The purpose of this paper is to investigate a comprehensive model of international tourists’ intentions to use mobile food information (MFI). The study compares the intentions of independent tourists and package tourists focusing on the influences of mobile design qualities and tourists’ perceptions. The model is based on the technology acceptance model. The results revealed that the proposed model more efficiently predicted intention in groups of independent tourists than in groups of package tour. Furthermore, there are some differences between these groups. The proposed model could contribute to future collaboration between tourist agents and mobile-based developers to achieve the implementation of MFI.
Sleep quality is highly significant for the people's overall health. A standard diagnosis for sleep-related syndromes and illnesses is Polysomnography (PSG) or a sleep test in a controlled laboratory. However, PSG requires a sleep specialist to interpret bio-signals collected. It is a time consuming procedure. One of the fundamental step in the PSG is Sleep Stage Classification (SSC). In this study, we propose an investigation of Automatic Sleep Stage Classification (ASSC) using data mining techniques as an alternative to the PSG in order to reduce the time necessary for accurately diagnosing sleep quality. We studied 2,535 subjects' polysomnographic data with 14 channels of biomedical signals from the Sleep Heart Health Study (SHHS) Dataset. Subsequently, four data mining techniques including Decision Trees, Random Forests, Neural Network, and k-Nearest Neighbors were selected to compare the classification performances. The classification results in k-Nearest Neighbors achieved the greatest accuracy at 83.76%.
Since the introduction of image pattern recognition and computer vision processing, the classification of cancer tissues has been a challenge at pixel-level, slide-level, and patient-level. Conventional machine learning techniques have given way to Deep Learning (DL), a contemporary, state-of-the-art approach to texture classification and localization of cancer tissues. Colorectal Cancer (CRC) is the third ranked cause of death from cancer worldwide. This paper proposes image-level texture classification of a CRC dataset by deep convolutional neural networks (CNN). Simple DL techniques consisting of transfer learning and fine-tuning were exploited. VGG-16, a Keras pre-trained model with initial weights by ImageNet, was applied. The transfer learning architecture and methods responding to VGG-16 are proposed. The training, validation, and testing sets included 5000 images of 150 × 150 pixels. The application set for detection and localization contained 10 large original images of 5000 × 5000 pixels. The model achieved F1-score and accuracy of 0.96 and 0.99, respectively, and produced a false positive rate of 0.01. AUC-based evaluation was also measured. The model classified ten large previously unseen images from the application set represented in false color maps. The reported results show the satisfactory performance of the model. The simplicity of the architecture, configuration, and implementation also contributes to the outcome this work.
Neonatal Hyperbilirubinemia, or jaundice, is a harmful disease found in newborns, a symptom of which is the yellowish discoloration of the skin. Visual examination is most frequently used for screening of Hyperbilirubinemia in neonates, however, blood specimen collection is the gold standard to identify the disease and its severity. We propose a Mobile Computer-Aided Diagnosis (mCADx) tool to identify the Neonatal Hyperbilirubinemia symptom using advanced digital image processing and data mining techniques. The mCADx was developed in a cross-platform environment. The mCADx works with smart devices run on either iOS or Android operating systems. With ethical committee approval, we collected and studied image data of 178 infant subjects with different jaundice severity levels. The severity of the disease was examined from blood test results, which were annotated by medical specialists. Data mining techniques included Decision Trees, k Nearest Neighbor, and the Conventional Neural Network was investigated in the dataset. An in-depth comparison between techniques was performed and discussed. The classification results in CNN gained the highest accuracy at 0.8099, 0.9251, 0.8086. This novel work can assist in identifying Neonatal Hyperbilirubinemia in newborns after discharging from the hospital. Reoccurring Neonatal Hyperbilirubinemia can be found with minimum awareness of parents. Limitations and future works were discussed in this work.
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