Insertion Detection System Employing Neural Network MLP and Detection Trees Using Different Techniques

by addressing intruder attacks, network security experts work to maintain services available at all times. The Intrusion Detection System (IDS) is one of the available mechanisms for detecting and classifying any abnormal behavior. As a result, the IDS must always be up to date with the most recent intruder attack signatures to maintain the confidentiality, integrity, and availability of the services. This paper shows how the NSL-KDD dataset may be used to test and evaluate various Machine Learning techniques. It focuses mostly on the NLS-KDD pre-processing step to create an acceptable and balanced experimental data set to improve accuracy and minimize false positives. For this study, the approaches J48 and MLP were employed. The Decision Trees classifier has been demonstrated to have the highest accuracy rate for detecting and categorizing all NSL-KDD dataset attacks

Pharmacy education and practice in 13 middle eastern countries

The Arab world has influenced the art and science of pharmacy for centuries. Pharmacy education and practice is continuing to evolve in the Arabic-speaking traditional Middle East countries, although relatively little information has been published in the English press. Our goal was to providea high-level synopsis of conditions in this region. We selected 13 countries for review. Information was obtained by reviewing the available published literature and individual university and program web sites, as well as contacting with program or country representatives. Seventy-eight active pharmacy schools in 12 countries were identified. At least 14,000 students (over 75% from Egypt) are admitted into baccalaureate degree programs every year. The 5-year baccalaureate degree remains the first professional degree to practice. While changes in pharmacy education have been relatively rapid over the past decade, the advancement of pharmacy practice, particularly in the private sector, appears to be slower. Hospital pharmacists often possess an advanced degree and tend to have a higher level of practice compared to that of community pharmacists. Despite the adversities that face academics and practitioners alike, there is a strong desire to advance the science and practice of pharmacy in the Middle East

Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling

Since its emergence as a pandemic in March 2020, coronavirus disease (COVID-19) outcome has been explored via several predictive models, using specific clinical or biochemical parameters. In the current study, we developed an integrative non-linear predictive model of COVID-19 outcome, using clinical, biochemical, immunological, and radiological data of patients with different disease severities. Initially, the immunological signature of the disease was investigated through transcriptomics analysis of nasopharyngeal swab samples of patients with different COVID-19 severity versus control subjects (exploratory cohort, n=61), identifying significant differential expression of several cytokines. Accordingly, 24 cytokines were validated using a multiplex assay in the serum of COVID-19 patients and control subjects (validation cohort, n=77). Predictors of severity were Interleukin (IL)-10, Programmed Death-Ligand-1 (PDL-1), Tumor necrosis factors-α, absolute neutrophil count, C-reactive protein, lactate dehydrogenase, blood urea nitrogen, and ferritin; with high predictive efficacy (AUC=0.93 and 0.98 using ROC analysis of the predictive capacity of cytokines and biochemical markers, respectively). Increased IL-6 and granzyme B were found to predict liver injury in COVID-19 patients, whereas interferon-gamma (IFN-γ), IL-1 receptor-a (IL-1Ra) and PD-L1 were predictors of remarkable radiological findings. The model revealed consistent elevation of IL-15 and IL-10 in severe cases. Combining basic biochemical and radiological investigations with a limited number of curated cytokines will likely attain accurate predictive value in COVID-19. The model-derived cytokines highlight critical pathways in the pathophysiology of the COVID-19 with insight towards potential therapeutic targets. Our modeling methodology can be implemented using new datasets to identify key players and predict outcomes in new variants of COVID-19

Exploring Media and Communication Students’ Perception of Egyptian Universities’ Use of Augmented Reality in Learning

The aim of this study was to investigate the perceptions of media students in Egypt universities about using augmented reality (AR) technology in learning. To achieve this, the study adopted Technology Acceptance Model (TAM) and utilized a survey questionnaire to collect data from students in seven universities across Egypt. The findings revealed that (i) the students had a positive perception about using AR in media and communication learning; (ii) many media students in Egypt were not fully aware of the various AR technology applications in media and communication education; (iii) the students identified several negative factors that may hinder their acceptance of AR technology as an instructional tool, such as poor connectivity, lack of free AR programs, and lack of training programs. Addressing these barriers could help promote the adoption of AR technology in media and communication learning among students in Egypt. The significance of the study lies in that it sheds light on the need for increased awareness and education of the potential benefits of using AR technology in media and communication learning

Carbon monoxide and respiratory symptoms in young adult passive smokers: A pilot study comparing waterpipe to cigarette

Objectives: Studies have correlated second hand smoke (SHS) with many diseases, especially respiratory effects. The goal of this study was to measure the impact of SHS on the respiratory symptoms and exhaled carbon monoxide. Material and Methods: The study population consisted of 50 young workers in restaurants serving waterpipes, 48 university students who sit frequently in the university cafeteria where cigarette smoking is allowed and 49 university students spending time in places where smoking is not allowed. Subjects completed questionnaires on socio-demographic characteristics, respiratory symptoms and exposure to SHS. Exhaled carbon monoxide levels were measured. ANOVA and Chi-square tests were used when applicable as well as linear and logistic regression analysis. Results: Exposure to cigarette smoke in university (adjusted odds ratio (ORa) = 6.06) and occupational exposure to waterpipe smoke (ORa = 7.08) were predictors of chronic cough. Being married (ORa = 6.40), living near a heavy traffic road (ORa = 9.49) or near a local power generator (ORa = 7.54) appeared responsible for chronic sputum production. Moreover, predictors of chronic allergies were: being male (ORa = 7.81), living near a local power generator (ORa = 5.52) and having a family history of chronic respiratory diseases (ORa = 17.01). Carbon monoxide levels were augmented by the number of weekly hours of occupational exposure to waterpipe smoke (β = 1.46) and the number of daily hours of exposure to cigarette smoke (β = 1.14). Conclusions: In summary, young non-smoker subjects demonstrated more chronic cough and elevated carbon monoxide levels when exposed to SHS while the effect of waterpipe was even more evident

Optimal Model for Path Loss Predictions using Feed-Forward Neural Networks

[EN] In this paper, an optimal model is developed for path loss predictions using the Feed-Forward Neural Network (FFNN) algorithm. Drive test measurements were carried out in Canaanland Ota, Nigeria and Ilorin, Nigeria to obtain path loss data at varying distances from 11 different 1,800 MHz base station transmitters. Single-layered FFNNs were trained with normalized terrain profile data (longitude, latitude, elevation, altitude, clutter height) and normalized distances to produce the corresponding path loss values based on the Levenberg-Marquardt algorithm. The number of neurons in the hidden layer was varied (1-50) to determine the Artificial Neural Network (ANN) model with the best prediction accuracy. The performance of the ANN models was evaluated based on different metrics: Mean Absolute error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), standard deviation, and regression coefficient (R). Results of the machine learning processes show that the FNN architecture adopting a tangent activation function and 48 hidden neurons produced the least prediction error, with MAE, MSE, RMSE, standard deviation, and R values of 4.21 dB, 30.99 dB, 5.56 dB, 5.56 dB, and 0.89, respectively. Regarding generalization ability, the predictions of the optimal ANN model yielded MAE, MSE, RMSE, standard deviation, and R values of 4.74 dB, 39.38 dB, 6.27 dB, 6.27 dB, and 0.86, respectively, when tested with new data not previously included in the training process. Compared to the Hata, COST 231, ECC-33, and Egli models, the developed ANN model performed better in terms of prediction accuracy and generalization ability.This work was supported by Covenant University [grant number CUCRID-SMARTCU-000343].Popoola, SI.; Adetiba, E.; Atayero, AA.; Faruk, N.; Tavares De Araujo Cesariny Calafate, CM. (2018). Optimal Model for Path Loss Predictions using Feed-Forward Neural Networks. Cogent Engineering. 5:1-19. https://doi.org/10.1080/23311916.2018.1444345S1195Adetiba, E., Iweanya, V. C., Popoola, S. I., Adetiba, J. N., & Menon, C. (2017). Automated detection of heart defects in athletes based on electrocardiography and artificial neural network. Cogent Engineering, 4(1). doi:10.1080/23311916.2017.1411220Adetiba, E., & Olugbara, O. O. (2015). Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features. The Scientific World Journal, 2015, 1-17. doi:10.1155/2015/786013Adeyemo, Z. K., Ogunremi, O. K., & Ojedokun, I. A. (2016). Optimization of Okumura-Hata Model for Long Term Evolution Network Deployment in Lagos, Nigeria. International Journal on Communications Antenna and Propagation (IRECAP), 6(3), 146. doi:10.15866/irecap.v6i3.9012Akhoondzadeh-Asl, L., & Noori, N. (2007). 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Applicability evaluation of Okumura, Ericsson 9999 and winner propagation models for coverage planning in 3.5 GHZ WiMAX systems.Erceg, V., Greenstein, L. J., Tjandra, S. Y., Parkoff, S. R., Gupta, A., Kulic, B., … Bianchi, R. (1999). An empirically based path loss model for wireless channels in suburban environments. IEEE Journal on Selected Areas in Communications, 17(7), 1205-1211. doi:10.1109/49.778178Farhoud, M., El-Keyi, A., & Sultan, A. (2013). Empirical correction of the Okumura-Hata model for the 900 MHz band in Egypt. 2013 Third International Conference on Communications and Information Technology (ICCIT). doi:10.1109/iccitechnology.2013.6579585Faruk, N., Adediran, Y. A., & Ayeni, A. A. (2013). Error bounds of empirical path loss models at VHF/UHF bands in Kwara State, Nigeria. Eurocon 2013. doi:10.1109/eurocon.2013.6625043Faruk, N., Ayeni, A., & Adediran, Y. A. (2013). ON THE STUDY OF EMPIRICAL PATH LOSS MODELS FOR ACCURATE PREDICTION OF TV SIGNAL FOR SECONDARY USERS. 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(2017, July 5–7). C-BRIG: A network architecture for real-time information exchange in smart and connected campuses. In Lecture notes in engineering and computer science: Proceedings of the world congress on engineering 2017 (pp. 398–401). London.Medeisis, A., & Kajackas, A. (s. f.). On the use of the universal Okumura-Hata propagation prediction model in rural areas. VTC2000-Spring. 2000 IEEE 51st Vehicular Technology Conference Proceedings (Cat. No.00CH37026). doi:10.1109/vetecs.2000.851585Mohtashami, V., & Shishegar, A. A. (2012). Modified wavefront decomposition method for fast and accurate ray-tracing simulation. IET Microwaves, Antennas & Propagation, 6(3), 295. doi:10.1049/iet-map.2011.0264Nimavat, V. D., & Kulkarni, G. (2012). Simulation and performance evaluation of GSM propagation channel under the urban, suburban and rural environments.Paper presented at the communication, information & computing technology (ICCICT), 2012 international conference on.. O. F. O. (2014). RADIO FREQUENCY OPTIMIZATION OF MOBILE NETWORKS IN ABEOKUTA, NIGERIA FOR IMPROVED QUALITY OF SERVICE. International Journal of Research in Engineering and Technology, 03(08), 174-180. doi:10.15623/ijret.2014.0308027Phillips, C., Sicker, D., & Grunwald, D. (2013). A Survey of Wireless Path Loss Prediction and Coverage Mapping Methods. IEEE Communications Surveys & Tutorials, 15(1), 255-270. doi:10.1109/surv.2012.022412.00172Popoola, S. I., Atayero, A. A., Badejo, J. A., John, T. M., Odukoya, J. A., & Omole, D. O. (2018). Learning analytics for smart campus: Data on academic performances of engineering undergraduates in Nigerian private university. Data in Brief, 17, 76-94. doi:10.1016/j.dib.2017.12.059Popoola, S. I., Atayero, A. A., & Faruk, N. (2018). Received signal strength and local terrain profile data for radio network planning and optimization at GSM frequency bands. Data in Brief, 16, 972-981. doi:10.1016/j.dib.2017.12.036Popoola, S. I., Atayero, A. A., Faruk, N., & Badejo, J. A. (2018). Data on the key performance indicators for quality of service of GSM networks in Nigeria. Data in Brief, 16, 914-928. doi:10.1016/j.dib.2017.12.005Popoola, S. I., Atayero, A. A., Faruk, N., Calafate, C. T., Adetiba, E., & Matthews, V. O. (2017, July 5–7). Calibrating the standard path loss model for urban environments using field measurements and geospatial data.Paper presented at the Lecture notes in engineering and computer science: Proceedings of the world congress on engineering 2017 (pp. 513–518). London.Popoola, S. I., Atayero, A. A., Faruk, N., Calafate, C. T., Olawoyin, L. A., & Matthews, V. O. (2017). Standard propagation model tuning for path loss predictions in built-up environments.Paper presented at the International Conference on Computational Science and Its Applications.Popoola, S. I., Atayero, A. A., Okanlawon, T. T., Omopariola, B. I., & Takpor, O. A. (2018). Smart campus: Data on energy consumption in an ICT-driven university. 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Comparative effectiveness of Anti-IL5 and Anti-IgE biologic classes in patients with severe asthma eligible for both.

BACKGROUND: Patients with severe asthma may present with characteristics representing overlapping phenotypes, making them eligible for more than one class of biologic. Our aim was to describe the profile of adult patients with severe asthma eligible for both anti-IgE and anti-IL5/5R and to compare the effectiveness of both classes of treatment in real life. METHODS: This was a prospective cohort study that included adult patients with severe asthma from 22 countries enrolled into the International Severe Asthma registry (ISAR) who were eligible for both anti-IgE and anti-IL5/5R. The effectiveness of anti-IgE and anti-IL5/5R was compared in a 1:1 matched cohort. Exacerbation rate was the primary effectiveness endpoint. Secondary endpoints included long-term-oral corticosteroid (LTOCS) use, asthma-related emergency room (ER) attendance, and hospital admissions. RESULTS: In the matched analysis (n = 350/group), the mean annualized exacerbation rate decreased by 47.1% in the anti-IL5/5R group and 38.7% in the anti-IgE group. Patients treated with anti-IL5/5R were less likely to experience a future exacerbation (adjusted IRR 0.76; 95% CI 0.64, 0.89; p < 0.001) and experienced a greater reduction in mean LTOCS dose than those treated with anti-IgE (37.44% vs. 20.55% reduction; p = 0.023). There was some evidence to suggest that patients treated with anti-IL5/5R experienced fewer asthma-related hospitalizations (IRR 0.64; 95% CI 0.38, 1.08), but not ER visits (IRR 0.94, 95% CI 0.61, 1.43). CONCLUSIONS: In real life, both anti-IgE and anti-IL5/5R improve asthma outcomes in patients eligible for both biologic classes; however, anti-IL5/5R was superior in terms of reducing asthma exacerbations and LTOCS use

Magnetic fusion with high energy self-colliding ion beams

Self-consistent equilibria are obtained for high beta plasma where almost all of the ions are ene with a gyroradius of the order of the plasma scale length. Magnetohydrodynamics would not apply to such a plasma. Recent experiments with tokamaks suggest that it would be insensitive to microinstabilities. Several methods are described for creating the plasma with intense neutralized ion beams

CT/MRI and CEUS LI-RADS Major Features Association with Hepatocellular Carcinoma: Individual Patient Data Meta-Analysis

Background The Liver Imaging Reporting and Data System (LI-RADS) assigns a risk category for hepatocellular carcinoma (HCC) to imaging observations. Establishing the contributions of major features can inform the diagnostic algorithm. Purpose To perform a systematic review and individual patient data meta-analysis to establish the probability of HCC for each LI-RADS major feature using CT/MRI and contrast-enhanced US (CEUS) LI-RADS in patients at high risk for HCC. Materials and Methods Multiple databases (MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and Scopus) were searched for studies from January 2014 to September 2019 that evaluated the accuracy of CT, MRI, and CEUS for HCC detection using LI-RADS (CT/MRI LI-RADS, versions 2014, 2017, and 2018; CEUS LI-RADS, versions 2016 and 2017). Data were centralized. Clustering was addressed at the study and patient levels using mixed models. Adjusted odds ratios (ORs) with 95% CIs were determined for each major feature using multivariable stepwise logistic regression. Risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) (PROSPERO protocol: CRD42020164486). Results A total of 32 studies were included, with 1170 CT observations, 3341 MRI observations, and 853 CEUS observations. At multivariable analysis of CT/MRI LI-RADS, all major features were associated with HCC, except threshold growth (OR, 1.6; 95% CI: 0.7, 3.6; P = .07). Nonperipheral washout (OR, 13.2; 95% CI: 9.0, 19.2; P = .01) and nonrim arterial phase hyperenhancement (APHE) (OR, 10.3; 95% CI: 6.7, 15.6; P = .01) had stronger associations with HCC than enhancing capsule (OR, 2.4; 95% CI: 1.7, 3.5; P = .03). On CEUS images, APHE (OR, 7.3; 95% CI: 4.6, 11.5; P = .01), late and mild washout (OR, 4.1; 95% CI: 2.6, 6.6; P = .01), and size of at least 20 mm (OR, 1.6; 95% CI: 1.04, 2.5; P = .04) were associated with HCC. Twenty-five studies (78%) had high risk of bias due to reporting ambiguity or study design flaws. Conclusion Most Liver Imaging Reporting and Data System major features had different independent associations with hepatocellular carcinoma; for CT/MRI, arterial phase hyperenhancement and washout had the strongest associations, whereas threshold growth had no association. © RSNA, 2021 Online supplemental material is available for this article