Cloud computing (CC) is on-demand accessibility of network resources, especially datastorage and processing power, without special and direct management by the users. CC recentlyhas emerged as a set of public and private datacenters that offers the client a single platform acrossthe Internet. Edge computing is an evolving computing paradigm that brings computation andinformation storage nearer to the end-users to improve response times and spare transmissioncapacity. Mobile CC (MCC) uses distributed computing to convey applications to cell phones.However, CC and edge computing have security challenges, including vulnerability for clients andassociation acknowledgment, that delay the rapid adoption of computing models. Machine learning(ML) is the investigation of computer algorithms that improve naturally through experience. In thisreview paper, we present an analysis of CC security threats, issues, and solutions that utilizedone or several ML algorithms. We review different ML algorithms that are used to overcomethe cloud security issues including supervised, unsupervised, semi-supervised, and reinforcementlearning. Then, we compare the performance of each technique based on their features, advantages,and disadvantages. Moreover, we enlist future research directions to secure CC models.
The increasing global population at a rapid pace makes road traffic dense; managing such massive traffic is challenging. In developing countries like Pakistan, road traffic accidents (RTA) have the highest mortality percentage among other Asian countries. The main reasons for RTAs are road cracks and potholes. Understanding the need for an automated system for the detection of cracks and potholes, this study proposes a decision support system (DSS) for an autonomous road information system for smart city development with the use of deep learning. The proposed DSS works in layers where initially the image of roads is captured and coordinates attached to the image with the help of global positioning system (GPS), communicated to the decision layer to find about the cracks and potholes in the roads, and eventually, that information is passed to the road management information system, which gives information to drivers and the maintenance department. For the decision layer, we projected a CNN-based model for pothole crack detection (PCD). Aimed at training, a K-fold cross-validation strategy was used where the value of K was set to 10. The training of PCD was completed with a self-collected dataset consisting of 6000 images from Pakistani roads. The proposed PCD achieved 98% of precision, 97% recall, and accuracy while testing on unseen images. The results produced by our model are higher than the existing model in terms of performance and computational cost, which proves its significance.
Objective: We aimed to compare the rate of complications of Gomco and Plastibell circumcision techniques ininfants. Study design:Prospective Randomized Clinical TrialStudy. Place and duration of study:Department of Pediatric Surgery, Sheikh Zayed Hospital, Rahim Yar Khanfor six months from August 2020 to July 2021. Patients and method: A total of 80 patients were enrolled in the study, equally divided into two groups. Group 1 included patients undergoing circumcision via the Gomco technique, while group 2 circumcised with the Plastibell method. All healthy male patients aged one day old to 4 years of age were included. Patients with any congenital abnormalities, e.g., urethral or penile shaft abnormality, local infection, hypospadias. Jaundice and bleeding disorders were excluded. All procedures weredone under local anesthesia, postoperatively topical antibiotic was prescribed to each patient. Data was entered and analyzed using SPSS 25.0. Frequencies and percentages were expressed for qualitative variables like gender and postoperative outcomes, i.e., bleeding, penile edema, and redundant skin. Mean ± S.D represented quantitative variables like age, weight, and BMI. A Chi-square test was used to compare the complication rate between both groups. A p-value ≤0.05 was considered significant. Results: In the Gomco technique, there was no Penile edema, surgical site infection, hematoma, and need for Repeat surgery/manipulation. We found that bleeding was more common in Gomco compared to Plastibell. On the other hand, penile edema, reductant skin, slipped ring, and the need for repeated surgery/manipulation was more often in the Plastibell technique. Conclusion: We propose using the Gomco method for circumcision because of its lower rate of complication and better aesthetic outcome than the Plastibell method. Keywords: Circumcision, Gomco, Infant, Plastibell.
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