A rapid increase in heart disease has occurred in recent years, which might be the result of unhealthy food, mental stress, genetic issues, and a sedentary lifestyle. There are many advanced automated diagnosis systems for heart disease prediction proposed in recent studies, but most of them focus only on feature preprocessing, some focus on feature selection, and some only on improving the predictive accuracy. In this study, we focus on every aspect that may have an influence on the final performance of the system, i.e., to avoid overfitting and underfitting problems or to solve network configuration issues and optimization problems. We introduce an optimally configured and improved deep belief network named OCI-DBN to solve these problems and improve the performance of the system. We used the Ruzzo-Tompa approach to remove those features that are not contributing enough to improve system performance. To find an optimal network configuration, we proposed a stacked genetic algorithm that stacks two genetic algorithms to give an optimally configured DBN. An analysis of a RBM and DBN trained is performed to give an insight how the system works. Six metrics were used to evaluate the proposed method, including accuracy, sensitivity, specificity, precision, F1 score, and Matthew’s correlation coefficient. The experimental results are compared with other state-of-the-art methods, and OCI-DBN shows a better performance. The validation results assure that the proposed method can provide reliable recommendations to heart disease patients by improving the accuracy of heart disease predictions by up to 94.61%.

Today’s rapidly developing communication technologies and dynamic collaborative business models made the security of data and resources more crucial than ever especially in multidomain environments like Cloud and Cyber-Physical Systems (CPS). It enforced the research community to develop enhanced access control techniques and models for resources across multi-domain distributed environments so that the security requirements of all participating organizations can be fulfilled through considering dynamicity of changing environments and versatility of access control policies. The popularity of Role-Based Access Control (RBAC) model is irrefutable because of low administrative overhead and large-scale implementation in business organizations. However, it does not incorporate the dynamically changing policies and lacks semantically meaningful business roles which could have a diverse impact upon access decisions in multi-domain business environments. This paper describes our proposed novel access control framework that uses semantic business roles and intelligent agents through implementation of our Intelligent RBAC (I-RBAC) model. It encompasses occupational entitlements as roles for multiple domains. We use the dataset of original occupational roles provided by Standard Occupational Classification (SOC), USA. The novelty of the paper lies in developing a core I-RBAC ontology using real-world semantic business roles and intelligent agent technologies together for achieving required level of access control in highly dynamic multi-domain environment. The intelligent agents use WordNet and bidirectional LSTM deep neural network for automated population of organizational ontology from unstructured text policies. This dynamically learned organizational ontology is further matched with our core I-RBAC ontology in order to extract unified semantic business roles. The proposed I-RBAC model is mathematically described and the overall I-RBAC framework and its implementation architecture is explained. At the end, the I-RBAC model is validated through the implementation results that show a linear runtime trend of the model in presence of a large number of permission assignments and multiple queries.

Advocates of software engineering and software project management stated in the literature that creeping of scope is one of the most common causes for the failure of software projects. Also, advocates believed that it could occur in almost every software project, which leads to compromise in quality, delayed schedules, increase cost and decreased customer satisfaction. However, the lack of empirical evidence demands a comprehensive investigation to identify the factors of scope creep and to propose a conceptual framework to empirically evaluate the impact of scope creep on software project success. To determine the scope creep factors in this study, two exploratory methods, i.e. a Systematic Literature Review (SLR) and interview from experts are performed. Following the analysis of these methods, a conceptual framework is proposed. To empirically evaluate the proposed conceptual framework, data is collected through a survey method. Next, the collected data is analyzed through Partial Least Squares’ Structural Equation Modelling (PLS-SEM). From the results, it is evident that the identified factors of scope creep are negatively associated with software project success. The results of empirical evaluation also second the findings of SLR. The outcome of the study may help the practitioners to understand the dynamics of factors, which undermine scope creep in software SMEs and to assist them in the development of effective control and mitigation strategies, therefore, to increase the project success rate.

Global software development (GSD) practice has been increasingly emerging in the recent few decades in the field of business and software industry. On the one hand, many software development organizations get the benefits of GSD, including but not limited to reduced cost, cheap labor, round the clock working and skilled professionals. On the other hand, these organizations have to face several challenges because of GSD. These challenges pose serious threats to the stability of the GSD projects. Communication between distributed team members is one of the most crucial challenges in GSD. Therefore, the current study aims to identify the communication risk in GSD and also evaluate the impact of these communication risks in GSD environment. A Systematic Literature Review (SLR) has been performed to identify all the communication-related issues in GSD. After that, a conceptual framework has been proposed for evaluating the impact of those issues on communication risk in GSD. An empirical evaluation has been performed on data collected from the software organizations of Pakistan working in GSD based environment. The finding of our study demonstrates that geographical distance, socio-temporal distance, socio-culture distance, team member’s attitude, team issues, organizational & architectural issue and customer issue have a significant direct impact on communication risk in GSD. The study also shows that there is a significant correlation between findings of SLR and empirical investigation (r=0.460, P=0.005). Further, we believe that the results of our research can help to tackle the issues related to communication in GSD. Therefore, it will help to improve the performance of the development activities of GSD organizations.