Multimodal Fake-News Recognition Using Ensemble of Deep Learners.

Social networks have drastically changed how people obtain information. News in social networks is accompanied by images and videos and thus receives more attention from readers as opposed to traditional sources. Unfortunately, fake-news publishers often misuse these advantages to spread false information rapidly. Therefore, the early detection of fake news is crucial. The best way to address this issue is to design an automatic detector based on fake-news content. Thus far, many fake-news recognition systems, including both traditional machine learning and deep learning models, have been proposed. Given that manual feature-extraction methods are very time-consuming, deep learning methods are the preferred tools. This study aimed to enhance the performance of existing approaches by utilizing an ensemble of deep learners based on attention mechanisms. To a great extent, the success of an ensemble model depends on the variety of its learners. To this end, we propose a novel loss function that enforces each learner to attend to different parts of news content on the one hand and obtain good classification accuracy on the other hand. Also, the learners are built on a common deep-feature extractor and only differ in their attention modules. As a result, the number of parameters is reduced efficiently and the overfitting problem is addressed. We conducted several experiments on some widely used fake-news detection datasets. The results confirm that the proposed method consistently surpasses the existing peer methods.

The relationship between chronic exposure to arsenic through drinking water and hearing function in exposed population aged 10-49 years: A cross-sectional study.

It has been documented that arsenic has a potential risk to human health and identified as a risk factor for hearing impairment. However, there are few studies that confirm the ototoxic effect of arsenic, especially on the human auditory system. Therefore, the current study was conducted to investigate the correlation between auditory thresholds at different frequencies (0.25, 0.5, 1, 2, 4 and 8 kHz) and arsenic levels in drinking water samples. A total of 240 people, divided into two equal groups: exposed and reference, were selected for the auditory tests. It should be noted that, at frequencies from 0.25 to 1 kHz, no hearing loss was observed in the both groups. Based on the results, no significant correlations (p > 0.05) were found between hearing thresholds and confounding variables including gender and BMI. However, smoking and age are known to be the main variables for hearing loss in univariate regression analysis. In the case of age, the hearing loss risk in the older participants was increased compared with the younger participants (4 kHz (OR =1.09; 95% CI: 1.04, 1.13) and 8 kHz (OR =1.12; 95% CI: 1.06, 1.18)). Smoking habits had significant associations with hearing loss risk at 4 kHz (OR = 3.48; 95% CI: 1.47, 8.22) and 8 kHz (OR = 3.01; 95% CI: 1.14, 7.95). The multivariate regression analysis showed that age, smoking status, and exposure to arsenic were significantly associated with increased risk of hearing loss. Moreover, no statistically significant correlation (p˃0.05) was observed between arsenic exposure and hearing loss in the logistic regression model compared to the reference group. These outcomes suggest that further investigation and cohort studies with a larger number of participants should be conducted to find an association between arsenic exposure and hearing loss in general population.

A non-monotone pattern search approach for systems of nonlinear equations.

In this paper, a new pattern search is proposed to solve the systems of nonlinear equations. We introduce a new non-monotone strategy which includes a convex combination of the maximum function of some preceding successful iterates and the current function. First, we produce a stronger non-monotone strategy in relation to the generated strategy by Gasparo et al. [Nonmonotone algorithms for pattern search methods, Numer. Algorithms 28 (2001), pp. 171-186] whenever iterates are far away from the optimizer. Second, when iterates are near the optimizer, we produce a weaker non-monotone strategy with respect to the generated strategy by Ahookhosh and Amini [An efficient nonmonotone trust-region method for unconstrained optimization, Numer. Algorithms 59 (2012), pp. 523-540]. Third, whenever iterates are neither near the optimizer nor far away from it, we produce a medium non-monotone strategy which will be laid between the generated strategy by Gasparo et al. [Nonmonotone algorithms for pattern search methods, Numer. Algorithms 28 (2001), pp. 171-186] and Ahookhosh and Amini [An efficient nonmonotone trust-region method for unconstrained optimization, Numer. Algorithms 59 (2012), pp. 523-540]. Reported are numerical results of the proposed algorithm for which the global convergence is established.

Prediction Effects of Personal, Psychosocial, and Occupational Risk Factors on Low Back Pain Severity Using Artificial Neural Networks Approach in Industrial Workers.

OBJECTIVES: This study aimed to provide an empirical model of predicting low back pain (LBP) by considering the occupational, personal, and psychological risk factor interactions in workers population employed in industrial units using an artificial neural networks approach. METHODS: A total of 92 workers with LBP as the case group and 68 healthy workers as a control group were selected in various industrial units with similar occupational conditions. The demographic information and personal, occupational, and psychosocial factors of the participants were collected via interview, related questionnaires, consultation with occupational medicine, and also the Rapid Entire Body Assessment worksheet and National Aeronautics and Space Administration Task Load Index software. Then, 16 risk factors for LBP were used as input variables to develop the prediction model. Networks with various multilayered structures were developed using MATLAB. RESULTS: The developed neural networks with 1 hidden layer and 26 neurons had the least error of classification in both training and testing phases. The mean of classification accuracy of the developed neural networks for the testing and training phase data were about 88% and 96%, respectively. In addition, the mean of classification accuracy of both training and testing data was 92%, indicating much better results compared with other methods. CONCLUSION: It appears that the prediction model using the neural network approach is more accurate compared with other applied methods. Because occupational LBP is usually untreatable, the results of prediction may be suitable for developing preventive strategies and corrective interventions.

Automatic determination of pubertal growth spurts based on the cervical vertebral maturation staging using deep convolutional neural networks.

BACKGROUND: This study aimed to develop a deep convolutional neural network (CNN) for automatic classification of pubertal growth spurts using cervical vertebral maturation (CVM) staging based on the lateral cephalograms of an Iranian subpopulation. MATERIAL AND METHODS: Cephalometric radiographs were collected from 1846 eligible patients (aged 5-18 years) referred to the orthodontic department of Hamadan University of Medical Sciences. These images were labeled by two experienced orthodontists. Two scenarios, including two- and three-class (pubertal growth spurts using CVM), were considered as the output for the classification task. The cropped image of the second to fourth cervical vertebrae was used as input to the network. After the preprocessing, the augmentation step, and hyperparameter tuning, the networks were trained with initial random weighting and transfer learning. Finally, the best architecture among the different architectures was determined based on the accuracy and F-score criteria. RESULTS: The CNN based on the ConvNeXtBase-296 architecture had the highest accuracy for automatically assessing pubertal growth spurts based on CVM staging in both three-class (82% accuracy) and two-class (93% accuracy) scenarios. Given the limited amount of data available for training the target networks for most of the architectures in use, transfer learning improves predictive performance. CONCLUSIONS: The results of this study confirm the potential of CNNs as an auxiliary diagnostic tool for intelligent assessment of skeletal maturation staging with high accuracy even with a relatively small number of images. Considering the development of orthodontic science toward digitalization, the development of such intelligent decision systems is proposed.

Identification of gene profiles related to the development of oral cancer using a deep learning technique.

BACKGROUND: Oral cancer (OC) is a debilitating disease that can affect the quality of life of these patients adversely. Oral premalignant lesion patients have a high risk of developing OC. Therefore, identifying robust survival subgroups among them may significantly improve patient therapy and care. This study aimed to identify prognostic biomarkers that predict the time-to-development of OC and survival stratification for patients using state-of-the-art machine learning and deep learning. METHODS: Gene expression profiles (29,096 probes) related to 86 patients from the GSE26549 dataset from the GEO repository were used. An autoencoder deep learning neural network model was used to extract features. We also used a univariate Cox regression model to select significant features obtained from the deep learning method (P < 0.05). High-risk and low-risk groups were then identified using a hierarchical clustering technique based on 100 encoded features (the number of units of the encoding layer, i.e., bottleneck of the network) from autoencoder and selected by Cox proportional hazards model and a supervised random forest (RF) classifier was used to identify gene profiles related to subtypes of OC from the original 29,096 probes. RESULTS: Among 100 encoded features extracted by autoencoder, seventy features were significantly related to time-to-OC-development, based on the univariate Cox model, which was used as the inputs for the clustering of patients. Two survival risk groups were identified (P value of log-rank test = 0.003) and were used as the labels for supervised classification. The overall accuracy of the RF classifier was 0.916 over the test set, yielded 21 top genes (FUT8-DDR2-ATM-CD247-ETS1-ZEB2-COL5A2-GMAP7-CDH1-COL11A2-COL3A1-AHR-COL2A1-CHORDC1-PTP4A3-COL1A2-CCR2-PDGFRB-COL1A1-FERMT2-PIK3CB) associated with time to developing OC, selected among the original 29,096 probes. CONCLUSIONS: Using deep learning, our study identified prominent transcriptional biomarkers in determining high-risk patients for developing oral cancer, which may be prognostic as significant targets for OC therapy. The identified genes may serve as potential targets for oral cancer chemoprevention. Additional validation of these biomarkers in experimental prospective and retrospective studies will launch them in OC clinics.

Sex determination from lateral cephalometric radiographs using an automated deep learning convolutional neural network.

Purpose: Despite the proliferation of numerous morphometric and anthropometric methods for sex identification based on linear, angular, and regional measurements of various parts of the body, these methods are subject to error due to the observer's knowledge and expertise. This study aimed to explore the possibility of automated sex determination using convolutional neural networks (CNNs) based on lateral cephalometric radiographs. Materials and Methods: Lateral cephalometric radiographs of 1,476 Iranian subjects (794 women and 682 men) from 18 to 49 years of age were included. Lateral cephalometric radiographs were considered as a network input and output layer including 2 classes (male and female). Eighty percent of the data was used as a training set and the rest as a test set. Hyperparameter tuning of each network was done after preprocessing and data augmentation steps. The predictive performance of different architectures (DenseNet, ResNet, and VGG) was evaluated based on their accuracy in test sets. Results: The CNN based on the DenseNet121 architecture, with an overall accuracy of 90%, had the best predictive power in sex determination. The prediction accuracy of this model was almost equal for men and women. Furthermore, with all architectures, the use of transfer learning improved predictive performance. Conclusion: The results confirmed that a CNN could predict a person's sex with high accuracy. This prediction was independent of human bias because feature extraction was done automatically. However, for more accurate sex determination on a wider scale, further studies with larger sample sizes are desirable.

Investigation of hand muscle fatigue and its influential factors in manual tasks.

Muscle fatigue (MF) can lead to musculoskeletal disorders (MSDs) in the long term; however, it can be managed if the causes are well known. This study aimed to examine the grip force (GF) and grip fatigue (GFa) of employees with light, moderate and heavy manual tasks using a dynamometer and find their possible relationship with other factors. The nature of heavy manual tasks led to more experience of GFa and GF of the right hand. Moreover, the equal need for both hands in occupations with light and moderate manual tasks is the reason for more GFa in the left hand. In this primary study, the height, weight and age of subjects and their exposure to vibration had a decisive effect on GF. In order to determine the accurate effects of the aforementioned risk factors on MF, it is recommended for future studies to be performed on larger populations.

What do the different ergonomic interventions accomplish in the workplace? A systematic review.

Introduction. Improving well-being and overall system performance are the ultimate goals of ergonomics, which are achieved through ergonomic interventions. This systematic review aimed to answer the question of what different ergonomic interventions accomplish in the workplace. Method. The systematic review followed PRISMA guidelines. Ergonomic interventions in workplaces focusing on any ergonomics health outcomes or productivity were identified in electronic databases up to June 1, 2019. Results. The 1635 articles collected from the literature screening stage were screened for their relevance to this study by the authors independently. The full-text review identified 22 papers qualified for inclusion in this systematic review. Most of the interventions implemented in the analyzed articles were ergonomic training programs, participatory ergonomics and workstation designs. The highlight results showed that interventions such as feedback, participatory ergonomics in short-term follow-ups and job rotation along with ergonomic guidelines did not significantly affect the risk of psychosocial factors. A significant reduction of musculoskeletal disorders in the upper limbs was reported with workplace improvements. Conclusion. There was no specific study method or intervention approach found to influence ergonomic outcomes. A multicomponent intervention program can be used to improve the impact of interventions on employees' health and system productivity.

Deterministic and probabilistic human health risk assessment approach of exposure to heavy metals in drinking water sources: A case study of a semi-arid region in the west of Iran.

In the current study, the concentration of heavy metals (Ba, Mn, Pb, and Cd) in drinking water resources of 328 villages in Hamadan Province were measured using ICP-OES apparatus during two dry (September 2018) and wet (April 2019) seasons. The assessment of the non-carcinogenic risk of selected heavy metals was conducted based on the recommendations of the USEPA. Also, sensitivity analysis and uncertainty of the effective variables were performed using Monte-Carlo simulations. Based on the results, Mn level in drinking water samples ranged 0.08-25.63 µg/L and 0.08-20.03 µg/L in dry and wet seasons, respectively. Similarly, Ba levels in water samples ranged 0.15-70.13 µg/L and 0.84-65 µg/L. Also, Cd and Pb concentrations in all sampling sites were below the limits of detection (LOD) of the ICP-OES apparatus. The hazard index (HI) values for adult and children were 2.17 × 10-3 and 3.29 × 10-3, respectively, which show a lack of non-carcinogenic risk for the examined heavy metals (Mn and Ba) to the local inhabitants. The results of the sensitivity analyses for adults and children revealed that two variables including metal concentration and ingestion rate of drinking water (IR) had the highest positive effects on the non-carcinogenic risk estimates. It was also found that there was no significant non-carcinogenic risk for the local residents in the studied area due to drinking water consumption.

Segmentation of medical images using mean value guided contour.

Partial differential equation-based (PDE-based) methods are extensively used in image segmentation, especially in contour models. Difficulties associated with the boundaries, namely troubles with developing initialization, inadequate convergence to boundary concavities, and difficulties connected to saddle points and stationary points of active contours make the contour models suffer from a feeble performance of referring to complex geometries. The present paper is designed to take advantage of mean value theorem rather than minimizing energy function for contours. It is efficiently capable of resolving above-mentioned problems by applying this theorem to the edge map gradient vectors, which is calculated from the image. Since the contour is computed in a straightforward manner from an edge map instead of force balance equation, it varies from other contour-based image segmentation methods. To illustrate the ability of the proposed model in complex geometries and ruptures, several experiments were also provided to validate the model. The experiments' results demonstrated that the proposed method, which is called mean value guided contour (MVGC), is capable of repositioning contours into boundary concavities and has suitable forcefulness in complex geometries.

Development of a noise prediction model based on advanced fuzzy approaches in typical industrial workrooms.

BACKGROUND: Noise prediction is considered to be the best method for evaluating cost-preventative noise controls in industrial workrooms. One of the most important issues is the development of accurate models for analysis of the complex relationships among acoustic features affecting noise level in workrooms. In this study, advanced fuzzy approaches were employed to develop relatively accurate models for predicting noise in noisy industrial workrooms. METHODS: The data were collected from 60 industrial embroidery workrooms in the Khorasan Province, East of Iran. The main acoustic and embroidery process features that influence the noise were used to develop prediction models using MATLAB software. Multiple regression technique was also employed and its results were compared with those of fuzzy approaches. RESULTS: Prediction errors of all prediction models based on fuzzy approaches were within the acceptable level (lower than one dB). However, Neuro-fuzzy model (RMSE=0.53dB and R2=0.88) could slightly improve the accuracy of noise prediction compared with generate fuzzy model. Moreover, fuzzy approaches provided more accurate predictions than did regression technique. CONCLUSIONS: The developed models based on fuzzy approaches as useful prediction tools give professionals the opportunity to have an optimum decision about the effectiveness of acoustic treatment scenarios in embroidery workrooms.

Feature Selection in Order to Extract Multiple Sclerosis Lesions Automatically in 3D Brain Magnetic Resonance Images Using Combination of Support Vector Machine and Genetic Algorithm.

This paper presents a new feature selection approach for automatically extracting multiple sclerosis (MS) lesions in three-dimensional (3D) magnetic resonance (MR) images. Presented method is applicable to different types of MS lesions. In this method, T1, T2, and fluid attenuated inversion recovery (FLAIR) images are firstly preprocessed. In the next phase, effective features to extract MS lesions are selected by using a genetic algorithm (GA). The fitness function of the GA is the Similarity Index (SI) of a support vector machine (SVM) classifier. The results obtained on different types of lesions have been evaluated by comparison with manual segmentations. This algorithm is evaluated on 15 real 3D MR images using several measures. As a result, the SI between MS regions determined by the proposed method and radiologists was 87% on average. Experiments and comparisons with other methods show the effectiveness and the efficiency of the proposed approach.

Segmentation of Multiple Sclerosis Lesions in Brain MR Images Using Spatially Constrained Possibilistic Fuzzy C-Means Classification.

This paper introduces a novel methodology for the segmentation of brain MS lesions in MRI volumes using a new clustering algorithm named SCPFCM. SCPFCM uses membership, typicality and spatial information to cluster each voxel. The proposed method relies on an initial segmentation of MS lesions in T1-w and T2-w images by applying SCPFCM algorithm, and the T1 image is then used as a mask and is compared with T2 image. The proposed method was applied to 10 clinical MRI datasets. The results obtained on different types of lesions have been evaluated by comparison with manual segmentations.

Uterine segmentation and volume measurement in uterine fibroid patients' MRI using fuzzy C-mean algorithm and morphological operations.

BACKGROUND: Uterine fibroids are common benign tumors of the female pelvis. Uterine artery embolization (UAE) is an effective treatment of symptomatic uterine fibroids by shrinkage of the size of these tumors. Segmentation of the uterine region is essential for an accurate treatment strategy. OBJECTIVES: In this paper, we will introduce a new method for uterine segmentation in T1W and enhanced T1W magnetic resonance (MR) images in a group of fibroid patients candidated for UAE in order to make a reliable tool for uterine volumetry. PATIENTS AND METHODS: Uterine was initially segmented using Fuzzy C-Mean (FCM) method in T1W-enhanced images and some morphological operations were then applied to refine the initial segmentation. Finally redundant parts were removed by masking the segmented region in T1W-enhanced image over the registered T1W image and using histogram thresholding. This method was evaluated using a dataset with ten patients' images (sagittal, axial and coronal views). RESULTS: We compared manually segmented images with the output of our system and obtained a mean similarity of 80%, mean sensitivity of 75.32% and a mean specificity of 89.5%. The Pearson correlation coefficient between the areas measured by the manual method and the automated method was 0.99. CONCLUSIONS: The quantitative results illustrate good performance of this method. By uterine segmentation, fibroids in the uterine may be segmented and their properties may be analyzed.