scholarly journals Predicting Grating Orientations With Cross-Frequency Coupling and Least Absolute Shrinkage and Selection Operator in V1 and V4 of Rhesus Monkeys

2021 ◽  
Vol 14 ◽  
Author(s):  
Zhaohui Li ◽  
Yue Du ◽  
Youben Xiao ◽  
Liyong Yin
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Rhesus Monkeys ◽  
Logistic Regression Model ◽  
Orientation Selectivity ◽  
Electrode Arrays ◽  
Frequency Coupling ◽  
The Cross ◽  
Selection Operator ◽  
Cross Frequency Coupling

Orientation selectivity, as an emergent property of neurons in the visual cortex, is of critical importance in the processing of visual information. Characterizing the orientation selectivity based on neuronal firing activities or local field potentials (LFPs) is a hot topic of current research. In this paper, we used cross-frequency coupling and least absolute shrinkage and selection operator (LASSO) to predict the grating orientations in V1 and V4 of two rhesus monkeys. The experimental data were recorded by utilizing two chronically implanted multi-electrode arrays, which were placed, respectively, in V1 and V4 of two rhesus monkeys performing a selective visual attention task. The phase–amplitude coupling (PAC) and amplitude–amplitude coupling (AAC) were employed to characterize the cross-frequency coupling of LFPs under sinusoidal grating stimuli with different orientations. Then, a LASSO logistic regression model was constructed to predict the grating orientation based on the strength of PAC and AAC. Moreover, the cross-validation method was used to evaluate the performance of the model. It was found that the average accuracy of the prediction based on the combination of PAC and AAC was 73.9%, which was higher than the predicting accuracy with PAC or AAC separately. In conclusion, a LASSO logistic regression model was introduced in this study, which can predict the grating orientations with relatively high accuracy by using PAC and AAC together. Our results suggest that the principle behind the LASSO model is probably an alternative direction to explore the mechanism for generating orientation selectivity.

scholarly journals Rice Seed Purity Identification Technology Using Hyperspectral Image with LASSO Logistic Regression Model

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4384
Author(s):  
Weihua Liu ◽  
Shan Zeng ◽  
Guiju Wu ◽  
Hao Li ◽  
Feifei Chen
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Recognition Accuracy ◽  
Rice Seed ◽  
Classification Feature ◽  
Rice Seeds ◽  
Selection Operator

Hyperspectral technology is used to obtain spectral and spatial information of samples simultaneously and demonstrates significant potential for use in seed purity identification. However, it has certain limitations, such as high acquisition cost and massive redundant information. This study integrates the advantages of the sparse feature of the least absolute shrinkage and selection operator (LASSO) algorithm and the classification feature of the logistic regression model (LRM). We propose a hyperspectral rice seed purity identification method based on the LASSO logistic regression model (LLRM). The feasibility of using LLRM for the selection of feature wavelength bands and seed purity identification are discussed using four types of rice seeds as research objects. The results of 13 different adulteration cases revealed that the value of the regularisation parameter was different in each case. The recognition accuracy of LLRM and average recognition accuracy were 91.67–100% and 98.47%, respectively. Furthermore, the recognition accuracy of full-band LRM was 71.60–100%. However, the average recognition accuracy was merely 89.63%. These results indicate that LLRM can select the feature wavelength bands stably and improve the recognition accuracy of rice seeds, demonstrating the feasibility of developing a hyperspectral technology with LLRM for seed purity identification.

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p < 0,0001), education (p < 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

scholarly journals Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes

2021 ◽  
Vol 11 (14) ◽  
pp. 6594
Author(s):  
Yu-Chia Hsu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Pattern Recognition ◽  
Logistic Regression ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Logistic Regression Model ◽  
National Football League ◽  
Performance Metrics ◽  
Betting Market

The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the first to apply a deep learning approach in financial time series modeling to predict sports match outcomes. The proposed approach has two main components: a convolutional neural network (CNN) classifier for implicit pattern recognition and a logistic regression model for match outcome judgment. First, the raw data used in the prediction are derived from the betting market odds and actual scores of each game, which are transformed into sports candlesticks. Second, CNN is used to classify the candlesticks time series on a graphical basis. To this end, the original 1D time series are encoded into 2D matrix images using Gramian angular field and are then fed into the CNN classifier. In this way, the winning probability of each matchup team can be derived based on historically implied behavioral patterns. Third, to further consider the differences between strong and weak teams, the CNN classifier adjusts the probability of winning the match by using the logistic regression model and then makes a final judgment regarding the match outcome. We empirically test this approach using 18,944 National Football League game data spanning 32 years and find that using the individual historical data of each team in the CNN classifier for pattern recognition is better than using the data of all teams. The CNN in conjunction with the logistic regression judgment model outperforms the CNN in conjunction with SVM, Naïve Bayes, Adaboost, J48, and random forest, and its accuracy surpasses that of betting market prediction.

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