The Internet of Things (IoT) has lately developed into an innovation for developing smart environments. Security and privacy are viewed as main problems in any technology's dependence on the IoT model. Privacy and security issues arise due to the different possible attacks caused by intruders. Thus, there is an essential need to develop an intrusion detection system for attack and anomaly identification in the IoT system. In this work, we have proposed a deep learning-based method Deep Belief Network (DBN) algorithm model for the intrusion detection system. Regarding the attacks and anomaly detection, the CICIDS 2017 dataset is utilized for the performance analysis of the present IDS model. The proposed method produced better results in all the parameters in relation to accuracy, recall, precision, F1-score, and detection rate. The proposed method has achieved 99.37% accuracy for normal class, 97.93% for Botnet class, 97.71% for Brute Force class, 96.67% for Dos/DDoS class, 96.37% for Infiltration class, 97.71% for Ports can class and 98.37% for Web attack, and these results were compared with various classifiers as shown in the results.

This paper proposes that interaction generated by tasks has previously been very difficult to analyse because of its highly indexical nature. Task-related actions and non-verbal communication could not be related easily to talk. A technological solution to this problem is presented, using a combination of task-tracking hardware and software, video recording and transcription. This enables a holistic approach, i.e. one in which all elements of behaviour can be integrated in analysis. Micro-analyses of multimodal data are undertaken, which provide revealing insights into the processes of task-based learning. A framework for describing and analysing task-based interaction from a holistic perspective is outlined. Keywords: task-based learning and teaching (TBLT), spoken interaction, Conversation Analysis (CA), task-based interaction, non-verbal communication Conceptions of 'task' and 'task-based interaction'There are well-known conceptual problems involved in the numerous different definitions of what is (and is not) a 'task', summarized in Ellis (2003: 2-9). In this section, however, we do not consider these definitions, but rather the different ways of conceptualizing a task as it evolves in time. We employ Breen's (1989) conception of the three phases of a task: task-as-workplan, task-in-process, and task-as-outcomes. The task-as-workplan is the intended pedagogy, the plan made prior to classroom implementation of what the Re-use of this article is permitted in accordance with the Terms and Conditions set out at

The majority of enhanced oil recovery (EOR) projects are being executed in the the U.S., Canada, Venezuela, Indonesia and China. The volume of oil produced by EOR methods increased considerably from 1.2 MMBD in 1990 to 2.5 MMBD in 2006 (Sandrea and Sandrea 2007). Current total world oil production from EOR is approaching 3 MMBD representing about 3.5% of the daily global oil production (Sandrea and Sandrea 2007). Thermal and CO2 methods are the major contributors to EOR production, followed by hydrocarbon gas injection and chemical EOR. Other more esoteric methods, e.g., microbial, have only been field tested, without any significant quantities being produced on a commercial scale. In recent years, the number of EOR projects has increased with escalating oil prices. The number of EOR projects in the Middle East (ME) has also increased over the past decade. In some countries like Oman, there has been no choice but to implement EOR projects aggressively due to dwindling "easy oil." Other countries in the region have also started to think EOR, and are including them in their strategic short-, medium- and long-term development plans. Furthermore, there are many projects on the drawing board and appropriate screening studies and EOR pilots are being pursued region-wide. This paper reviews the current ME EOR projects from full-field development to field trials, including those on the drawing board. The option of advanced secondary recovery (ASR) — also known as improved oil recovery (IOR) — technologies before full-field deployment of EOR is also discussed. A case is made that they are a better first option before deployment of capital-intensive EOR projects. The ME’s general drive towards "ultimate" oil recovery — instead of immediate oil recovery — is highlighted in the context of EOR. Some of the enablers for EOR in the ME are also discussed in the paper. It highlights the opportunities and challenges of EOR specific to the region.

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Internet of Things (IoT) technology has recently been applied in healthcare systems as an Internet of Medical Things (IoMT) to collect sensor information for the diagnosis and prognosis of heart disease. The main objective of the proposed research is to classify data and predict heart disease using medical data and medical images. The proposed model is a medical data classification and prediction model that operates in two stages. If the result from the first stage is efficient in predicting heart disease, there is no need for stage two. In the first stage, data gathered from medical sensors affixed to the patient’s body were classified; then, in stage two, echocardiogram image classification was performed for heart disease prediction. A hybrid linear discriminant analysis with the modified ant lion optimization (HLDA-MALO) technique was used for sensor data classification, while a hybrid Faster R-CNN with SE-ResNet-101 modelwass used for echocardiogram image classification. Both classification methods were carried out, and the classification findings were consolidated and validated to predict heart disease. The HLDA-MALO method obtained 96.85% accuracy in detecting normal sensor data, and 98.31% accuracy in detecting abnormal sensor data. The proposed hybrid Faster R-CNN with SE-ResNeXt-101 transfer learning model performed better in classifying echocardiogram images, with 98.06% precision, 98.95% recall, 96.32% specificity, a 99.02% F-score, and maximum accuracy of 99.15%.

This study investigated the feasibility of coupling a subsurface numerical reservoir simulator (POWERS) with a surface network modeling simulator, to assist in making better field management decisions according to business need. Coupled simulation models have two advantages over uncoupled models. First, interdependence of the reservoir and surface facilities are properly modeled in coupled simulation models to accommodate rapid variations in production strategies. Coupled simulation models are likely to give more accurate production forecasts compared with modeling the reservoir or the surface separately. Second, given that most surface network modeling tools have a built-in optimizer, it is possible to allocate rates among wells based on a user's objective optimizing function, -e.g., reducing or maintaining a watercut level for a given production target -taking into consideration any system production constraints applied on a well, a group of wells or trunkline levels. To improve the quality of simulation results, a new algorithm is implemented in POWERS to calculate the inflow performance relationship (IPR), based on drainage pressure, i.e., a reservoir pressure calculated as the average of several neighboring cells in the simulation model as opposed to the single cell pressure. The current study shows that it is feasible to run coupled POWERS-surface network models and gain the benefit from the optimization algorithm of the surface network modeling tool.

In this study, a survey of multiple sclerosis (MS) classification and segmentation process is presented, which is based on magnetic resonance imaging. Knowledge of MS lesions is gained by determining the number of sample lesions in order that the lesion development level can be followed precisely; therefore, the effects of pharmaceuticals in medical tests can be accurately assessed. Accurate recognition of MS lesions in magnetic resonance images is an additionally complex process because of their changing shapes and sizes which can be very difficult to identify based on anatomical positions in various subjects. This can be determined by precise segmentation; manual segmentation would be very difficult to perform as it requires high level knowledge which takes additional time. Inter-and intra-expert variability need to be determined in order to perform the automated segmentation of lesions. The principal aim of this survey effort is to provide an analysis of the different categorization and segmentation methods and their techniques. This survey work will be valuable for researchers working in MS by considering and carefully evaluating the past work. The benefits and drawbacks of existing techniques are reviewed and the issue of MS lesion segmentation and classification is elucidated.

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