in various applications, including systems based on wireless, cloud, and mobile sensor networks (WSN). The systematic review approach is adopted on a two-way basis to produce a clear view of the research work to date and to provide a field that can be used for future work.
Computer
Dhuha Abd Almoanf; Shaimaa H. Shaker
Abstract
Computed tomography (CT) is used to diagnose diseases and tumors. A special dye called contrast material is used in CT scans to assist emphasize the parts of the body being examined. Therefore, an enhancement technique to improve CT images' degradation is needed. This paper aims to present a method to ...
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Computed tomography (CT) is used to diagnose diseases and tumors. A special dye called contrast material is used in CT scans to assist emphasize the parts of the body being examined. Therefore, an enhancement technique to improve CT images' degradation is needed. This paper aims to present a method to enhance the quality of Ct-Scan images based on discrete wavelets transform and the Retinex algorithm. The proposed methods are based on the Retinex algorithm parameters or Dark Channel Prior algorithm parameters, according to the output image from preprocessing and discrete wavelets algorithm steps to increase the lightness degree of an image, remove possible noise from the image improve the contrast. The results of experiments of the enhanced image outputted from the Retinex model compared with one outputted from the Dark Channel Prior method. Hence, image quality based on the DCP method is a higher degree of enhanced rate and reasonable rate of noise removal-based enhancement measures, which were SI, MSE, IQI, SNR, and SNR, but was very attentive to the percentage values of IQI and SI. So the DCP with WT was recorded as the highest rate of enhancement.
Computer
Heba Mohammed Fadhil; Mohammed Najm Abdullah; Mohammed Issam Younis
Abstract
Testing is a vital phase in software development, and having the right amount of test data is an important aspect in speeding up the process. As a result of the integrationist optimization challenge, extensive testing may not always be practicable. There is also a shortage of resources, expenses, and ...
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Testing is a vital phase in software development, and having the right amount of test data is an important aspect in speeding up the process. As a result of the integrationist optimization challenge, extensive testing may not always be practicable. There is also a shortage of resources, expenses, and schedules that impede the testing process. One way to explain combinational testing (CT) is as a basic strategy for creating new test cases. CT has been discussed by several scholars while establishing alternative tactics depending on the interactions between parameters. Thus, an investigation into current CT methods was started in order to better understand their capabilities and limitations. In this study, 97 publications were evaluated based on a variety of criteria, including the generation technology, test strategy method, supported interactions, mixed coverage ,and support constraints between parameters. CT analysis had a wide range of interaction assistance options available to researchers. Since 2010, a unified interaction has been the most common style of interaction between the two parties. The year 2018 was hailed as the most successful in terms of CT by researchers. Researchers should focus on one test at a time and metaheuristic search strategies for t-way CT. There has also been a significant increase in the popularity of other trends, such as deep learning (DL). CT appears to be a useful testing technique for balancing and fault detection capabilities for a variety of systems and applications, according to our research. Future research and software development may benefit from this information.
Computer
Zainab F. Makhrib; Abdulamir A. karim
Abstract
The Digital watermarking is a field of information hiding that entails hiding the crucial information in the original data in order to prevent illegal duplication and distribution of multimedia data such as image, video, text and ect.. In this paper, we present two techniques to embed watermarks in the ...
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The Digital watermarking is a field of information hiding that entails hiding the crucial information in the original data in order to prevent illegal duplication and distribution of multimedia data such as image, video, text and ect.. In this paper, we present two techniques to embed watermarks in the cover image. The first is the Least Significant Bit (LSB) method, which is a spatial domain technique and considered fragile against attacks and other operations. The second method is the frequency domain technique, which uses Discrete Wavelet Transform (DWT) and is considered robust against attacks. The efficiency and performance of these techniques are evaluated based on Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). From the results, the value of PSNR is above 37 dB, which ensures better imperceptibility and shows better robustness. The comparison between the two techniques shows that the hybrid method was more robust than the LSB method, hence it achieves good invisibility.
Computer
Zainab A. Kamal; Rana F. Ghani
Abstract
the primary concerns with manual transactions include corruption, lack oftransparency, fraud, and mismanagement of distribution operations, all of which arecreated by traditional centralized applications, necessitating the migration to blockchaintechnology. In this work, a system is presented to secure ...
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the primary concerns with manual transactions include corruption, lack oftransparency, fraud, and mismanagement of distribution operations, all of which arecreated by traditional centralized applications, necessitating the migration to blockchaintechnology. In this work, a system is presented to secure and monitor correspondencebetween several nodes and store it in a decentralized database in order to securedistributed ledger transactions and safeguard against fraud and tampering whentransactions are shared by multiple parties. The hashing that blockchain technologydelivers in each transaction ensures a high level of security. The hashing associated witheach transaction confirms all sending and receiving transactions. When a transaction issent from one node to another, the other node checks the hash accompanying thetransaction to see if it came from a registered node or an external node. Within theblockchain system, the nodes will check transaction correspondences. The system hasdemonstrated its effectiveness by delivering a more secure messaging system with highcredibility and tamper resistance. In addition, the time it takes to authenticate will be inreal time.
Computer
Rasha Ismail Ahmed; Rasha Mohammed Mohsin Mohammed Mohsin; Rana Fareed Ghani
Abstract
The rapid growth of the human population and the spread of many bad habits affect the health of human beings, this leads to many health problems, such as heart disease, blood pressure, and diabetes. Some of these diseases require earlier detection and fast treatment, to avoid major risks, such as permanent ...
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The rapid growth of the human population and the spread of many bad habits affect the health of human beings, this leads to many health problems, such as heart disease, blood pressure, and diabetes. Some of these diseases require earlier detection and fast treatment, to avoid major risks, such as permanent damage or even life loss. The fast evolution of communications and nanotechnology nowadays is being facilitated to help with saving lives before great damage happens. This paper suggests a special architecture through which to monitor patients remotely using an Internet of Things (IoT)architecture for the purpose of notifying the paramedics and health care centers to rescue the life of cardiovascular patients. The experiments, after attempts with several types of classification algorithms, showed that The result shows that The decision tree has high accuracy for predicting the new heart attack and saving lives before happening of great damage .the result reached between( 83 to 87) percentage, which is a good percentage to avoid the risk of heart attacks and thus reducing death rates.
Computer
Raad Abdul Ameer Qasim; Bashar Saadoon Mahdi
Abstract
Image Protection is one of the most important issues that have created problems in technology challenges in the past and present years, whether they are stored or when sent to other parties. And how to develop the techniques adopted in encrypting it, devise new methods, or integrate the available technologies ...
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Image Protection is one of the most important issues that have created problems in technology challenges in the past and present years, whether they are stored or when sent to other parties. And how to develop the techniques adopted in encrypting it, devise new methods, or integrate the available technologies to provide high security in encrypting images or any important data and preserving them from loss or hacking. This paper presents a new method to generate a random keychain using genetic algorithm techniques to develop new generations and XOR technology to encrypt digital images. Where the results showed the high efficiency of the encryption method with its ease of use and the preservation of the original image data after decoding with high accuracy and speed in implementing the strategy used. The efficiency of these switches for use has been tested using the National Institute of Standards and Technology (NIST) and the statistical randomness test, and the tests were successfully passed.
