Software developers, particularly in open-source projects, rely on bug repositories to organize their work. On a bug report, the component field is used to indicate to which team of developers a bug should be routed. Researchers have shown that incorrect categorization of newly received bug reports to components can cause potential delays in the resolution of bug reports. Approaches have been developed that consider the use of machine learning approaches, specifically Support Vector Machines (svm), to automatically categorize bug reports into the appropriate component to help streamline the process of solving a bug. One drawback of an SVM-based approach is that the results of categorization can be uneven across various components in the system if some components receive less reports than others. In this paper, we consider broadening the consistency of the recommendations produced by an automatic approach by investigating three approaches to automating bug report categorization: an approach similar to previous ones based on an SVM classifier and Term Frequency Inverse Document Frequency(svm-tf-idf), an approach using Latent Dirichlet Allocation (LDA) with SVM (svm-lda) and an approach using LDA and Kullback Leibler divergence (lda-kl). We found that lda-kl produced recalls similar to those found previously but with better consistency across all components for which bugs must be categorized.

The global standards in the field of industrial automation are maintained in industries by completely digitizing their manufacturing process with industry 4.0 standard. Internet of Things (IoT) enables the conservation of cultural heritage with proper assistance on data management on the data collected from the sensors. However, energy efficient conservation is required to monitor the IoT sensors in order to deal with building a better infrastructure. In this paper, we develop a bio-inspired algorithm which can automate the entire furnace monitoring and controlling system in order to eliminate the human intervention involved in the physical process. The algorithm is blended as a web-based remote application for the better control of the tasks involved, energy utilized, and its subsequent log-report maintenance. The entire system employs Wi-Fi communication for data transfer from device to cloud where the stored data including temperature log, forth coming schedule, and process graphic are maintained by the proposed algorithm to predict the machine failure at an earlier stage. The real-time prototype system is supported by a heat treatment process that is completely automated using IoT to monitor and maintain the temperature during the production of metal casting process.

Considering task dependencies, the balancing of the Internet of Health Things (IoHT) scheduling is considered important to reduce the make span rate. In this paper, we developed a smart model approach for the best task schedule of Hybrid Moth Flame Optimization (HMFO) for cloud computing integrated in the IoHT environment over e-healthcare systems. The HMFO guarantees uniform resource assignment and enhanced quality of services (QoS). The model is trained with the Google cluster dataset such that it learns the instances of how a job is scheduled in cloud and the trained HMFO model is used to schedule the jobs in real time. The simulation is conducted on a CloudSim environment to test the scheduling efficacy of the model in hybrid cloud environment. The parameters used by this method for the performance assessment include the use of resources, response time, and energy utilization. In terms of response time, average run time, and lower costs, the hybrid HMFO approach has offered increased response rate with reduced cost and run time than other methods.

In the age of ubiquitous computing, people around the globe are benefiting from a more contemporary and intelligent way of living with the help of IoMT (Internet of Medical Things). The Internet of Medical Things (IoMT) and e-healthcare are envisioned to make it simple for individuals to access high-quality home healthcare. Smart healthcare is a convergence of improvements in IoMT, smart wearables, and communication technologies. Using communication technology and software, IoMT unites smart trackers, patients, caretakers, and healthcare providers on a unified platform. The healthcare system has undergone a transformation attributable to IoMT, and it possesses much more potential. By effectively integrating IoT sensors and communication technologies, IoMT has considerably lowered the cost and energy consumption of e-health care. The importance of social and mental health cannot be overstated. According to a global poll, 33 percent of adults worldwide are alone. Unfortunately, this number is swelling every day. In this paper, we present a unique model to monitor a person's social interactions using particular metrics and data collected by wearable tech and sent to the cloud through IoMT. The sigmoid function is used to evaluate the data, and a particular threshold value is used to determine a person's appropriate social behavior. The proposed work is one of a kind.