This paper investigates the effect of bitrate control methods on QoE of multi-view video and audio streaming with MPEG-DASH. We adopt three bitrate control methods for conventional single-view video streaming to the M...
This paper investigates the effect of bitrate control methods on QoE of multi-view video and audio streaming with MPEG-DASH. We adopt three bitrate control methods for conventional single-view video streaming to the MVV-A system with MPEG-DASH. We conduct a subjective experiment changing available network bandwidth and investigate the effect of the methods on QoE.
Multi-step ahead time series forecasting is essential in Internet of Things (IoT) applications in smart cities and smart homes to make accurate future predictions and precise decision-making. Thus, this study introduc...
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Stress detection is a growing topic in the field of natural language processing. The study of stress detection for mental health prediction has been proven to benefit the development of recommender systems and automat...
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Stress detection is a growing topic in the field of natural language processing. The study of stress detection for mental health prediction has been proven to benefit the development of recommender systems and automated mental health assessments in previous studies. Additionally, the widespread usage of social media has served as a potential data source for developing such models. Our research tried to detect whether the users of social media were under stress or not. We used a dataset from Dreaddit consisting of posts from one of the popular social media platforms, Reddit. We propose a machine learning model consisting of Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Random Forest, Bag of Words, and Term Frequency – inverse document frequency (TF-IDF) for stress detection. The final evaluation of the model achieved an 80.00% F-1 Score and 75.00% accuracy, and both were scored by SVM.
This paper uses YOLO deep learning to create a predict system for Meteorological Service for Air Navigation with a total of 5 types of forecast results: BKN, CAVOK, FEW, OVER and SCT. Indicative information for evalua...
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ISBN:
(数字)9798350383591
ISBN:
(纸本)9798350383607
This paper uses YOLO deep learning to create a predict system for Meteorological Service for Air Navigation with a total of 5 types of forecast results: BKN, CAVOK, FEW, OVER and SCT. Indicative information for evaluating the image is observing the nature of the clouds arranged together and comparing them to quantities in the sky using the unit of measurement “okta”, which is divided into 8 parts of the image. Therefore, the nature of using deep learning in the article is to teach deep learning to recognize various image characteristics both during the day and at night in order to use the results of the forecast to inform the weather conditions in the sky. The process in this article consists of 3 steps: The first step is to train the data with images with different characteristics from the 5 data types. The next step is to test the accuracy of the weights generated from the training and training steps. The final step is to use water values to create a decision-making system for users. From the experiment, private dataset in this paper, more than 2500 images were used to participate in the experiment. The results of the experiment found that the average results of precision, recall, mAP50, and mAP50-95 could be measured at 84.7%, 92.1%, 95.7%, and 95.3%, respectively.
This paper presents a novel algorithm for reachability analysis of nonlinear discrete-time systems. The proposed method combines constrained zonotopes (CZs) with polyhedral relaxations of factorable representations of...
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The increasing problem of air pollution has led to the advancement and improvement of air quality prediction studies. Predicting air quality in advance is crucial for mitigating the detrimental effects of air pollutio...
The increasing problem of air pollution has led to the advancement and improvement of air quality prediction studies. Predicting air quality in advance is crucial for mitigating the detrimental effects of air pollution on public health and economic activities. This study focuses on the development and evaluation of the Nonlinear Autoregressive Exogenous Neural Network (NARX) and Support Vector Machine (SVM) models for multi-step prediction of Malaysia's Air Pollutant Index (API). The models were constructed using a dataset from air quality monitoring stations in Malaysia's three prominent industrial areas: Pasir Gudang, Larkin, and TTDI Jaya. The model development process began by constructing a single-step API predictor, then developing and analyzing a multi-step API predictor using the recursive approach. The prediction performance was assessed using the Root Mean Square Error (RMSE) and Coefficient of Determination values $(\mathrm{R}^{2})$ . The results indicate that the recursive NARX demonstrates promising performance compared to the recursive SVM in multi-step API prediction. However, additional analysis shows that the outliers strongly affected multi-step NARX's prediction, suggesting that the recursive NARX model cannot predict sudden fluctuations not seen in training.
Colorectal cancer is the third most diagnosed cancer in the world, but it has a higher mortality rate in men compared to women. However, we are not close to understanding how and why sex influences the outcome of the ...
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The placement of distributed generation (DG) units in power systems is an efficient way for energy loss reduction, especially when the penetration of DG in modern systems is growing due to their impacts on environment...
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It is important to figure out the patterns of woven fabrics before producing woven fabric with a machine. Recognition of woven fabric pattern usually with the help of the human eye can understand the fabric pattern. H...
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Hyperparameter optimization (HPO) is paragon to maximize performance when designing machine learning models. Among different HPO methods, Genetic Algorithm (GA) based optimization is considered effective because it al...
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