Al-Iraqia Journal for Scientific Engineering Research

Utilizing blockchain technology for different applications has seen a growing interest in recent years. Academic institutions can consider implementing a student information system (SIS) based on blockchain technology as a promising area. This research paper explores in detail the design and execution aspects of an SIS powered by blockchain technology implemented through ASP.net, C#, Ethereum, Solidity, Ganache and IPFS. The main goal of the proposed system is to tackle important problems concerning data security and accessibility in conventional paper-based or centralized systems. In the proposed SIS system based on blockchain technology there are five distinct user categories: students, stage managers, department managers, college managers, and super administrators. Every user possesses specific functionalities like accessing grades, attendance, subjects, schedules, and profiles. Data integrity and privacy are ensured by the system's design using cryptographic techniques and smart contracts implemented in Solidity. The paper also addresses the technical implementation aspects, involving using ASP.net and C# to develop both the user interface and backend logic. To store decentralized data, the Ethereum blockchain is utilized. The proposed system is evaluated through a series of experiments and performance tests. The assessment of the blockchain-based SIS includes evaluating its efficiency, security, and usability through these tests. The paper highlights the effectiveness of the system in enhancing data security, accessibility, and transparency by providing detailed results and findings.

This study presents a pioneering quantum encryption technique, the Generalized Novel Enhancement Quantum Representation (GNEQR), to ensure enhanced security for color medical images in E-health systems. The proposed method is seamlessly integrated into an ASP.NET Web API and Blazor Web Assembly environment. During the encryption process, the GNEQR algorithm utilizes advanced bit-plane scrambling to obscure the original color medical image. A 5D chaotic map is employed to generate an image key, which is then used to create a scrambled version of the key image. Applying an XOR operation between the scrambled image and the key image produces a highly secure quantum-encrypted color image. In the decryption process, the inverse of the encryption steps is implemented, leveraging the 5D chaotic map-generated key to accurately reconstruct the original color medical image. This research aims to significantly enhance E-health security by protecting patient data, including medical images and histories, through quantum encryption. The incorporation of GNEQR within an ASP.NET Web API and Blazor Web Assembly framework facilitates seamless deployment and underscores the potential of this method to revolutionize healthcare data privacy and integrity.

Author Biographies

The Internet of Things (IoT) has recast the way we interact with technology and the world around us. The IoT is almost everywhere in our daily lives, and this huge growth in using the IoT increases the need for implementing a high level of security framework for this technology. This study presents an analysis of some of the recent IoT cyber-attack detection systems to provide an evaluation of these systems and useful future research directions in this field. This analysis is introduced along with an overview of the IoT's security challenges and solutions and the types of security attacks in the IoT environment. Although the most recent approaches to cyber-attack detection have high percentages of attack detection accuracy, these classical DL models that were learned with local datasets need more enhancement to maintain privacy and data storage when involved in networks of cooperative nodes in IoTs. Furthermore, current techniques still lack the ability to provide a generalization for new attacks, cover the binary and multiclass classifications of cyber-attacks, and develop feature selection algorithms to reduce dataset dimension.‎

Facial expression recognition (FER) is a significant area of study in computer vision and affective computing. In numerous applications, such as human-computer interaction, emotion detection, and behavior analysis. Feature extraction is a crucial stage in facial expression recognition systems, as it involves extracting pertinent information from facial images in order to accurately represent various facial expressions. The purpose of this paper is to investigate and compare the various feature extraction techniques used in facial expression recognition, as well as their merits, limitations, and impact on overall system performance. Using benchmark datasets and performance metrics, the evaluation provides insight into the efficacy of various feature extraction methods. In this study, we propose a method for facial expression recognition that integrates deep learning, Principal Component Analysis (PCA), and Gray Level Co-occurrence Matrix (GLCM). Use 1D-CNN for classification.

Image compression is a process applied to reduce the size of an image file, byte by byte, without reducing its quality acceptability levels. By reducing the file size, more images can be stored dona given amount of disk or memory space. Thus, bandwidth is required when images are transmitted over the Internet or downloaded from a web page, reducing network congestion and speeding contented delivery. The proposed system adopted the LZW algorithm to compress images without loss. This algorithm was named after the names of the scientists who developed this algorithm in recognition of their efforts, namely Abraham Lempel, Jakob Ziv and Terry Welch, which is based on a dynamic dictionary, where this dictionary checks the file for duplicate data and then stores it in the dictionary, where this algorithm replaces strings of characters with individual symbols, as this proposed system performs 12-bit codes, and it was applied to many images and we got impressive results. The importance of this system lies in the benefit it provides by reducing the size of the image file without losing data, and thus reducing the space needed to store and send it easily over the Internet and high quality. The Python language has been used to implement this system.

Thermoelectric coolers (TEC) are devices that conversion of electrical power into a temperature gradient. TEC modules have the benefits of high dependability, small dimensions, lightweight, absence of mechanical moving parts and working fluid. Thermoelectric cooling systems have experienced significant utilisation in recent years. These technologies have been used in automobiles to cool and maintain the quality of goods during transportation and in portable cooling bags, computers, and medical apparatus. The TEC manufacture datasheet only offers limited data on characteristics and maximum parameters, which is insufficient for studying thermoelectric systems. This paper presents an analytical modelling methodology that provides a direct method for assessing the performance of module parameters and determining the fundamental thermal physical parameters of a TEC. Furthermore, this article outlines the procedure for choosing the appropriate TEC module from a selection of three options to integrate it with a vapour compression refrigeration (VCR) system. The objective is to attain low temperatures while reducing power consumption and improving performance. The findings indicates that The TEC1-12706 module was the most suitable option among three different types of TEC modules for integration with the VCR system to achieve low temperatures. This choice was based on its lower power consumption than the TEC127010 and TEC127015 modules, resulting in 36.5% and 59.3% energy savings, respectively. Additionally, the heat dissipation from the hot side of the TEC12706 is lower than the TEC12710 and TEC12715 modules by 32.2% and 59%, respectively.

This study investigates the impact of Particle Swarm Optimization (PSO) on power loss and production cost reduction in overloaded transmission lines. PSO is used to manage power flow in congested transmission lines and reduce power losses. The proposed technique was tested using the IEEE large-scale 57-bus test system. Optimal power flow (OPF) is crucial for modern power systems, aiming to minimize active and reactive power distribution while considering technological and economic constraints. PSO is presented as a solution to the OPF problem, focusing on actual power losses, hypothetical power losses, and overload consequence. PSO is presents.as a solution, and various methods, such as genetic algorithms and linear programming, will be used to compare PSO results. The suggested PSO yields reduced power losses with higher loads in case studies. The approach is evaluated for IEEE -57 buses and produces superior results when used with the MATLAB environment. PSO reduces the target function's real power loss from its starting condition (24.98) MW to its ideal state (16.75) MW with a reduction of 8.23 MW. Additionally, PSO reduces the target function's active power loss by 20% overload from its initial state (56.042 MW) to the optimal state of 46.23 MW with a reduction of 9.812 MW.

As the Internet of Things (IoT) continues to grow, integrating Software-Defined Networking (SDN) has brought numerous benefits to IoT deployments. However, this convergence also introduces new challenges in terms of cybersecurity. This review paper explores the landscape of cyber-attacks in SDN-based IoT environments, providing an overview of the various attack vectors, vulnerabilities, and potential security risks associated with this emerging paradigm. This paper examines the unique characteristics of SDN-based IoT networks and their implications for cybersecurity. It delves into different types of cyber-attacks that can target SDN-based IoT deployments, including port scanning, Operating System (OS) fingerprinting, fuzzing, Denial-of-Service (DoS), and Distributed Denial-of-Service (DDoS) attacks. In addition, this paper examines the existing research and case studies that leverage Deep Learning (DL) techniques for cyber-attack detection and prevention in SDN-based IoT environments. It highlights the advantages of using DL, including its ability to learn complex patterns and adapt to evolving attack strategies. This paper emphasizes the need for robust datasets and the importance of feature selection and preprocessing techniques to enhance the effectiveness of DL models in the context of SDN-based IoT security. It also discusses the integration of DL with other security measures, such as encryption and access control, to provide a comprehensive defense mechanism. In summary, this review paper contributes to understanding how Artificial Intelligence (AI) can enhance the security of SDN-based IoT environments. It serves as a valuable resource for researchers and practitioners in the field of cyber security, exploring the current research, obstacles, and potential solutions for using DL to identify and mitigate cyber-attacks in SDN-based IoT settings.

Indoor localization has gained significant attention recently due to the growing demand for location-aware applications within indoor environments. Among various techniques, Wi-Fi fingerprinting combined with the Internet of Things (IoT) has emerged as a promising solution for accurate and cost-effective indoor localization. This paper surveys the existing research related to indoor localization using Wi-Fi fingerprinting with the IoT, aiming to provide a comprehensive understanding of the advancements, challenges, and potential applications in this field. The paper begins by introducing the fundamental concepts of indoor localization and the role of Wi-Fi fingerprinting in achieving accurate position estimation. In addition, this paper focuses on the contributions of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in improving localization accuracy, robustness, and scalability. The reviewed papers have been examined from various aspects, including system architecture, deployment strategies, fingerprint creation techniques, and localization algorithms, with a discussion about the advantages and limitations of each approach. In addition to discussing the state-of-the-art techniques, this paper identifies research gaps and open challenges in indoor localization using Wi-Fi fingerprinting with the IoT. The findings presented in this paper can guide future research efforts, leading to the development of intelligent and context-aware IoT applications within indoor environments.

The educational buildings within universities are the main spaces for students' academic activities and life during the daytime, students spend 80% of their time inside these buildings. Therefore, achieving student thermal comfort requires universities to spend time and cost to prepare these buildings to be comfortable. The University of Mosul (UoM) included 24 colleges with 117 scientific departments, which contained hundreds of educational buildings occupied by 65000 students in addition to 4099 faculty staff and 3170 administrative staff. The thermal comfortability in UoM educational buildings partially achieved using the traditional method of air-conditioning units and some of the buildings still use electrical and gasoline heaters, which cost and increase the air pollution with less efficiency function. Therefore, the study is a trial to find a guideline for designing and treating the educational building using the passive and active design focusing on the facades as most elements of the building facade the outside environments and affect the internal thermal comfortability. The methodology applied simulation and analysis of a successful double-skin façade to eliminate the main steps of the design. The case study selected the following criterion of uncomfortable buildings in the UoM considering the questionnaire thermal characteristics of educational buildings. The case study is the hostel building (Al-Zahraa residential Building) within the University of Mosul. The results show three main steps that should followed by the designers in the university to achieve thermal comfortability, additionally considering the possibility of redesigning the facades.