IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING

Robotics involves designing, simulating, and controlling robots for various industries and daily tasks. Manipulator arm robots are precisionoriented industrial robots designed for one-plane movement. Planar robotic manipulators are used in cargo loading, assembly lines, handling radioactive materials, and military applications. In this study theoretical analysis of manipulator robot kinematics using Denavit- Hartenberg notation and the Euler-Lagrange method for a three-degree of freedom (3- DOF) system, requiring an advanced controller for accurate control. A new hybrid controller, Computed Torque Control-Neural-Network -Grey Wolf Optimization (CTC- NNGWO), is proposed for path planning of 3-DOF MR. It uses kinematics and dynamic models, computes torque magnitudes of stepper motors, and denormalizes output values from NN. MATLAB programming is used for the simulation of the theoretical result. A comparison study of CTC-NN-GWO and CTC-PSO found that CTC-NN-GWO outperforms CTC-PSO in minimizing path planning errors, the maximal value of the error of joint angular position ????????1 is (0.29 rad), whereas ????????2 and ????????3 are approximately (0.15 rad) and (0.07 rad), respectively, also, reducing torque magnitudes, improving the performance of 3-DOF MR.

unlike continuously triggered techniques, event-triggered strategies update the control actions based on specific conditions. Consequently, the energy consumption will be reduced while maintaining performance and stability. Compared to the traditional (periodic) implementation of SMC, event-triggered sliding mode controller (ET-SMC) requires 101 control action modifications, 33.9 % less than periodic SMC implementation. The proposed triggering rule technique is shown to be feasible and closed-loop stable. A positive lower bound for inter-event execution time prevents Zeno- type behavior. The mathematical model of a linear time invariant system which is chosen to be a Direct Current servo motor (DC servo motor) is simulated to show the advantages of the recommended technique over conventional sliding mode technique. The Direct Current servo motor (DC servo motor) is chosen as the mathematical model of a linear time invariant system which simulated to show the advantages of the recommended technique over conventional sliding mode technique.

Ransomware is a type of computer malware that is currently widespread and highly dangerous. Ransomware attacks have become a major cybersecurity risk, posing significant risks to individuals and organizations alike. Also, traditional techniques for malware analysis, such as signature matching and heuristics, are no longer viable due to the exponential growth of malware. Researchers have explored various approaches to address this issue, but there is a lack of proper documentation and comparison of existing works. This paper presents an analysis of ransomware detection systems, which is part of an ongoing research project aiming to develop an open-source ransomware detection system to address the identified gaps in the field. To prevent such attacks, it is recommended to regularly backup files and avoid clicking on untrusted email links and attachments. Machine learning has been suggested by researchers as a more effective method of detecting malware. With internet use on the rise, honeypots offer a viable option for reducing security threats and safeguarding classified data from hackers. Honeypots are regarded as valuable resources for thwarting attacks and giving important insight about the origin and behavior of such attacks, which is useful for analysts who conduct such investigations. This paper provides a general view of cybersecurity, machine learning (ML), cyber threats, and honeypot systems towards mitigating system attacks and understanding their origin and behavior.

Intelligent traffic systems are emerging and becoming part of smart city infrastructure that requires computer vision applications to provide traffic information like vehicle classification and counting in real-time. Vehicle counting helps detect heavy traffic on roads, and vehicle classification helps enforce further processes like speed estimation to enforce speed limit laws based on the vehicle class. Deep learning computer vision-based systems provide automatic feature extractions that are robust to changes in lighting, shadows, and occlusions. This paper proposes a software solution for a real- time traffic monitoring system based on a cutting-edge single-stage deep learning model through the state-of-the-art YOLOv8 algorithm. YOLOv8 is the most recent model of the YOLO family, which provides object detection and classification through its CNN architecture. The proposed work detects vehicles and counts them based on their class. The four common vehicle classes are sedan cars, buses, trucks, and motorcycles, and a counter for each class is displayed on the system’s output screen in real-time and recorded in a log file. The results of the proposed system running on the Nvidia GTX 1070 GPU show an average accuracy of 96.58% with an average error of 3.42% for vehicle detection and an average accuracy of 97.54% with a 2.46% average error for vehicle counting. For vehicle classification, the results for the four vehicle classes (car, bus, truck, and motorcycle) show an accuracy of (94.7%, 94.7%, 96.2%, 99.7%), precision (95%, 100%, 81.4%, 100%), recall (97.9%, 36.3%, 100%, 66.6%), and the f1-score (96.3%, 53.2%, 89.7%, 79.9%), respectively.

The retina, a layer of light-sensitive tissue on the inner surface of the eye, is what allows for vision. That propose to design and analytically examine an image quantizer coding that is inspired by the way the retina manages and compresses visual features based on past studies. Retina-inspired coding is a new cipher algorithm which is very promising. Comparing the Double-Density Discrete Wavelet Transform (DDDWT) to earlier traditional approaches, it is more difficult but also produces excellent results. This efficient coding is credited to a streamlined account that, by the way, deals with 2-D and 3-D pictures, transformation matrices, and the conversion of the number DDWT as if via matrix multiplication. Naturally, this code makes a lot of interesting points of view available. In this proposal, we'll try to show how various theoretical model extensions can be applied to applications in video surveillance, information technology, and neuroscience. Therefore, that believe that using a multidisciplinary approach will help system achieve objectives. This strategy would apply signal processing methods with data from neurophysiology. There are hoping that our efforts will result in some innovative new coding algorithms. The proposed codes will be implemented in MATLAB for ease of experimentation. To implement this code more successfully, a low-level programming language is required.

Maintenance scheduling is a critical challenge in all major industrial sectors, such as aeronautics, automotive manufacturing, and power generation plants. The goal of power plant maintenance scheduling is to establish a schedule plan for carrying out preventive maintenance shutdowns for each unit within a defined planning horizon. In this paper, a mathematical model for optimizing the maintenance schedule was established to maximize the supply, minimize fuel costs, and CO2 emissions from generating units. A developed Whale Optimization Algorithm called Binary Pareto Multi-Objective Whale Optimization Algorithm BPMOWOA is proposed and implemented to find optimal maintenance scheduling for a power plant. The proposed algorithm uses two different approaches. The first approach includes binary encoding, in which each generating unit and each time interval are represented in binary form. Then, in the second approach, a fixed-sized repository is integrated into the WOA for saving and retrieving the Pareto optimal solutions, and a grid mechanism is integrated into the WOA to maintain diversity in the population of non-dominated solutions. A case study was conducted on fourteen generating units adapted from a real-world power plant to validate the algorithm's efficiency. The results illustrate that the proposed algorithm was effective in optimizing the maintenance schedule in terms of coverage and non-dominated solution and improved the power plant performance by increasing electricity generation by 11.48% and decreasing fuel expenses by 6.56%, which are the main goals of the considered power plant.

Medical imaging is a sensitive and important type of data that requires robust encryption to protect it during transmission over networks. Confidentiality is a critical aspect to consider when securing information systems, and encryption techniques. These images have unique characteristics, including large data size, redundancies, and similarities between neighboring pixels, making them different from text. To confirm the security of medical images and prevent unauthorized access or manipulation, highly secure algorithms are necessary due to the rapid advancement of network technologies. This survey aims to explain the different methods currently used to protect medical data, such as encryption, steganography, steganography, and methods of hiding, as well as the difference between full encryption of the medical image and partial encryption only of the important sections of the images. In order to arrive at the most effective approach, a number of prior experiences and studies were used. This article discusses recent advancements in this field, highlighting the need for secure encryption schemes to withstand adversarial attacks and ensure the confidentiality of medical images.

Plug-in electric vehicles (PEVs) present a promising solution for decreasing greenhouse gas emissions and conserving natural oil reserves. However, the growing number of PEVs connected to the electric grid raises concerns regarding the reliable and safe operation of the network. This concern stems from the fact that the charging operation of electric vehicles (EVs) involves a significantly high level of electricity consumption due to the size of EVs' battery charging period. Unscheduled charging activities can lead to increased electricity consumption, increase in PEV charging costs and overload on the distribution grid. To address this issue, this paper proposes a coordinated charging schedule for PEVs, utilizing an optimized EV battery charging model with an optimal control method that deals with finding the best possible control signal for a dynamic system over period of time aiming to optimize a particular performance index, while satisfying various constraints such as boundary condition, state, and control path. The results demonstrate that the proposed optimized charging schedule reduces charging costs by up to 21% compared to an unscheduled charging pattern.