Early detection of Autism Spectrum Disorder (ASD) needs to be increased to prevent further adverse impacts. Thus, the classifi-cation between ASD and Typically Development (TD) individuals is an intriguing task. This ...
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Early detection of Autism Spectrum Disorder (ASD) needs to be increased to prevent further adverse impacts. Thus, the classifi-cation between ASD and Typically Development (TD) individuals is an intriguing task. This review study has collected 26 related papers to answer four research questions, i.e., what are the most used data inputs, brain atlases, and machine learning models for ASD classification, as also to discover the significant parts of the brain correlated with the ASD. It was eventually found that functional connectivity matrix, Support Vector Machine, and CC200 are the most frequently used data input, model, and brain atlas, respectively. Researchers also concluded that the posterior temporal fusiform cortex, intracalcarine cortex, cuneal cortex, subcallosal cortex, occipital pole, and lateral occipital cortex are the brain regions highly correlated with ASD.
TikTok, a social networking site for uploading short videos, has become one of the most popular. Despite this, not all users are happy with the app; there are criticisms and suggestions, one of which is reviewed via t...
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TikTok, a social networking site for uploading short videos, has become one of the most popular. Despite this, not all users are happy with the app; there are criticisms and suggestions, one of which is reviewed via the TikTok app on the Google Play Store. The reviews were extracted and then used for training a sentiment analysis model. The VADER sentiment method was utilized to offer the review's initial labeling (positive, neutral, and negative). The result revealed that most reviews were classified as positive, meaning that the data were imbalanced and challenging to handle in further analysis. Therefore, Random Under-sampling (RUS) and Random Over-sampling (ROS) methods were deployed to deal with that condition. The labeled reviews were subsequently pre-processed using tools such as case folding, noise removal, normalization, and stopwords before being used for training a Support Vector Machine (SVM) model for sentiment classification. The SVM trained without resampling produced the most favorable results, with an F1-score of 0.80.
Measuring clock skew of devices over a network fully relies on the offsets, the differences between sending and receiving times. Offsets that shape a thick line are the most ideal one as their slope is directly the cl...
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With the advancement of technology, information systems have become increasingly necessary in almost all areas, including healthcare. One of the technologies used to facilitate this progress is electronic medical reco...
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The swift progress of Internet of Things (IoT) technology has unveiled vast opportunities for interfacing and monitoring physical devices. Developers frequently encounter obstacles when conducting human-in-the-loop te...
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The use of the Internet of Things (IoT) has been widely adopted for a lot of purposes. Business process automation is one type of implementation that has been carried out. In addition, the data successfully captured b...
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ISBN:
(数字)9798331508579
ISBN:
(纸本)9798331508586
The use of the Internet of Things (IoT) has been widely adopted for a lot of purposes. Business process automation is one type of implementation that has been carried out. In addition, the data successfully captured by the IoT ecosystem can be utilized for various computer modeling needs, including the decision model construction in assessing room quality. The decision model itself is a computer model supporting the decision maker in decision making process. This research, via four simple research stages, aims to develop an IoT-based decision model to evaluate room quality based on two parameters: temperature and humidity. The design of the IoT environment and the implementation of temperature and humidity data readings in a room are used as input data for a decision model to assess the comfort level of the room. In this study, the primary method used to design the model is object-oriented, while fuzzy logic is employed to build the model. The developed decision model can simulate data captured from the designed IoT ecosystem, successfully assessing the comfort level of a room. From the simulation results using 18 data points (from 9 days of data capturing) captured by temperature and humidity sensors, the average comfort level of the room was found to be 19.7873.
This article presents our approach for the Style Change Detection Task at PAN 2022 using discourse markers. Discourse markers (such as ‘what’, ‘I have’, etc.) are words or expressions used to connect, organise and...
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Recently, Wang et al. proposed a computationally transferable authenticated key agreement protocol for smart healthcare by adopting the certificateless public-key cryptography. They claimed that their protocol could e...
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Given a black box oracle that evaluates a univariate polynomial p(x) of a degree d, we seek its zeros, aka the roots of the equation. At FOCS 2016, Louis and Vempala approximated within an absolutely largest zero of s...
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computer vision has been used in many areas such as medical, transportation, military, geography, etc. The fast development of sensor devices inside camera and satellite provides not only red-greed-blue (RGB) images b...
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