This research proposes a methodology for identifying the optimal feature combination using Support Vector Machine (SVM) based on edge and texture features. Canny, Sobel, and Prewitt for edge detection and GLCM, Gabor,...
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In the modern era, an increasing number of diseases are emerging because of human lifestyle choices and bacterial transmission. Gastritis, characterized by inflammation in the stomach lining leading to frequent abdomi...
In the modern era, an increasing number of diseases are emerging because of human lifestyle choices and bacterial transmission. Gastritis, characterized by inflammation in the stomach lining leading to frequent abdominal pain, is one such ailment. Recognizing the significance of addressing these health issues, there is a need for an expert system application specifically designed for diagnosing gastritis. The primary objective is to disseminate information to the public regarding the early symptoms of gastritis, promoting awareness from a young age. The research method used is the web-based Expert System Development Life Cycle (ESDLC). The diagnostic process employs the Certainty Factor (CF) method to ensure accurate results. By utilizing symptom CF values chosen by the user, the Certainty Factor approach facilitates a diagnosis based on calculations established by medical specialists. The research culminates in the development of an expert system application capable of accurately diagnosing the disease Android based. The accuracy rates for the first, second, and third test cases are reported as 98.848%, 99.8464%, and 99.99115264%, respectively. System testing, conducted through black-box testing, validates the application's functionality, demonstrating a 100% success rate for each component.
In the delayed match-to-sample task, responses of inferior temporal neurons to adjacent stimuli in the sequence are correlated to each other when the monkey was trained repeatedly with the sequence of visual stimuli, ...
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In the delayed match-to-sample task, responses of inferior temporal neurons to adjacent stimuli in the sequence are correlated to each other when the monkey was trained repeatedly with the sequence of visual stimuli, although the monkey was not required to associate the stimuli with each other. This correlation, however, is not observed for a monkey with lesions of the rhinal cortex, which is not consistently explained by existing models of such correlated responses. In the present study, we construct a model consisting of two networks corresponding to area TE and the perirhinal cortex, and show that perirhinal plasticity may underlie the mechanism of implicit association learning.
Diphtheria is an infectious disease that affects the upper respiratory system and throat, arising suddenly and caused by Corynebacterium diphtheriae. The symptoms of diphtheria such as fever, swollen neck and slimy no...
Diphtheria is an infectious disease that affects the upper respiratory system and throat, arising suddenly and caused by Corynebacterium diphtheriae. The symptoms of diphtheria such as fever, swollen neck and slimy nose are often interpreted as influenza or laryngitis. Diphtheria is a contagious and deadly disease that requires specialized and rapid treatment and care. In today's technological era, it can be overcome with a system that can diagnose diphtheria like a doctor's diagnosis ability commonly called an expert system. This study describes the application of an expert system to diagnose diphtheria disease. The research method used is the web-based Expert System Development Life Cycle (ESDLC). The method used to process input values is Forward Chaining. Fuzzy Tsukamoto to calculate the input value from the user. The research has been tested with Blackbox testing showing the results that the expert system works well and accuracy testing with 92% results. The results show effectiveness with a satisfactory level of accuracy in detecting diphtheria disease. This research contributes to the development of expert systems in the health sector with a focus on diagnosing diphtheria disease using a fuzzy logic approach.
Gastroenteritis is a common gastrointestinal disorder with varying degrees of severity, including cases without dehydration, mild dehydration, moderate dehydration, and severe dehydration. This research focuses on the...
Gastroenteritis is a common gastrointestinal disorder with varying degrees of severity, including cases without dehydration, mild dehydration, moderate dehydration, and severe dehydration. This research focuses on the development of a mobile-based expert system for detecting gastroenteritis, capable of distinguishing between these different dehydration levels. The methodology employed integrates the Extreme Structure Development Life Cycle (ESDLC) and the Best First Search algorithm. The expert system is designed to provide swift and accurate responses to users inputting symptoms, categorizing gastroenteritis cases based on the severity of dehydration. The application of ESDLC ensures a structured and organized development process, while the Best First Search method enhances the efficiency of the diagnostic process, facilitating timely and precise recommendations. Accessible through mobile platforms, the system allows users to input symptoms conveniently and receive rapid recommendations. Rigorous testing and evaluation affirm the system's high accuracy in discerning dehydration levels in gastroenteritis patients. In conclusion, the developed mobile-based expert system exhibits promising potential for advancing diagnostic services, particularly in the categorization of dehydration levels in gastroenteritis cases. The successful implementation of ESDLC and Best First Search paves the way for similar solutions in other healthcare domains, contributing positively to early and accurate disease management efforts. This innovation marks a significant stride towards improving healthcare technology for enhanced decision support in gastroenteritis diagnosis.
Reconfigurable architecture for the probabilistic neural network (PNN) is proposed and the PNN hardware system is developed using FPGAs. In the system, preprocessing circuits as well as the PNN can be reconfigured ada...
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Reconfigurable architecture for the probabilistic neural network (PNN) is proposed and the PNN hardware system is developed using FPGAs. In the system, preprocessing circuits as well as the PNN can be reconfigured adapting each task. Potential performance of the PNN cannot be brought out until the total reconfigurability including the preprocessing is achieved. The developed system shows high recognition accuracy with high processing speed for some image recognition tasks.
The energy control of a Wireless Sensor Network (WSN) often leads to an unbalanced state between the battery storage system, energy extraction through photovoltaic systems energy, and energy utilization in the WSN. Th...
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Misdelivery in logistic services leads to increased costs and degradation of packages. For deliveries using trucks, erroneous deliveries are prevented by checking identification numbers on packages as the packages pas...
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In the analysis of real-world data, two significant challenges often arise: high-dimensional signals and their temporal interactions. To address these issues and identify transitions in process conditions through end-...
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ISBN:
(数字)9798331510589
ISBN:
(纸本)9798331510596
In the analysis of real-world data, two significant challenges often arise: high-dimensional signals and their temporal interactions. To address these issues and identify transitions in process conditions through end-to-end learning, we propose a self-supervised representation learning framework that conceptualizes signals as images. This straightforward approach classifies imaged signals as time-specific conditions by maximizing mutual information within a domain-specific feature space. We applied this methodology to two labeled open datasets and one unlabeled real-world process dataset, yielding promising results.
We propose a novel architecture for variable comparator named RPCA (Reconfigurable Parallel Comparation Architecture) making full use of the reconfigurability of FPGA. In the RPCA, an appropriate comparator to each ta...
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We propose a novel architecture for variable comparator named RPCA (Reconfigurable Parallel Comparation Architecture) making full use of the reconfigurability of FPGA. In the RPCA, an appropriate comparator to each task can be chosen and implemented under the speed/data-size tradeoff. An RPCA-based pack-filter prototype using XILINX XCV300E-6PQ240C and RealTeK RTL8201BL is developed and filtering speed of 120ns for 128 IPs and 1,360ns for 2,048 IPs are obtained under the speed/data-size trade-off.
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