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.
Control
Ali M. Majeed; Safanah M. Raafat; Najat M. Ramadhan
Abstract
Wireless Sensor Network (WSN) represents a key network in the present and future Internet of Things (IoT) technology. WSN has an uncountable number of applications and is commonly used to aggregate information and control the physical environment remotely through small embedded devices known as wireless ...
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Wireless Sensor Network (WSN) represents a key network in the present and future Internet of Things (IoT) technology. WSN has an uncountable number of applications and is commonly used to aggregate information and control the physical environment remotely through small embedded devices known as wireless sensor nodes. Power consumption is one of the main challenges in WSN due to the limitation of power resources. Consequently, several techniques have been followed to optimize power consumption. The feedback control system is one of the routes that has been utilized to minimize power consumption in WSN using the mathematical model of power and rate control in WSN. In this paper, a concise review of various types of control systems that are deployed for power saving in WSN will be discussed. The comparison between the applied control strategies is the key finding.
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.
Control
Hadeel I. Abdulameer; Mohamed J. Mohamed
Abstract
Four Fractional/Integer Order Fuzzy Proportional Integral Derivative controller structures are designed in this study to successfully control a nonlinear, coupled, multi-input, multi-output, three-link rigid robotic manipulator system. The performance of Fractional Order Fuzzy Proportional Integral Derivative ...
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Four Fractional/Integer Order Fuzzy Proportional Integral Derivative controller structures are designed in this study to successfully control a nonlinear, coupled, multi-input, multi-output, three-link rigid robotic manipulator system. The performance of Fractional Order Fuzzy Proportional Integral Derivative and Integer Order Fuzzy Proportional Integral Derivative controllers is evaluated for reference trajectory tracking, changing beginning circumstances, disturbance rejection, and model uncertainty. These controllers' parameters are tuned using a meta-heuristic optimization approach called the most valuable player algorithm for the objective function, which is defined as the integral of the time-squared error. Simulation results show that the suggested Fractional Order Fuzzy Proportional Integral Derivative controllers outperform Integer Order Fuzzy Proportional Integral Derivative controllers for tracking performance, stability, and robustness for all structures. Fractional Order Fuzzy Proportional Derivative Fractional Order Proportional Integral Derivative controller is the best one for trajectory tracking, disturbances rejection, and parameter variation with the least integral of time square error equal to 2.7420×10-6, 3.4×10-3 and 2.0108×10-4 respectively and the response of the angular position for all links for trajectory tracking has minimum settling time which is equal to 0.0290 s for the first link, 0.0160 s for the second link and 0.0050 s for the third link. When the initial condition is changed, the One Block Fractional Order Fuzzy Proportional Integral Derivative controller is the best one, since the integral of time square error is minimum and equal to 1.6253×10-4.
Control
Mustafa Laith Muhammed; Amjad Jaleel Humaidi; Enass Hassan Flaieh
Abstract
The search algorithms are characterized by their ability to find the optimal path in a short calculation time. In this study, a comparative analysis has been conducted to perform path planning of planar manipulator for static obstacle avoidance based on graph search algorithms. Four methods have been ...
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The search algorithms are characterized by their ability to find the optimal path in a short calculation time. In this study, a comparative analysis has been conducted to perform path planning of planar manipulator for static obstacle avoidance based on graph search algorithms. Four methods have been taken into account to establish a comparison platform; namely, conventional A*, modified A*, Chaos A*, and circulation heuristic search (CHS) algorithms. The performance of comparison is evaluated in terms of length of optimal path and consumption time of calculation. All algorithms have been coded and simulated within the MATLAB software environment. According to computer simulation, the results showed that CHS algorithms outperform the other graph search ones in terms of generated path length, while the Choas A* could give the least calculation time as compared to its counterparts.
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.
Control
Ayat Naji Hussain; Sahar Adil Abboud; Basim Abdul baki Jumaa; Mohammed Najm Abdullah
Abstract
Human gait data follows distinct and identifiable patterns that are critical for movement analysis and evaluation Like other biological signals. The success of a rehabilitation program is dependent on the execution of proper progress monitoring. To ensure success, diagnosis of gait anomalies, as well ...
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Human gait data follows distinct and identifiable patterns that are critical for movement analysis and evaluation Like other biological signals. The success of a rehabilitation program is dependent on the execution of proper progress monitoring. To ensure success, diagnosis of gait anomalies, as well as the implementation of therapy to address them, must be validated in a constant and timely manner in developing youngsters. In this paper, machine learning techniques were utilized to classify foot diseases and the purpose is to increase the accuracy of disease detection and diagnosis because intelligent systems can contribute significantly in the medical field and have proven their worth in diagnosing many diseases. The results show high accuracy of the used machine learning algorithms, where the accuracy of the classifiers reached 100% for Random Forest (RF), Decision Tree (DT), and k-nearest neighbors (KNN), while it reached 98% for Logistic Regression.
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.
Computer
Salah Sabah Abed; Mohammed Natiq Fadhil
Abstract
The most attractive study framework among academics is Software Define Networking Networking SDN, which aims to create the Internet with an architecture- independent architecture that will lead to the most significant advances in the network field. This can solve many network problems to deal with high ...
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The most attractive study framework among academics is Software Define Networking Networking SDN, which aims to create the Internet with an architecture- independent architecture that will lead to the most significant advances in the network field. This can solve many network problems to deal with high demand changes and reduce Replenishment, changes and less manual work. Because of the limited architecture of traditional networks, which requires modifications in the basic design, network expansion has been mature and slow. Since 2010, until now, the ODL - OpenDayLight model has been proposed to solve most of the problems that guide network engineers in the process of managing complex high-volume networks by top research-oriented universities around the world. Now is the time to turn dreams into reality by putting the presented ideas into action, which will result in a solution that meets the expectations of the researcher regarding the process of managing complex networks and all forms of networks. This document is an attempt to assist researchers in implementing a software identification network infrastructure on which the research community may focus on further analysis and development. We demonstrated an incremental approach to implementing ODL - OpenDayLight Controller (ODL is a JVM program and can run from any operating system and device as long as it supports Java) from the Software Define Network, as well as creating and executing required scenarios, and illustrate the working nature of ODL - OpenDayLight compared to ryu contrlloer, in this paper Research.
Computer
Yasser A. Yasser; Ahmed T. Sadiq; Wasim AlHamdani
Abstract
Honeywords are fake passwords that are typically companions to the real password “sugarword.” The honeyword technique is a password cracking detection technique that works effectively to improve the security of hashed passwords by making password cracking simpler to detect. The password database ...
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Honeywords are fake passwords that are typically companions to the real password “sugarword.” The honeyword technique is a password cracking detection technique that works effectively to improve the security of hashed passwords by making password cracking simpler to detect. The password database will contain many honeywords for each user in the system. A silent alarm will trigger, indicating that the password database has been compromised if the hacker signs in using a honeyword. The honeychecker is a separate server in charge of recognizing the real password and raising the silent alarm. Many honeyword creation techniques have been presented previously. They all have limitations in the generating process, supporting characteristics, and strengths of honeyword. The bees algorithm, an optimization metaheuristic swarm intelligence algorithm, is used in this article to suggest a novel approach for generating honeywords. The proposed bee algorithm succeeded in addressing the limitations of the previous methods by enhancing the honeyword generating process, supporting the honeyword characteristics, and addressing the honeyword system problems. The most important characteristics of the honeyword (flatness, DoS resistance, and storage) were supported by the proposed method to present unconditionally flatness, strong DoS resistance, and moderate storage.
