Adopting effective hierarchal IoMTs computing with K-efficient clustering to control and forecast COVID-19 cases

The Internet of Medical Things (IoMTs) based on fog/cloud computing has been effectively proven to improve the controlling, monitoring, and care quality of Coronavirus disease 2019 (COVID-19) patients. One of the convenient approaches to assess symptomatic patients is to group patients with comparable symptoms and provide an overview of the required level of care to patients with similar conditions. Therefore, this study adopts an effective hierarchal IoMTs computing with K-Efficient clustering to control and forecast COVID-19 cases. The proposed system integrates the K-Means and K-Medoids clusterings to monitor the health status of patients, early detection of COVID-19 cases, and process data in real-time with ultra-low latency. In addition, the data analysis takes into account the primary requirements of the network to assist in understanding the nature of COVID-19. Based on the findings, the K-Efficient clustering with fog computing is a more effective approach to analyse the status of patients compared to that of K-Means and K-Medoids in terms of intra-class, inter-class, running time, the latency of network, and RAM consumption. In summary, the outcome of this study provides a novel approach for remote monitoring and handling of infected COVID-19 patients through real-time personalised treatment services.

Partial differential equations modeling of thermal transportation in Casson nanofluid flow with arrhenius activation energy and irreversibility processes

The formation of entropy in a mixed convection Casson nanofluid model with Arhenius activation energy is examined in this paper using magnetohydrodynamics (MHD). The expanding sheet, whose function of sheet velocity is nonlinear, confines the Casson nanofluid. The final equations, which are obtained from the first mathematical formulations, are solved using the MATLAB built-in solver bvp4c. Utilizing similarity conversion, ODEs are converted in their ultimate form. A number of graphs and tabulations are also provided to show the effects of important flow parameters on the results distribution. Slip parameter was shown to increase fluid temperature and decrease entropy formation. On the production of entropy, the Brinkman number and concentration gradient have opposing effects. In the presence of nanoparticles, the Eckert number effect's augmentation of fluid temperature is more significant. Furthermore, a satisfactory agreement is reached when the findings of the current study are compared to those of studies that have been published in the past.

Improving prediction of plant disease using k-efficient clustering and classification algorithms

<span lang="EN-US">Because plant disease is main cause of most plants’ damage, improving prediction plans for early detection of plant where it has disease or not is an essential interest of decision makers in the agricultural sector for providing proper plant care at appropriate time. Clustering and classification algorithms have proven effective in early detection of plant disease. Making clusters of plants with similar features is an excellent strategy for analyzing features and providing an overview of care quality provided to similar plants. Thus, in this article, we present an artificial intelligence (AI) model based on k-nearest neighbors (k-NN) classifier and k-efficient clustering that integrates k-means with k-medoids to take advantage of both k-means and k-medoids to improve plant disease prediction strategies. Objectives of this article are to determine performance of k-mean, k-medoids and k-efficient also we compare k-NN before clustering and with clustering in prediction of soybean disease for selecting best one for plant disease forecasting. These objectives enable us to analysis data of plant that help to understand nature of plant. Results indicate that k-NN with k-efficient is more efficient than other in terms of inter-class, intra-class, normal mutual information (NMI), accuracy, precision, recall, F-measure, and running time.</span>

Enabling image optimisation and artificial intelligence technologies for better Internet of Things framework to predict COVID

Sensor technology advancements have provided a viable solution to fight COVID and to develop healthcare systems based on Internet of Things (IoTs). In this study, image processing and Artificial Intelligence (AI) are used to improve the IoT framework. Computed Tomography (CT) image‐based forecasting of COVID disease is among the important activities in medicine for measuring the severity of variability in the human body. In COVID CT images, the optimal gamma correction value was optimised using the Whale Optimisation Algorithm (WOA). During the search for the optimal solution, WOA was found to be a highly efficient algorithm, which has the characteristics of high precision and fast convergence. Whale Optimisation Algorithm is used to find best gamma correction value to present detailed information about a lung CT image, Also, in this study, analysis of important AI techniques has been done, such as Support Vector Machine (SVM) and Deep‐Learning (Deep‐Learning (DL)) for COVID disease forecasting in terms of amount of data training and computational power. Many experiments have been implemented to investigate the optimisation: SVM and DL with WOA and without WOA are compared by using confusion matrix parameters. From the results, we find that the DL model outperforms the SVM with WOA and without WOA.

Integrating of Promising Computer Network Technology with Intelligent Supervised Machine Learning for Better Performance

The Software defined network (SDN) controller has such networks universal sight and allows for centralized management and control for the networks. The algorithms of Machine learning used alone or combined with the SDN controller's northbound applications in order to make intelligent SDN. SDN is such potential networking design that blends network's programmability with central administration. The control and the data planes are separated in SDN, and the network with central management point is called SDN controller, which may be programmed and utilized as a brain of the network. Lately, the community of researchers have shown a greater willingness to take advantage of current advances in artificial intelligence to give the SDN best decision making and learning skills. Our research found that combining SDN with Intelligent Supervised Machine Learning (ISML) is very important for performance improvement. ISML is the development of algorithms that can generate broad patterns and assumptions from external source instances in order to portend the predestination of future instances. The ISML algorithms of classification goal is to categorize data based on past information. In data science problems, classification is used rather frequently. To solve such problems, a number of successful approaches were already presented, including rule-based techniques, instance-based techniques, logic-based techniques, and stochastic techniques. This study examined the ISML algorithms' efficiency by checking the precision, accuracy, and with or without SDN recall.

Development of an Integrate E-Medical System Using Software Defined Networking and Machine Learning

Scholars and medical professionals have recognizes the importance of electronic medical monitoring services for tracking elderly people's health. These platforms generate a large amount of data, requiring privacy and data security. on the contrary, Using Software Defined Networking (SDN) to maintain network efficiency and flexibility, which is especially important in the case of healthcare observation, could be a viable solution. Moreover, machine learning can additionally utilized as a game changing tool which incorporated with SDN for optimal level of privacy and security. Even so, integrating SDN into machine learning, which heavily relies on health sensors of patients, is incredibly difficult. In this paper, an Integrate Medical Platform (IMP) with a focus on SDN and Machine learning integration is proposed. We produce a platform that reduces complexity by identifying high level SDN regulations based on the extracted flow classes and utilizing machine learning traffic flow classification techniques. F or various types of traffic, We employ supervised learning approaches based on models that have already been trained. We use four algorithms for supervised learning: Random forest, Logistic Regression classifiers, K-NN, and SVM, with different characteristics. Finally, we evaluated IMP by using accuracy, precision, TPR, TNR, FPR, MAE, and energy consumption.

Improved Methods for Mammogram Breast Cancer Using by Denoising Filtering

In diagnosing breast cancer, digital mammograms have shown their effectiveness as an appropriate and simple instrument in the early detection of tumor. Mammograms offer helpful cancer symptoms information, including microcalcifications and masses, which are not easy to distinguish because there are some flaws with the mammography images, including low contrast, high noise, fuzzy and blur. Additionally, there is a major problem with mammography because of a high density of the breast which conceals As a result of the mammographic image, it is more difficult to distinguish between the tissues with normal dense and the tissues that are cancerous. Therefore, mammography images need to be improved in order to accurately identify and diagnose breast cancer. The most typical goals of images enhancement are to remove noise and improve image details. With the aid of mammography image processing techniques, a special data including distinctive characteristics of tumors can be differentiated, this could help distinguish between malignant and benign cancers. This work focuses on removing noise of pepper & salt, improving image to increase the quality of mammography and enhance early detection of breast cancer. A specific approach is employed to do this, including of two phases of image denoising base filtration and one phase to improve contrast. The stages of filtering contain the using of wiener and median filters. The contrast enhancement stage utilizes (CLAHE) which is an abbreviation for contrast limited adaptive histogram equalization. Evaluating the performance is done via contrast histogram for the CLAHE and MSE & PSNF for the filters. The results demonstrate that the work technique is doing better when put to comparison with other approaches in term of low MSE (1.1645) and high PSNR (47.4750). The technique will be assessed with additional kinds of noise for future work.