Recent advances in the domain of software defect prediction (SDP) include the integration of multiple classification techniques to create an ensemble or hybrid approach. This technique was introduced to improve the prediction performance by overcoming the limitations of any single classification technique. This research provides a systematic literature review on the use of the ensemble learning approach for software defect prediction. The review is conducted after critically analyzing research papers published since 2012 in four well-known online libraries: ACM, IEEE, Springer Link, and Science Direct. In this study, five research questions that cover the different aspects of research progress on the use of ensemble learning for software defect prediction are addressed. To extract the answers to identified questions, 46 most relevant papers are shortlisted after a thorough systematic research process. This study will provide compact information regarding the latest trends and advances in ensemble learning for software defect prediction and provide a baseline for future innovations and further reviews. Through our study, we discovered that frequently employed ensemble methods by researchers are the random forest, boosting, and bagging. Less frequently employed methods include stacking, voting and Extra Trees. Researchers proposed many promising frameworks, such as EMKCA, SMOTE-Ensemble, MKEL, SDAEsTSE, TLEL, and LRCR, using ensemble learning methods. The AUC, accuracy, F-measure, Recall, Precision, and MCC were mostly utilized to measure the prediction performance of models. WEKA was widely adopted as a platform for machine learning. Many researchers showed through empirical analysis that feature selection and data sampling were important pre-processing steps that improve the performance of ensemble classifiers.

The current world faces a considerable traffic rate on roads due to the increase in various types of vehicles. It caused emergency vehicles to delay and increasing the patients' health risk factor. Internet of vehicle-based artificial neural network (IoV-ANN) has been proposed for effective health autonomous system in medical things. The proposed IoV-ANN provides a secure network to monitor and track the vehicle's motion using the global positioning system. It consists of an autonomous system which is enabled with an artificial neural network (ANN). ANN model has three layers. First layers collect the data using IoV sensors. Second or hidden layers process the sensor data, predict the road's traffic condition and reroute the emergency vehicle to an exact route. IoV-ANN helps to reduce road congestion in this article to enhance the timely functioning of an emergency vehicle. ANN categorizes the congestion networks of traffic. Traffic restrictions such as changing the queue gap in the road signals and the alternative roads are altered automatically due to congestion. It allows the government to develop ideas for alternate routes to enhance traffic control. The output layer gives commands to the driver autonomously. The simulation analysis of the proposed method proved that the system could work independently. The IoV-ANN achieves the highest performance rate of (97.89%), with a reduced error rate (9.12%) traffic congestion rate (10.31%), travel period (32 s), vehicle detection rate (93.61%), classification accuracy (95.02%), MAPE (8.4%), throughput rate (93.50%) when compared to other popular methods.Keywords Internet of vehicles Á Artificial neural network Á Hidden layer Á Global positioning system Á Emergency Á Autonomous system Á Vehicle motion Á IoV sensors Communicated by Vicente Garcia Diaz.

Non-governmental organizations (NGOs) in under-developed countries are receiving funds from donor agencies for various purposes, including relief from natural disasters and other emergencies, promoting education, women empowerment, economic development, and many more. Some donor agencies have lost their trust in NGOs in under-developed countries, as some NGOs have been involved in the misuse of funds. This is evident from irregularities in the records. For instance, in education funds, on some occasions, the same student has appeared in the records of multiple NGOs as a beneficiary, when in fact, a maximum of one NGO could be paying for a particular beneficiary. Therefore, the number of actual beneficiaries would be smaller than the number of claimed beneficiaries. This research proposes a blockchain-based solution to ensure trust between donor agencies from all over the world, and NGOs in under-developed countries. The list of National IDs along with other keys would be available publicly on a blockchain. The distributed software would ensure that the same set of keys are not entered twice in this blockchain, preventing the problem highlighted above. The details of the fund provided to the student would also be available on the blockchain and would be encrypted and digitally signed by the NGOs. In the case that a record inserted into this blockchain is discovered to be fake, this research provides a way to cancel that record. A cancellation record is inserted, only if it is digitally signed by the relevant donor agency.

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This study is a literature review on cloud computing cloud computing trends as one the fastest growing technologies in the computer industry and their benefits and opportunities for all types of organizations. In addition, it addresses the challenges and problems that contribute to increasing the number of customers willing to adopt and use the technology. A mixed research study approach was adopted for the study, that is, by collecting and analyzing both quantitative and qualitative information within the same literature review and summarizing the findings of previous (related) studies. Results highlights the current and future trends of cloud computing and exposes readers to the challenges and problems associated with cloud computing. The reviewed literature showed that the technology is promising and is expected to grow in the future. Researchers have proposed many techniques to address the problems and challenges of cloud computing, such as security and privacy risks, through mobile cloud computing and cloud-computing governance.

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