Traffic Violation Detection system using radio frequency identification (RFID) has been applied to detect vehicles with limited power sources through RFID Tag. Camera sensors are also applied to identify a vehicle and...
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Malaria is a severe disease caused by parasites of the genus Plasmodium, which are transmitted to humans through the bite of an infected female Anopheles mosquito. Symptoms of malaria begin to appear at least within 1...
Malaria is a severe disease caused by parasites of the genus Plasmodium, which are transmitted to humans through the bite of an infected female Anopheles mosquito. Symptoms of malaria begin to appear at least within 10 to 15 days. If malaria is not treated immediately, it is feared that it will cause respiratory problems, shortness of breath, and death. To avoid the occurrence of these events, the idea arose to create an AI (Artificial Intelligence) project that can recognize the presence of malaria parasites in blood cells. Thus, the main objective of this project is to find out how to create a Machine Learning model that can efficiently identify malaria parasites in the human body. The AI project uses CNN (Convolutional Neural network) as an algorithm to recognize the presence or absence of parasites in blood cell images that will be inputted by the user. Process of implementing CNN, using VGG19 which is an advanced CNN that has pre-trained layers and a good understanding of describing an image, both the shape, color, and structure of the image. After implementing the Transfer Learning algorithm on the dataset, the result is a Transfer Learning algorithm that can detect the presence of Malaria parasites in blood cells with an accuracy rate of 92 percent a specificity of 95 percent, and a sensitivity of 89 percent. The accuracy can still increase depending on the diversity of the data provided. The more often we train and input test data as train data, the accuracy of AI will also increase.
Recently, the research on daily health monitoring using a wearable sensor has been continually evolving. In the future, when this system is actually implemented, a vast amount of data transmission will be conducted fr...
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Adaptive Mesh Refinement (AMR) is a widely known technique to adapt the accuracy of a solution in critical areas of the problem domain instead of using regular or irregular but static meshes. The MARE2DEM is a paralle...
Adaptive Mesh Refinement (AMR) is a widely known technique to adapt the accuracy of a solution in critical areas of the problem domain instead of using regular or irregular but static meshes. The MARE2DEM is a parallel application that employs the AMR technique to model 2D electromagnetics in oil and gas exploration. The modeling consists in iteratively applying a data inversion based on a set of measurements collected and registered by a survey on an area of interest. The parallelism of the MARE2DEM works by dividing the workload into a set of refinement groups that represent overlapping areas of the problem domain. Each refinement group can be computed independently of the others by a set of workers, carrying out the AMR in the meshes when necessary. The shape and compute performance of the refinement group depend directly of a set of user-defined parameters. In this article, we provide a method to estimate the MARE2DEM performance for all possible values that can be used in the influencing parameters of the application for a given case study. Our relatively cheap method enables the geologist to configure MARE2DEM correctly and extract the best performance for a given cluster configuration. We detail how the method works and evaluate its effectiveness with success, pinpointing the best values for the creating refinement groups using a real case study from the Marlim field on the coast of Rio de Janeiro, Brazil. Although we demonstrate our evaluation with this scenario, our method works for any input of MARE2DEM.
In this paper, we propose a switching scheme of GCR Block Ack and GCR Unsolicited Retry, standardized in IEEE 802.11aa, according to network conditions for video and audio groupcast over wireless LANs. We utilize thre...
In this paper, we propose a switching scheme of GCR Block Ack and GCR Unsolicited Retry, standardized in IEEE 802.11aa, according to network conditions for video and audio groupcast over wireless LANs. We utilize three transmission modes in the proposed method: GCR Block Ack with four retries, GCR Block Ack with two retries, and GCR Unsolicited Retry with twice transmission. The proposed method is compared with the three individual methods by computer simulation under various network conditions to evaluate application-level QoS. We then assess QoE by a subjective experiment. We show that the proposed method can choose an appropriate mode and achieve better QoE than the individual methods.
This research focused on social media applications that had been used by large-scale users. Data might be in the form of text, image, video, each with its own data processing complexity. In this study, the researchers...
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Many smoking-related diseases are difficult to treat and often fatal. Rather than treating diseased smokers, preventing the diseased is more achievable, though, many of them deny to being smokers, leading to another p...
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ISBN:
(数字)9798331519643
ISBN:
(纸本)9798331519650
Many smoking-related diseases are difficult to treat and often fatal. Rather than treating diseased smokers, preventing the diseased is more achievable, though, many of them deny to being smokers, leading to another problem. Thus, this study aims to detect important aspects that can detect that the person is a smoker or not through their bio-signals through using SHAP, along with a comprehensive analysis of the used methods, gradient-boosting algorithms XGBoost, LightGBM, and CatBoost, known for their efficiency in handling complex datasets and non-linear relationships. The study then found that triglyceride, Gtp, hemoglobins significantly affect the body's responses to smoking, based on the CatBoosts’ results, having an AUC score of up to 0.8612 and an accuracy score of up to 0.7776 with the selected features.
Because financial time series forecasting is sensitive to political, economic, and social factors, it is not a simple task. As a result, those who make investments in currency exchange and financial markets typically ...
Because financial time series forecasting is sensitive to political, economic, and social factors, it is not a simple task. As a result, those who make investments in currency exchange and financial markets typically search for reliable models that can guarantee they will maximize their profile and minimize their losses. Fortunately, many studies have used a method from Artificial Neural Networks (ANNs) called Backpropagation, could improve the predictive accuracy of the behavior of the financial data over time. This paper aims to forecast stock share prediction from closing value of PT. Bank Central Asia Tbk, and PT. Bank Maybank Indonesia Tbk. The results show that the using Backpropagation gives the closest result. And for the rating of judgement for cast accuracy, it exceeded 10% accuracy, which means high accurate from the prediction. For further checking, comparing the results of research from Victor’s results, it almost hits the same accuracy percentage. Which means, these prediction are accurate enough to do time series forecasting.
Six-sigma is an approach to appraise a company's prospect in generating a number of piece with homogenized processes without any production defects or zero faults. It is operated not only for declining defect numb...
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The outbreak of acute respiratory syndrome virus disease in China at the end of 2019 has caused a global epidemic as well as high mortality rates in affected countries. This research aimed at examining the extent of t...
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