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Real-count data time series often show the phenomenon of over-dispersion. In this paper, we introduce the first-order integer-valued autoregressive process with seasonal structure. The univariate marginal distribution is derived from the Delaporte distribution and the innovations are convolution of Poisson with α-fold zero modified geometric distribution, based on binomial thinning operator, for modeling integer-valued time series with over-dispersion. Some properties of the model are derived. The methods of Yule-Walker, conditional least squares, and conditional maximum likelihood are used to estimate the parameters. The Monte Carlo experiment is conducted to evaluate the performances of these estimators in finite samples. At the end, this model is illustrated using a real data set and is compared to some INAR(1) models.

Abstract Mental workload refers to the cognitive effort required to perform tasks, and it is an important factor in various fields, including system design, clinical medicine, and industrial applications. In this paper, we propose innovative methods to assess mental workload from EEG data that use effective brain connectivity for the purpose of extracting features, a hierarchical feature selection algorithm to select the most significant features, and finally machine learning models. We have used the Simultaneous Task EEG Workload (STEW) dataset, an open-access collection of raw EEG data from 48 subjects. We extracted brain-effective connectivities by the direct directed transfer function and then selected the top 30 connectivities for each standard frequency band. Then we applied three feature selection algorithms (forward feature selection, Relief-F, and minimum-redundancy-maximum-relevance) on the top 150 features from all frequencies. Finally, we applied sevenfold cross-validation on four machine learning models (support vector machine (SVM), linear discriminant analysis, random forest, and decision tree). The results revealed that SVM as the machine learning model and forward feature selection as the feature selection method work better than others and could classify the mental workload levels with accuracy equal to 89.53% (± 1.36).

Abstract This study aims to develop a machine learning approach leveraging clinical data and blood parameters to predict non-alcoholic steatohepatitis (NASH) based on the NAFLD Activity Score (NAS). Using a dataset of 181 patients, we performed preprocessing including normalization and categorical encoding. To identify predictive features, we applied sequential forward selection (SFS), chi-square, analysis of variance (ANOVA), and mutual information (MI). The selected features were used to train machine learning classifiers including SVM, random forest, AdaBoost, LightGBM, and XGBoost. Hyperparameter tuning was done for each classifier using randomized search. Model evaluation was performed using leave-one-out cross-validation over 100 repetitions. Among the classifiers, random forest, combined with SFS feature selection and 10 features, obtained the best performance: Accuracy: 81.32% ± 6.43%, Sensitivity: 86.04% ± 6.21%, Specificity: 70.49% ± 8.12% Precision: 81.59% ± 6.23%, and F1-score: 83.75% ± 6.23% percent. Our findings highlight the promise of machine learning in enhancing early diagnosis of NASH and provide a compelling alternative to conventional diagnostic techniques. Consequently, this study highlights the promise of machine learning techniques in enhancing early and non-invasive diagnosis of NASH based on readily available clinical and blood data. Our findings provide the basis for developing scalable approaches that can improve screening and monitoring of NASH progression.

Background: In this study, we want to evaluate the response to Lutetium-177 (177Lu)-DOTATATE treatment in patients with neuroendocrine tumors (NETs) using single-photon emission computed tomography (SPECT) and computed tomography (CT), based on image-based radiomics and clinical features. Methods: The total volume of tumor areas was segmented into 61 SPECT and 41 SPECT-CT images from 22 patients with NETs. A total of 871 radiomics and clinical features were extracted from the SPECT and SPECT-CT images. Subsequently, a feature reduction method called maximum relevance minimum redundancy (mRMR) was used to select the best combination of features. These selected features were modeled using a decision tree (DT), random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM) classifiers to predict the treatment response in patients. For the SPECT and SPECT-CT images, ten and eight features, respectively, were selected using the mRMR algorithm. Results: The results revealed that the RF classifier with feature selection algorithms through mRMR had the highest classification accuracies of 64% and 83% for the SPECT and SPECT-CT images, respectively. The accuracy of the classifications of DT, KNN, and SVM for SPECT-CT images is 79%, 74%, and 67%, respectively. The poor accuracy obtained from different classifications in SPECT images (≈64%) showed that these images are not suitable for predicting treatment response. Conclusions: Modeling the selected features of SPECT-CT images based on their anatomy and the presence of extensive gray levels makes it possible to predict responses to the treatment of 177Lu-DOTATATE for patients with NETs.

Abstract This study aimed to investigate the diagnostic performance of machine learning-based radiomics analysis to diagnose coronary artery disease status and risk from rest/stress Myocardial Perfusion Imaging (MPI) single-photon emission computed tomography (SPECT). A total of 395 patients suspicious of coronary artery disease who underwent 2-day stress-rest protocol MPI SPECT were enrolled in this study. The left ventricle myocardium, excluding the cardiac cavity, was manually delineated on rest and stress images to define a volume of interest. Added to clinical features (age, sex, family history, diabetes status, smoking, and ejection fraction), a total of 118 radiomics features, were extracted from rest and stress MPI SPECT images to establish different feature sets, including Rest-, Stress-, Delta-, and Combined-radiomics (all together) feature sets. The data were randomly divided into 80% and 20% subsets for training and testing, respectively. The performance of classifiers built from combinations of three feature selections, and nine machine learning algorithms was evaluated for two different diagnostic tasks, including 1) normal/abnormal (no CAD vs. CAD) classification, and 2) low-risk/high-risk CAD classification. Different metrics, including the area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE), were reported for models’ evaluation. Overall, models built on the Stress feature set (compared to other feature sets), and models to diagnose the second task (compared to task 1 models) revealed better performance. The Stress-mRMR-KNN (feature set-feature selection-classifier) reached the highest performance for task 1 with AUC, ACC, SEN, and SPE equal to 0.61, 0.63, 0.64, and 0.6, respectively. The Stress-Boruta-GB model achieved the highest performance for task 2 with AUC, ACC, SEN, and SPE of 0.79, 0.76, 0.75, and 0.76, respectively. Diabetes status from the clinical feature family, and dependence count non-uniformity normalized, from the NGLDM family, which is representative of non-uniformity in the region of interest were the most frequently selected features from stress feature set for CAD risk classification. This study revealed promising results for CAD risk classification using machine learning models built on MPI SPECT radiomics. The proposed models are helpful to alleviate the labor-intensive MPI SPECT interpretation process regarding CAD status and can potentially expedite the diagnostic process.

Purpose: This study aims to diagnose the severity of important pathological indices, i.e., fibrosis, steatosis, lobular inflammation, and ballooning from the pathological images of the liver tissue based on extracted features by radiomics methods. Materials and Methods: This research uses the pathological images obtained from liver tissue samples for 258 laboratory mice. After preprocessing the images and data augmentation, a collection of texture feature sets extracted by gray-level-based algorithms, including Global, Gray-level Co-Occurrence Matrix (GLCM), Gray-level Run length Matrix (GLRLM), Gray-level Size Zone Matrix (GLSZM), and Neighboring Gray Tone Difference Matrix (NGTDM) algorithms. Then, advanced methods of classification, namely Support Vector Machine (SVM), Random Forest (RF), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbors (KNN), Logistic Regression (LR), Naïve Bayes (NB), and Multi-layer Perceptrons (MLP) are employed. This procedure is provided separately for each of the four indices of fibrosis level in 6 grading classes, steatosis in 5 grading classes, inflammation in 4 grading classes, and ballooning in 3 grading classes. For a comparison of the output of these algorithms, the accuracy value obtained from the evaluation data is presented for the performance of different methods. Results: The results showed that, compared to other methods, the Gaussian SVM algorithm provides a better response to the classification of the grading of liver disease among all the indices from the pathological images due to its structural features. This value of accuracy was calculated at 84.30% for fibrosis, 90.55% for steatosis, 81.11% for inflammation, and 95.98% for ballooning. Conclusion: This fully automatic framework based on advanced radiomics algorithms and machine learning from pathological images can be very useful in clinical procedures and be considered as an assistant or a substitute for pathologists’ diagnoses.

Abstract Prediction of response to Repetitive Transcranial Magnetic Stimulation (rTMS) can build a very effective treatment platform that helps Major Depressive Disorder (MDD) patients to receive timely treatment. We proposed a deep learning model powered up by state-of-the-art methods to classify responders (R) and non-responders (NR) to rTMS treatment. Pre-treatment Electro-Encephalogram (EEG) signal of public TDBRAIN dataset and 46 proprietary MDD subjects were utilized to create time–frequency representations using Continuous Wavelet Transform (CWT) to be fed into the two powerful pre-trained Convolutional Neural Networks (CNN) named VGG16 and EfficientNetB0. Equipping these Transfer Learning (TL) models with Bidirectional Long Short-Term Memory (BLSTM) and attention mechanism for the extraction of most discriminative spatiotemporal features from input images, can lead to superior performance in the prediction of rTMS treatment outcome. Five brain regions named Frontal, Central, Parietal, Temporal, and occipital were assessed and the highest evaluated performance in 46 proprietary MDD subjects was acquired for the Frontal region using the TL-LSTM-Attention model based on EfficientNetB0 with accuracy, sensitivity, specificity, and Area Under the Curve (AUC) of 97.1%, 97.3%, 97.0%, and 0.96 respectively. Additionally, to test the generalizability of the proposed models, these TL-BLSTM-Attention models were evaluated on a public dataset called TDBRAIN and the highest accuracy of 82.3%, the sensitivity of 80.2%, the specificity of 81.9% and the AUC of 0.83 were obtained. Therefore, advanced deep learning methods using a time–frequency representation of EEG signals from the frontal brain region and the convolutional recurrent neural networks equipped with the attention mechanism can construct an accurate platform for the prediction of response to the rTMS treatment.

Background: This study aims to extract carotid wall vibrations non-invasively and evaluate changes in the carotid artery caused by age, BMI, and sex. Such evaluation can increase the possibility of detecting wall stiffness and atherosclerosis in the early stages and prevent heart attack and death. Method: To extract small vibrations of the carotid wall, the image-tracking method, and optical flow with four different methods were used. The study involved twenty participants, comprising six females and fourteen males, with a mean age of 36.25 years, mean weight of 75.2 Kg, and mean BMI of 25. The posterior wall motion and vibration were extracted using ultrasound RF signals. Result: 4 optical flow methods, Gunnar-Farneback, Horn- Schunk, Lucas-Kanade, and Lucas-Kanade derivative were used for all samples, and covariance, correlation, P-value, and R-squared were estimated. Results showed the differences in parameters such as age and BMI with carotid wall vibration. These values for age are ( , , , ) and for BMI are ( , , , ), respectively. For gender as a new parameter, a comparison between men’s and women’s vibrations was estimated. The range of measured vibrations by optical flow methods is about to and the mean standard deviation is . Conclusion: The results presented that gender affects the vibration of the vessel wall, which in men is more than in women. In addition, increasing age and BMI may increase the stiffness of the carotid wall and reduce vibrations that were evaluated previously. Using the Gunner-Farneback method as image tracking for small vibrations is the best way with the highest accuracy.

Background: The Gleason grading system has been the most effective prediction for prostate cancer patients. This grading system provides this possibility to assess prostate cancer’s aggressiveness and then constitutes an important factor for stratification and therapeutic decisions. However, determining Gleason grade requires highly-trained pathologists and is time-consuming and tedious, and suffers from inter-pathologist variability. To remedy these limitations, this paper introduces an automatic methodology based on transfer learning with pretrained convolutional neural networks (CNNs) for automatic Gleason grading of prostate cancer tissue microarray (TMA). Methods: Fifteen pretrained (CNNs): Efficient Nets (B0-B5), NasNetLarge, NasNetMobile, InceptionV3, ResNet-50, SeResnet 50, Xception, DenseNet121, ResNext50, and inception_resnet_v2 were fine-tuned on a dataset of prostate carcinoma TMA images. Six pathologists separately identified benign and cancerous areas for each prostate TMA image by allocating benign, 3, 4, or 5 Gleason grade for 244 patients. The dataset was labeled by these pathologists and majority vote was applied on pixel-wise annotations to obtain a unified label. Results: Results showed the NasnetLarge architecture is the best model among them in the classification of prostate TMA images of 244 patients with accuracy of 0.93 and area under the curve of 0.98. Conclusion: Our study can act as a highly trained pathologist to categorize the prostate cancer stages with more objective and reproducible results.

The purpose of this study was to develop and validate a program for educating adolescents in cyberspace. The present study is a qualitative theory grounded and the research data were obtained using semi-structured and in-depth interviews with parents of Tehran’s Area 13 who met the entry criteria. The study measure was in-depth interview and sampling was continued until complete saturation. Data were coded at three levels. Regarding to the theoretical code of cyberspace psychopathology, the central codes of mental, social, physical, functional and educational pathologies along with 22 open codes were extracted and in relation to the theoretical code of coping strategies for dealing with cyberspace injuries, Parental awareness, legislation and effective interaction with adolescents were extracted with 32 open codes. Open, conceptual, and theoretical codes based on the Strauss-Korbin model were validated by counselings and psychologists with validation by constructive and internal method of evaluations. Then, a conceptual model of two-way interaction of psychopathologies and coping strategies to deal with cyberspace were designed. Finally, based on the conceptual model, a program for educating adolescents in cyberspace was developed. According to the obtained model, the pathologies and strategies of dealing with cyberspace interact with each other and have a two-way relationships. Therefore, the six-stage program of parenting education in cyberspace is able to suggest effective strategies in the field of parenting with regard to the harms of cyberspace for parents.

AbstractEstimation of mental workload from electroencephalogram (EEG) signals aims to accurately measure the cognitive demands placed on an individual during multitasking mental activities. By analyzing the brain activity of the subject, we can determine the level of mental effort required to perform a task and optimize the workload to prevent cognitive overload or underload. This information can be used to enhance performance and productivity in various fields such as healthcare, education, and aviation. In this paper, we propose a method that uses EEG and deep neural networks to estimate the mental workload of human subjects during multitasking mental activities. Notably, our proposed method employs subject-independent classification. We use the “STEW” dataset, which consists of two tasks, namely “No task” and “simultaneous capacity (SIMKAP)-based multitasking activity”. We estimate the different workload levels of two tasks using a composite framework consisting of brain connectivity and deep neural networks. After the initial preprocessing of EEG signals, an analysis of the relationships between the 14 EEG channels is conducted to evaluate effective brain connectivity. This assessment illustrates the information flow between various brain regions, utilizing the direct Directed Transfer Function (dDTF) method. Then, we propose a deep hybrid model based on pre-trained Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for the classification of workload levels. The accuracy of the proposed deep model achieved 83.12% according to the subject-independent leave-subject-out (LSO) approach. The pre-trained CNN + LSTM approaches to EEG data have been found to be an accurate method for assessing the mental workload. [ABSTRACT FROM AUTHOR]

Background: Motor Imagery (MI) Brain Computer Interface (BCI) directly links central nervous system to a computer or a device. Most MI-BCI structures rely on features of a single channel of Electroencephalogram (EEG). However, to provide more valuable features, the relationships among EEG channels in the form of effective brain connectivity analysis must be considered. Objective: This study aims to identify a set of robust and discriminative effective connectivity features from EEG signals and to develop a hierarchical machine learning structure for discrimination of left and right hand MI task effectively.Material and Methods: In this analytical study, we estimated effective connectivity using Granger Causality (GC) methods namely, Generalized Partial Directed Coherence (GPDC), Directed Transfer Function (DTF) and direct Directed Transfer Function (dDTF). These measures determine the transient causal relation between different brain areas. Then a feature subset selection method based on Kruskal–Wallis test was performed to choose most significant directed causal connection between channels. Moreover, the minimal-redundancy-maximal-relevance feature selection method is applied to discard non-significance features. Finally, support vector machine method is used for classification. Results: The maximum value of the classification accuracies using GC methods over different frequency bands in 29 subjects in 60 trial is approximately 84% in Mu (8−12 Hz) - Beta1 (12−15 Hz) frequency band using GPDC method. Conclusion: This new hierarchical automated BCI system could be applied for discrimination of left and right hand MI tasks from EEG signal, effectively.

Purpose: This study aims to extract carotid wall vibrations non-invasively and evaluate changes in the carotid artery caused by age, BMI, and sex. Such evaluation can increase the possibility of detecting wall stiffness and atherosclerosis in the early stages and prevent heart attack and death. Materials and Methods: To extract small vibrations of the carotid wall, the image-tracking method, and optical flow with four different methods were used. The study involved twenty participants, comprising six females and fourteen males, with a mean age of 36.25 years, mean weight of 75.2 Kg, and mean BMI of 25. The posterior wall motion and vibration were extracted using ultrasound RF signals. Results: 4 optical flow methods, Gunnar-Farneback, Horn-Schunk, Lucas-Kanade, and Lucas-Kanade derivative were used for all samples, and covariance, correlation, P-value, and R-squared were estimated. Results showed the differences in parameters such as age and BMI with carotid wall vibration. These values for age are (Cov = -0.1891, Corr = -0.6770, P-value < 0.001, r2 = -0.4590) and for BMI are (Cov = -0.0564, Corr = -0.6510, P-value < 0.001, r2 = 0.4238), respectively. For gender as a new parameter, a comparison between men's and women's vibrations was estimated. The range of measured vibrations by optical flow methods is about 0.0002 µm to 0.1µm, and the mean standard deviation is 0.04967 µm. Conclusion: The results presented that gender affects the vibration of the vessel wall, which in men is more than in women. In addition, increasing age and BMI may increase the stiffness of the carotid wall and reduce vibrations that were evaluated previously. Using the Gunner-Farneback method as image tracking for small vibrations is the best way with the highest accuracy. [ABSTRACT FROM AUTHOR]

Atomization of liquid jets in gaseous crossflows has many natural and industrial applications, for example, in fuel atomization in gas turbine engines, rocket engines, film cooling, and, recently, suspension and solution precursor plasma spraying processes for the development of advanced coatings. Viscosity and density of the gaseous medium may significantly vary in applications such as plasma spraying, which can affect the instability waves on the liquid jet column, resulting in a major change in the mechanism of primary and secondary breakups. In this study, a numerical model is used to investigate the impact of gas viscosity on breakup mechanisms for a wide range of density ratios and Weber numbers. Due to many challenges, only a few comprehensive atomization measurements have been performed on this subject. However, novel computational models could provide the atomization process with a thorough picture in the last two decades. The incompressible variable-density Navier–Stokes equations are solved by using finite volume schemes, and a geometric volume-of-fluid technique is used to track the gas–liquid interface. In our parametric study, three sets of density ratios and Weber numbers are chosen. In each set, four cases with different orders of magnitude of gaseous Reynolds number are simulated. Different characteristics of jet atomization are analyzed, such as the jet trajectory, breakup location, and surface instabilities generated along the jet column. Ultimately, the effects of gaseous Reynolds number, density ratio, and Weber number on jet deformation and breakup mechanisms are discussed.