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The rise of cyber-attacks such as Distributed Denial of Service (DDoS) and SQL injection has become a significant concern for organizations and individuals who rely on the internet. Traditional detection methods have become increasingly ineffective in addressing these challenges, necessitating the development of new and innovative solutions. This paper proposes using Convolutional Neural Networks (CNNs) to detect DDoS and SQL injection attacks. Our paper proposes a Convolutional Neural Network model with a multi-layer neural and relu activation function optimizer that reaches a higher degree of precision than previous Deep Learning models. In this study, we tested the relatively new dataset CIC-IDS-2018, which contains different types of attacks. With this dataset, our model achieves an unprecedented accuracy of>96%, minimizing computational time. [ABSTRACT FROM AUTHOR]

Monkeypox disease was declared a Public Health Emergency of International Concern (PHEIC) by the World Health Organization (WHO) on July 23rd, 2022. The purpose of this designation is to encourage the global community to take coordinated measures to contain the spread of the disease and protect communities. The recovery period for Monkeypox disease typically ranges from 2 to 4 weeks, and in some cases, the disease can resolve spontaneously with or without treatment. However, severe cases have been reported, which can lead to fatalities. The fatality ratio for Monkeypox disease is estimated between 3% to 6% at present. It is essential to take precautionary measures to prevent the spread of this disease and seek medical attention if any symptoms are observed. Monkeypox is not highly contagious, and it generally spreads through close contact with an infected person. However, in May 2022, a significant number of cases of Monkeypox disease were identified, indicating that the disease was spreading rapidly. In light of this situation, it is crucial to track and isolate suspected cases promptly to prevent further transmission. Rapid and effective contact tracing, along with timely isolation of cases, is essential to contain the spread of the disease and prevent it from becoming an epidemic. Identifying, controlling, and taking measures to control the spread of Monkeypox disease is of utmost importance. To aid in this effort, this paper aims to create a large-scale dataset of Monkeypox disease images using Generative Adversarial Networks (GANs). GANs have proven to be highly effective in generating high-quality images and can aid in creating a more comprehensive and diverse dataset. The generated images will be used to train different deep learning models for image classification to diagnose Monkeypox disease. The deep learning models will be compared, and their performance will be evaluated using images of infected human skin with Monkeypox disease and healthy skin images. The results obtained from this paper will improve the accuracy of previous works, aiding in the early diagnosis and treatment of Monkeypox disease. [ABSTRACT FROM AUTHOR]

This paper reviewed the Hand Sign Recognition system from 2018 to 2022. Based on the review, much research is done on Deep learning-based hand sign recognition. Many of these studies are published annually in article, journals, and conference proceedings. Additionally, we also examined the performance of the HGR system in terms of recognition accuracy. In this paper we concentrate on the data environment, the data acquisition, and the hand gesture presentation— three crucial components of the vision-based HGR system. We also looked at how well the vision-based HGR system performed regarding recognition accuracy. [ABSTRACT FROM AUTHOR]

Recently, imagery of the chest has become the key clinical procedure for diagnosing and predicting chest infection in the lungs. Computed tomography (CT) images of the chest were thus considered in this study as a screening strategy for early-stage detection of chest infections and other abnormalities in the human lung. Raw computed tomography is difficult to interpret, leading to a need to develop computer algorithm diagnostic (CAD) approaches to improve the detection of abnormalities in the resulting CT images. The data samples used in this paper were obtained from Al-Hussein Teaching Hospital and the Radiology Department of Imam Al Hujja Charity Hospital in Iraq, Kerbala. The number of images in the assembled dataset was 150 across two different class types, normal and with confirmed lung disease. The Fuzzy C-Means Clustering (FCMC), K-Means Clustering (KMC), and Gaussian Mixed Model Clustering (GMMC) techniques were then applied to the chest CT images to test the detection and classification of the normal and infected scans. The features of the CT images in this paper were then filtered to remove clusters identified as belonging to normal areas to develop a full algorithm for the identification of abnormal areas by mans of anatomic segmentation of chest infection Region of Interest (ROI), which is presented as part of this work. The experimental results show that the proposed segmentation techniques offered clustering accuracy of 93.12 %, 90.23%, and 91.41% for FCMC, KMC, and GMMC, respectively. The performance metrics thus show that the FCMC algorithm outperforms the KMC and GMMC algorithms, as well as being fully autonomous, and having the capability to isolate abnormal infected regions in the lung tissue where such anomalies exist accurately to the benefit of radiologists, a function few other computational algorithms can offer. [ABSTRACT FROM AUTHOR]

Electro-kinetic (EK) remediation is one of the main techniques used to remediate soil contaminated with organic, inorganic, or mixed pollutants. The electro-kinetic method is popular due to its lower operation costs and minimal negative impact on environmental systems. This paper offers a brief overview of studies on the performance of EK technology with respect to treating soil and sediment, based on examination of the parameters that influence the constituents of the treated soil and sediment. The outcome of this is the identification of the most important factor affecting the migration of metals from the soil, namely pH level. Based on this, this paper offers an overview of the methods devoted to increasing the removal efficiencies of pollutants from contaminated soil and sediment, including leaching solutions extraction solution. [ABSTRACT FROM AUTHOR]

This work presents a review of the current state-of-the-art in composite joints, which are commonly considered one of the most promising techniques emerging in the past few decades. The paper thus assesses existing work on composite joints for various connections, with a focus on the effects of their interaction. The investigation included experimental studies, numerical studies, design code literature reviews, cyclic loading studies, seismic studies, and monotonic load studies on composite joints, along with an examination of recent papers investigating beam-column connections. This work thus summarises the extant literature on composite joints for various connections to outline the resulting knowledge gaps and to offer possible research directions on composite joints that might enable a greater understanding of such joints to be developed. [ABSTRACT FROM AUTHOR]

Steel-fibre reinforced polymer (FRP) composite bar (SFCB) is a novel form of rebar. The main technique for SFCB manufacturing, which is described briefly in this paper, is a manual technique, and this study cast eight slabs using this procedure to investigate the flexural behavior of SFCB-reinforced concrete slabs by comparing them with concrete slabs reinforced with ordinary steel bars. The study included an investigation of changes in the thickness of slab, the effects of the CFRP sheet length wrapped around the steel bars, and concrete compressive strengths. The outcomes showed that, increasing the slab thickness from 80 to 100 mm led to an increase in the ultimate load capacity of 55%, while utilising SFCB reinforcement enhanced the ultimate load capacity, with maximum percentage increases ranging from 23% to 41%. Furthermore, the results showed that replacing NC with RPC offered slight enhancements in the ultimate load: this was about 12% in the control slab, while when SFCB was used, the improvement was 16%. Finally, a length of reinforcement two-thirds of the full length of the bar was found to be the optimum length when used for strengthening. Within this paper, the methodology and results are first outlined, then a brief conclusion offered with regard to the investigation overall. [ABSTRACT FROM AUTHOR]

Traffic volumes have increased rapidly in recent years across all major highways of Iraq due to the ongoing increases in economic growth in the region. The ability of a pavement structure to perform its main function decreases as traffic load increases, especially where overloaded heavy vehicles pass along a road. The majority of the road pavement is designed with vehicle legal load limits in mind: however, these may not always be adhered to, as most transportation businesses seek to reduce transportation costs by avoiding repetition of vehicle routes. Excessive truck traffic can cause economic, environmental, and social harm, however, and in this paper, the Iraqi State Commissions of Roads and Bridges (SCRP) code, AASHTO, and the Asphalt Institute method are used to analyse the effects of overloaded vehicles on road pavement thickness as compared to standard conditions. For both standard conditions and overloading cases, the traffic load expressed by Equivalent Single Axle Load (ESAL) and truck factor were thus calculated, and the differences resulting from overloading thus identified. Traffic data from the Khalis–Odayam road were used in this paper to form a case study, allowing assessment of the impact of repetitive load conditions on the structural design, and any increase in layer thickness above the legal axle load limit that ensues. The results obtained from this work show that the total thickness of the paving layers as assessed using the AASHTO method was 805 mm and that by the Asphalt Institute method was 800 mm; these results were thus relatively close, despite the fact that the value of the ESAL was valued at 9.8*10^8 in the AASHTO method and 7.9*10*8 in the asphalt institute method, while the thickness values required by the two methods was greater than the current thickness of the road, indicating failures and distortions in the road after each successive road maintenance. According to these findings, overloaded vehicles on the road thus have a significant impact on pavement service life and necessitate to laying of thicker pavement. [ABSTRACT FROM AUTHOR]

The intention of this article is to explore the radiation effects of MHD free convective heat transmission past an oscillating hot perpendicular surface in porous medium. The fluid is understood to be viscous, incompressible and the flow is considered as time dependent. Since the paper has aim to study MHD flow problem, the fluid should be electrically conducting. The problem has been modelled mathematically by system of PDEs. The systems of PDEs are worked out by adopting Laplace transform method. In this paper, a closed form of solution have been obtained. The results for the velocity, temperature, Nusselt number and skin-friction have been determined. The given graphs and tables deal with the flow characteristics with reference to the effects of magnetic parameter, Prandtl number, Grashof number, permeability, heat source/sink parameter and radiation parameter. The results show that the proposed method can effectively predict these values with accuracy and may be valuable in many engineering applications. [ABSTRACT FROM AUTHOR]

This paper emphasizes on the characterization techniques for high energy and high-power cell dedicated to electric vehicles (EV's), as across the past few years the number of EV's has rapidly increased. Projections are such that, more than 250 million EV's will be used for transportation by next few years. The main requirement of energy storage is provided by the Li-ion cells/batteries. Lithium-ion cells are now being widely adapted owing to its properties such as specific power and energy density. In this paper, in order to achieve high performance of battery cells and to carry out the ageing study, we have designed and implemented a test bench to characterize energy and power optimized cells. [ABSTRACT FROM AUTHOR]