Brain tumors are difficult to treat and cause substantial fatalities worldwide. Medical professionals visually analyze the images and mark out the tumor regions to identify brain tumors, which is time-consuming and prone to error. Researchers have proposed automated methods in recent years to detect brain tumors early. These approaches, however, encounter difficulties due to their low accuracy and large false-positive values. An efficient tumor identification and classification approach is required to extract robust features and perform accurate disease classification. This paper proposes a novel multiclass brain tumor classification method based on deep feature fusion. The MR images are preprocessed using min-max normalization, and then extensive data augmentation is applied to MR images to overcome the lack of data problem. The deep CNN features obtained from transfer learned architectures such as AlexNet, GoogLeNet, and ResNet18 are fused to build a single feature vector and then loaded into Support Vector Machine (SVM) and K-nearest neighbor (KNN) to predict the final output. The novel feature vector contains more information than the independent vectors, boosting the proposed method’s classification performance. The proposed framework is trained and evaluated on 15,320 Magnetic Resonance Images (MRIs). The study shows that the fused feature vector performs better than the individual vectors. Moreover, the proposed technique performed better than the existing systems and achieved accuracy of 99.7%; hence, it can be used in clinical setup to classify brain tumors from MRIs.

BackgroundPFPS is one of the most frequently occurring overuse injuries affecting the lower limbs. A variety of functional and self-reported outcome measures have been used to assess clinical outcomes of patients with PFPS, however, only the Anterior Knee Pain Scale (AKPS) has been designed for PFPS patients.Material/MethodsWe followed international recommendations to perform a cross-cultural adaptation of the AKPS. The Arabic AKPS and the Arabic RAND 36-item Health Survey were administered to 40 patients who were diagnosed with PFPS. Participants were assessed at baseline and after 2 to 3 days assessed with the Arabic AKPS only. The measurements tested were reliability, validity, and feasibility.ResultsThe Arabic AKPS showed high reliability for both temporal stability, internal consistency (Cronbach’s alpha was 0.81 for the first assessment and 0.75 for the second), excellent test-retest reliability (Intraclass Correlation Coefficients ICC=0.96; 95% confidence interval (CI): 0.93, 0.98) and good agreement (standard error of measurement SEM=1.8%). The Arabic AKPS was significantly correlated with physical components of the RAND 36-Item Health Survey (Spearman’s rho=0.69: p<0.001). No ceiling or floor effects were observed.ConclusionsThe Arabic AKPS is a valid and reliable tool and is comparable to the original English version and other translated versions.

Objective: The use of Electronic Nicotine Delivery Systems (ENDS) is increasing rapidly. However, its discoloring effect on dental restorations is not known. This study aimed to evaluate the effect of ENDS aerosol when compared to conventional cigarette smoke (CS) on the color stability of dental ceramic (DC) and resin composite (RC). Methods: This research project was conducted from November 2018 to May 2019. In this study 30 discs each for DC and RC materials were fabricated to be equally divided into groups of exposure to CS, ENDS aerosol and storage in distilled water (No smoke; NS) respectively (n=10). Specimens were exposed for a total of 7 days, with a rate of 10 cycles per day, each cycle represented 10 puffs. The color change was assessed using the CIELAB color space, by calculating ΔE. Data was analysed using ANOVA and multiple comparisons test. Results: Ceramic specimens in CS (2.422 ± 0.771) and ENDS (2.396 ± 0.396) groups showed comparable ΔE (color change) (p=0.992). Similarly, composite specimens in CS (42.871 ± 2.442) and ENDS (46.866 ± 3.64) groups showed comparable ΔE (p>0.05). NS specimens in both composite and ceramic samples showed lower ΔE than CS and ENDS specimens respectively. Conclusions: Aerosol from Electronic nicotine delivery systems (ENDS) showed similar discoloration levels as cigarette smoking (CS). The level of discoloration for ceramic samples for both ENDS and CS was below clinically perceptible levels (Mean ΔE < 2.5). Discoloration of composite resin due to CS and ENDS was visually perceptible (Mean ΔE > 4.0). doi: https://doi.org/10.12669/pjms.36.5.2303 How to cite this:Vohra F, Andejani AF, Alamri O, Alshehri A, Al-Hamdan RS, Almohareb T, et al. Influence of electronic nicotine delivery systems (ENDS) in comparison to conventional cigarette on color stability of dental restorative materials. Pak J Med Sci. 2020;36(5):---------.  doi: https://doi.org/10.12669/pjms.36.5.2303 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Decoupled data and control planes in Software Defined Networks (SDN) allow them to handle an increasing number of threats by limiting harmful network links at the switching stage. As storage, high-end servers, and network devices, Network Function Virtualization (NFV) is designed to replace purpose-built network elements with VNFs (Virtualized Network Functions). A Software Defined Network Function Virtualization (SDNFV) network is designed in this paper to boost network performance. Stateful firewall services are deployed as VNFs in the SDN network in this article to offer security and boost network scalability. The SDN controller’s role is to develop a set of guidelines and rules to avoid hazardous network connectivity. Intruder assaults that employ numerous socket addresses cannot be adequately protected by these strategies. Machine learning algorithms are trained using traditional network threat intelligence data to identify potentially malicious linkages and probable attack targets. Based on conventional network data (DT), Bayesian Network (BayesNet), Naive-Bayes, C4.5, and Decision Table (DT) algorithms are used to predict the target host that will be attacked. The experimental results shows that the Bayesian Network algorithm achieved an average prediction accuracy of 92.87%, Native–Bayes Algorithm achieved an average prediction accuracy of 87.81%, C4.5 Algorithm achieved an average prediction accuracy of 84.92%, and the Decision Tree algorithm achieved an average prediction accuracy of 83.18%. There were 451 k login attempts from 178 different countries, with over 70 k source IP addresses and 40 k source port addresses recorded in a large dataset from nine honeypot servers.

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