Dengue fever is a tropical disease caused by dengue virus. This virus is transmitted by mosquitoes. Dengue can also be transmitted via infected blood products and through organ donation. There are many infected people...
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(纸本)9781538623176
Dengue fever is a tropical disease caused by dengue virus. This virus is transmitted by mosquitoes. Dengue can also be transmitted via infected blood products and through organ donation. There are many infected people in each year. Thus, if the number of patients can be predicted, vaccines for dengue fever will be able to prepare for serving all patients in that area. This research proposed a two-step prediction method that combines time series forecasting analysis and supervisedlearning techniques to predict the number of dengue fever patient cases. In generally, the result of prediction from only the number of patient cases is not good enough for application;accuracy and confidence are low. In this research, the environmental factors are considered and predicted. Then, these factors are used for predicting the number patient cases. The experimental results show that the proposed two-step prediction technique is a good choice for dengue fever prediction. The accuracy is better than the simple prediction technique.
This paper introduces a novel approach to speed-sensorless predictive torque control (PTC) in an autonomous wind energy conversion system, specifically utilizing an asymmetric double star induction generator (ADSIG). ...
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This paper introduces a novel approach to speed-sensorless predictive torque control (PTC) in an autonomous wind energy conversion system, specifically utilizing an asymmetric double star induction generator (ADSIG). To achieve accurate estimation of non-linear quantities, the Gaussian Process Regression algorithm (GPR) is employed as a powerful machine learning tool for designing speed and flux estimators. To enhance the capabilities of the GPR, two improvements were implemented, (a) hyperparametric optimization through the Bayesian optimization (BO) algorithm and (b) curation of the input vector using the gray box concept, leveraging our existing knowledge of the ADSIG. Simulation results have demonstrated that the proposed GPR-PTC would remain robust and unaffected by the absence of a speed sensor, maintaining performance even under varying magnetizing inductance. This enables a reliable and cost-effective control solution.
The present study investigates the flow and thermal fields of Rayleigh-Benard convection (RBC) in a rectangular channel with an internal circular cylinder. The parameters considered are Rayleigh number (10(4) <= Ra...
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The present study investigates the flow and thermal fields of Rayleigh-Benard convection (RBC) in a rectangular channel with an internal circular cylinder. The parameters considered are Rayleigh number (10(4) <= Ra <= 10(6) ), Prandtl number (Pr = 0.7), and irreversibility distribution ratio (phi = 1). The vertical distance (delta) in the range of -0.2 <= delta <= 0.2 is the major simulation parameter in present study. The results are analyzed based on the iso-surface of temperature, vortical structure with orthogonal enstrophy distribution, and entropy generations. Additionally, Nusselt number ( Nu ) and Bejan number ( Be ) are obtained to analyze the heat transfer characteristics and irreversibility, respectively. The Rayleigh number and the vertical distance significantly influence the flow and thermal characteristics within the channel. Besides, an artificial neural network (ANN) model is used to predict the distribution of local Nusselt number. The performance of present ANN model is evaluated by comparing the tendency and quantitative values with the direct numerical simulation (DNS) results. The results show that the ANN model used in this study can precisely predict the correlation between the input parameters and output parameter with lesser computational time and cost compared to the DNS.
Electrocardiogram (ECG) is a noninvasive method used to detect arrhythmias or heart abnormalities. It describes the electrical activity of the heart. Physicians are faced with difficulties in detecting irregular heart...
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Electrocardiogram (ECG) is a noninvasive method used to detect arrhythmias or heart abnormalities. It describes the electrical activity of the heart. Physicians are faced with difficulties in detecting irregular heartbeats due to the presence of noise and subtle changes in the signal amplitude and duration. Depending on human visual detection alone may lead to misdiagnosis or insignificant detection of cardiovascular diseases. A computer-aided diagnosis of the ECG will assist physicians to significantly detect the cardiovascular diseases. Non-linear method is useful to extract and capture hidden information in the ECG signal. In this study we present a combination of two nonlinear methods; Higher Order Statistics (HOS) cumulants and Independent Component Analysis (ICA), performed on the dynamics ECG signals for arrhythmia detection. The abnormal heartbeats focused in this study are Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Atrial Premature Contraction (APC), Ventricular Premature Contraction (VPC) and Paced beat (P). The HOS and ICA features were fed to the Support Vector Machine (SVM) with kernel functions for automatic classification and neural network. In our study, we obtained the highest accuracy of 96.34% with Radial Basis Function (RBF) kernel and neural network.
Protein fold recognition using machine learning-based methods is crucial in the protein structure discovery, especially when the traditional sequence comparison methods fail because the structurally-similar proteins s...
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Protein fold recognition using machine learning-based methods is crucial in the protein structure discovery, especially when the traditional sequence comparison methods fail because the structurally-similar proteins share little in the way of sequence homology. Many different machine learning-based fold classification methods have been proposed with still increasing accuracy and the main aim of this article is to cover all the major results in this field.
Nowadays, there is a great interest in developing accurate wireless indoor localization mechanisms enabling the implementation of many consumer-oriented services. Among the many proposals, wireless indoor localization...
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Nowadays, there is a great interest in developing accurate wireless indoor localization mechanisms enabling the implementation of many consumer-oriented services. Among the many proposals, wireless indoor localization mechanisms based on the Received Signal Strength Indication (RSSI) are being widely explored. Most studies have focused on the evaluation of the capabilities of different mobile device brands and wireless network technologies. Furthermore, different parameters and algorithms have been proposed as a means of improving the accuracy of wireless-based localization mechanisms. In this paper, we focus on the tuning of the RSSI fingerprint to be used in the implementation of a Bluetooth Low Energy 4.0 (BLE4.0) Bluetooth localization mechanism. Following a holistic approach, we start by assessing the capabilities of two Bluetooth sensor/receiver devices. We then evaluate the relevance of the RSSI fingerprint reported by each BLE4.0 beacon operating at various transmission power levels using feature selection techniques. Based on our findings, we use two classification algorithms in order to improve the setting of the transmission power levels of each of the BLE4.0 beacons. Our main findings show that our proposal can greatly improve the localization accuracy by setting a custom transmission power level for each BLE4.0 beacon.
Virtual reality is an immersive interactive technology that captures the entire location within 360 degrees with the help of special cameras, mounts and software. This paper discusses the role of artificial intelligen...
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Virtual reality is an immersive interactive technology that captures the entire location within 360 degrees with the help of special cameras, mounts and software. This paper discusses the role of artificial intelligence technology and virtual reality technology in the cultural communication of tourist attractions. In tourist attractions, VR technology provides a glimpse of information about the tourist attraction with the help of VR photography and VR video. This study provides a new idea for the design of interactive cultural communication devices and uses supervised learning algorithms to make their versatility and interactivity fully reflected in the communication effect . The results of the study show that by using supervised learning algorithms, artificial intelligence based virtual reality provides 97% high accuracy.
The evaluation of car drivers' stress condition is gaining interest as research on Autonomous Driving Systems (ADS) progresses. The analysis of the stress response can be used to assess the acceptability of ADS an...
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The evaluation of car drivers' stress condition is gaining interest as research on Autonomous Driving Systems (ADS) progresses. The analysis of the stress response can be used to assess the acceptability of ADS and to compare the driving styles of different autonomous drive algorithms. In this contribution, we present a system based on the analysis of the Electrodermal Activity Skin Potential Response (SPR) signal, aimed to reveal the driver's stress induced by different driving situations. We reduce motion artifacts by processing two SPR signals, recorded from the hands of the subjects, and outputting a single clean SPR signal. Statistical features of signal blocks are sent to a supervised learning algorithm, which classifies between stress and normal driving (non-stress) conditions. We present the results obtained from an experiment using a professional driving simulator, where a group of people is asked to undergo manual and autonomous driving on a highway, facing some unexpected events meant to generate stress. The results of our experiment show that the subjects generally appear more stressed during manual driving, indicating that the autonomous drive can possibly be well received by the public. During autonomous driving, however, significant peaks of the SPR signal are evident during unexpected events. By examining the electrocardiogram signal, the average heart rate is generally higher in the manual case compared to the autonomous case. This further supports our previous findings, even if it may be due, in part, to the physical activity involved in manual driving.
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