Computer
Teaba Wala aldeen khairi
Abstract
Many companies recognize the importance of cloud computing all around the world. However, various worries keep businesses from adopting cloud computing. Data security, privacy, and trust difficulties are among them. Recently, there have been rapid developments in the progression of cloud computing services. ...
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Many companies recognize the importance of cloud computing all around the world. However, various worries keep businesses from adopting cloud computing. Data security, privacy, and trust difficulties are among them. Recently, there have been rapid developments in the progression of cloud computing services. This paper focuses on the design and implementation of the secure cloud services framework by providing secure and trusted storage for user data. Proposed framework generated an encryption key based on a chaotic map generator and encrypted user data. proposed work shows that integration of key with defensive options is more efficient than approaches from those categories of using external keys. A test has been applied on the frame work in cloud slime services and show the effectiveness of the proposed solution to provide secure cloud services. Our model of cloud services show valid ad promising performance with multiple users trail.
Computer
Mohammed Abdulwahid Jebur; Hasanen Abdullah
Abstract
The university courses timetabling problem (UCTP) is a big topic among academics and institutions since it occurs every academic year. In general, UCTP is the distribution of events across slots time for each room based on a list of restrictions provided in one semester, such as (hard constraint and ...
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The university courses timetabling problem (UCTP) is a big topic among academics and institutions since it occurs every academic year. In general, UCTP is the distribution of events across slots time for each room based on a list of restrictions provided in one semester, such as (hard constraint and soft constraint), with the objective of avoiding conflicts in such assignments. Hard constraints should never be breached when striving to satisfy as many soft constraints as possible. There are many different methods used in automating the problems of the university timetabling course in higher education institutions. This paper presents an improved algorithm for the Meerkat Clan to solve the UCTP. This is done by studying the behavior of the Meerkat Clan Algorithm and Specifying the points that are able to improve without changing the main behavior of the Meerkat Clan Algorithm. And by testing with four datasets of different sizes, good results were obtained by optimizing this algorithm.
Control
Fatimah I. Hussein; Safanah Raafat
Abstract
The control technique for an exoskeleton system for lower limb rehabilitation is complicated, and numerous internal and external elements must be taken into account, in addition to the uncertainties in the system model. In this paper, through the analysis of the lower extremity exoskeleton is utilized ...
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The control technique for an exoskeleton system for lower limb rehabilitation is complicated, and numerous internal and external elements must be taken into account, in addition to the uncertainties in the system model. In this paper, through the analysis of the lower extremity exoskeleton is utilized to obtain the corresponding equation and its linearized form. The nonlinear differential equations have been linearized by using Jacobean’s method in order to facilitate the controller design. Considering the interior and external factors of the connecting rod, the uncertain elements are introduced and therefore the optimal control technique is applied to regulate the system. An optimal state feedback control strategy of Linear Quadratic Regulator (LQR), and LQR-Servo have been implemented in this work. Finally, the physical parameters of the Knee-Ankle Orthosis (KAO) exoskeleton are used, and the simulation results show the advantage and applicability of the proposed controller’s design of the Knee-Ankle orthosis system.
Computer
Wildan J. Jameel; Suhad M. Kadhem; Ayad R. Abbas
Abstract
The main reason for the emergence of a deepfake (deep learning and fake) term is the evolution in artificial intelligence techniques, especially deep learning. Deep learning algorithms, which auto-solve problems when giving large sets of data, are used to swap faces in digital media to create fake media ...
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The main reason for the emergence of a deepfake (deep learning and fake) term is the evolution in artificial intelligence techniques, especially deep learning. Deep learning algorithms, which auto-solve problems when giving large sets of data, are used to swap faces in digital media to create fake media with a realistic appearance. To increase the accuracy of distinguishing a real video from fake one, a new model has been developed based on deep learning and noise residuals. By using Steganalysis Rich Model (SRM) filters, we can gather a low-level noise map that is used as input to a light Convolution neural network (CNN) to classify a real face from fake one. The results of our work show that the training accuracy of the CNN model can be significantly enhanced by using noise residuals instead of RGB pixels. Compared to alternative methods, the advantages of our method include higher detection accuracy, lowest training time, a fewer number of layers and parameters.
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.
Computer
Sama Salam Samaan; Hassan Awheed Jeiad
Abstract
Modelling computer networks in general, particularly Software Defined Networking (SDN) as a graph, is beneficial in network planning and design, configuration management, traffic analysis, and security. According to the dynamic nature of SDN, it needs a fast response due to the rapid changes in the network ...
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Modelling computer networks in general, particularly Software Defined Networking (SDN) as a graph, is beneficial in network planning and design, configuration management, traffic analysis, and security. According to the dynamic nature of SDN, it needs a fast response due to the rapid changes in the network state. The SDN network topology can be modelled as a graph and stored in a graph database, and the traffic load of each switch is stored in the created graph. Consequently, a graph processing framework can be used to process the stored traffic data, and the results are utilized in traffic engineering to assist the SDN controller in network management. This paper provides a comprehensive literature survey involving graph techniques applied to SDN. Then, a summary of graph algorithms is presented. In addition, an overview of graph databases and graph processing frameworks is displayed. Finally, a model is suggested to integrate the graph database and graph processing framework in SDN traffic analysis.
Control
Zahraa Ali Waheed; Amjad Jaleel Humaidi
Abstract
Physiotherapeutic exoskeleton devices have recently been developed to helppeople rehabilitate impaired limb mobility and replace the use of physiotherapists. Suchsystems are characterized by high nonlinear and time-varying coefficients. In order tocope with such difficult control challenges, a need arose ...
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Physiotherapeutic exoskeleton devices have recently been developed to helppeople rehabilitate impaired limb mobility and replace the use of physiotherapists. Suchsystems are characterized by high nonlinear and time-varying coefficients. In order tocope with such difficult control challenges, a need arose for reliable nonlinearcontrollers. While in this study the Sliding Mode Control (SMC) was used to track thetrajectory of the knee exoskeleton-system (KES) while having parameter uncertainty. Inaddition, the whale optimization algorithm (WOA) was introduced and developed toadjust the thickness design parameters for further optimization of its performance. Thesimulation was performed on a calculator using the MATLAB-Simulink program toconduct a comparative study between the optimal and Classical SMC where the resultsof comparison with the test parameters used by the SMC showed, the results of theproposed optimal SMC revealed that the positioning inaccuracy of the knee increased by31.8807% and it follows from this result that the controller could successfully performtracking the track well. Also, the control system created at the optimal thickness has abetter dynamic performance than the classical thickness.
Sura Sabah Rasheed; Ahmed T. Sadiq
Volume 21, Issue 2 , June 2021, , Page 132-142
Abstract
Social media have been increasing obviously and widely due to the fact that it is a mediafor users who express their emotions using reviews and comments on a variety of areas in life. In thepresent study, a modest model has been suggested for the assessment of service departments with theuse of reviews ...
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Social media have been increasing obviously and widely due to the fact that it is a mediafor users who express their emotions using reviews and comments on a variety of areas in life. In thepresent study, a modest model has been suggested for the assessment of service departments with theuse of reviews and comments in social media pages of those departments from various governorates.The utilization of the text mining for the sentiment classification has been used through collectingIraqi dialect reviews on service department pages on Facebook to be analyzed with the use of thesentiment analysis to track the emotions from the comments and posts. Those have been classifiedafter that to positive, neutral or negative comment with the use of the algorithms of Naive Bayesian,Rough Set Theory, and K-Nearest Neighbors. Out of 13 Iraqi capital (Baghdad) service departmentshave been tackled, it has been found that 11% of those departments had very good assessment, 18%from these service departments have good assessment, 21% from these service departments havemedium assessment, 24% from these service departments have acceptance assessment and 26% fromthese service departments have bad assessment. The results of the evaluation showed the poorservices provided by service departments in the capital Baghdad. Experimental results were helpfulfor the service departments in improving their work and programs had responded quickly andsufficiently to the customer demands.
Computer
shayma Ashor; Hanaa Mohsin Ahmed
Abstract
In the last few years, many applications have viewed great development, such as smart city applications, social media, smartphones, security systems, etc. In most of these applications, facial recognition played a major role. The work of these applications begins by locating the face within the image ...
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In the last few years, many applications have viewed great development, such as smart city applications, social media, smartphones, security systems, etc. In most of these applications, facial recognition played a major role. The work of these applications begins by locating the face within the image and then recognizing the face. The circumstances surrounding the person at the moment of taking the picture greatly affect the accuracy of these applications, especially the inappropriate lighting. Therefore, the stage of preparing the images is very important in the work. To solve this problem, we proposed a system that combines the use of gamma and Histogram Equalization algorithm (HE) to improve the images before starting to detect the face using the Viola-Jones. Then extract the facial features and identify the person using convolutional neural networks. The proposed system achieved a very small error rate and an accuracy during training that reached 100%.
Computer
Roaa E. Alhassany; Rana F. Ghani
Abstract
In recent years, especially with COVID-19, video conference applications have become very important. Millions of peoples around the world have become to communicate with each other through using video conference applications. The most critical factor in the performance success of video conference applications ...
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In recent years, especially with COVID-19, video conference applications have become very important. Millions of peoples around the world have become to communicate with each other through using video conference applications. The most critical factor in the performance success of video conference applications is the user's perception of the quality of the experience. In this work, an Extreme Learning Machine (ELM) model was proposed for predicting video quality of experience. The proposed system extracts several features from videos that have a significant impact on the quality of the experience. The model performance was validated with unseen data. Spearman’s Rank Correlation Coefficient (SRCC), Root Mean Square Error (RMSE), Pearson’s Linear Correlation Coefficient (PLCC) metrics have been used to measure the accuracy of the model and correlation. The results demonstrate that the proposed model had better performance than models used by the previous researchers that were used for predicting video QoE in terms of precision, correlation, and running time.
Computer
Mohammed Abduljabbar Ali; Abir Jaafar Hussain; Ahmed T. Sadiq
Abstract
Crowd detection has various applications nowadays. However, detecting humans in crowded circumstances is difficult because the features of different objects conflict, making cross-state detection impossible. Detectors in the overlapping zone may therefore overreact. The proposal uses the YOLO v5 (You ...
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Crowd detection has various applications nowadays. However, detecting humans in crowded circumstances is difficult because the features of different objects conflict, making cross-state detection impossible. Detectors in the overlapping zone may therefore overreact. The proposal uses the YOLO v5 (You Only Look Once) method to improve crowd recognition and counting. This algorithm is entirely accurate and detects things in real-time. The idea relies on edge enhancement and pre-processing to solve overlapping feature regions in the image and improve performance. The CrowdHuman data set is used to train YOLO v5. The system counts the number of humans in the image to detect a crowd. Before training, this model enhanced the image with several filters. The YOLO v5 algorithm distinguishes a person inside a crowd by utilizing the surrounding box on the head and overall body. Therefore, the number of head detection is x- coordinated compared to the body. Assume the detected heads outnumber the bodies. A square of the head will be extracted, but not a body square. Also, cropping the image reduces interference between human beings and enhances the edge features. Thus, YOLOv5 can detect it. The idea improves head and body detection by 2.17 and 4.1 percent, respectively.
Computer
Samara Mohammed Radhi; Raheem Ogla
Abstract
Securing information is difficult in the modern internet era, asterabytes of data are generated daily online and online transactions occurvirtually every second. The current world's information security relies heavily oncryptography, which makes the internet a safer environment. Making informationincoherent ...
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Securing information is difficult in the modern internet era, asterabytes of data are generated daily online and online transactions occurvirtually every second. The current world's information security relies heavily oncryptography, which makes the internet a safer environment. Making informationincoherent to an unauthorized person is done through the use of cryptography.Providing legitimate users with confidentiality as a result. There are a widevariety of cryptographic algorithms suitable for this purpose. An idealcryptography method would allow the user to do their job without breaking thebank. Unfortunately, there is no magic formula that can address everyissue.Several algorithms balance cost and performance. A banking applicationneeds robust security at a high cost, while a gaming software that sends userpatterns for analytics cares more about speed and cost. Thus, choosing theappropriate encryption technique for the user. This study offers importantinsights in the process of selecting cryptographic algorithms in terms of eachalgorithm's strengths, weaknesses, cost, and performance . In order todemonstrate an entire performance analysis in this article, as opposed to justtheoretical comparisons, this research developed and thoroughly examined thecost and performance of commonly used cryptographic algorithms, includingDES, 3DES, AES, RSA, and blowfish. According to the findings, blowfishrequires the smallest amount of time to decrypt files of various sizes (25K, 50K,1M, 2M, 3M, and 4M), and it also consumes the smallest amount of memory. Thismakes it approximately three times faster than other cryptographic algorithms.
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
Sarah Saadoon Jasim; Alia Karim Abdul Hassan; Scott Turner
Abstract
Artificial Neural Networks (ANNs) are utilized to solve a variety of problems in many domains. In this type of network, training and selecting parameters that define networks architecture play an important role in enhancing the accuracy of the network's output; Therefore, Prior to training, those parameters ...
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Artificial Neural Networks (ANNs) are utilized to solve a variety of problems in many domains. In this type of network, training and selecting parameters that define networks architecture play an important role in enhancing the accuracy of the network's output; Therefore, Prior to training, those parameters must be optimized. Grey Wolf Optimizer (GWO) has been considered one of the efficient developed approaches in the Swarm Intelligence area that is used to solve real-world optimization problems. However, GWO still faces a problem of the slump in local optimums in some places due to insufficient diversity. This paper proposes a novel algorithm Levy Flight- Chaotic Chen mapping on Wolf Pack Algorithm in Neural Network. It efficiently exploits the search regions to detect driving sleepiness and balance the exploration and exploitation operators, which are considered implied features of any stochastic search algorithm. Due to the lack of dataset availability, a dataset of 15 participants has been collected from scratch to evaluate the proposed algorithm's performance. The results show that the proposed algorithm achieves an accuracy of 99.3%.
Computer
hala khalid Hussien; Ra'ad A. Muhajjar; Bashar saadoon mahdi
Abstract
Ease of access to digital images and the many images editing programs available, like photoshop. All this makes the Issue of protecting images against modification becomes essential. Some images contain crucial information that can risk a patient's life, such as medical images and e- government images ...
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Ease of access to digital images and the many images editing programs available, like photoshop. All this makes the Issue of protecting images against modification becomes essential. Some images contain crucial information that can risk a patient's life, such as medical images and e- government images that relate to citizen information and state or ministry security. The watermark was one of the essential methods for this type of protection, especially the fragile watermark, which is very sensitive to any attack. Because of its other characteristics, it was one of the techniques that proved its efficiency in detecting tampering and the authenticity of images— also, watermarking focuses on protecting the image itself, not about protecting the secret message. A fragile watermark is a watermarking which inserts some information to cover an image to secure it .fragile watermarking could use in such a way and implement in spatial or frequency domain or in both so, making it a hybrid watermarking scheme. The Paper presented set of fragile watermark techniques used by the researchers with the performance metrics of an algorithm used in spatial and frequency domains, also showing how to use artificial intelligence with a watermarking technique to protect Document images from manipulation and forgery.
Communication
Rawia Abdullah Muhammed; Maisa'a Abid Ali Al-Dabbas; Ashwak Mahmood Alabaichi
Abstract
The Internet of things (IoT) is one of those emerging technologies, which aregoing to rule the world in the next few decades. The IoT environment not only enablesHuman to Machine interaction but also fosters Machine to Machine connectivity.Numerous IoT devices have poor security and insufficient computing ...
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The Internet of things (IoT) is one of those emerging technologies, which aregoing to rule the world in the next few decades. The IoT environment not only enablesHuman to Machine interaction but also fosters Machine to Machine connectivity.Numerous IoT devices have poor security and insufficient computing power, making themprime targets for hackers. The IoT environment uses lightweight cryptographic techniquesto address security requirements. Another security method for IoT devices issteganography. In the contemporary Internet era, the ability to secure private informationis crucial, and steganography offers this capability. Due to its great ability to concealsensitive data, video has drawn the attention of numerous academics among all forms ofdigital media. The main goal of this work is to examine several methods for fusing videosteganography and cryptography techniques. Additionally, a thorough investigation andevaluation of a variety of video steganography methods in both compressed and rawdomains are also emphasized. The comprehensive analysis of prior material makes iteasier to have in-depth knowledge while creating approaches that combine cryptographywith steganography.
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
Rana Mohammed Zaki; Hala Bahjat Abdul wahab
Abstract
Thomas and Patrik are working on a stream cipher called SNOW 3G. In 2006, it was chosen as the centerpiece of a new set of confidentiality and integrity algorithms for the Universal Mobile Telecommunications System (UMTS). In 2008, Böhm published an article named "Statistical Evaluation of Stream Cipher ...
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Thomas and Patrik are working on a stream cipher called SNOW 3G. In 2006, it was chosen as the centerpiece of a new set of confidentiality and integrity algorithms for the Universal Mobile Telecommunications System (UMTS). In 2008, Böhm published an article named "Statistical Evaluation of Stream Cipher SNOW 3G." He put the randomness of the SNOW 3G key stream generator to the test. As a randomness test tool, Böhm uses the NIST test statistics package, which consists of three kinds of tests: lengthy key stream data, short key stream data, and initialization vector data. Only the short key stream set of data failed eight random chance test results out of three kinds of tests, according to the report's findings. The SNOW 3G suggestions, he claims, fail due to a flaw with in cipher's initialization. In this paper, we use the PLL algorithm to modify the SNOW 3G algorithm for key initialization and generation keystream. We employ the same itself Böhm employed. The modify SNOW 3G algorithm exceed whole of the statistic exam in a experiment. The findings show that PLL has an effect on algorithm randomness.
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
Zainab Kareem Mahyob; Raheem AbdulSahib Ogla; Suhiar Mohammed Zeki
Abstract
The Internet is a massive network that connects millions of users from all over the world and the data transmitted via it needs great protection, especially since that are in the age of big data. To solve part of this problem, IPsec was utilized, which is a set of protocols necessary to offer security ...
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The Internet is a massive network that connects millions of users from all over the world and the data transmitted via it needs great protection, especially since that are in the age of big data. To solve part of this problem, IPsec was utilized, which is a set of protocols necessary to offer security to units of the Internet in general and the IP layer in particular. It is mostly based on major exchange protocols. The most frequent mechanism for transferring key materials and establishing security linkages between two entities is Internet Key Exchange (IKE). In the present work, it is proposed to use a public key that works together with Diffie-Hellman cryptography and the main advantages of a single-stage contribution (as opposed to the two-stage in standard IKE) it is better in terms of improved transfer and time (more time for the corresponding negotiation) to make the proposed IKE more secure with Simple account constraints
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
Rasheed Abdul Ameer Rasheed; Ahmed Sabah Al-Araji; Mohammed Najm Abdullah; Hamed S Al-Raweshidy
Abstract
A client-server network is one of the most important topics in a computer network. In this work, a real-time computer control system is designed based on the Client-Server Model for a Multi-Agent Mobile Robot System (CSM-MAMRS) and is applied to a building that consists of (N) floors and uses one mobile ...
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A client-server network is one of the most important topics in a computer network. In this work, a real-time computer control system is designed based on the Client-Server Model for a Multi-Agent Mobile Robot System (CSM-MAMRS) and is applied to a building that consists of (N) floors and uses one mobile robot on each floor that has specific actions in different types of environments. The new proposed CSM-MAMRS consists of four layers. The first stage manages the network communication for each agent. The second stage solves the major problems of path planning by using a proposed hybrid algorithm that combines the Rapidly Exploring Random Tree Star (RRT*) and the Particle Swarm Optimization (PSO) algorithms in order to provide the shortest and smoothest path with collision avoidance between the starting and the target points in a static and dynamic robot environments are used.. In the third stage, a velocity planner controller is based on an inverse kinematic mobile robot model. In the fourth stage, the Hypertext Transfer Protocol HTTP is used to send velocity values to real mobile robots via the Wireless Network Control Administration. The simulation results and experimental works successfully achieved to significant improvement in real-time when using three missions of three mobile robots on different static map floors in the building. The maximum tracking pose errors for three robots in the static enviormnets are 0.39 cm, 0.02 cm, 0.32 cm respectively, but the maximum tracking pose error in the dynamic environments are 2.94 cm and 2.8 cm only for two mobile robots along the maximum distance of 250 cm.
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.
Mohamed J. Mohamed; Mustafa Y. Abbas
Abstract
One of the major problems in the field of mobile robots is the trajectory tracking problem. There are a big number of investigations for different control strategies that have been used to control the motion of the mobile robot when the nonlinear kinematic model of mobile robots was considered. The trajectory ...
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One of the major problems in the field of mobile robots is the trajectory tracking problem. There are a big number of investigations for different control strategies that have been used to control the motion of the mobile robot when the nonlinear kinematic model of mobile robots was considered. The trajectory tracking control of autonomous wheeled mobile robot in a changing unstructured environment needs to take into account different types of uncertainties. Type-1 fuzzy logic sets present limitations in handling those uncertainties while type-2 fuzzy logic sets can manage these uncertainties to give a superior performance. This paper focuses on the design of interval type-2 fuzzy like proportional-integral-derivative (PID) controller for the kinematic model of mobile robot. The firefly optimization algorithm has been used to find the best values of controller’s parameters. The aim of this controller is trying to force the mobile robot tracking a pre-defined continuous path with minimum tracking error. The Matlab simulation results demonstrate the good performance and robustness of this controller. These were confirmed by the obtained values of the position tracking errors and a very smooth velocity, especially with regards to the presence of external disturbance or change in the initial position of mobile robot. Finally, in comparison with other proposed controllers, the results of nonlinear IT2FLC PID controller outperform the nonlinear PID neural controller in minimizing the MSE for all control variables and in the robustness measure.
