In software engineering community, defect prediction is one the active domain. For the software’s success, it is essential to reduce the software engineering and data-mining gap. Software defects prediction forecasts the source code errors before the testing phase. Methods for predicting software defects, such as clustering, statistical methods, mixed algorithms, metrics based on neural networks, black box testing, white box testing and machine learning are frequently used to explore the effect area in software. The main contribution of this research is the use of feature selection for the first time to increase the accuracy of machine learning classifiers in defects pre-diction. The objective of this study is to improve the defects prediction accuracy in five data sets of NASA namely; CM1, JM1, KC2, KC1, and PC1. These NASA data sets are open to public. In this research, the feature selection technique is use with machine-learning techniques; Random Forest, Logistic Regression, Multilayer Perceptron, Bayesian Net, Rule ZeroR, J48, Lazy IBK, Support Vector Machine, Neural Networks, and Decision Stump to achieve high defect prediction accuracy as compared to without feature selection (WOFS). The research workbench, a machine-learning tool called WEKA (Waikato Environment for Knowledge Analysis), is used to refine da-ta, preprocess data, and apply the mentioned classifiers. To assess statistical analyses, a mini tab statistical tool is used. The results of this study reveals that accuracy of defects prediction with feature selection (WFS) is improve in contrast with the accuracy of WOFS.

Ever since its emergence, the concept internet of things (IoT) has been applied in many fields. In the area of medical sciences, a new concept “Internet of Medical Things” (IoMT) has been explored. IoMT establishes a connection between humans & machines and serves both of them. It has been expected that, by 2025, services of IoMT would reach the entire world. IoMT has covered a wide scope pertaining to health but unfortunately been facing many security challenges. Healthcare systems consist of sensitive and significant data, which is unorganized and noisy and needs additional power to be calculated for effective analysis & workable results. This data is worked upon for the purpose of making critical decisions. Therefore, it has become the main target of Cyber Criminals. The need of robust security and privacy (S&P) is gradually increasing as more and more devices are getting connected to the IoMT. The S&P of the IoMT has now become a great challenge, considering the utmost significance and vulnerability of the data in the healthcare industry. Lack of sufficient S&P in IoMT devices keeps the patients’ privacy at high stake. This research is intended to propose a Security Model to cope with these Security threats, attacks, issues and challenges. The proposed model has been developed by thoroughly investigating all the major security models through a detailed systematic literature review. The SLR has been conducted to explore all the security threats, security attacks, security issues and security challenges. Extensive meta-analysis has been performed for each of the defined category in order to prioritize these risks. After analyzing these risks, a comprehensive security model has been proposed. The interface has been developed in Python which is well structured, user friendly and easy to implement. The developed module not only identify and prioritize the risks but also automatically control different level of threats. The developed system also contain user intimation modules in case of any threat. This research is based on a very flexible and comprehensive model, which would be highly beneficial to future researchers who desire to work on existing models for the improvement as well as to those who wish to create new security models for IoMT.

One of the epistemological results emerging from this initial study is that the different forms of co-relational inference , known in the Islamic jurisprudence as qiyās , represent an innovative and sophisticated form of reasoning that not only provides new epistemological insights into legal reasoning in general but also furnishes a fine-grained pattern for parallel reasoning which can be deployed in a wide range of problem-solving contexts and does not seem to reduce to the standard forms of analogical argumentation studied in contemporary philosophy of science. However, in the present paper we will only discuss the case of so-called co-relational inferences of the occasioning factor and only in the context of Islamic jurisprudence.