Brain-computer interface (BCI) has been widely used to predict the intention of users in motor imagery-based (MI-based) task. Although the overall MI classification accuracy has been largely enhanced from previous efforts, applying MI-BCI to the so-called BCI-illiterate subjects remains as an unsolved problem. This study proposed a physiological approach for improving MI-BCI performance, by measuring the baseline attention level estimated by coefficient F from the electroencephalogram (EEG) band-activities. In this endeavor, a total of 9 MI-EEG recordings were retrieved from an open BCI dataset. A measure of attention level was calculated for each trial to select high attention trials. High attention trial-based machine learning model showed higher MI classification performance (median accuracy = 62.50% (interquartile range (IQR) = 55.21 - 82.29%)) than the conventional approach (median accuracy = 57.64% (IQR = 54.17 - 62.50 % )) with statistical significance (Wilcoxon rank sum test, p = 0.037). This study found that machine learning models trained from high attention trials yield improved classification accuracy to the models derived from total trial regardless of both BCI illiterate and literate.
Biometrics are getting more and more attention in recent years for security and other concerns. So far, only fingerprint recognition has seen limited success for on-line security check, since other biometrics verification and identification systems require more complicated and expensive acquisition interfaces and recognition processes. Hand-Geometry can be used for biometric verification and identification because of its acquisition convenience and good performance for verification and identification performance. It could also be a good candidate for online checks. Therefore, this paper proposes a Hand-Geometry recognition system based on geometrical features of hand. From anatomical point of view, human hand can be characterized by its length, width, thickness, geometrical composition, shapes of the palm, and shape and geometry of the fingers. This paper proposes thirty relevant features for a Hand-Geometry recognition system. This system presents verification results based on hand measurements of 20 individuals. The verification process has been tested on a size of $320{\times}240$ image, and result of the verification process have hit rate of 95% and FAR of 0.020.
Biomarkers using EEG could be used to detect and analyze various mental diseases. Many researchers are working on biomarkers that are mostly based on spectral analysis, resulting in poor reproducibility so restricted to being used in a clinical environment. In this study, we proposed new biomarker, a potential biomarker for MDD using resting state EEG and EEG connectivity algorithm. To develop new biomarker, the $PRED+CT$ dataset consisting of 43 MDD patients and 73 healthy control was used. wPLI connectivity for the low alpha band was acquired from 60 EEG channels. An independent sample t-test was conducted, and only statistically significant connectivity ( $p < 0.001$ ) was selected among the connectivity. As a result, there was the biggest difference in connectivity between CPz and left hemisphere connectivity among MDD patients and healthy control. A decrease in connectivity between the central parietal region and frontal/temporal region located in the left hemisphere of MDD patients is also confirmed. New biomarker could be utilized in a diagnosis of MDD, and we expect it to be used in a clinical environment.
Networks within the Internet of Things (IoT) have some of the most targeted devices due to their lightweight design and the sensitive data exchanged through smart city networks. One way to protect a system from an attack is to use machine learning (ML)-based intrusion detection systems (IDSs), significantly improving classification tasks. Training ML algorithms require a large network traffic dataset; however, large storage and months of recording are required to capture the attacks, which is costly for IoT environments. This study proposes an ML pipeline using the conditional tabular generative adversarial network (CTGAN) model to generate a synthetic dataset. Then, the synthetic dataset was evaluated using several types of statistical and ML metrics. Using a decision tree, the accuracy of the generated dataset reached 0.99, and its lower complexity reached 0.05 s training and 0.004 s test times. The results show that synthetic data accurately reflect real data and are less complex, making them suitable for IoT environments and smart city applications. Thus, the generated synthetic dataset can further train models to secure IoT networks and applications.
Background: The inter- and intrarater variability of conventional computed tomography (CT) classification systems for evaluating the extent of ischemic-edematous insult following traumatic brain injury (TBI) may hinder the robustness of TBI prognostic models. Objective: This study aimed to employ fully automated quantitative densitometric CT parameters and a cutting-edge machine learning algorithm to construct a robust prognostic model for pediatric TBI. Methods: Fifty-eight pediatric patients with TBI who underwent brain CT were retrospectively analyzed. Intracranial densitometric information was derived from the supratentorial region as a distribution representing the proportion of Hounsfield units. Furthermore, a machine learning-based prognostic model based on gradient boosting (i.e., CatBoost) was constructed with leave-one-out cross-validation. At discharge, the outcome was assessed dichotomously with the Glasgow Outcome Scale (favorability: 1–3 vs. 4–5). In-hospital mortality, length of stay (>1 week), and need for surgery were further evaluated as alternative TBI outcome measures. Results: Densitometric parameters indicating reduced brain density due to subtle global ischemic changes were significantly different among the TBI outcome groups, except for need for surgery. The skewed intracranial densitometry of the unfavorable outcome became more distinguishable in the follow-up CT within 48 h. The prognostic model augmented by intracranial densitometric information achieved adequate AUCs for various outcome measures [favorability = 0.83 (95% CI: 0.72–0.94), in-hospital mortality = 0.91 (95% CI: 0.82–1.00), length of stay = 0.83 (95% CI: 0.72–0.94), and need for surgery = 0.71 (95% CI: 0.56–0.86)], and this model showed enhanced performance compared to the conventional CRASH-CT model. Conclusion: Densitometric parameters indicative of global ischemic changes during the acute phase of TBI are predictive of a worse outcome in pediatric patients. The robustness and predictive capacity of conventional TBI prognostic models might be significantly enhanced by incorporating densitometric parameters and machine learning techniques.
DTW 알고리즘은 두 시계열 간의 유사도 측정 방법으로 비선형 방식의 뒤틀림을 통해 두 시계열 간 최적의 정렬을 찾는 데 사용되고 있다. 그러나 DTW 알고리즘은 O(mn)의 시간 복잡도를 가지기 때문에 대규모 시계열 데이터에서의 사용이 제한된다. 본 논문에서는 DTW 알고리즘의 시간 복잡도를 개선하기 위해 DTW 행렬 계산을 병렬화하여 연산 횟수를 줄이는 알고리즘을 제안한다. DTW 행렬 계산은 인접 요소 간 의존성에 의해 병렬 연산을 할 수 없기 때문에 병렬 연산이 가능하도록 Shear-Mapping 방법을 적용하여 거리 행렬을 변환하였다. 제안된 방법의 성능 분석을 위해 다양한 길이의 시계열 데이터를 사용하여 알고리즘의 실행 시간을 측정하였으며, 그 결과 시계열의 길이가 길수록 기존의 DTW 알고리즘보다 성능이 우수함을 확인할 수 있었다.
Smart contracts on blockchain platforms are susceptible to security issues that can lead to significant financial losses. This study converts the Solidity code into abstract syntax trees and generates control flow graphs and data flow graphs. These graphs train a graph convolutional network model to detect security weaknesses. The proposed system outperforms traditional tools, achieving higher accuracy, recall, precision, and F1 scores when detecting weaknesses such as integer overflow/underflow, reentrancy, delegate call to the untrusted callee, and time-based issues. This study demonstrates that leveraging control and data flow analysis with graph neural networks significantly enhances smart contract security and provides a robust and reliable solution.
Failure of cerebral autoregulation and subsequent hypoperfusion is common during the acute phase of traumatic brain injury (TBI). The cerebrovascular pressure-reactivity index (PRx) indirectly reflects cerebral autoregulation and has been used to derive optimal cerebral perfusion pressure (CPP). This study provides a method for the use of a combination of PRx, CPP, and intracranial pressure (ICP) to better evaluate the extent of cerebral hypoperfusion during the first 24 hours after TBI, allowing for a more accurate prediction of mortality risk.
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