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The deployment of executable specifications has increased significantly in the last few years. Just as with any other specification documents, these specifications must be examined to ensure the necessary degree of quality. A common and successful technique used for examining traditional specifications is inspection. Now the question has arisen whether inspections on executable specification are the best choice, or if other techniques which use the execution capabilities of the models perform better.

In this paper, we empirically compare several defect detection techniques for executable specifications. In particular, we examine inspections, testing, and ad-hoc simulation. Here, we use the specification languages Statemate and Matlab/Simulink. Also, we take a closer look at the inspection process itself and try to quantify the benefits of an inspection meeting for executable specifications.

The sentiment analysis relying on the aspect of online reviews is utilized for identifying the polarity of the given review. Nowadays, many methods are introduced for aspect-based sentiment analysis (ABSA) using neural networks, and many methods failed to consider contextual information exploitation to make the performance more accurate. Hence, this research proposed an optimized deep learning method for the detection of the aspect and to identify the polarity. Hence, in this research, an optimized deep learning technique for the ABSA is introduced by considering the online reviews, in which the deep learning classifiers are trained with the proposed Fabricius ringlet optimization (FRO) algorithm to reduce the loss that helps to enhance the accuracy of sentiment polarity prediction. The proposed FRO is developed by the hybridization of the behavioral nature of the Fabricius and the ringlet in feeding for the determination of the global best solution. The tuning of the weights and biases of the classifier enhance the performance of the classifier. The objective behind the tuning is to minimize the loss function while training and to enhance the accuracy of aspect extraction and polarity prediction of sentiment. Based on a study of the existing approach, the suggested FRO-based hybrid deep learning method is significantly improved; its accuracy, sensitivity, and specificity are 87.06%, 90.83%, and 79.37%, respectively, with a training percentage of 40%. The accuracy, sensitivity, and specificity of the existing technique have also been enhanced for aspect restaurant values, which are 87.53%, 96.06%, and 79.88% with a 60% training percentage. Similar to that, Twitter values for accuracy, sensitivity, and specificity are reported to be 89.08%, 99.35%, and 79.70%, respectively, with an 80% training percentage. The proposed method obtained the 90.13%, 99.35%, and 81.10% accuracy, sensitivity, and specificity from the assessment of the FRO-based hybrid deep learning.

Epilepsy is an unavoidable major persistent and critical neurological disorder that influences the human brain. Moreover, this is apparently distinguished via its recurrent malicious seizures. A seizure is a phase of synchronous, abnormal innervations of a neuron’s population which might last from seconds to a few minutes. In addition, epileptic seizures are transient occurrences of complete or partial irregular unintentional body movements that combine with consciousness loss. As epileptic seizures rarely occurred in each patient, their effects based on physical communications, social interactions, and patients’ emotions are considered, and treatment and diagnosis are undergone with crucial implications. Therefore, this survey reviews 65 research papers and states an important analysis on various machine-learning approaches adopted in each paper. The analysis of different features considered in each work is also done. This survey offers a comprehensive study on performance attainment in each contribution. Furthermore, the maximum performance attained by the works and the datasets used in each work is also examined. The analysis on features and the simulation tools used in each contribution is examined. At the end, the survey expanded with different research gaps and their problem which is beneficial to the researchers for promoting advanced future works on epileptic seizure detection.

As maintenance of buildings and facilities is one of the key tasks in improving the sustainability of buildings, the design for maintainability (DfM) has recently become one of the hot issues due to the complexity associated with maintaining buildings and facilities. While the body of knowledge on DfM has started increasing, there is a lack of the systematic and comprehensive review on DfM-related issues. Thus, this chapter aims to conduct a comprehensive review, from the managerial and technical perspectives, in the relevant research areas. The findings from the managerial perspective were mainly: (1) maintainability implementation status and significant barriers and (2) critical management factors affecting DfM. Furthermore, the findings from the technical perspective involved (1) identification of maintainability considerations and criteria, (2) critical building components, (3) maintainability scoring systems, and (4) innovations and technical advancement to achieve DfM. This study also analyzed the state of the art and trends in DfM research and proposed future research directions. The findings from this study offer preliminary results of the critical review and will serve as a platform for both researchers and practitioners to retrieve the latest developments and trends in DfM.