Ranking Cloud Computing Criteria in Developing Electronic Communications Services Using MCDM

The main purpose of this study was ranking cloud computing criteria in developing electronic communications services using multiple-criteria decision analysis methodology (MCDM). This correlational research was applied in terms of purpose. Statistical population of the study included ICT experts of the steel industry in Yazd province (industrial experts), among which, 312 individuals were selected using purposeful nonprobability (judgmental) sampling method. Considering the conducted investigations and critically reviewing the related books and articles, the variables, criteria, and scales were identified using cloud computing technology. To analyze the data, MCDM and fuzzy logic calculations were utilized in Expert Choice software. According to the results and considering fuzzy calculations related to the capabilities of cloud computing in developing electronic communications services, the most important criteria in the "IT management in steel industries" cluster having (A) network code was "communicating with steel industries` costumers" having (AB) network code and fuzzy network weight equal to 0.096; the most important criteria in "cloud computing capabilities" cluster having (B) network code were "reducing steel industries` costs" having (BA) network code and fuzzy network weight equal to 0.191; and "providing rapid services to steel industries` costumers" having (BB) network code and fuzzy network weight equal to 0.120. on the other hand, the most important criteria in "developing electronic communications services" cluster having (C) network code was "storing the data in electronic communications services" having (CD) network code and fuzzy network weight equal to 0.123, since based on fuzzy logic calculation, they had the highest fuzzy rank in Matlab programming environment regarding cloud computing capabilities in developing electronic communications services

Auto-ASD-Network: A technique based on Deep Learning and Support Vector Machines for diagnosing Autism Spectrum Disorder using fMRI data

Quantitative analysis of brain disorders such as Autism Spectrum Disorder (ASD) is an ongoing field of research. Machine learning and deep learning techniques have been playing an important role in automating the diagnosis of brain disorders by extracting discriminative features from the brain data. In this study, we propose a model called Auto-ASD-Network in order to classify subjects with Autism disorder from healthy subjects using only fMRI data. Our model consists of a multilayer perceptron (MLP) with two hidden layers. We use an algorithm called SMOTE for performing data augmentation in order to generate artificial data and avoid overfitting, which helps increase the classification accuracy. We further investigate the discriminative power of features extracted using MLP by feeding them to an SVM classifier. In order to optimize the hyperparameters of SVM, we use a technique called Auto Tune Models (ATM) which searches over the hyperparameter space to find the best values of SVM hyperparameters. Our model achieves more than 70% classification accuracy for 4 fMRI datasets with the highest accuracy of 80%. It improves the performance of SVM by 26%, the stand-alone MLP by 16% and the state of the art method in ASD classification by 14%. The implemented code will be available as GPL license on GitHub portal of our lab (https://github.com/PCDS)

Similarity based classification of ADHD using Singular Value Decomposition

Attention deficit hyperactivity disorder (ADHD) is one of the most common brain disorders among children. This disorder is considered as a big threat for public health and causes attention, focus and organizing difficulties for children and even adults. Since the cause of ADHD is not known yet, data mining algorithms are being used to help discover patterns which discriminate healthy from ADHD subjects. Numerous efforts are underway with the goal of developing classification tools for ADHD diagnosis based on functional and structural magnetic resonance imaging data of the brain. In this paper, we used Eros, which is a technique for computing similarity between two multivariate time series along with k-Nearest-Neighbor classifier, to classify healthy vs ADHD children. We designed a model selection scheme called J-Eros which is able to pick the optimum value of k for k-Nearest-Neighbor from the training data. We applied this technique to the public data provided by ADHD-200 Consortium competition and our results show that J-Eros is capable of discriminating healthy from ADHD children such that we outperformed the best results reported by ADHD-200 competition more than 20 percent for two datasets

GPU-DFC: A GPU-based parallel algorithm for computing dynamic-functional connectivity of big fMRI data

Studying dynamic-functional connectivity (DFC) using fMRI data of the brain gives much richer information to neuroscientists than studying the brain as a static entity. Mining of dynamic connectivity graphs from these brain studies can be used to classify diseased versus healthy brains. However, constructing and mining dynamic-functional connectivity graphs of the brain can be time consuming due to size of fMRI data. In this paper, we propose a highly scalable GPU-based parallel algorithm called GPU-DFC for computing dynamic-functional connectivity of fMRI data both at region and voxel level. Our algorithm exploits sparsification of correlation matrix and stores them in CSR format. Further reduction in the correlation matrix is achieved by parallel decomposition techniques. Our GPU-DFC algorithm achieves 2 times speed-up for computing dynamic correlations compared to state-of-the-art GPU-based techniques and more than 40 times compared to a sequential CPU version. In terms of storage, our proposed matrix decomposition technique reduces the size of correlation matrices more than 100 times. Reconstructed values from decomposed matrices show comparable results as compared to the correlations with original data. The implemented code is available as GPL license on GitHub portal of our lab (https://github.com/pcdslab/GPU-DFC)

RATERS’ FATIGUE AND THEIR COMMENTS DURING SCORING WRITING ESSAYS: A CASE OF IRANIAN EFL LEARNERS

Rating accuracy in writing among EFL learners is crucial in determining their English proficiency. Despite the importance of its accuracy, little is known about various factors may affect the accuracy of rating writing essays. This study examines how raters’ comments on EFL writing tasks change as a result of fatigue. To this end, four raters were selected and each given 28 essays to score and comment on. Six general types of raters’ comments (i.e., those on grammar, choice of words, organization, punctuation, dictation, and capitalization) were into focus in this study. Overall, results suggested that fatigue affects raters’ frequency of comments on grammar, choice of words, and organization, and that raters’ comments on punctuation, dictation, and capitalization do not seem to change significantly due to the effect of fatigue. Furthermore, this study revealed that the most and least frequent comments in 112 scored essays were those on grammar and dictation, respectively

GPU-PCC: A GPU Based Technique to Compute Pairwise Pearson’s Correlation Coefficients for Big fMRI Data

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive brain imaging technique for studying the brain’s functional activities. Pearson’s Correlation Coefficient is an important measure for capturing dynamic behaviors and functional connectivity between brain components. One bottleneck in computing Correlation Coefficients is the time it takes to process big fMRI data. In this paper, we propose GPU-PCC, a GPU based algorithm based on vector dot product, which is able to compute pairwise Pearson’s Correlation Coefficients while performing computation once for each pair. Our method is able to compute Correlation Coefficients in an ordered fashion without the need to do post-processing reordering of coefficients. We evaluated GPU- PCC using synthetic and real fMRI data and compared it with sequential version of computing Correlation Coefficient on CPU and existing state-of-the-art GPU method. We show that our GPU-PCC runs 94.62× faster as compared to the CPU version and 4.28× faster than the existing GPU based technique on a real fMRI dataset of size 90k voxels. The implemented code is available as GPL license on GitHub portal of our lab at https://github.com/pcdslab/GPU-PCC

The effect of socioeconomic status on ambulance requests

Introduction: Emergency medical events are not randomly distributed over a certain area. Many hidden patterns may influence this distribution due to several socioeconomic, demographic, and geospatial factors. Identifying these patterns will help health policy makers have a better planning for emergency medical services (EMS) in finding high-risk places, and people at high risk. Methods: Mashhad city EMS calls records have been analyzed retrospectively. The privacy of the data was considered by eliminating the identification information such as the name or phone number of the patients. To recognize the location of the requests all the recorded addresses were mapped into a single number representing the municipality region of the address. To express the relationship between the predictors, correlation coefficient has been employed.   Results: 154528 calls in a citywide registry from March 21, 2013, to March 20, 2014, were investigated. The average of age was 42.43 years (S.D = 21.7) with 50.5% male, 40.7% female and 8.8% of the sex were not registered. 64% of the calls were medical related and the remaining 36% were trauma-related requests. Aside from traffic accident that was the top most in all regions, other top five reasons for ambulance request including weakness, seizure, unconsciousness, nervous stress, and dyspnea were recognized. Although the regions with lower socioeconomic status are more vulnerable, they request ambulances less frequently than the regions with higher socioeconomic status. Conclusion: There is a relationship between the socioeconomic status of people and their calls to EMS. The results of this study can help policymakers in finding people in potentially high-risk locations and provide facilities to reduce mortality and morbidity