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.

Road surface defects are crucial problems for safe and smooth traffic flow. Due to climate changes, low quality of construction material, large flow of traffic, and heavy vehicles, road surface anomalies are increasing rapidly. Detection and repairing of these defects are necessary for the safety of drivers, passengers, and vehicles from mechanical faults. In this modern era, autonomous vehicles are an active research area that controls itself with the help of in-vehicle sensors without human commands, especially after the emergence of deep learning (DNN) techniques. A combination of sensors and DNN techniques can be useful for unmanned vehicles for the perception of their surroundings for the detection of tracks and obstacles for smooth traveling based on the deployment of artificial intelligence in vehicles. One of the biggest challenges for autonomous vehicles is to avoid the critical road defects that may lead to dangerous situations. To solve the accident issues and share emergency information, the Intelligent Transportation System (ITS) introduced the concept of vehicular network termed as vehicular ad hoc network (VANET) for achieving security and safety in a traffic flow. A novel mechanism is proposed for the automatic detection of road anomalies by autonomous vehicles and providing road information to upcoming vehicles based on Edge AI and VANET. Road images captured via camera and deployment of the trained model for road anomaly detection in a vehicle could help to reduce the accident rate and risk of hazards on poor road conditions. The techniques Residual Convolutional Neural Network (ResNet-18) and Visual Geometry Group (VGG-11) are applied for the automatic detection and classification of the road with anomalies such as a pothole, bump, crack, and plain roads without anomalies using the dataset from different online sources. The results show that the applied models performed well than other techniques used for road anomalies identification.

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Increasing man-machine trust has burgeoned during the last few decades. The growing interest in trust-building has led to the study of the non-dichotomous nature of trust. Trust as social behavior is an integral part of effective team building. The major focus has been offered to study how humans build trust towards machines, whereas few attempts have been made to study the reverse. Studies have shown that trustworthiness perceptions initialize trust behavior whereas trust behavior influences subsequent trustworthiness perceptions. This paper presents the design and comparative analysis of evidential fuzzy multi-criteria decision-making (EFMCDM) based on multi-dimensional trust quantification schemes to quantify trust level with the human agent in a collaborative environment.

By the dramatic growth of the population in cities requires the traffic systems to be designed efficiently and sustainably by taking full advantage of modern-day technology. Dynamic traffic flow is a significant issue which brings about a block of traffic movement. Thus, for tackling this issue, this paper aims to provide a mechanism to predict the traffic congestion with the help of Artificial Neural Networks (ANN) which shall control or minimize the blockage and result in the smoothening of road traffic. Proposed Modeling Smart Road Traffic Congestion Control using Artificial Back Propagation Neural Networks (MSR2C-ABPNN) for road traffic increase transparency, availability and efficiency in services offered to the citizens. In this paper, the prediction of congestion is operationalized by using the algorithm of backpropagation to train the neural network. The proposed system aims to provide a solution that will increase the comfort level of travellers to make intelligent and better transportation decision, and the neural network is a plausible approach to find traffic situations. Proposed MSR2C-ABPNN with Time series gives attractive results concerning MSE as compared to the fitting approach.

The application of confusion and diffusion processes on the three individual components of an RGB image is not secure and efficient, so this problem needs to be addressed. In this paper, a novel RGB image cipher is proposed using chaotic systems, 15-puzzle artificial intelligence problem and DNA computing. First of all the given color image is decomposed into its red, green and blue gray scale images. Then these gray scale images are concatenated to make a single gray scale image. This single gray scale image is further divided into different blocks. A block level permutation (BLP) is proposed on this gray scale image by using the 15-puzzle problem. A pixel level permutation is applied to further randomize the image pixels. This confused image is then DNA encoded. Afterwards, a diffusion process is applied on this DNA encoded image. Lastly this DNA diffused image is converted back into the decimal. Further, this single gray scale image is broken into three gray scale images. These three images are combined to get the final color cipher image. To create the plaintext sensitivity, SHA 256 hash function has been used. Both the simulation and a comprehensive security analyses suggest the robustness and the impregnability of the proposed scheme which in turn signals towards the real world applicability of the scheme.