Al-Iraqia Journal for Scientific Engineering Research

Wireless Sensor Networks (WSNs) are gaining popularity due to their low cost and ease of maintenance and management. One of the most challenging issues in WSN is energy conservation. The clustering strategy has the potential to extend the lifespan of WSNs. The selection of Cluster Head (CH) in each cluster is regarded as the capable approach for energy efficient routing in the clustering model, which reduces the transmission latency in the WSN. As a result, the routing method between nodes and sinks must take into account the efficient use of energy. In Low-Energy Adaptive Clustering Hierarchy (LEACH), direct data connection from source cluster head to sink is addressed, leading to inequitable energy consumption of cluster heads CHs and network routing gaps. In this paper, the conceptual framework of clustering in wireless sensor networks based on traditional and optimization categories is explored. In addition, the most relevant up-to-date researches in routing protocols are presented. The study presents a new taxonomy for categorizing current hierarchical energy efficiency routing protocols for WSNs and examines their functionality and performance. Next, the study examines current routing protocols in WSNs, ranging from traditional routing protocols to swarm intelligence-based protocols. LEACH variations are discussed using a taxonomy based on distinct classes, residual energy, centralization, distance, mobility, energy efficiency, and so on. The CH selection, hop count, mobility, scalability, energy efficiency, topology, and deployment are all compared in detail. Finally, Energy efficiency, data aggregation, location awareness, QoS, scalability, load balancing, fault tolerance, query based, and multipath are summarized for the hierarchical routing protocols that fall into both classes.

This disorder can be considered as neurodevelopmental which has an affection on an individual’s behavior, communicating as well as learning. Its diagnosis needs a lot of time and money. And if it is detected early, it is going to be very helpful via giving the infected ones the suitable treatment in good time. As the development of AI and ML, this disorder is able to be predicted very soon. Thus, the goal here can be the proposing of a system called ASD prediction system by utilizing Naïve bayes classifier for predicting if a person is infected with ASD or not to develop the screening method used to detect the illness. The experimental outcomes showed that the Naïve bayes classifier achieved a great precision, recall and F- measure of about (98. 975%, 98.972 % and 98 .972 %) in kid's dataset. On the other hand, it was noticed that the precision, recall and F- measure in the dataset of the teenagers can be approximately (98.136%, 98.076% and 98.067%) with an estimated error rate that is (0.044).

Facial expression recognition (FER) systems accurately identify facial expressions by extracting facial features. The extraction of robust facial features comes after automatic face detection in this procedure. On the FAR2013 dataset, a five-step system developed to assess the performance of machine learning algorithms. Components of the system include preprocessing, feature extraction, model training and testing, classification, and evaluation. Three machine-learning algorithms utilized in this study: logistic regression (LR), random forest (RF), and AdaBoost (ADA). The RF algorithm achieved the highest degree of precision with a 61% success rate. The purpose of the study was to evaluate the performance of machine learning algorithms on the FAR2013 dataset. The study highlights the importance of facial feature extraction in FER systems and the precision of machine learning algorithms in facial expression recognition.

Nowadays, image manipulation algorithms are increasingly being used even by people who do not have any deep knowledge of technology, and one can easily find an application to manipulate face image for various purposes. After the development of image processing algorithms, the research community highlighted the need to develop a technology that detects image manipulation, but it did not highlight the recovery of the facial region after manipulation localization, which would be very useful in practical applications. In this paper, a new face image modification detection and recovery scheme based on image watermarking and bicubic interpolation algorithm is presented. Several experiments were conducted to evaluate the performance of the proposed scheme, which proved its effectiveness in generating high-quality watermarked images, detecting different types of manipulation, localizing the manipulated blocks in the face region, and restoring the face region with good visual quality. A comparison with the latest detection techniques demonstrates the superiority of the proposed scheme.

In the coming decade, we'll see the rollout of the intelligent information society—a highly digitalized, intelligent civilization that draws inspiration from AI and runs on global data. Next-generation or modern communication networks are crucial to bringing this ambitious plan to fruition because of their promise of universal connectivity, full 360-degree wireless coverage, and the seamless integration of all necessary functions to power truly vertical apps. Research in this area has shown that the Intelligent Reflective Surface (IRS) combined with wireless environment management capability is a viable solution for 6G technology. To be more specific, the IRS is capable of precise 3D passive beam formation due to the wavefront's phases, amplitude, frequency, and polarization being controlled intelligently by tunable components. In this article, we provide an overview of the IRS, including categorizing recent IRS research theory, and we also review IRS's structure, controller, tunable chips, and design hardware architecture. Finally, as a timely review of the IRS, our summary will be of interest to both researchers and practitioners involved.

The integration regarding various power electronic devices has led to an increase in the complexity of power systems. Limiting the fault currents is crucial for protecting these systems, the augmentation of fault reliability, and the stability. Many Faults Current Limiters (FCLs) were used in power systems because they quickly and effectively fault current limiters. The presented work gives a thorough literature review regarding the use of various FCL types in the power systems. The non-superconducting and superconducting FCL applications have been divided into five categories: (a) application in transmission, distribution and generation networks; (b) applications in the systems of alternating current (AC)/direct current (DC); (c) applications in the integration of renewable sources of energy; (d) applications in the distributed generation (DG); (e) applications for improving stability, reliability, and fault ride through capabilities. With examples of their practical implementation in various nations, impact, modeling, and control approaches of various FCLs in the power systems have been shown. With the alteration of its structures, appropriate control design, and optimal placement recommendations have been given in order to enhance performance regarding FCLs in the power systems. In order to incorporate the continuing research advances in practical systems, industry and researchers working on power system stability concerns can benefit greatly from this study.

Embedded items (things) are items that are connected to the Internet and have access to global services and people. Wireless Sensor Networks (WSNs) are increasingly becoming the Internet of Things' core network. Moreover, WSNs must support numerous applications concurrently and process vast volumes of data as IoT becomes more integrated into daily life. Thus, using the WSN multi-sink is crucial. Wireless Sensor Networks with multiple sinks are prone to congestion in sink nodes, lowering the efficiency of data collection and processing. The majority of Internet of Things (IoT) applications use the RPL routing protocol, which is offered by Internet Engineering Task Force (IETF). RPL considers IoT deployment requirements across many applications to be highly interoperable and versatile. Nonetheless, several issues still need to be resolved, particularly in large-scale networks. Congestion is one of the problems with RPL, which leads to packet losses at the sink, as a result of the memory overflow incident. This paper introduces Multi sink Load-Balancing Algorithm (MSLBA) that proposed to resolve the congestion issue across several sinks. The suggested approach effectively balances load across sinks by dynamically updating RPL in accordance with DAG Size (DS), Hope Count (HC), and Node Rank (NR). As a result, RPL is able to appropriately spread the load among the sinks. In terms of Packet Delivery Ratio (PDR), MSLBA greatly outperformed the standard RPL algorithm. Throughput, and Delay. The PDR was improved by 9%, while the throughput and latency were improved by 8.5% and 68%, respectively.

Due to advancement, there are now more smart devices connected to the internet., which causes massive data traffic in the network. Resulting in many problems such as slow response time, largely consumed energy, high load in transmission channels, and bad use of the network resources in the traditional cloud. Edge computing facilities and brings the cloud's service to the network's edge. Edge computing a distributed computing near to source of data. This technology solved and helps to solve many problems in the cloud, but also has challenges and open issues, for example, the limited lifetime of the IoT devices, limited resources, and computation offloading. The performance of edge computing is affected by offloading. So, many optimization methods are used to solve this problem and improve the performance in edge computing. This paper presents a survey of the studies related to optimizing task offloading in edge computing. The difference between this work and the previous surveys is that this survey combined offloading and optimization with the types of optimization methods and takes into consideration the three layers of edge computing architecture IoT, the Edge layer, and the cloud layer. The previous surveys did not include all types of optimization or combine the offloading with optimization. The architecture of edge computing, challenges, and open issues, optimization methods are presented.