METHODS: A retrospective analysis was performed on 145 HIV-positive Malaysians of Chinese descent from two centers at the University Hospital Kuala Lumpur (UHKL) and the General Hospital Kuala Lumpur (GHKL) from March 1997 to February 1998. Demographic data and clinical data were analyzed.
RESULTS: The analysis showed that 104 out of 145 patients had mucocutaneous disorders (71.7%). In the study, there were 100 men (96.2%) and four women (3.8%). The majority of patients were in the age group 20-50 years. The patients who presented with mucocutaneous disease also had low CD4+ T-lymphocyte counts and most had acquired immunodeficiency syndrome (AIDS) defining illness. The number of cases with generalized hyperpigmentation was very high in the group (35.9%), followed by nodular prurigo (29.7%) and xerosis (27.6%). Seborrheic dermatitis was seen in 20.7% of cases, with psoriasis in 8.3%. The most common infections were oral candidiasis (35.9%), tinea corporis and onychomycosis (9.7%), and herpes infection (5.5%); however, mucocutaneous manifestations of Kaposi's sarcoma were rare.
CONCLUSIONS: The results suggest that mucocutaneous findings are useful clinical predictors of HIV infection or signs of the presence of advanced HIV infection.
OBJECTIVES: In this manuscript, the Robotic Facial Recognition System using the Compound Classifier (RERS-CC) is introduced to improve the recognition rate of human faces. The process is differentiated into classification, detection, and recognition phases that employ principal component analysis based learning. In this learning process, the errors in image processing based on the extracted different features are used for error classification and accuracy improvements.
RESULTS: The performance of the proposed RERS-CC is validated experimentally using the input image dataset in MATLAB tool. The performance results show that the proposed method improves detection and recognition accuracy with fewer errors and processing time.
CONCLUSION: The input image is processed with the knowledge of the features and errors that are observed with different orientations and time instances. With the help of matching dataset and the similarity index verification, the proposed method identifies precise human face with augmented true positives and recognition rate.
OBJECTIVES: This paper discusses activity detection and analysis (ADA) using security robots in workplaces. The application scenario of this method relies on processing image and sensor data for event and activity detection. The events that are detected are classified for its abnormality based on the analysis performed using the sensor and image data operated using a convolution neural network. This method aims to improve the accuracy of detection by mitigating the deviations that are classified in different levels of the convolution process.
RESULTS: The differences are identified based on independent data correlation and information processing. The performance of the proposed method is verified for the three human activities, such as standing, walking, and running, as detected using the images and sensor dataset.
CONCLUSION: The results are compared with the existing method for metrics accuracy, classification time, and recall.
OBJECTIVES: In this manuscript, the Interaction Modeling and Classification Scheme (IMCS) is introduced to improve the accuracy of HRI. This scheme consists of two phases, namely error classification and input mapping. In the error classification process, the input is analyzed for its events and conditional discrepancies to assign appropriate responses in the input mapping phase. The joint process is aided by a linear learning model to analyze the different conditions in the event and input detection.
RESULTS: The performance of the proposed scheme shows that it is capable of improving the interaction accuracy by reducing the ratio of errors and interaction response by leveraging the information extraction from the discrete and successive human inputs.
CONCLUSION: The fetched data are analyzed by classifying the errors at the initial stage to achieve reliable responses.
METHODS: Publications were collected from PubMed, EMBASE, Cochrane Library, Web of Science, CNKI, Sino Med, VIP, and Wan Fang Databases. Relative risk (RR) was calculated with a 95 % confidence interval (CI). Heterogeneity was assessed. The Cochrane Collaboration's tool was used to assess the risk of bias.
RESULTS: 10 studies with 2702 patients showed that the combination therapy significantly reduced the incidence of HCC in patients with post-hepatitis B cirrhosis at 1, 3, and 5 years. However, the preventive effects of TCM were in compensated cirrhosis, but not the decompensated cirrhosis. Furthermore, TCM correlated with improved liver function and enhanced virological response.
CONCLUSION: Combination therapy with TCM demonstrated the certain potential in reducing the incidence of HCC in patients with hepatitis B cirrhosis. This is attrinuted to the improvement of liver function and enhancement of the viral response. However, the efficacy of TCM in the field still needs more high-quality RCTs to provide stronger evidence in the future.