A Novel Deep-Learning-Based Bug Severity Classification Technique Using Convolutional Neural Networks and Random Forest with Boosting.

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.

DengueFog: A Fog Computing-Enabled Weighted Random Forest-Based Smart Health Monitoring System for Automatic Dengue Prediction.

Dengue is a distinctive and fatal infectious disease that spreads through female mosquitoes called Aedes aegypti. It is a notable concern for developing countries due to its low diagnosis rate. Dengue has the most astounding mortality level as compared to other diseases due to tremendous platelet depletion. Hence, it can be categorized as a life-threatening fever as compared to the same class of fevers. Additionally, it has been shown that dengue fever shares many of the same symptoms as other flu-based fevers. On the other hand, the research community is closely monitoring the popular research fields related to IoT, fog, and cloud computing for the diagnosis and prediction of diseases. IoT, fog, and cloud-based technologies are used for constructing a number of health care systems. Accordingly, in this study, a DengueFog monitoring system was created based on fog computing for prediction and detection of dengue sickness. Additionally, the proposed DengueFog system includes a weighted random forest (WRF) classifier to monitor and predict the dengue infection. The proposed system's efficacy was evaluated using data on dengue infection. This dataset was gathered between 2016 and 2018 from several hospitals in the Delhi-NCR region. The accuracy, F-value, recall, precision, error rate, and specificity metrics were used to assess the simulation results of the suggested monitoring system. It was demonstrated that the proposed DengueFog monitoring system with WRF outperforms the traditional classifiers.

An ensemble learning-based experimental framework for smart landslide detection, monitoring, prediction, and warning in IoT-cloud environment.

Landslides occur every year during the monsoon season in hilly areas. This natural disaster annually leads to several fatalities, injuries, and property destruction. Monitoring landslides and promptly alerting people to looming disasters in light of these injuries and fatalities are crucial. To date, no efficient technique is in practice to predict landslides. The tools that are now available monitor landslides at a very high cost and do not offer early warning or forecasts of soil movement. An innovative, low-cost Internet of Things (IoT)-based system for landslip warning, monitoring, and prediction is the major objective of this research. Its assessment, implementation, and development are described in detail. This study proposes an IoT-based smart landslide detection, warning, prediction, and monitoring system. The pre and post-measures use sensors and other hardware to deal with landslide disasters. It uses real-time environment monitoring (landslide site) for any changes and provides appropriate output by comparing the threshold values. The proposed system is tested on a prototype model, which performed well in our tests. The database was updated 2.5 s after the landslide thanks to a steady Internet connection. In less than 5 s after the event, the Thingspeak channel can display a graphical depiction of the data and its position. Multiple readings showed an 80-85% system accuracy rate. Further, the proposed ensemble learning-based risk prediction model is applied to static and dynamic data to predict the landslide for future reference. The ensemble classifier model has 98.67% recall, 96.56% accuracy, 97.35% F1-value, and 96.07% precision. The alert SMS is also sent to concerned authorities for medical emergency/PWD department/district administration.