Missing Data Calculation Using the Antioxidant Activity in Selected Herbs

A Novel Method for Detection of Tuberculosis in Chest Radiographs Using Artificial Ecosystem-Based Optimisation of Deep Neural Network Features

Tuberculosis (TB) is is an infectious disease that generally attacks the lungs and causes death for millions of people annually. Chest radiography and deep-learning-based image segmentation techniques can be utilized for TB diagnostics. Convolutional Neural Networks (CNNs) has shown advantages in medical image recognition applications as powerful models to extract informative features from images. Here, we present a novel hybrid method for efficient classification of chest X-ray images. First, the features are extracted from chest X-ray images using MobileNet, a CNN model, which was previously trained on the ImageNet dataset. Then, to determine which of these features are the most relevant, we apply the Artificial Ecosystem-based Optimization (AEO) algorithm as a feature selector. The proposed method is applied to two public benchmark datasets (Shenzhen and Dataset 2) and allows them to achieve high performance and reduced computational time. It selected successfully only the best 25 and 19 (for Shenzhen and Dataset 2, respectively) features out of about 50,000 features extracted with MobileNet, while improving the classification accuracy (90.2% for Shenzen dataset and 94.1% for Dataset 2). The proposed approach outperforms other deep learning methods, while the results are the best compared to other recently published works on both datasets.

A Bidirectional Searching Strategy to Improve Data Quality Based on K-Nearest Neighbor Approach

Traffic data are the basis of traffic control, planning, management, and other implementations. Incomplete traffic data that are not conducive to all aspects of transport research and related activities can have adverse effects such as traffic status identification error and poor control performance. For intelligent transportation systems, the data recovery strategy has become increasingly important since the application of the traffic system relies on the traffic data quality. In this study, a bidirectional k-nearest neighbor searching strategy was constructed for effectively detecting and recovering abnormal data considering the symmetric time network and the correlation of the traffic data in time dimension. Moreover, the state vector of the proposed bidirectional searching strategy was designed based the bidirectional retrieval for enhancing the accuracy. In addition, the proposed bidirectional searching strategy shows significantly more accuracy compared to those of the previous methods.