Empirical Assessment of Software Quality Assurance Performs and Challenges in A Development

Quality assurance is critical in the process of software development because it ensures that the software system and other software products are reliable and simple to maintain. Quality standards, Quality control, process automation, quality management, quality planning, and improvement are only a few of the tasks that make up software quality assurance. While our work focused on quality planning, observance to standard methods, and the issues that come with them, our scope is to comprise quality control, software process development, and participation in global quality standards associations. The goal is to produce more reliable findings that can help the software community make better decisions. To collect data from software practitioners, a qualitative research technique was used, particularly the use of questionnaire research instruments

Measuring the Need of Using Virtual Laboratory Based on Multimedia Technology as a Teaching and Learning Tool for Colleges Students at Tikrit University

Over the years, virtual reality has become an essential part of education. Therefore, to determine the advantages and strengths of this technique and technology is understanding how can developed it and measured is crucial. In the same context, there has been changes in the educational field, Changing from the traditional lab environment to a virtual lab. Therefore, this study focuses on virtual laboratories. Indeed, the use of VL to remotely is to understand the practical part in an effective education. In other words, this study focused on educational research and student profiling in the virtual laboratory by making a qualitative analysis. So far, there is no research that provides student data connectivity in virtual labs and learning analytics. To develop this concept, the analysis for active learners and their learning understandings and knowledge was conducted at differents colleges of Tikrit University by using a qualitative method (discussion). To achieve that, this study aims to examine whether virtual laboratory is required in Tikrit University colleges to supporting traditional lab in the learning process. Finally, the results show that there is a strong need for the virtual lab to process the difficult practical aspects for some of the students in the sciences field such as medicine, dental, biology, and chemistry

A Systematic Review of Intelligent Smart Traffic Control Systems (ITCs) using Image Processing Techniques

Automatic number plate recognition (ANPR) is a technology to control traffic in smart cities. The ANPR technology has become very important in daily life with the addition of vehicles, traffic violations, and controlling traffic issues. This article aims to review different smart traffic control methodologies used to develop better results in the past few years. This paper presents a comprehensive review of intelligent smart traffic control systems (ITCs) using image processing techniques. We first investigate the major challenges, including the detection of number plate formats and the recognition of characters. We specifically investigate different ANPR techniques that include the acquisition, segmentation, classification, detection, and recognition of number plates. The paper determined that utilizing additional image processing techniques resulted in successful outcomes. Finally, the paper suggests potential areas for future study to improve the accuracy and correctness of image processing methods

A Systematic Review of Vehicle License Plate Recognition Algorithms Based on Image Segmentation

Recently, vehicle license plate recognition (VLPR) is a very significant topic in smart transportation. License plate (LP) has become an important and difficult research problem in recent years due to its difficulties such as detection speed, noise, effects, various lighting, and others. In the same context, most VLPR algorithms include should have many methods to be able to identify LP images based on different letters, colors, languages, complex backgrounds, distortions, hazardous situations, obstructions, vehicle speeds, vertical or horizontal lines, horizontal slopes, and lighting.  Therefore, this study provides a comprehensive review of current VLPR algorithms in the context of detection, and segmentation. Where, available VLPR algorithms are classified according to image segmentation methods (characteristics, and features) and are compared in terms of simplicity, complexity, uptime, problems, and obstacles

Performance analysis of medical image compression using DCT and FFT Transforms

There is a high demand for image compression since it reduces the computational time, which in turn reduces the storage and transmission costs. Image compression involves reducing excessive and irrelevant data while maintaining reasonable image quality. Image compression techniques such as the Discrete Cosine Transform (DCT) and Fast Fourier Transform (FFT) are the focus of this study. These tools were selected because of their wide application in image processing; one example is JPEG (Joint Photographic Experts Group), which uses DCT for compression. A comparison is made between DCT and FFT, two compression methods implemented in MATLAB. CT and MRI images are used for an experiment, the quality of an image is determined by various parameters. To perform DCT the filter mask is used and a threshold is used for FFT to keep the top coefficient values. The experimental findings are compared and evaluated in terms of Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR)

A New Ranking Technique to Enhance the Infection Size in Complex Networks

Detecting the spreaders/sources in complex networks is an essential manner to understand the dynamics of the information spreading process. Consider the k-Shell centrality metric, which is taken into account the structural position of a node within the network, a more effective metric in picking the node which has more ability on spreading the infection compared to other centrality metrics such the degree, between and closeness.  However, the K-Shell method suffers from some boundaries, it gives the same K-Shell index to a lot of the nodes, and it uses only one indicator to rank the nodes. A new technique is proposed in this research to develop the K-Shell metric by using the degree of the node, and a coreness of its rounding friends to estimate the ability of the node in spreading the infection within the network. The experimental results, which were done on four types of real and synthetic networks, and using an epidemic propagation model SIR, demonstrate that the suggested technique can measure the node effect more precisely and offer a unique ordering group than other centrality measures

Virtual Machine’s Network Security

Network virtualization has become progressively unmistakable lately. It enables the creation of organizational frameworks that are expressly tailored to the requirements of distinctive organizational applications and facilitates the introduction of favorable circumstances for the occurrence and evaluation of new designs and conventions. Despite the extensive materiality of organizational virtualization, the widespread use of communication channels and steering devices raises a number of safety-related issues. To enable their use in real, large-scale settings, virtual organization foundations must be given security. In this paper, we see the details of industry's top practices for virtual organization security. We discuss some of the major risks, the main challenges associated with this type of climate, as well as the arrangements suggested in the text that aim to handle various security vantage points. Virtualization is a notable thought having applications in different fields of registering. This strategy takes into consideration the production of numerous virtual stages on a solitary actual framework, taking into consideration the execution of heterogeneous models on a similar equipment. It might likewise be used to streamline the use of actual assets, on the grounds that a manager can progressively make and erase virtual hubs to satisfy fluctuated degrees of need. Virtual Machine’s Network Security is an important topic in today’s world, due to the rapid increase in the use of virtual machines. Virtual machines provide a more efficient, cost effective and secure way of running applications and services. However, there are some security risks associated with virtual machines which must be tackled to ensure the safety and security of the network. This paper presents security principal known as Nonrepudiation which authenticates the delivery of messages and transaction using Digital Signature method. Furthermore, an overview of the security threats and solutions associated with virtual machines and their networks, including the different types of threats, solutions and best practices to protect against them. Additionally, the paper discusses the importance of monitoring and logging in virtual machines. Finally, the paper concludes with a few recommendations for countermeasure the security of virtual machines and their networks

An Efficient Exponential Estimator of Population Mean in the Presence of Median of the Study Variable

Survey sampling practitioners have been working on efficiency improvement and bias reduction in finite population parameter estimation. We proposed an exponential estimator of population mean in the presence of median of study variable. The bias and mean square error of the proposed estimator were obtained using Taylor series method. The relative performance of the proposed estimators with respect to conventional and some existing estimators were assessed using three (3) natural dataset information. The novel median based estimator perform better than the conventional, usual mean, ratio, regression and other existing estimators considered in the study have been established. The empirical results shown that the proposed estimator is more efficient than the conventional and some existing estimators considered in the study

Enhancing Security and Energy Efficiency in Wireless Sensor Network Routing with IOT Challenges: A Thorough Review

Wireless sensor networks (WSNs) have emerged as a crucial component in the field of networking due to their cost-effectiveness, efficiency, and compact size, making them invaluable for various applications. However, as the reliance on WSN-dependent applications continues to grow, these networks grapple with inherent limitations such as memory and computational constraints. Therefore, effective solutions require immediate attention, especially in the age of the Internet of Things (IoT), which largely relies on the effectiveness of WSNs. This study undertakes a comprehensive review of research conducted between 2018 and 2020, categorizing it into six main domains: 1) Providing an overview of WSN applications, management, and security considerations. 2) Focusing on routing and energy-saving techniques. 3) Reviewing the development of methods for information gathering, emphasizing data integrity and privacy. 4) Emphasizing connectivity and positioning techniques. 5) Examining studies that explore the integration of IoT technology into WSNs with an eye on secure data transmission. 6) Highlighting research efforts aimed at energy efficiency. The study addresses the motivation behind employing WSN applications in IoT technologies, as well as the challenges, obstructions, and solutions related to their application and development. It underscores that energy consumption remains a paramount issue in WSNs, with untapped potential for improving energy efficiency while ensuring robust security. Furthermore, it identifies existing approaches' weaknesses, rendering them inadequate for achieving energy-efficient routing in secure WSNs. This review sheds light on the critical challenges and opportunities in the field, contributing to a deeper understanding of WSNs and their role in secure IoT applications

Impact of Edge Detection Algorithms on Different Types of Images using PSNR and MSE

Edge detection is the process of detecting sharp changes in image brightness in a digital image. It aids in the recognition of an object and its shape in an image. As a result, edge detection plays a vital role in image processing, especially in domains like segmentation, image registration, and object identification. This paper is an attempt to study the impact of several edge detection algorithms such as Sobel, Prewitt, Robert, Kirsch, Robinson, Laplacian of Gaussian (LOG) and Canny. The three different types of images such as medical , natural and satellite images are considered for experiment. Performance measures used for comparison are Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR)