Editorial: Emerging applications of text analytics and natural language processing in healthcare

WOS:001033976100001Text analytics and natural language processing (NLP) have emerged as powerful tools in healthcare, revolutionizing patient care, clinical research, and public health administration. Over the years, as healthcare databases expand exponentially, healthcare providers, pharmaceutical and biotech industries are utilizing both tools to enhance patient outcome

Predicting occupational injury causal factors using text-based analytics : A systematic review

Workplace accidents can cause a catastrophic loss to the company including human injuries and fatalities. Occupational injury reports may provide a detailed description of how the incidents occurred. Thus, the narrative is a useful information to extract, classify and analyze occupational injury. This study provides a systematic review of text mining and Natural Language Processing (NLP) applications to extract text narratives from occupational injury reports. A systematic search was conducted through multiple databases including Scopus, PubMed, and Science Direct. Only original studies that examined the application of machine and deep learning-based Natural Language Processing models for occupational injury analysis were incorporated in this study. A total of 27, out of 210 articles were reviewed in this study by adopting the Preferred Reporting Items for Systematic Review (PRISMA). This review highlighted that various machine and deep learning-based NLP models such as K-means, Naïve Bayes, Support Vector Machine, Decision Tree, and K-Nearest Neighbors were applied to predict occupational injury. On top of these models, deep neural networks are also included in classifying the type of accidents and identifying the causal factors. However, there is a paucity in using the deep learning models in extracting the occupational injury reports. This is due to these techniques are pretty much very recent and making inroads into decision-making in occupational safety and health as a whole. Despite that, this paper believed that there is a huge and promising potential to explore the application of NLP and text-based analytics in this occupational injury research field. Therefore, the improvement of data balancing techniques and the development of an automated decision-making support system for occupational injury by applying the deep learning-based NLP models are the recommendations given for future research

A Comprehensive Survey of the Recent Studies with UAV for Precision Agriculture in Open Fields and Greenhouses

The increasing world population makes it necessary to fight challenges such as climate change and to realize production efficiently and quickly. However, the minimum cost, maximum income, environmental pollution protection and the ability to save water and energy are all factors that should be taken into account in this process. The use of information and communication technologies (ICTs) in agriculture to meet all of these criteria serves the purpose of precision agriculture. As unmanned aerial vehicles (UAVs) can easily obtain real-time data, they have a great potential to address and optimize solutions to the problems faced by agriculture. Despite some limitations, such as the battery, load, weather conditions, etc., UAVs will be used frequently in agriculture in the future because of the valuable data that they obtain and their efficient applications. According to the known literature, UAVs have been carrying out tasks such as spraying, monitoring, yield estimation, weed detection, etc. In recent years, articles related to agricultural UAVs have been presented in journals with high impact factors. Most precision agriculture applications with UAVs occur in outdoor environments where GPS access is available, which provides more reliable control of the UAV in both manual and autonomous flights. On the other hand, there are almost no UAV-based applications in greenhouses where all-season crop production is available. This paper emphasizes this deficiency and provides a comprehensive review of the use of UAVs for agricultural tasks and highlights the importance of simultaneous localization and mapping (SLAM) for a UAV solution in the greenhouse

A hybrid end-to-end learning approach for breast cancer diagnosis: Convolutional recurrent network

WOS:000917422800001In this study, mammography images are classified as normal, benign, and malignant using the Mammographic Image Analysis Society (MIAS) and INbreast datasets. After the preprocessing of each image, the processed images are given as input to two different end-to-end deep networks. The first network contains only a Convolutional Neural Network (CNN), while the second network is a hybrid structure that includes both the CNN and Bidirectional Long Short Term Memories (BiLSTM). The classification accuracy obtained using the first and second hybrid architectures is 97.60% and 98.56% for the MIAS dataset, respectively. In addition, experiments performed for the INbreast dataset at the study's end prove the proposed method's effectiveness. These results are comparable to those obtained in previous popular studies. The proposed study contributes to previous studies in terms of preprocessing steps, deep network design, and high diagnostic accuracy

A robust semantic lung segmentation study for CNN-based COVID-19 diagnosis

This paper aims to diagnose COVID-19 by using Chest X-Ray (CXR) scan images in a deep learning-based system. First of all, COVID-19 Chest X-Ray Dataset is used to segment the lung parts in CXR images semantically. DeepLabV3+ architecture is trained by using the masks of the lung parts in this dataset. The trained architecture is then fed with images in the COVID-19 Radiography Database. In order to improve the output images, some image preprocessing steps are applied. As a result, lung regions are successfully segmented from CXR images. The next step is feature extraction and classification. While features are extracted with modified AlexNet (mAlexNet), Support Vector Machine (SVM) is used for classification. As a result, 3-class data consisting of Normal, Viral Pneumonia and COVID-19 class are classified with 99.8% success. Classification results show that the proposed method is superior to previous state-of-the-art methods

Discriminative power of geometric parameters of different cultivars of sour cherry pits determined using machine learning

WOS:000735271000001The aim of this study was to develop models based on linear dimensions or shape factors, and the sets of combined linear dimensions and shape factors for discrimination of sour cherry pits of different cultivars (‘Debreceni botermo’, ‘Łutówka’, ‘Nefris’, ‘Kelleris’). The geometric parameters were calculated using image processing. The pits of different sour cherry cultivars statistically significantly differed in terms of selected dimensions and shape factors. The discriminative models built based on linear dimensions produced average accuracies of up to 95% for distinguishing the pit cultivars in the case of ‘Nefris’ vs. ‘Kelleris’ and 72% for all four cultivars. The average accuracies for the discriminative models built based on shape factors were up to 95% for the ‘Nefris’ and ‘Kelleris’ pits and 73% for four cultivars. The models combining the linear dimensions and shape factors produced accuracies reaching 96% for the ‘Nefris’ vs. ‘Kelleris’ pits and 75% for all cultivars. The geometric parameters with high discriminative power may be used for distinguishing different cultivars of sour cherry pits. It can be of great importance for practical applications. It may allow avoiding the adulteration and mixing of different cultivars. © 2021 by the authors

A novel proposal for deep learning-based diabetes prediction: Converting clinical data to image data

Diabetes, one of the most common diseases worldwide, has become an increasingly global threat to humans in recent years. However, early detection of diabetes greatly inhibits the progression of the disease. This study proposes a new method based on deep learning for the early detection of diabetes. Like many other medical data, the PIMA dataset used in the study contains only numerical values. In this sense, the application of popular convolutional neural network (CNN) models to such data are limited. This study converts numerical data into images based on the feature importance to use the robust representation of CNN models in early diabetes diagnosis. Three different classification strategies are then applied to the resulting diabetes image data. In the first, diabetes images are fed into the ResNet18 and ResNet50 CNN models. In the second, deep features of the ResNet models are fused and classified with support vector machines (SVM). In the last approach, the selected fusion features are classified by SVM. The results demonstrate the robustness of diabetes images in the early diagnosis of diabetes

An ensemble learning estimation of the effect of magnetic coupling on switching frequency value in wireless power transfer system for electric vehicles

WOS:000515158800065Wireless power transmission (WPT) systems of small power levels used in the medical and communications sectors have been developed in recent years for tens of kW power levels for charging stations of electric vehicles. In wireless charging systems, power transfer is provided by magnetic coupling using coreless transformers, and in these systems, power electronics circuit design is the crucial point. The inductor behavior as a series resonance circuit element required in the power electronics circuit of WPT systems varies according to the magnetic coupling positioning errors between the primary and secondary sides of the coreless transformer. Therefore, the considering that the resonant capacitor value is constant in the resonance tank circuit, the switching frequency value in the power electronics circuit must be adaptively controlled so that the transferred power value can be carried out efficiently. In this study, the parametric simulations have been performed using Ansys-Electronics software to adaptively control the switching frequency value in the inverter circuit depending on the magnetic coupling coefficients in the WPT circuit designed at a power value of 25 kW. Based on the data obtained from these simulation studies according to different scenarios, the switching frequency value can be changed adaptively and thus the WPT efficiency can be kept at a certain level by providing resonance in each condition. Also, 105 efficiency data were obtained by using Ansys-Electronics parametric solver for many variables such as coreless transformer, resonant circuit parameters of power electronic circuit, switching frequency and magnetic coupling. The WPT efficiency is predicted by ensemble decision trees algorithm. The results show that the estimation with ensemble decision trees is quite successful

Authentication of tomato (Solanum lycopersicum L.) cultivars using discriminative models based on texture parameters of flesh and skin images

WOS:000784865800003Image analysis can provide reproducible, objective and accurate results using a non-destructive, inexpensive procedure. The aim of this study was to evaluate the usefulness of the textures calculated from images of the flesh and skin of tomato fruit for cultivar discrimination. The color images of six tomato cultivars were acquired for whole peeled tomatoes and whole tomatoes with skin using a digital camera. The images were converted to color channels R, G, B, L, a, b, X, Y, and Z. The discriminative models were built based on a set of selected textures from all color channels, each color channel, and color space, separately. For images of tomato flesh and skin, the highest average accuracies equal to 92.67% and 94.33%, respectively, were obtained for models combining textures selected from all channels. For color spaces, the correctness reached 92% and 94% for tomato flesh and skin images, respectively. In the case of color channels, cultivar discrimination reached 86.33% for channel a for tomato flesh and 88.67% for channel b for tomato skin. The developed models can be used to evaluate the authenticity of tomato cultivars

Goal distance-based UAV path planning approach, path optimization and learning-based path estimation: GDRRT*, PSO-GDRRT* and BiLSTM-PSO-GDRRT*

WOS:000996114200001The basic conditions for mobile robots to be autonomous are that the mobile robot localizes itself in the environment and knows the geometric structure of the environment (map). After these conditions are met, this mobile robot is given a specific task, but how the robot will navigate for this task is an important issue. Especially for Unmanned Aerial Vehicles (UAV), whose application has increased recently, path planning in a three-dimensional (3D) environment is a common problem. This study performs three experimental applications to discover the most suitable path for UAV in 3D environments with large and many obstacles. Inspired by Rapidly Random-Exploring Tree Star (RRT*), the first implementation develops the Goal Distance-based RRT* (GDRRT*) approach, which performs intelligent sampling taking into account the goal distance. In the second implementation, the path discovered by GDRRT* is shortened using Particle Swarm Optimization (PSO) (PSO-GDRRT*). In the final application, a network with a Bidirectional Long/Short Term Memory (BiLSTM) layer is designed for fast estimation of optimal paths found by PSO-GDRRT* (BiLSTM-PSO-GDRRT*). As a result of these applications, this study provides important novelties: GDRRT* converges to the goal faster than RRT* in large and obstacle-containing 3D environments. To generate groundtruth paths for training the learning-based network, PSO-GDRRT* finds the shortest paths relatively quickly. Finally, BiLSTM-PSO-GDRRT* provides extremely fast path planning for real-time UAV applications. This work is valuable for real-time autonomous UAV applications in a complex and large environment, as the new methods it offers have fast path planning capability