The rapid development of Internet of Things (IoT) devices and their increasing numbers have caused a tremendous increase in network traffic and a wider range of cyber-attacks. This growing trend has complicated the detection process for traditional intrusion detection systems and heightened the challenges faced by these devices, such as imbalanced and large training data. This study presents a cohesive methodology of a series of intelligent techniques to prepare clean and balanced data for training the first (core) layer of a robust hierarchical intrusion detection system. The methodology was built by cleaning and compressing the data using an Autoencoder and preparing a strong latent space for balancing using a hybrid method that combines Grey Wolf Optimization (GWO) with Borderline-SMOTE. Particle Swarm Optimization (PSO) was used to select the most important features that provide the greatest amount of information for training the first layer, which was built using deep learning techniques, and linking them in a hybrid manner that combines a Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) and the Attention mechanism. The proposed model was evaluated using two different types of datasets: the CICIOT2023 dataset, which is characterized by its large size and significant variation in the number of attacks, and the UNSW-NB15 dataset, which is characterized by its simplicity and less imbalance compared to the first dataset, to prepare and generalize the system across multiple environments. The proposed class showed binary classification results with an accuracy of 0.94, an Area Under the Curve (AUC) of 0.93, an optimized F1-score of 0.338, and a Matthews Correlation Coefficient (MCC) of 0.324 at the best threshold on the CICIoT2023 dataset. It also achieved an accuracy of approximately 0.96, an AUC of 0.985, and an MCC of over 0.82 on the UNSW-NB15 dataset. These results confirmed the construction of a strong and resilient layer, preparing the foundation for a robust hierarchical offside detection system.
Porosity and pore structure are important characteristics of pharmaceutical tablets, since they influence the physical properties, such as mechanical strength, density and disintegration time. This paper is an attempt to investigate the pore structure of four different paracetamol tablets based on mercury porosimetry. The intrusion volumes of mercury were used to calculate the pore diameter, pore volume and pore size distribution. The result obtained indicate that the variation of the pore volume in the tablets followed the sequence:- S.D.I. Iraq? Pharmacare,Dubai-U.A.E.? Bron and Burk(UK) London?Lark Laboratories(India), while the variation of surface area followed the sequence:- S.D.I. Iraq? Lark Laboratories(India)? Pharmacare,Dubai-U.A
... Show MoreLung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens. Distinguishing the size, shape, and location of lymphatic nodes can identify the spread of the disease around these nodes. Thus, identifying lung cancer at the early stage is remarkably helpful for doctors. Lung cancer can be diagnosed successfully by expert doctors; however, their limited experience may lead to misdiagnosis and cause medical issues in patients. In the line of computer-assisted systems, many methods and strategies can be used to predict the cancer malignancy level that plays a significant role to provide precise abnormality detectio
... Show MoreIn this paper, a simulation model and practical testbed for green Internet of Things (IoT) edge devices are proposed based on solar harvester with constant voltage-maximum power point tracking (CV-MPPT) technique. Billions of connected edge devices represent the essential part of the IoT through the IP-enabled sensor networks based on IPv6 over Low power Wireless Personal Area Network (6LoWPAN). In traditional IoT edge devices, the stored energy in the non-rechargeable battery determines the node lifetime while it is being depleted with time. Therefore, purchasing billions of such batteries is costly and must be disposed of efficiently. This paper is aimed at simulating and implementing a new class of green IoT edge devices that can report
... Show MoreThe researcher focused on the importance of the physical abilities of the tennis game, as this game is one of the games that are characterized by its specificity in performance as this game is characterized by continuous movement and dealing with different elements, so this game requires the development of muscle strength, which plays an important role in Performance skills in the game of tennis. There are several methods to develop strength, including flat hierarchical technique, which is one of the most common forms of training in the development of muscle strength. As for the research problem, the researcher found a method that has an effect on the development of force. Therefore, the researcher tried to diversify a
... Show MoreIraq suffers from serious pollution with harmful particles that have important direct and indirect effects on human activities and human health. In this research, a system for detecting pollutants in the air was designed and manufactured using infrared laser technology. This system was used to detect the presence of pollutants in the dust storms that swept the city of Baghdad which could have a negative impact on human health and living organisms.
The designed detection system based on the use of infrared laser (IR) with a wavelength of 1064 nm was used for the purposes of detecting pollutants based on the scattering of the laser beam from these pollutants. The system was aligned to obtain the best signal for the scattered rays, w
... Show MoreIn this study, the dynamic modeling and step input tracking control of single flexible link is studied. The Lagrange-assumed modes approach is applied to get the dynamic model of a planner single link manipulator. A Step input tracking controller is suggested by utilizing the hybrid controller approach to overcome the problem of vibration of tip position through motion which is a characteristic of the flexible link system. The first controller is a modified version of the proportional-derivative (PD) rigid controller to track the hub position while sliding mode (SM) control is used for vibration damping. Also, a second controller (a fuzzy logic based proportional-integral plus derivative (PI+D) control scheme) is developed for both vibra
... Show MoreMachine learning models have recently provided great promise in diagnosis of several ophthalmic disorders, including keratoconus (KCN). Keratoconus, a noninflammatory ectatic corneal disorder characterized by progressive cornea thinning, is challenging to detect as signs may be subtle. Several machine learning models have been proposed to detect KCN, however most of the models are supervised and thus require large well-annotated data. This paper proposes a new unsupervised model to detect KCN, based on adapted flower pollination algorithm (FPA) and the k-means algorithm. We will evaluate the proposed models using corneal data collected from 5430 eyes at different stages of KCN severity (1520 healthy, 331 KCN1, 1319 KCN2, 1699 KCN3 a
... Show MoreThe purpose of this paper is to develop a hybrid conceptual model for building information modelling (BIM) adoption in facilities management (FM) through the integration of the technology task fit (TTF) and the unified theory of acceptance and use of technology (UTAUT) theories. The study also aims to identify the influence factors of BIM adoption and usage in FM and identify gaps in the existing literature and to provide a holistic picture of recent research in technology acceptance and adoption in the construction industry and FM sector.