Optimal deep learning driven intrusion detection in SDN-Enabled IoT environment

In recent years, wireless networks are widely used in different domains. This phenomenon has increased the number of Internet of Things (IoT) devices and their applications. Though IoT has numerous advantages, the commonly-used IoT devices are exposed to cyber-attacks periodically. This scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic networks. The Software-Defined Networking (SDN) technique and the Machine Learning (ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT networks. The Intrusion Detection System (IDS) models can be employed to secure the SDN-enabled IoT environment in this scenario. The current study devises a Harmony Search algorithm-based Feature Selection with Optimal Convolutional Autoencoder (HSAFS-OCAE) for intrusion detection in the SDN-enabled IoT environment. The presented HSAFS-OCAE method follows a three-stage process in which the Harmony Search Algorithm-based FS (HSAFS) technique is exploited at first for feature selection. Next, the CAE method is leveraged to recognize and classify intrusions in the SDN-enabled IoT environment. Finally, the Artificial Fish Swarm Algorithm (AFSA) is used to fine-tune the hyperparameters. This process improves the outcomes of the intrusion detection process executed by the CAE algorithm and shows the work’s novelty. The proposed HSAFS-OCAE technique was experimentally validated under different aspects, and the comparative analysis results established the supremacy of the proposed model

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Image region completion by structure reconstruction and texture synthesis

In this thesis, we present a new image completion method that automates the filling in of holes left by the removal of undesired areas in images so that the final output image is visually plausible. The reconstruction of the hole is based on the assumption that regions, particularly in natural images, tend to be spatially continuous and are only separated by the hole and must therefore be linked. Therefore, our approach is based on first creating image structure (regions boundaries) in the hole and then propagating texture from surrounding areas constrained by this structure. Structure reconstruction is performed in order to preserve the global structure of the image, by creating regions in the hole with well defined boundaries such that they match the surroundings. The images are first segmented into homogeneous regions. The regions touching the hole are then relabelled based on their colour and spatial distances. Similar regions are then linked resulting in creating a new area in the hole that will be flood-filled and then synthesised to match the surrounding structure. This reconstructed image is then used for texture synthesis as a constraint. Our texture synthesis method proposes two modifications to the generic texture synthesis method and this includes a parallel synthesis order and an iterative synthesis scheme. The parallel synthesis, in which a pixel being synthesised is independent of other pixels during any given iteration and not affected by other previously synthesised pixels, helps reducing the directional bias caused by sequential scanning orders such as the raster scan. The iterative synthesis scheme allows global randomness which will progressively converge towards fine detailed texture. This scheme ensures that the created texture has sufficient, but not excessive, randomness and does not have replications of entire patches. As a result, the method is able to convert gradually the input image into plausibly synthesised image and to remove visible boundary artifacts. The combination of the image structure and texture synthesis methods results in having an image completion method that is capable of dealing with images with large holes that are surrounded by different types of structure and texture areas.EThOS - Electronic Theses Online ServiceSaudi Institute of Diplomatic StudiesGBUnited Kingdo

Autonomous Short-Term Traffic Flow Prediction Using Pelican Optimization with Hybrid Deep Belief Network in Smart Cities

Accurate and timely traffic flow prediction not just allows traffic controllers to evade traffic congestion and guarantee standard traffic functioning, it even assists travelers to take advantage of planning ahead of schedule and modifying travel routes promptly. Therefore, short-term traffic flow prediction utilizing artificial intelligence (AI) techniques has received significant attention in smart cities. This manuscript introduces an autonomous short-term traffic flow prediction using optimal hybrid deep belief network (AST2FP-OHDBN) model. The presented AST2FP-OHDBN model majorly focuses on high-precision traffic prediction in the process of making near future prediction of smart city environments. The presented AST2FP-OHDBN model initially normalizes the traffic data using min–max normalization. In addition, the HDBN model is employed for forecasting the traffic flow in the near future, and makes use of DBN with an adaptive learning step approach to enhance the convergence rate. To enhance the predictive accuracy of the DBN model, the pelican optimization algorithm (POA) is exploited as a hyperparameter optimizer, which in turn enhances the overall efficiency of the traffic flow prediction process. For assuring the enhanced predictive outcomes of the AST2FP-OHDBN algorithm, a wide-ranging experimental analysis can be executed. The experimental values reported the promising performance of the AST2FP-OHDBN method over recent state-of-the-art DL models with minimal average mean-square error of 17.19132 and root-mean-square error of 22.6634

Artificial Intelligence-Based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems

Unmanned Aerial Vehicles (UAVs), or drones, provided with camera sensors enable improved situational awareness of several emergency responses and disaster management applications, as they can function from remote and complex accessing regions. The UAVs can be utilized for several application areas which can hold sensitive data, which necessitates secure processing using image encryption approaches. At the same time, UAVs can be embedded in the latest technologies and deep learning (DL) models for disaster monitoring areas such as floods, collapsed buildings, or fires for faster mitigation of its impacts on the environment and human population. This study develops an Artificial Intelligence-based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems (AISCC-DE2MS). The proposed AISCC-DE2MS technique majorly employs encryption and classification models for emergency disaster monitoring situations. The AISCC-DE2MS model follows a two-stage process: encryption and image classification. At the initial stage, the AISCC-DE2MS model employs an artificial gorilla troops optimizer (AGTO) algorithm with an ECC-Based ElGamal Encryption technique to accomplish security. For emergency situation classification, the AISCC-DE2MS model encompasses a densely connected network (DenseNet) feature extraction, penguin search optimization (PESO) based hyperparameter tuning, and long short-term memory (LSTM)-based classification. The design of the AGTO-based optimal key generation and PESO-based hyperparameter tuning demonstrate the novelty of our work. The simulation analysis of the AISCC-DE2MS model is tested using the AIDER dataset and the results demonstrate the improved performance of the AISCC-DE2MS model in terms of different measures

Lead-Tolerant Bacillus Strains Promote Growth and Antioxidant Activities of Spinach (Spinacia oleracea) Treated with Sewage Water

Irrigation with sewage-contaminated water poses a serious threat to food security, particularly in developing countries. Heavy metal tolerant bacteria are sustainable alternatives for the removal of wastewater contaminants. In the present study, four lead (Pb)-tolerant strains viz. Bacillus megaterium (N8), Bacillus safensis (N11), Bacillus sp. (N18), and Bacillus megaterium (N29) were inoculated in spinach and grown in sewage water treated earthen pots separately and in combination with canal water. Results showed that Pb-tolerant strains significantly improved plant growth and antioxidant activities in spinach and reduces metal concentration in roots and leaves of spinach plants irrigated with treated wastewater. Strain Bacillus sp. (N18) followed by B. safensis (N11) caused the maximum increase in shoot length, root length, shoot fresh weight, root fresh weight, shoot dry weight, root dry weight, and leaf area compared to the uninoculated control of sewage water treated plants. These strains also improved antioxidant enzymatic activity including catalase, guaiacol peroxidase dismutase, superoxide dismutase, and peroxidases activities compared to the uninoculated control under sewage water conditions. Strain Bacillus sp. (N18) followed by B. safensis (N11) showed the highest reduction in nickel, cadmium, chromium, and Pb contents in roots and leaves of spinach compared to the uninoculated control plants treated with the sewage water. Such potential Pb-tolerant Bacillus strains could be recommended for the growth promotion of spinach after extensive evaluation under field conditions contaminated with wastewater