A great diversity comes in the field of medical sciences because of computing capabilities and improvements in techniques, especially in the identification of human heart diseases. Nowadays, it is one of the world’s most dangerous human heart diseases and has very serious effects the human life. Accurate and timely identification of human heart disease can be very helpful in preventing heart failure in its early stages and will improve the patient’s survival. Manual approaches for the identification of heart disease are biased and prone to interexaminer variability. In this regard, machine learning algorithms are efficient and reliable sources to detect and categorize persons suffering from heart disease and those who are healthy. According to the recommended study, we identified and predicted human heart disease using a variety of machine learning algorithms and used the heart disease dataset to evaluate its performance using different metrics for evaluation, such as sensitivity, specificity, F-measure, and classification accuracy. For this purpose, we used nine classifiers of machine learning to the final dataset before and after the hyperparameter tuning of the machine learning classifiers, such as AB, LR, ET, MNB, CART, SVM, LDA, RF, and XGB. Furthermore, we check their accuracy on the standard heart disease dataset by performing certain preprocessing, standardization of dataset, and hyperparameter tuning. Additionally, to train and validate the machine learning algorithms, we deployed the standard K-fold cross-validation technique. Finally, the experimental result indicated that the accuracy of the prediction classifiers with hyperparameter tuning improved and achieved notable results with data standardization and the hyperparameter tuning of the machine learning classifiers.

Convolutiona neural network (CNN) is one of the best neural networks for classification, segmentation, natural language processing (NLP), and video processing. The CNN consists of multiple layers or structural parameters. The architecture of CNN can be divided into three sections: convolution layers, pooling layers, and fully connected layers. The application of CNN became most demanding due to its ability to learn features from images automatically, involving massive amount of training data and high computational resources like GPUs. Due to the availability of the above-stated resources, multiple CNN architectures have been reported. This study focuses on the working of convolution, pooling, and the fully connected layers of CNN architecture, origin of architectures, limitation, benefits of reported architectures, and comparative analysis of contemporary architecture concerning the number of parameters, architectural depth, and significant contribution.

It is quite natural that the crops may be affected from a number of diseases due to many factors namely, change in climate, variations in environmental changings, deficiency of urea etc. Among these factors, the deficiency of the natural nutrients is one of the common reasons that may effect on the overall production of a certain crop. The grape leaves are one of crops that are affected by the deficiency of nutrients like Potassium, Magnesium, Nitrogen and Phosphorus. Furthermore, the effects of these nutrients may have similar disorders on the grape leaves like tilting off the leaf from edges, change in color, rusting off from the root etc. and it is hard to find the identify the nutrient which is likely to be deficient in the grape leaf. In order to ensure the good quality and high production, it is necessary to design an automated system that helps in classifying the affected grape leaf in any of the four classes namely, Potassium-deficient (K-deficient), Phosphorus-deficient (P-deficient), Nitrogen-deficient (N-deficient) or Magnesium-deficient (Mg-deficient). To achieve this target, we performed a series of experiment in which we first created a dataset of grape leaves affected from the deficiency of nutrients, from the crop fields in a controlled environment. The dataset is also augmented since the data instances were not in appropriate amount to achieve the negotiable results. After preprocessing, the Convolution Neural Network (CNN) classifier is used to achieve the average individual accuracies of 77.97, 77.74, 81.81 and, 78.09 for K-, Mg-, P- and N-deficient grape leaves, respectively using conventional training testing ratio and while for the same sequence individual accuracies achieved are 95.95, 92.70, 90.91 and , 94.76 using n-fold cross validation approaches on the original dataset. These accuracies were improved when these approaches are applied on the augmented dataset. The results were also compared with recent studies concluding that our proposed approach outperformed the previous studies. Our experimental results are equally applicable and beneficial when implemented on mobile devices for getting real-time results.

Containers have evolved to support microservice architecture as a low-cost alternative to virtual machines. Containers are increasingly prevalent in the virtualization landscape because of better working; containers can bear considerably less overhead than the conventional hypervisor-based component virtual machines. However, containers directly communicate with the host kernel, and attackers can co-locate containers in the host system quicker than virtual machines. This causes significant security issues in container technology. The security hardening system is currently targeted at implementing universal access management regulations that make it difficult to assess the required procedure for accessing containers. Security mechanisms include an explicit awareness of the purpose and actions of the container and entail manual interaction and configuration. A user-friendly container protection scheme implemented an access policy to comply with its anticipated and legitimate application performance. In this study, container technology constraints have been overcome by proposing a unique Docker-sec mechanism. Docker-sec uses four mechanisms; the original collection has been improved during container runtime by additional rules that constrain the capacity of the container, further representing the applications in practice, file system, processes, network isolation, and vulnerability scanning of Docker images over different workload. Different vulnerabilities have been scanned with a CVE severity level. Results showed that inter-container communication with the system is more secure containers from zero vulnerabilities with an overhead of 3.45%.

Modified source code validation is done by regression testing. In regression testing, the time and resources are limited, in which we have to select the minimal test cases from test suites to reduce execution time. The test case minimization process deals with the optimization of the regression testing by removing redundant test cases or prioritizing the test cases. This study proposed a test case prioritization approach based on multiobjective particle swarm optimization (MOPSO) by considering minimum execution time, maximum fault detection ability, and maximum code coverage. The MOPSO algorithm is used for the prioritization of test cases with parameters including execution time, fault detection ability, and code coverage. Three datasets are selected to evaluate the proposed MOPSO technique including TreeDataStructure, JodaTime, and Triangle. The proposed MOPSO is compared with the no ordering, reverse ordering, and random ordering technique for evaluating the effectiveness. The higher values of results represent the more effectiveness and the efficiency of the proposed MOPSO as compared to other approaches for TreeDataStructure, JodaTime, and Triangle datasets. The result is presented to 100-index mode relevant from low to high values; after that, test cases are prioritized. The experiment is conducted on three open-source java applications and evaluated using metrics inclusiveness, precision, and size reduction of a matrix of the test suite. The results revealed that all scenarios performed well in acceptable mode, and the technique is 17% to 86% more effective in terms of inclusiveness, 33% to 85% more effective in terms of precision, and 17% minimum to 86% maximum in size reduction of metrics.

Activated microglia release harmful substances to retinal ganglion cells (RGCs), but may also benefit by removing cellular debris and secreting neurotrophic factors. These paradoxical roles remain controversial because the nature and time-course of the injury that defines their role is unknown. The aim of this study was to determine if pharmacological manipulation of microglia to acquire a pro-inflammatory or pro-survival phenotype will exacerbate or enhance neuronal survival after injury. Treated HAPI (highly aggressively proliferating immortalized) microglia were injected into the vitreous or tail vein (TV) of female Sprague-Dawley rats. Retinas were examined at 4-14 days following optic nerve crush (ONC) and the number of surviving RGCs was determined. Injection of untreated HAPI cells resulted in the greater loss of RGCs early after ONC when injected into the vitreous and later after ONC when injected into the TV. LPS activated HAPI cells injected into the vitreous resulted in greater RGC loss with and without injury. When injected into the TV with ONC there was no loss of RGCs 4 days after ONC but greater loss afterwards. Minocycline treated HAPI cells injected into the vitreous resulted in greater RGC survival than untreated HAPI cells. However, when injected into the TV with ONC there was greater loss of RGCs. These results suggest that optic nerve signals attract extrinsic microglia to the retina, resulting in a proinflammatory response. Neuroprotection or cytotoxicity of microglia depends on the type of activation, time course of the injury, and if they act on the axon or cell body. Graphical Abstract HAPI microglia migrate to the retina or optic nerve following optic nerve injury when injected into the vitreous or tail vein, respectively. Pretreatment with LPS or minocycline differentially effects retinal ganglion cell survival. In most cases, the result late in the injury process is greater retinal ganglion cell loss.