A big data study of language use and impact in radio broadcasting in China

Broadcasting more educating and language-reviving contents are ways radio stations can help revitalize the use of the English language in the Hunan province of China. The challenges faced in communicating in English in Chinese radio stations are majorly caused by the lack of language professionals and linguists in the broadcast stations. The absence of these professionals is a major constraint to the development of the community. The broadcast media can help manage multilingualism through the introduction of new words which would give little or no room for lexicon dearth but would expand the language lexicon. Using the English language during broadcast reduces language dearth, and helps reach a much larger audience, even those not in China. Programmes anchored in English in places where the language is barely spoken enhances the vocabulary, comprehension and language vitality of the listeners. This study examined the impact of the English language used in radio broadcasting using a descriptive Big Data survey research design. The study’s population comprises of the inhabitants of the Hunan province in China, from which a sample of 50 broadcast staff and 150 regular inhabitants was drawn using a stratified random sampling technique. The instrument of data collection was a structured questionnaire with closed questions and a self-structured interview. The sample employed frequency distribution tables, percentages, and charts in the presentation and analysis of data. The results revealed that majority of the respondents in Hunan listened to radio broadcast indicating that the use of English language can have massive impact on the people. The study also found that majority of the respondents use their indigenous languages in their day-to-day activities as well as their schools with English being used majorly only in schools with only English-speaking students. The study recommends, amongst others, that the Broadcasting Corporation of China (BCC) review their policy on the allocated time of broadcast in English languages, and that more English language experts and linguists should be incorporated into the broadcast system

An archetypal determination of mobile cloud computing for emergency applications using decision tree algorithm.

Numerous users are experiencing unsafe communications due to the growth of big network mediums, where no node communication is detected in emergency scenarios. Many people find it difficult to communicate in emergency situations as a result of such communications. In this paper, a mobile cloud computing procedure is implemented in the suggested technique in order to prevent such circumstances, and to make the data transmission process more effective. An analytical framework that addresses five significant minimization and maximization objective functions is used to develop the projected model. Additionally, all mobile cloud computing nodes are designed with strong security, ensuring that all the resources are allocated appropriately. In order to isolate all the active functions, the analytical framework is coupled with a machine learning method known as Decision Tree. The suggested approach benefits society because all cloud nodes can extend their assistance in times of need at an affordable operating and maintenance cost. The efficacy of the proposed approach is tested in five scenarios, and the results of each scenario show that it is significantly more effective than current case studies on an average of 86%

FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images

In this paper, we propose an end-to-end deep neural network for solving the problem of imbalanced large and small organ segmentation in head and neck (HaN) CT images. To conduct radiotherapy planning for nasopharyngeal cancer, more than 10 organs-at-risk (normal organs) need to be precisely segmented in advance. However, the size ratio between large and small organs in the head could reach hundreds. Directly using such imbalanced organ annotations to train deep neural networks generally leads to inaccurate small-organ label maps. We propose a novel end-to-end deep neural network to solve this challenging problem by automatically locating, ROI-pooling, and segmenting small organs with specifically designed small-organ sub-networks while maintaining the accuracy of large organ segmentation. A strong main network with densely connected atrous spatial pyramid pooling and squeeze-and-excitation modules is used for segmenting large organs, where large organs' label maps are directly output. For small organs, their probabilistic locations instead of label maps are estimated by the main network. High-resolution and multi-scale feature volumes for each small organ are ROI-pooled according to their locations and are fed into small-organ networks for accurate segmenting small organs. Our proposed network is extensively tested on both collected real data and the \emph{MICCAI Head and Neck Auto Segmentation Challenge 2015} dataset, and shows superior performance compared with state-of-the-art segmentation methods.Comment: MICCAI 201

Non-invasive methods to evaluate liver fibrosis in patients with non-alcoholic fatty liver disease

Non-alcoholic Fatty Liver Disease (NAFLD) is a chronic liver disease that is strongly related to insulin resistance and metabolic syndrome, and it has become the most common liver disorder in developed countries. NAFLD embraces the full pathological process of three conditions: steatosis, non-alcoholic steatohepatitis, and finally, cirrhosis. As NAFLD progresses, symptoms will become increasingly severe as fibrosis develops. Therefore, evaluating the fibrosis stage is crucial for patients with NAFLD. A liver biopsy is currently considered the gold standard for staging fibrosis. However, due to the limitations of liver biopsy, non-invasive alternatives were extensively studied and validated in patients with NAFLD. The advantages of non-invasive methods include their high safety and convenience compared with other invasive approaches. This review introduces the non-invasive methods, summarizes their benefits and limitations, and assesses their diagnostic performance for NAFLD-induced fibrosis

Cloud-based bug tracking software defects analysis using deep learning

Cloud technology is not immune to bugs and issue tracking. A dedicated system is required that will extremely error prone and less cumbersome and must command a high degree of collaboration, flexibility of operations and smart decision making. One of the primary goals of software engineering is to provide high-quality software within a specified budget and period for cloud-based technology. However, defects found in Cloud-Based Bug Tracking software's can result in quality reduction as well as delay in the delivery process. Therefore, software testing plays a vital role in ensuring the quality of software in the cloud, but software testing requires higher time and cost with the increase of complexity of user requirements. This issue is even cumbersome in the embedded software design. Early detection of defect-prone components in general and embedded software helps to recognize which components require higher attention during testing and thereby allocate the available resources effectively and efficiently. This research was motivated by the demand of minimizing the time and cost required for Cloud-Based Bug Tracking Software testing for both embedded and general-purpose software while ensuring the delivery of high-quality software products without any delays emanating from the cloud. Not withstanding that several machine learning techniques have been widely applied for building software defect prediction models in general, achieving higher prediction accuracy is still a challenging task. Thus, the primary aim of this research is to investigate how deep learning methods can be used for Cloud-Based Bug Tracking Software defect detection with a higher accuracy. The research conducted an experiment with four different configurations of Multi-Layer Perceptron neural network using five publicly available software defect datasets. Results of the experiments show that the best possible network configuration for software defect detection model using Multi-Layer Perceptron can be the prediction model with two hidden layers having 25 neurons in the first hidden layer and 5 neurons in the second hidden layer