scholarly journals State-of-the-Art Techniques for Diagnosis of Medical Parasites and Arthropods

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1545
Author(s):  
Pichet Ruenchit

Conventional methods such as microscopy have been used to diagnose parasitic diseases and medical conditions related to arthropods for many years. Some techniques are considered gold standard methods. However, their limited sensitivity, specificity, and accuracy, and the need for costly reagents and high-skilled technicians are critical problems. New tools are therefore continually being developed to reduce pitfalls. Recently, three state-of-the-art techniques have emerged: DNA barcoding, geometric morphometrics, and artificial intelligence. Here, data related to the three approaches are reviewed. DNA barcoding involves an analysis of a barcode sequence. It was used to diagnose medical parasites and arthropods with 95.0% accuracy. However, this technique still requires costly reagents and equipment. Geometric morphometric analysis is the statistical analysis of the patterns of shape change of an anatomical structure. Its accuracy is approximately 94.0–100.0%, and unlike DNA barcoding, costly reagents and equipment are not required. Artificial intelligence technology involves the analysis of pictures using well-trained algorithms. It showed 98.8–99.0% precision. All three approaches use computer programs instead of human interpretation. They also have the potential to be high-throughput technologies since many samples can be analyzed at once. However, the limitation of using these techniques in real settings is species coverage.

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Yi-Hui Qiu ◽  
Xin Yang ◽  
Zong-Ze Li ◽  
Chiyu Zhang ◽  
Shui-Xuan Chen

Abstract Artificial intelligence (AI) technology has been widely applied in various fields in recent years. Nevertheless, no systematic study has yet been conducted on the effects of AI technology on different fields. In this study, the impacts of the latest AI technology on technological innovation in different fields were analysed and quantized systematically from a patent perspective. Moreover, trends on AI technological innovation in some fields were analysed thoroughly. We conducted this study on a dataset of patents related to AI technology. Based on the patent dataset, we carried out a statistical analysis on technology fields, which we defined and classified based on international patent classification (IPC) number. Distributions of IPC in different fields were also analysed to determine the trends on AI technological innovation. The research conclusions can provide useful information to investors and enterprises, who are interested in the state of the art concerning AI technology.


Author(s):  
Ben Bright Benuwa ◽  
Yong Zhao Zhan ◽  
Benjamin Ghansah ◽  
Dickson Keddy Wornyo ◽  
Frank Banaseka Kataka

The rapid increase of information and accessibility in recent years has activated a paradigm shift in algorithm design for artificial intelligence. Recently, deep learning (a surrogate of Machine Learning) have won several contests in pattern recognition and machine learning. This review comprehensively summarises relevant studies, much of it from prior state-of-the-art techniques. This paper also discusses the motivations and principles regarding learning algorithms for deep architectures.


Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1128
Author(s):  
Chern-Sheng Lin ◽  
Yu-Ching Pan ◽  
Yu-Xin Kuo ◽  
Ching-Kun Chen ◽  
Chuen-Lin Tien

In this study, the machine vision and artificial intelligence algorithms were used to rapidly check the degree of cooking of foods and avoid the over-cooking of foods. Using a smart induction cooker for heating, the image processing program automatically recognizes the color of the food before and after cooking. The new cooking parameters were used to identify the cooking conditions of the food when it is undercooked, cooked, and overcooked. In the research, the camera was used in combination with the software for development, and the real-time image processing technology was used to obtain the information of the color of the food, and through calculation parameters, the cooking status of the food was monitored. In the second year, using the color space conversion, a novel algorithm, and artificial intelligence, the foreground segmentation was used to separate the vegetables from the background, and the cooking ripeness, cooking unevenness, oil glossiness, and sauce absorption were calculated. The image color difference and the distribution were used to judge the cooking conditions of the food, so that the cooking system can identify whether or not to adopt partial tumbling, or to end a cooking operation. A novel artificial intelligence algorithm is used in the relative field, and the error rate can be reduced to 3%. This work will significantly help researchers working in the advanced cooking devices.


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