An intrusion detection system (IDS) is an important protection instrument for detecting complex network attacks. Various machine learning (ML) or deep learning (DL) algorithms have been proposed for ...implementing anomaly-based IDS (AIDS). Our review of the AIDS literature identifies some issues in related work, including the randomness of the selected algorithms, parameters, and testing criteria, the application of old datasets, or shallow analyses and validation of the results. This paper comprehensively reviews previous studies on AIDS by using a set of criteria with different datasets and types of attacks to set benchmarking outcomes that can reveal the suitable AIDS algorithms, parameters, and testing criteria. Specifically, this paper applies 10 popular supervised and unsupervised ML algorithms for identifying effective and efficient ML-AIDS of networks and computers. These supervised ML algorithms include the artificial neural network (ANN), decision tree (DT), k-nearest neighbor (k-NN), naive Bayes (NB), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) algorithms, whereas the unsupervised ML algorithms include the expectation-maximization (EM), k-means, and self-organizing maps (SOM) algorithms. Several models of these algorithms are introduced, and the turning and training parameters of each algorithm are examined to achieve an optimal classifier evaluation. Unlike previous studies, this study evaluates the performance of AIDS by measuring the true positive and negative rates, accuracy, precision, recall, and F-Score of 31 ML-AIDS models. The training and testing time for ML-AIDS models are also considered in measuring their performance efficiency given that time complexity is an important factor in AIDSs. The ML-AIDS models are tested by using a recent and highly unbalanced multiclass CICIDS2017 dataset that involves real-world network attacks. In general, the k-NN-AIDS, DT-AIDS, and NB-AIDS models obtain the best results and show a greater capability in detecting web attacks compared with other models that demonstrate irregular and inferior results.
The objective of this study was to assess the biological potency and chemical composition of
aboveground parts using GC-MS. In this approach, 44 components were investigated, comprising 99.99% of the ...total volatile compounds. The major components were classified as fatty acids and lipids (51.36%), oxygenated hydrocarbons (33.59%), amines (7.35%), carbohydrates (6.06%), steroids (1.21%), and alkaloids (0.42%). The major components were interpreted as 1,3-dihydroxypropan-2-yl oleate (oxygenated hydrocarbons, 18.96%), ethyl 2-hydroxycyclohexane-1-carboxylate (ester of fatty acid, 17.56%), and 2-propyltetrahydro-2
-pyran-3-ol (oxygenated hydrocarbons, 11.18%). The DPPH antioxidant activity of the extracted components of
verified that the shoot extract was the most potent with IC
= 28.89 mg/L, with the percentages of radical scavenging activity at 74.28% ± 3.51%. The extracted plant, on the other hand, showed substantial antibacterial activity against the diverse bacterial species, namely,
(23.46 ± 1.69),
(22.91 ± 0.96),
(21.07 ± 0.80), and
(17.83 ± 0.67). In addition, the extracted plant was in vitro assessed as a considerable anticancer agent on HepG2 cells, in which MTT, cell proliferation cycle, and DNA fragmentation assessments were applied on culture and treated cells. The larvicidal efficacy of the extracted plant was also evaluated against
, the dengue disease vector. As a result, we may infer that
extract increased cytocompatibility and cell migratory capabilities, and that it may be effective in mosquito control without causing harm.
In healthcare applications, numerous sensors and devices produce massive amounts of data which are the focus of critical tasks. Their management at the edge of the network can be done by Fog ...computing implementation. However, Fog Nodes suffer from lake of resources That could limit the time needed for final outcome/analytics. Fog Nodes could perform just a small number of tasks. A difficult decision concerns which tasks will perform locally by Fog Nodes. Each node should select such tasks carefully based on the current contextual information, for example, tasks' priority, resource load, and resource availability. We suggest in this paper a Multi-Agent Fog Computing model for healthcare critical tasks management. The main role of the multi-agent system is mapping between three decision tables to optimize scheduling the critical tasks by assigning tasks with their priority, load in the network, and network resource availability. The first step is to decide whether a critical task can be processed locally; otherwise, the second step involves the sophisticated selection of the most suitable neighbor Fog Node to allocate it. If no Fog Node is capable of processing the task throughout the network, it is then sent to the Cloud facing the highest latency. We test the proposed scheme thoroughly, demonstrating its applicability and optimality at the edge of the network using iFogSim simulator and UTeM clinic data.
Background and purpose: Kidney diseases impose significant global health challenges. Potassium dichromate (PD) is a heavy metal frequently associated with nephrotoxicity. PD prompts oxidative and ...inflammatory injuries in renal tissues. L-carnitine is a naturally-occurring amino acid commonly used as a supplement.
Experimental approach: Forty rats were randomly allocated into 5 groups. Group 1 (normal) received only saline. Nephrotoxicity was induced in the remaining groups by PD (15 mg/kg; i.p). Group 2 served as a nephrotoxic group. Groups 3-5 received L-carnitine (25, 50, and 100 mg/kg; p.o.), respectively for 4 weeks.
Findings/Results: PD administration resulted in elevated serum creatinine and blood urea nitrogen accompanied by diminished reduced glutathione and elevated malondialdehyde, tumor necrosis factor-alpha, and transforming growth factor-beta renal tissue contents relative to normal rats. PD also produced apoptotic histopathological injuries and down-regulated PI3K/Akt signaling pathway; signifying ongoing apoptosis. In the current work, L-carnitine use in the selected dose levels resulted in improvement of all the aforementioned serum, renal tissue, and histological parameters relative to nephrotoxic rats. L-carnitine up-regulated PI3K/Akt signaling pathway that was down-regulated post PD use.
Conclusion and implications: Collectively, the study highlighted that the possible mechanisms beyond the beneficial effects of L-carnitine are mainly through its antioxidant as well as anti-inflammatory actions. L- carnitine significantly abrogated apoptosis via up-regulation of PI3K/Akt signaling pathway and signified restoration of normal renal cell proliferation and functionality.
The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the ...spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID-19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result.
Systems based on fog computing produce massive amounts of data; accordingly, an increasing number of fog computing apps and services are emerging. In addition, machine learning (ML), which is an ...essential area, has gained considerable progress in various research domains, including robotics, neuromorphic computing, computer graphics, natural language processing (NLP), decision-making, and speech recognition. Several researches have been proposed that study how to employ ML to settle fog computing problems. In recent years, an increasing trend has been observed in adopting ML to enhance fog computing applications and provide fog services, like efficient resource management, security, mitigating latency and energy consumption, and traffic modeling. Based on our understanding and knowledge, there is no study has yet investigated the role of ML in the fog computing paradigm. Accordingly, the current research shed light on presenting an overview of the ML functions in fog computing area. The ML application for fog computing become strong end-user and high layers services to gain profound analytics and more smart responses for needed tasks. We present a comprehensive review to underline the latest improvements in ML techniques that are associated with three aspects of fog computing: management of resource, accuracy, and security. The role of ML in edge computing is also highlighted. Moreover, other perspectives related to the ML domain, such as types of application support, technique, and dataset are provided. Lastly, research challenges and open issues are discussed.
The quick spread of the Coronavirus Disease (COVID-19) infection around the world considered a real danger for global health. The biological structure and symptoms of COVID-19 are similar to other ...viral chest maladies, which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease. In this study, an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods (e.g., artificial neural network (ANN), support vector machine (SVM), linear kernel and radial basis function (RBF), k-nearest neighbor (k-NN), Decision Tree (DT), and CN 2 rule inducer techniques) and deep learning models (e.g., MobileNets V2, ResNet50, GoogleNet, DarkNet and Xception). A large X-ray dataset has been created and developed, namely the COVID-19 vs. Normal (400 healthy cases, and 400 COVID cases). To the best of our knowledge, it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases. Based on the results obtained from the experiments, it can be concluded that all the models performed well, deep learning models had achieved the optimum accuracy of 98.8% in ResNet50 model. In comparison, in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBF accuracy 94% for the prediction of coronavirus disease 2019.
Breast cancer is considered to be one of the most threatening issues in clinical practice. However, existing breast cancer diagnosis methods face questions of complexity, cost, human-dependency, and ...inaccuracy. Recently, many computerized and interdisciplinary systems have been developed to avoid human errors in both quantification and diagnosis. A computerized system can be further improved to optimize the efficiency of breast tumour identification. The current paper presents an effort to automate characterization of breast cancer from ultrasound images using multi-fractal dimensions and backpropagation neural networks. In this study, a total of 184 breast ultrasound images (72 abnormal (tumour cases) and 112 normal cases) were examined. Various setups were employed to achieve a decent balance between positive and negative rates of the diagnosed cases. The obtained results manifested in high rates of precision (82.04%), sensitivity (79.39%), and specificity (84.75%).
Automatic information extraction from online published scientific documents is useful in various applications such as tagging, web indexing and search engine optimization. As a result, automatic ...information extraction has become among the hottest areas of research in text mining. Although various information extraction techniques have been proposed in the literature, their efficiency demands domain specific documents with static and well-defined format. Furthermore, their accuracy is challenged with a slight modification in the format. To overcome these issues, a novel ontological framework for information extraction (OFIE) using fuzzy rule-base (FRB) and word sense disambiguation (WSD) is proposed. The proposed approach is validated with a significantly wider document domains sourced from well-known publishing services such as IEEE, ACM, Elsevier, and Springer. We have also compared the proposed information extraction approach against state-of-the-art techniques. The results of the experiment show that the proposed approach is less sensitive to changes in the document format and has a significantly better average accuracy of 89.14% and F-score as 89%.
Liver fibrosis (LF) is a life-threatening complication of most chronic liver diseases resulting from a variety of injurious agents and hepatotoxic insults. To date, there are no specific therapies ...for LF, and all the currently available drugs have been developed for other indications. Thus, there is a pressing need to develop new drugs for treatment of LF. Therefore, the current study aimed to elucidate the potential antifibrotic effect of Pirfenidone (PFD) against concanavalin A (ConA)-induced immunological model of liver fibrosis in mice.
Hepatic fibrosis was induced in mice by injecting ConA (10 mg/kg/wk./i.v) for 4 weeks. Then, the mice were treated with or without PFD (125 mg/kg/ip/day) for 2 weeks. Hepatic fibrosis was determined by Masson Trichrome staining; Haematoxylin & eosin (H&E) staining, immunohistochemistry staining of type II and IV collagens, and colorimetric assessment of hydroxyprolline (HP) content in the liver tissues. In addition, the expression of α-SMA mRNA was determined by real time RT-PCR. The serum levels of TGF-β, TNF-α, TIMP-1 and MMP-2 were measured by ELISA.
Treatment with PFD significantly reduced ConA-induced expression of type II and IV collagens, α-SMA mRNA expression, and HP content and decreased inflammatory cells infiltration in hepatic tissues. Furthermore, serum levels of TGF-β, TNF-α, and TIMP-1 were significantly reduced with concomitant increase in MMP-2 expression.
Treatment with PFD ameliorates concanavalin A-induced hepatic inflammation and fibrosis in mice. Thus, PFD may represent a promising therapeutic option for hepatic fibrosis and its related complications.
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