The accurate severity classification of a bug report is an important aspect of bug fixing. The bug reports are submitted into the bug tracking system with high speed, and owing to this, bug repository size has been increasing at an enormous rate. This increased bug repository size introduces biases in the bug triage process. Therefore, it is necessary to classify the severity of a bug report to balance the bug triaging process. Previously, many machine learning models were proposed for automation of bug severity classification. The accuracy of these models is not up to the mark because they do not extract the important feature patterns for learning the classifier. This paper proposes a novel deep learning model for multiclass severity classification called Bug Severity classification to address these challenges by using a Convolutional Neural Network and Random forest with Boosting (BCR). This model directly learns the latent and highly representative features. Initially, the natural language techniques preprocess the bug report text, and then n-gram is used to extract the features. Further, the Convolutional Neural Network extracts the important feature patterns of respective severity classes. Lastly, the random forest with boosting classifies the multiple bug severity classes. The average accuracy of the proposed model is 96.34% on multiclass severity of five open source projects. The average F-measures of the proposed BCR and the existing approach were 96.43% and 84.24%, respectively, on binary class severity classification. The results prove that the proposed BCR approach enhances the performance of bug severity classification over the state-of-the-art techniques.

Across all countries, both developing and developed, women face the greatest risk of breast cancer. Patients who have their breast cancer diagnosed and staged early have a better chance of receiving treatment before the disease spreads. The automatic analysis and classification of medical images are made possible by today’s technology, allowing for quicker and more accurate data processing. The Internet of Things (IoT) is now crucial for the early and remote diagnosis of chronic diseases. In this study, mammography images from the publicly available online repository The Cancer Imaging Archive (TCIA) were used to train a deep transfer learning (DTL) model for an autonomous breast cancer diagnostic system. The data were pre-processed before being fed into the model. A popular deep learning (DL) technique, i.e., convolutional neural networks (CNNs), was combined with transfer learning (TL) techniques such as ResNet50, InceptionV3, AlexNet, VGG16, and VGG19 to boost prediction accuracy along with a support vector machine (SVM) classifier. Extensive simulations were analyzed by employing a variety of performances and network metrics to demonstrate the viability of the proposed paradigm. Outperforming some current works based on mammogram images, the experimental accuracy, precision, sensitivity, specificity, and f1-scores reached 97.99%, 99.51%, 98.43%, 80.08%, and 98.97%, respectively, on the huge dataset of mammography images categorized as benign and malignant, respectively. Incorporating Fog computing technologies, this model safeguards the privacy and security of patient data, reduces the load on centralized servers, and increases the output.

Developing precise and robust algorithms that can help in obtaining maximum power yield in a variable speed wind turbine is an important area of research in wind engineering. The present manuscript proposes a technique that utilizes a second-generation CRONE controller for the maximum power tracking technique (MPPT) to maximize power generation in a wind energy conversion system (WECS) based on a double-fed induction generator (DFIG). The authors propose this novel method because the classical controllers cannot provide adequate performance in terms of extracting the maximum energy from variable speed wind turbines when applying a real wind profile and they cannot guarantee the high stability of the WECS. Moreover, this novel controller sufficiently handles problems related to the control effort level. The performance of the second-generation CRONE method was mathematically modeled using MATLAB/Simulink and compared with four other types of MPPT control techniques, which include a proportional-integral linear controller (PI), nonlinear sliding mode controller (SMC), backstepping controller (BS), and fuzzy logic controller (FLC). Two different wind profiles, a step wind profile and a real wind profile, were considered for the comparative study. The response time, dynamic error percentage, and static error percentage were the quantitative parameters compared, and the qualitative parameters included set-point tracking and precision. This test demonstrated the superiority of the second-generation CRONE controller in terms of all of the compared parameters.

Human gait recognition has emerged as a branch of biometric identification in the last decade, focusing on individuals based on several characteristics such as movement, time, and clothing. It is also great for video surveillance applications. The main issue with these techniques is the loss of accuracy and time caused by traditional feature extraction and classification. With advances in deep learning for a variety of applications, particularly video surveillance and biometrics, we proposed a lightweight deep learning method for human gait recognition in this work. The proposed method includes sequential steps–pretrained deep models selection of features classification. Two lightweight pretrained models are initially considered and fine-tuned in terms of additional layers and freezing some middle layers. Following that, models were trained using deep transfer learning, and features were engineered on fully connected and average pooling layers. The fusion is performed using discriminant correlation analysis, which is then optimized using an improved moth-flame optimization algorithm. For final classification, the final optimum features are classified using an extreme learning machine (ELM). The experiments were carried out on two publicly available datasets, CASIA B and TUM GAID, and yielded an average accuracy of 91.20 and 98.60%, respectively. When compared to recent state-of-the-art techniques, the proposed method is found to be more accurate.

Location information plays an important role in most of the applications in Wireless Sensor Network (WSN). Recently, many localization techniques have been proposed, while most of these deals with two Dimensional applications. Whereas, in Three Dimensional applications the task is complex and there are large variations in the altitude levels. In these 3D environments, the sensors are placed in mountains for tracking and deployed in air for monitoring pollution level. For such applications, 2D localization models are not reliable. Due to this, the design of 3D localization systems in WSNs faces new challenges. In this paper, in order to find unknown nodes in Three-Dimensional environment, only single anchor node is used. In the simulation-based environment, the nodes with unknown locations are moving at middle & lower layers whereas the top layer is equipped with single anchor node. A novel soft computing technique namely Adaptive Plant Propagation Algorithm (APPA) is introduced to obtain the optimized locations of these mobile nodes. These mobile target nodes are heterogeneous and deployed in an anisotropic environment having an Irregularity (Degree of Irregularity (DOI)) value set to 0.01. The simulation results present that proposed APPA algorithm outperforms as tested among other meta-heuristic optimization techniques in terms of localization error, computational time, and the located sensor nodes.

A solution to reduce the emission and generation cost of conventional fossil-fuel-based power generators is to integrate renewable energy sources into the electrical power system. This paper outlines an efficient hybrid particle swarm gray wolf optimizer (HPS-GWO)-based optimal power flow solution for a system combining solar photovoltaic (SPV) and wind energy (WE) sources with conventional fuel-based thermal generators (TGs). The output power of SPV and WE sources was forecasted using lognormal and Weibull probability density functions (PDFs), respectively. The two conventional fossil-fuel-based TGs are replaced with WE and SPV sources in the existing IEEE-30 bus system, and total generation cost, emission and power losses are considered the three main objective functions for optimization of the optimal power flow problem in each scenario. A carbon tax is imposed on the emission from fossil-fuel-based TGs, which results in a reduction in the emission from TGs. The results were verified on the modified test system that consists of SPV and WE sources. The simulation results confirm the validity and effectiveness of the suggested model and proposed hybrid optimizer. The results confirm the exploitation and exploration capability of the HPS-GWO algorithm. The results achieved from the modified system demonstrate that the use of SPV and WE sources in combination with fossil-fuel-based TGs reduces the total system generation cost and greenhouse emissions of the entire power system.