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General General

Immersive virtual reality application for intelligent manufacturing: Applications and art design.

In Mathematical biosciences and engineering : MBE

Intelligent manufacturing (IM), sometimes referred to as smart manufacturing (SM), is the use of real-time data analysis, machine learning, and artificial intelligence (AI) in the production process to achieve the aforementioned efficiencies. Human-machine interaction technology has recently been a hot issue in smart manufacturing. The unique interactivity of virtual reality (VR) innovations makes it possible to create a virtual world and allow users to communicate with that environment, providing users with an interface to be immersed in the digital world of the smart factory. And virtual reality technology aims to stimulate the imagination and creativity of creators to the maximum extent possible for reconstructing the natural world in a virtual environment, generating new emotions, and transcending time and space in the familiar and unfamiliar virtual world. Recent years have seen a great leap in the development of intelligent manufacturing and virtual reality technologies, yet little research has been done to combine the two popular trends. To fill this gap, this paper specifically employs Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to conduct a systematic review of the applications of virtual reality in smart manufacturing. Moreover, the practical challenges and the possible future direction will also be covered.

Lei Yu, Su Zhi, He Xiaotong, Cheng Chao

2023-Jan

** immersive Human-machine interaction , intelligent manufacturing , virtual reality **

General General

An application of artificial intelligence for investigating the effect of COVID-19 lockdown on three-dimensional temperature variation in equatorial Africa.

In Geoscience frontiers

We present interesting application of artificial intelligence for investigating effect of the COVID-19 lockdown on 3-dimensional temperature variation across Nigeria (2°-15° E, 4°-14° N), in equatorial Africa. Artificial neural networks were trained to learn time-series temperature variation patterns using radio occultation measurements of atmospheric temperature from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC). Data used for training, validation and testing of the neural networks covered period prior to the lockdown. There was also an investigation into the viability of solar activity indicator (represented by the sunspot number) as an input for the process. The results indicated that including the sunspot number as an input for the training did not improve the network prediction accuracy. The trained network was then used to predict values for the lockdown period. Since the network was trained using pre-lockdown dataset, predictions from the network are regarded as expected temperatures, should there have been no lockdown. By comparing with the actual COSMIC measurements during the lockdown period, effects of the lockdown on atmospheric temperatures were deduced. In overall, the mean altitudinal temperatures rose by about 1.1 °C above expected values during the lockdown. An altitudinal breakdown, at 1 km resolution, reveals that the values were typically below 0.5 °C at most of the altitudes, but exceeded 1 °C at 28 and 29 km altitudes. The temperatures were also observed to drop below expected values at altitudes of 0-2 km, and 17-20 km.

Okoh Daniel, Onuorah Loretta, Rabiu Babatunde, Obafaye Aderonke, Audu Dauda, Yusuf Najib, Owolabi Oluwafisayo

2022-Mar

COVID-19 lockdown, Equatorial Africa, Neural network, Sunspot number, Temperature, Time-series

General General

Strengths and limitations of relative wealth indices derived from big data in Indonesia.

In Frontiers in big data

Accurate relative wealth estimates in Low and Middle-Income Countries (LMICS) are crucial to help policymakers address socio-demographic inequalities under the guidance of the Sustainable Development Goals set by the United Nations. Survey-based approaches have traditionally been employed to collect highly granular data about income, consumption, or household material goods to create index-based poverty estimates. However, these methods are only capture persons in households (i.e., in the household sample framework) and they do not include migrant populations or unhoused citizens. Novel approaches combining frontier data, computer vision, and machine learning have been proposed to complement these existing approaches. However, the strengths and limitations of these big-data-derived indices have yet to be sufficiently studied. In this paper, we focus on the case of Indonesia and examine one frontier-data derived Relative Wealth Index (RWI), created by the Facebook Data for Good initiative, that utilizes connectivity data from the Facebook Platform and satellite imagery data to produce a high-resolution estimate of relative wealth for 135 countries. We examine it concerning asset-based relative wealth indices estimated from existing high-quality national-level traditional survey instruments, the USAID-developed Demographic Health Survey (DHS), and the Indonesian National Socio-economic survey (SUSENAS). In this work, we aim to understand how the frontier-data derived index can be used to inform anti-poverty programs in Indonesia and the Asia Pacific region. First, we unveil key features that affect the comparison between the traditional and non-traditional sources, such as the publishing time and authority and the granularity of the spatial aggregation of the data. Second, to provide operational input, we hypothesize how a re-distribution of resources based on the RWI map would impact a current social program, the Social Protection Card (KPS) of Indonesia and assess impact. In this hypothetical scenario, we estimate the percentage of Indonesians eligible for the program, which would have been incorrectly excluded from a social protection payment had the RWI been used in place of the survey-based wealth index. The exclusion error in that case would be 32.82%. Within the context of the KPS program targeting, we noted significant differences between the RWI map's predictions and the SUSENAS ground truth index estimates.

Sartirano Daniele, Kalimeri Kyriaki, Cattuto Ciro, Delamónica Enrique, Garcia-Herranz Manuel, Mockler Anthony, Paolotti Daniela, Schifanella Rossano

2023

index, machine learning, poverty, survey, wealth

Radiology Radiology

Automatic Segmentation of Periapical Radiograph Using Color Histogram and Machine Learning for Osteoporosis Detection.

In International journal of dentistry

Osteoporosis leads to the loss of cortical thickness, a decrease in bone mineral density (BMD), deterioration in the size of trabeculae, and an increased risk of fractures. Changes in trabecular bone due to osteoporosis can be observed on periapical radiographs, which are widely used in dental practice. This study proposes an automatic trabecular bone segmentation method for detecting osteoporosis using a color histogram and machine learning (ML), based on 120 regions of interest (ROI) on periapical radiographs, and divided into 60 training and 42 testing datasets. The diagnosis of osteoporosis is based on BMD as evaluated by dual X-ray absorptiometry. The proposed method comprises five stages: the obtaining of ROI images, conversion to grayscale, color histogram segmentation, extraction of pixel distribution, and performance evaluation of the ML classifier. For trabecular bone segmentation, we compare K-means and Fuzzy C-means. The distribution of pixels obtained from the K-means and Fuzzy C-means segmentation was used to detect osteoporosis using three ML methods: decision tree, naive Bayes, and multilayer perceptron. The testing dataset was used to obtain the results in this study. Based on the performance evaluation of the K-means and Fuzzy C-means segmentation methods combined with 3 ML, the osteoporosis detection method with the best diagnostic performance was K-means segmentation combined with a multilayer perceptron classifier, with accuracy, specificity, and sensitivity of 90.48%, 90.90%, and 90.00%, respectively. The high accuracy of this study indicates that the proposed method provides a significant contribution to the detection of osteoporosis in the field of medical and dental image analysis.

Widyaningrum Rini, Sela Enny Itje, Pulungan Reza, Septiarini Anindita

2023

General General

Effects of ultrasound with an automatic vessel detection system using artificial intelligence on the selection of puncture points among ultrasound beginner clinical nurses.

In The journal of vascular access

BACKGROUND : Ultrasound guidance increases the success rate of peripheral intravenous catheter placement. However, the longer time required to obtain ultrasound-guided access poses difficulties for ultrasound beginners. Notably, interpretation of ultrasonographic images is considered as one of the main reasons of difficulty in using ultrasound for catheter placement. Therefore, an automatic vessel detection system (AVDS) using artificial intelligence was developed. This study aimed to investigate the effectiveness of AVDS for ultrasound beginners in selecting puncture points and determine suitable users for this system.

METHODS : In this crossover experiment involving the use of ultrasound with and without AVDS, we enrolled 10 clinical nurses, including 5 with some experience in peripheral intravenous catheterization using ultrasound-aided methods (categorized as ultrasound beginners) and 5 with no experience in ultrasound and less experience in peripheral intravenous catheterization using conventional methods (categorized as inexperienced). These participants chose two puncture points (those with the largest and second largest diameter) as ideal in each forearm of a healthy volunteer. The results of this study were the time required for the selection of puncture points and the vein diameter of the selected points.

RESULTS : Among ultrasound beginners, the time required for puncture point selection in the right forearm second candidate vein with a small diameter (<3 mm) was significantly shorter when using ultrasound with AVDS than when using it without AVDS (mean, 87 vs 247 s). Among inexperienced nurses, no significant difference in the time required for all puncture point selections was found between the use of ultrasound with and without AVDS. In the vein diameter, significant difference was shown only in the absolute difference at left second candidate among inexperienced participants.

CONCLUSION : Ultrasonography beginners needed less time to select the puncture points in a small diameter vein using ultrasound with AVDS than without AVDS.

Abe-Doi Mari, Murayama Ryoko, Takahashi Toshiaki, Matsumoto Masaru, Tamai Nao, Nakagami Gojiro, Sanada Hiromi

2023-Mar-09

Artificial intelligence, catheter placement, peripheral intravenous catheter, ultrasound, vein assessment

Radiology Radiology

CoviExpert: COVID-19 detection from chest X-ray using CNN.

In Measurement. Sensors

COVID-19 continues to threaten the world with its impact and severity. This pandemic has created a sense of havoc and shook the world stretching the medical fraternity to an unimaginable extent, who are now facing fatigue and exhaustion. Due to the rapid increase in cases all across the globe demanding extensive medical care, people are hunting for resources like testing facilities, medical drugs and even hospital beds. Even people with mild to moderate infection are panicking and mentally giving up due to anxiety and desperation. To combat these issues, it is necessary to find an inexpensive and faster way to save lives and bring about a much-needed change. The most fundamental way through which this can be achieved is with the help of radiology which involves examination of Chest X rays. They are primarily used for the diagnosis of this disease. But due to panic and severity of this disease a recent trend of performing CT scans has been observed. This has been under scrutiny since it exposes patients to a very high level of radiation known to increase the probability of cancer. As quoted by the AIIMS Director, one CT scan is equivalent to around 300-400 Chest X-rays. Also, it is relatively a much costlier testing method. Hence, in this report, we have presented a Deep learning approach which can detect covid 19 positive cases from Chest X ray images. It involves creation of a Deep learning based Convolutional Neural Network (CNN) using Keras (python library) and integrating the model with a front-end user interface for ease of use. This leads up to the creation of a software which we have named as CoviExpert. It uses the sequential Keras model which is built layer by layer. All the layers are trained independently to make independent predictions which are then combined to give the final output. 1584 images of Chest X-rays of both COVID-19 positive and negative patients have been used as training data. 177 images have been used as testing data. The proposed approach gives a classification accuracy of 99%. CoviExpert can be used on any device by any medical professional to detect Covid positive patients within a few seconds.

Arivoli A, Golwala Devdatt, Reddy Rayirth

2022-Oct

CNN, COVID-19, CT scan, CoviExpert, Deep learning, X-ray