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.

Feature Importance Score-Based Functional Link Artificial Neural Networks for Breast Cancer Classification.

Growth of malignant tumors in the breast results in breast cancer. It is a cause of death of many women across the world. As a part of treatment, a woman might have to go through painful surgery and chemotherapy that may further lead to severe side effects. However, it is possible to cure it if it is diagnosed in the initial stage. Recently, many researchers have leveraged machine learning (ML) techniques to classify breast cancer. However, these methods are computationally expensive and prone to the overfitting problem. A simple single-layer neural network, i.e., functional link artificial neural network (FLANN), is proposed to overcome this problem. Further, the F-score is used to reduce the issue of overfitting by selecting features having a higher significance level. In this paper, FLANN is proposed to classify breast cancer using Wisconsin Breast Cancer Dataset (WBCD) (with 699 samples) and Wisconsin Diagnostic Breast Cancer (WDBC) (with 569 samples) datasets. Experimental results reveal that the proposed models can diagnose breast cancer with higher performance. The proposed model can be used in the early breast cancer diagnosis with 99.41% accuracy.

FVC-NET: An Automated Diagnosis of Pulmonary Fibrosis Progression Prediction Using Honeycombing and Deep Learning.

Pulmonary fibrosis is a severe chronic lung disease that causes irreversible scarring in the tissues of the lungs, which results in the loss of lung capacity. The Forced Vital Capacity (FVC) of the patient is an interesting measure to investigate this disease to have the prognosis of the disease. This paper proposes a deep learning-based FVC-Net architecture to predict the progression of the disease from the patient's computed tomography (CT) scan and the patient's metadata. The input to the model combines the image score generated based on the degree of honeycombing for a patient identified based on segmented lung images and the metadata. This input is then fed to a 3-layer net to obtain the final output. The performance of the proposed FVC-Net model is compared with various contemporary state-of-the-art deep learning-based models, which are available on a cohort from the pulmonary fibrosis progression dataset. The model showcased significant improvement in the performance over other models for modified Laplace Log-Likelihood (-6.64). Finally, the paper concludes with some prospects to be explored in the proposed study.

Analysis of the Effect of Incentive Nursing Intervention in Children with Severe Viral Encephalitis and Myocarditis during Rehabilitation Based on Diffusion Weighted MRI.

BACKGROUND: Severe viral encephalitis in children causes a viral infection that damages their central nervous system. This situation arises the mental abnormalities, sudden rise in body temperature, disturbance of consciousness, and so forth in children, which can be life-threatening. OBJECTIVE: This work aimed at exploring the effect of diffusion weighted MRI on children with severe viral encephalitis and myocarditis. METHODS: This work presents a diffusion weighted MRI scanning method that involves scanning through a serial imaging device, axial scanning, and sagittal and coronal scanning. 60 children with severe viral encephalitis and myocarditis who admitted to Brain Hospital of Hunan Province from April 2017 to May 2020 were deemed as research subjects. All the children underwent CT and MRI examination, blood routine examination, and cerebrospinal fluid examination after admission. This work uses the random number table method to classify the subjects into control group and observation group, each consisting of 30 cases. Children in the control group were provided with the routine nursing intervention, whereas children in the observation group were subjected to incentive nursing intervention. The baseline data, ECG monitoring indicators, body abnormalities, and clinical symptom relief time of the two groups of children were compared and the results of diffusion weighted MRI scans were analyzed and the ADC values were counted. RESULTS: The two groups of children were compared on the basis of baseline data, and the variation was not statistically substantial (P > 0.05). The cases of children in the control group had higher heart rate and respiration, and physical dysfunction, language dysfunction, unconsciousness, and nervous dysfunction were more than those in the observation group. However, the cases of blood oxygen saturation were less than those of the observation group. After nursing intervention done for the control group, remission time of clinical symptoms such as convulsion, physical dysfunction, unconsciousness, and nerve dysfunction was longer relative to the observation group (all P < 0.05 are considered). CONCLUSION: The diffusion weighted MRI had diagnostic significance for severe viral encephalitis and myocarditis. For children with severe viral encephalitis and myocarditis, clinical incentive nursing intervention was particularly imperative. It can not only help children to relieve symptoms and control the deterioration of the disease in a short time but also help improve the quality of life of the children and the confidence of family members to cope with the disease.