Testing is considered as one of the expensive activities in software development process. Fixing the defects during testing process can increase the cost as well as the completion time of the project. Cost of testing process can be reduced by identifying the defective modules during the development (before testing) stage. This process is known as “Software Defect Prediction”, which has been widely focused by many researchers in the last two decades. This research proposes a classification framework for the prediction of defective modules using variant based ensemble learning and feature selection techniques. Variant selection activity identifies the best optimized versions of classification techniques so that their ensemble can achieve high performance whereas feature selection is performed to get rid of such features which do not participate in classification and become the cause of lower performance. The proposed framework is implemented on four cleaned NASA datasets from MDP repository and evaluated by using three performance measures, including: F-measure, Accuracy, and MCC. According to results, the proposed framework outperformed 10 widely used supervised classification techniques, including: “Naïve Bayes (NB), Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), kStar (K*), One Rule (OneR), PART, Decision Tree (DT), and Random Forest (RF)”.

The exponent increase in the use of online information systems triggered the demand of secure networks so that any intrusion can be detected and aborted. Intrusion detection is considered as one of the emerging research areas now days. This paper presents a machine learning based classification framework to detect the Denial of Service (DoS) attacks. The framework consists of five stages, including: 1) selection of the relevant Dataset, 2) Data pre-processing, 3) Feature Selection, 4) Detection, and 5) reflection of Results. The feature selection stage incudes the Decision Tree (DT) classifier as subset evaluator with four well known selection techniques including: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Best First (BF), and Rank Search (RS). Moreover, for detection, Decision Tree (DT) is used with bagging technique. Proposed framework is compared with 10 widely used classification techniques including Naïve Bayes (NB), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), K-Nearest Neighbor (kNN), Decision Tree (DT), Radial Basis Function (RBF), One Rule (OneR), PART, Bayesian Network (BN) and Random Tree (RT). A part of NSL-KDD dataset related to Denial of Service attack is used for experiments and performance is evaluated by using various accuracy measures including: Precision, Recall, F measure, FP rate, Accuracy, MCC, and ROC. The results reflected that the proposed framework outperformed all other classifiers.

Software defect prediction is one of the emerging research areas of software engineering. The prediction of defects at early stage of development process can produce high quality software at lower cost. This research contributes by presenting a feature selection based ensemble classification framework which consists of four stages: 1) Dataset selection, 2) Feature Selection, 3) Classification, and 4) Results. The proposed framework is implemented from two dimensions, one with feature selection and second without feature selection. The performance is evaluated through various measures including: Precision, Recall, F-measure, Accuracy, MCC and ROC. 12 Cleaned publically available NASA datasets are used for experiments. The results of both the dimensions of proposed framework are compared with the other widely used classification techniques such as: “Naïve Bayes (NB), Multi-Layer Perceptron (MLP). Radial Basis Function (RBF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), kStar (K*), One Rule (OneR), PART, Decision Tree (DT), and Random Forest (RF)”. Results reflect that the proposed framework outperformed other classification techniques in some of the used datasets however class imbalance issue could not be fully resolved.

Cellular technology progressed miraculously in the last decade. It has redefined communication paradigm. Statistics provided by Ericson and Cisco show the number of mobile connected devices will reach figures of 9.2 billion and 11.6 billion respectively by 2020. Overall connected devices will surpass 50 billion then. Extremely higher data rates, zero latency, massively scalable, connecting everything anywhere is what that 5G promises. To meet such ambitious goals which apparently seems challenging, the tools and technologies that mobile communication has in its repertoire and what it needs more either enhancement in existing solutions or new solution or joint venture of both, is a question that demands an answer. To realize 5G, evolution and revolution both approaches are being employed. Evolution seeks enhancements in existing technologies while revolution looks for new innovations and technologies. Extension in frequency spectrum, network densification, MIMO, carrier aggregation, Centralized-RAN, HetNets, and Network Functionality Virtualization are the key enablers. This paper disseminates information about ongoing research and development of 5G.