Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

Surgery Surgery

Deep Learning-Based Approaches for Decoding Motor Intent From Peripheral Nerve Signals.

In Frontiers in neuroscience ; h5-index 72.0

Previous literature shows that deep learning is an effective tool to decode the motor intent from neural signals obtained from different parts of the nervous system. However, deep neural networks are often computationally complex and not feasible to work in real-time. Here we investigate different approaches' advantages and disadvantages to enhance the deep learning-based motor decoding paradigm's efficiency and inform its future implementation in real-time. Our data are recorded from the amputee's residual peripheral nerves. While the primary analysis is offline, the nerve data is cut using a sliding window to create a "pseudo-online" dataset that resembles the conditions in a real-time paradigm. First, a comprehensive collection of feature extraction techniques is applied to reduce the input data dimensionality, which later helps substantially lower the motor decoder's complexity, making it feasible for translation to a real-time paradigm. Next, we investigate two different strategies for deploying deep learning models: a one-step (1S) approach when big input data are available and a two-step (2S) when input data are limited. This research predicts five individual finger movements and four combinations of the fingers. The 1S approach using a recurrent neural network (RNN) to concurrently predict all fingers' trajectories generally gives better prediction results than all the machine learning algorithms that do the same task. This result reaffirms that deep learning is more advantageous than classic machine learning methods for handling a large dataset. However, when training on a smaller input data set in the 2S approach, which includes a classification stage to identify active fingers before predicting their trajectories, machine learning techniques offer a simpler implementation while ensuring comparably good decoding outcomes to the deep learning ones. In the classification step, either machine learning or deep learning models achieve the accuracy and F1 score of 0.99. Thanks to the classification step, in the regression step, both types of models result in a comparable mean squared error (MSE) and variance accounted for (VAF) scores as those of the 1S approach. Our study outlines the trade-offs to inform the future implementation of real-time, low-latency, and high accuracy deep learning-based motor decoder for clinical applications.

Luu Diu K, Nguyen Anh T, Jiang Ming, Xu Jian, Drealan Markus W, Cheng Jonathan, Keefer Edward W, Zhao Qi, Yang Zhi


convolutional neural network, deep learning, feature extraction, motor decoding, neural decoder, neuroprosthesis, peripheral nerve interface, recurrent neural network

General General

Transmission trend of the COVID-19 pandemic predicted by dendritic neural regression.

In Applied soft computing

In 2020, a novel coronavirus disease became a global problem. The disease was called COVID-19, as the first patient was diagnosed in December 2019. The disease spread around the world quickly due to its powerful viral ability. To date, the spread of COVID-19 has been relatively mild in China due to timely control measures. However, in other countries, the pandemic remains severe, and COVID-19 protection and control policies are urgently needed, which has motivated this research. Since the outbreak of the pandemic, many researchers have hoped to identify the mechanism of COVID-19 transmission and predict its spread by using machine learning (ML) methods to supply meaningful reference information to decision-makers in various countries. Since the historical data of COVID-19 is time series data, most researchers have adopted recurrent neural networks (RNNs), which can capture time information, for this problem. However, even with a state-of-the-art RNN, it is still difficult to perfectly capture the temporal information and nonlinear characteristics from the historical data of COVID-19. Therefore, in this study, we develop a novel dendritic neural regression (DNR) method to improve prediction performance. In the DNR, the multiplication operator is used to capture the nonlinear relationships between input feature signals in the dendrite layer. Considering the complex and large landscape of DNR's weight space, a new scale-free state-of-matter search (SFSMS) algorithm is proposed to optimize the DNR, which combines the state-of-matter search algorithm with a scale-free local search. The SFSMS achieves a better global search ability and thus can effectively reduce the possibility of falling into local minima. In addition, according to Takens's theorem, phase space reconstruction techniques are used to discover the information hidden in the high-dimensional space of COVID-19 data, which further improves the precision of prediction. The experimental results suggest that the proposed method is more competitive in solving this problem than other prevailing methods.

Dong Minhui, Tang Cheng, Ji Junkai, Lin Qiuzhen, Wong Ka-Chun


COVID-19, Neural network, Optimization, Prediction, Regression

General General

A new composite approach for COVID-19 detection in X-ray images using deep features.

In Applied soft computing

The new type of coronavirus, COVID 19, appeared in China at the end of 2019. It has become a pandemic that is spreading all over the world in a very short time. The detection of this disease, which has serious health and socio-economic damages, is of vital importance. COVID-19 detection is performed by applying PCR and serological tests. Additionally, COVID detection is possible using X-ray and computed tomography images. Disease detection has an important position in scientific researches that includes artificial intelligence methods. The combined models, which consist of different phases, are frequently used for classification problems. In this paper, a new combined approach is proposed to detect COVID-19 cases using deep features obtained from X-ray images. Two main variances of the approach can be presented as single layer-based (SLB) and feature fusion-based (FFB). SLB model consists of pre-processing, deep feature extraction, post-processing, and classification phases. On the other side, the FFB model consists of pre-processing, deep feature extraction, feature fusion, post-processing, and classification phases. Four different SLB and six different FFB models were developed according to the number and binary combination of layers used in the feature extraction phase. Each model is employed for binary and multi-class classification experiments. According to experimental results, the accuracy performance for COVID-19 and no-findings classification of the proposed FFB3 model is 99.52%, which is better than the best performance accuracy (of 98.08%) in the literature. Concurrently, for multi-class classification, the proposed FFB3 model has an accuracy performance of 87.64% outperforming the best existing work (which reported an 87.02% classification performance). Various metrics, including sensitivity, specificity, precision, and F1-score metrics are used for performance analysis. For all performance metrics, the FFB3 model recorded a higher success rate than existing work in the literature. To the best of our knowledge, these accuracy rates are the best in the literature for the dataset and data split type (five-fold cross-validation). Composite models (SLBs and FFBs), which are generated in this paper, are successful ways to detect COVID-19. Experimental results show that feature extraction, pre-processing, post-processing, and hyperparameter tuning are the steps are necessary to obtain a higher success. For prospective works, different types of pre-trained models and other hyperparameter tuning methods can be implemented.

Ozcan Tayyip


COVID-19 detection in X-ray images, Data processing, Deep features, Feature extraction, Feature fusion, Pre-trained models

General General

Understanding and predicting COVID-19 clinical trial completion vs. cessation.

In PloS one ; h5-index 176.0

As of March 30 2021, over 5,193 COVID-19 clinical trials have been registered through Among them, 191 trials were terminated, suspended, or withdrawn (indicating the cessation of the study). On the other hand, 909 trials have been completed (indicating the completion of the study). In this study, we propose to study underlying factors of COVID-19 trial completion vs. cessation, and design predictive models to accurately predict whether a COVID-19 trial may complete or cease in the future. We collect 4,441 COVID-19 trials from to build a testbed, and design four types of features to characterize clinical trial administration, eligibility, study information, criteria, drug types, study keywords, as well as embedding features commonly used in the state-of-the-art machine learning. Our study shows that drug features and study keywords are most informative features, but all four types of features are essential for accurate trial prediction. By using predictive models, our approach achieves more than 0.87 AUC (Area Under the Curve) score and 0.81 balanced accuracy to correctly predict COVID-19 clinical trial completion vs. cessation. Our research shows that computational methods can deliver effective features to understand difference between completed vs. ceased COVID-19 trials. In addition, such models can also predict COVID-19 trial status with satisfactory accuracy, and help stakeholders better plan trials and minimize costs.

Elkin Magdalyn E, Zhu Xingquan


General General

Intrusion detection by machine learning for multimedia platform.

In Multimedia tools and applications

The multimedia service company, Netflix, increased the number of new subscribers during the Coronavirus pandemic age. Intrusion detection systems for multimedia platforms can prevent the platform from network attacks. An intelligent intrusion detection system is proposed for the security IP Multimedia Subsystem (IMS) based on machine learning technology. For increasing the accuracy of the classifiers, it is vital to select the critical features to construct the intrusion detection system. Two-class classifiers, including the Decision Tree, Support Vector Machine, and Naive Bayesian, are selected to evaluate intrusion detection accuracy. According to the three classifiers' accuracy values, the most critical features are selected based on the features' ranking orders. Six critical features are selected:Service, dst_host_same_srv_rate, Flag, Protocol Type, Dst_host_rerror_rate, and Count. Numerical comparison with state_of_the_art shows that critical features improve intrusion detection accuracy, which can be better than the deep learning method.

Hsu Chih-Yu, Wang Shuai, Qiao Yu


Coronavirus pandemic, Decision tree, Intrusion detection, Machine learning, Naive Bayesian classifier, Streaming service, Support vector machine

General General

Studying the Effect of Taking Statins before Infection in the Severity Reduction of COVID-19 with Machine Learning.

In BioMed research international ; h5-index 102.0

Statins can help COVID-19 patients' treatment because of their involvement in angiotensin-converting enzyme-2. The main objective of this study is to evaluate the impact of statins on COVID-19 severity for people who have been taking statins before COVID-19 infection. The examined research patients include people that had taken three types of statins consisting of Atorvastatin, Simvastatin, and Rosuvastatin. The case study includes 561 patients admitted to the Razi Hospital in Ghaemshahr, Iran, during February and March 2020. The illness severity was encoded based on the respiratory rate, oxygen saturation, systolic pressure, and diastolic pressure in five categories: mild, medium, severe, critical, and death. Since 69.23% of participants were in mild severity condition, the results showed the positive effect of Simvastatin on COVID-19 severity for people that take Simvastatin before being infected by the COVID-19 virus. Also, systolic pressure for this case study is 137.31, which is higher than that of the total patients. Another result of this study is that Simvastatin takers have an average of 95.77 mmHg O2Sat; however, the O2Sat is 92.42, which is medium severity for evaluating the entire case study. In the rest of this paper, we used machine learning approaches to diagnose COVID-19 patients' severity based on clinical features. Results indicated that the decision tree method could predict patients' illness severity with 87.9% accuracy. Other methods, including the K-nearest neighbors (KNN) algorithm, support vector machine (SVM), Naïve Bayes classifier, and discriminant analysis, showed accuracy levels of 80%, 68.8%, 61.1%, and 85.1%, respectively.

Davoudi Alireza, Ahmadi Mohsen, Sharifi Abbas, Hassantabar Roshina, Najafi Narges, Tayebi Atefeh, Kasgari Hamideh Abbaspour, Ahmadi Fatemeh, Rabiee Marzieh