scholarly journals fMRI-Based Machine Learning Analysis of Neural Substrates of Pediatric Anxiety: Temporal Pole and Emotional Face-Responses.

2020 ◽  
Author(s):  
Jeffrey Sawalha ◽  
Muhammad Yousefnezhad ◽  
Alessandro Selvitella ◽  
Bo Cao ◽  
Andrew Greenshaw ◽  
...  
Keyword(s):  
Machine Learning ◽  
Separation Anxiety ◽  
Childhood Anxiety ◽  
Fearful Faces ◽  
Pediatric Anxiety ◽  
Machine Learning Model ◽  
Temporal Pole ◽  
The Right ◽  
Emotional Face ◽  
Learning Analysis

Abstract A prominent cognitive aspect of anxiety is dysregulation of emotional interpretation of facial expressions, associated with neural activity from the amygdala and prefrontal cortex. We report machine learning analysis of fMRI results supporting a key role for a third area, the temporal pole (TP) for childhood anxiety in this context. This finding is based on differential fMRI responses to emotional faces (e.g. angry versus fearful faces) in children with one or more of generalized anxiety, separation anxiety, and social phobia (n = 22) compared with matched controls (n = 23). In our machine learning model, the right TP distinguished anxious from control children (accuracy = 81%). Involvement of the TP as significant for neurocognitive aspects of pediatric anxiety is a novel finding worthy of further investigation.

scholarly journals Predicting pediatric anxiety from the temporal pole using neural responses to emotional faces

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jeffrey Sawalha ◽  
Muhammad Yousefnezhad ◽  
Alessandro M. Selvitella ◽  
Bo Cao ◽  
Andrew J. Greenshaw ◽  
...  
Keyword(s):  
Machine Learning ◽  
Separation Anxiety ◽  
Childhood Anxiety ◽  
Neural Responses ◽  
Adaptive Boosting ◽  
Emotional Faces ◽  
Fearful Faces ◽  
Pediatric Anxiety ◽  
Temporal Pole ◽  
The Right

AbstractA prominent cognitive aspect of anxiety is dysregulation of emotional interpretation of facial expressions, associated with neural activity from the amygdala and prefrontal cortex. We report machine learning analysis of fMRI results supporting a key role for a third area, the temporal pole (TP) for childhood anxiety in this context. This finding is based on differential fMRI responses to emotional faces (angry versus fearful faces) in children with one or more of generalized anxiety, separation anxiety, and social phobia (n = 22) compared with matched controls (n = 23). In our machine learning (Adaptive Boosting) model, the right TP distinguished anxious from control children (accuracy = 81%). Involvement of the TP as significant for neurocognitive aspects of pediatric anxiety is a novel finding worthy of further investigation.

scholarly journals Text and Data Formatting for Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 2756-2760
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Data Preprocessing ◽  
Learning Model ◽  
Poor Quality ◽  
Big Data Analysis ◽  
Model Quality ◽  
Machine Learning Model ◽  
Learning Analysis

Machine learning is a prominent tool for getting data from large amounts of information. Whereas a good amount of machine learning analysis has targeted on increasing the accuracy and potency of coaching and reasoning algorithms, there is less attention within the equally vital issues of observing the standard of information fed into the machine learning model. The standard of huge information is far away from good. Recent studies have shown that poor quality will bring serious errors to the result of big data analysis and this could have an effect on in making additional precise results from the information. Advantages of data preprocessing within the context of ML are advanced detection of errors, model-quality improves by the usage of better data, savings in engineering hours to debug issues

scholarly journals SafeOne Machine Learning model to predict industrial incidents in Chemical and Gas Industries

2021 ◽  
Vol 12 (2) ◽  
pp. 1123-1131
Author(s):  
Ganapathy Subramaniam Balasubramanian, Et. al.
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Workplace Safety ◽  
Machine Learning Algorithms ◽  
Industrial Safety ◽  
Machine Learning Model ◽  
Helpful Factors ◽  
The Right ◽  
Aws Lambda ◽  
Safety Strategy

Understanding activity incidents is one of the necessary measures in workplace safety strategy. Analyzing the trends of the activity incident information helps to spot the potential pain points and helps to scale back the loss. Optimizing the Machine Learning algorithms may be a comparatively new trend to suit the prediction model and algorithms within the right place to support human helpful factors. This research aims to make a prediction model spot the activity incidents in chemical and gas industries. This paper describes the design and approach of building and implementing the prediction model to predict the reason behind the incident which may be used as a key index for achieving industrial safety specific to chemical and gas industries. The implementation of the grading algorithmic program including the prediction model ought to bring unbiased information to get a logical conclusion. The prediction model has been trained against incident information that has 25700 chemical industrial incidents with accident descriptions for the last decade. Inspection information and incident logs ought to be chomped high of the trained dataset to verify and validate the implementation. The result of the implementation provides insight towards the understanding of the patterns, classifications, associated conjointly contributes to an increased understanding of quantitative and qualitative analytics. Innovative cloud-based technology discloses the gate to method the continual in-streaming information, method it, and output the required end in a period. The first technology stack utilized in this design is Apache Kafka, Apache Spark, KSQL, Data frames, and AWS Lambda functions. Lambda functions are accustomed implement the grading algorithmic program and prediction algorithmic program to put in writing out the results back to AWS S3 buckets. Proof of conception implementation of the prediction model helps the industries to examine through the incidents and can layout the bottom platform for the assorted protective implementations that continuously advantage the workplace's name, growth, and have less attrition in human resources.

scholarly journals Clinical Features at Presentation for Glioblastoma Patients Impact Survival Predictions in a Machine Learning Model

2021 ◽  
Vol 23 (Supplement_4) ◽  
pp. iv18-iv18
Author(s):  
Alistair Lawrence ◽  
Rohit Sinha ◽  
Stefan Mitrasinovic ◽  
Stephen Price
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Machine Learning Algorithms ◽  
Analysis Model ◽  
Tree Model ◽  
Machine Learning Model ◽  
Validation Set ◽  
Learning Analysis ◽  
Prognostic Modelling

Abstract Aims To generate an accurate prediction model for greater than median survival using Random Forest machine learning analysis and to compare the model to a traditional logistic regression analysis model on the same Glioblastoma Dataset. Method In this single centre retrospective cohort study, all patients with histologically diagnosed primary GB from October 2014 to April 2019 were included (n=466). Machine learning algorithms encompassing multiple logistic regression and a Random Forest, Gini index-based decision tree model with 100,000 trees were used. 17 clinical, molecular and treatment specific binarily categorised variables were used. The dataset was split 70:30 into training and validating sets. Results The dataset contained 466 patients. 326 patients made up the training set and 140 the validation set. The Random Forest model’s accuracy for predicting 18-month survival was 86.4% compared to the Logistic Regression model’s accuracy of 85.7%. The top 5 factors that the Random Forest model used to predict survival over 18 months were; mean MGMT status >10%, if the patient underwent gross total resection, whether the patient had adjuvant temozolomide, whether the patient had a neurological deficit on presentation, and the sex of the patient. Conclusion Machine learning can be applied in the context of GB prognostic modelling. The models show that as well as the known factors that affect GB survival, the presenting symptom may also have an impact on prognostication.

scholarly journals Uplift modeling VS conventional predictive model: A reliable machine learning model to solve employee turnover

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Davin Wijaya ◽  
Jumri Habbeyb DS ◽  
Samuelta Barus ◽  
Beriman Pasaribu ◽  
Loredana Ioana Sirbu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Success Rate ◽  
Predictive Model ◽  
Employee Turnover ◽  
Performance Comparison ◽  
Uplift Modeling ◽  
Machine Learning Model ◽  
Average Accuracy ◽  
Synthetic Datasets ◽  
The Right

Employee turnover is the loss of talent in the workforce that can be costly for a company. Uplift modeling is one of the prescriptive methods in machine learning models that not only predict an outcome but also prescribe a solution. Recent studies are focusing on the conventional predictive models to predict employee turnover rather than uplift modeling. In this research, we analyze whether the uplifting model has better performance than the conventional predictive model in solving employee turnover. Performance comparison between the two methods was carried out by experimentation using two synthetic datasets and one real dataset. The results show that despite the conventional predictive model yields an average prediction accuracy of 84%; it only yields a success rate of 50% to target the right employee with a retention program on the three datasets. By contrast, the uplift model only yields an average accuracy of 67% but yields a consistent success rate of 100% in targeting the right employee with a retention program.

scholarly journals The Classification of Elbow Extension and Flexion: A feature selection investigation

Mekatronika ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 68-73
Author(s):  
Mohamad Ilyas Rizan ◽  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Amirul Abdullah ◽  
Mohd Azraai Mohd Razman ◽  
Anwar P. P. Abdul Majeed
Keyword(s):  
Machine Learning ◽  
Information Gain ◽  
Advanced Technology ◽  
Modern World ◽  
K Nearest Neighbors ◽  
Rehabilitation Process ◽  
Machine Learning Model ◽  
Elbow Movement ◽  
The Time Domain ◽  
The Right

Nowadays, the worldwide primary reasons of long-term disability is stroke. When the blood supply to your brain is interupted and reduced, stroke occurs as it depriving brain tissue of nutrients and oxygen. In the modern world, advanced technology are revolutionizing the rehabilitation process. This research uses mechanomyography (MMG) and machine learning models to classify the elbow movement, extension and flexion of the elbow joint. The study will aid in the control of an exoskeleton for stroke patient's rehabilitation process in future studies. Five volunteers (21 to 23 years old) were recruited in Universiti Malaysia Pahang (UMP) to execute the right elbow movement of extension and flexion. The movements are repeated five times each for two active muscles for the extension and flexion motion, namely triceps and biceps. From the time domain based MMG signals, twenty-four features were extracted from the MMG before being classified by the machine learning model, namely k-Nearest Neighbors (k-NN). The k-NN has achieved the classification accuracy (CA) with 88.6% as the significant features are identified through the information gain approach. It may well be stated that the suggested process was able to classify the elbow movement well

scholarly journals PHOTONAI—A Python API for rapid machine learning model development

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254062
Author(s):  
Ramona Leenings ◽  
Nils Ralf Winter ◽  
Lucas Plagwitz ◽  
Vincent Holstein ◽  
Jan Ernsting ◽  
...  
Keyword(s):  
Machine Learning ◽  
External Validation ◽  
Model Development ◽  
Learning Model ◽  
Complex Data ◽  
Algorithm Selection ◽  
Iterative Model ◽  
Machine Learning Model ◽  
High Level ◽  
Learning Analysis

PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom algorithm sequences. It is especially designed to support the iterative model development process and automates the repetitive training, hyperparameter optimization and evaluation tasks. Importantly, the workflow ensures unbiased performance estimates while still allowing the user to fully customize the machine learning analysis. PHOTONAI extends existing solutions with a novel pipeline implementation supporting more complex data streams, feature combinations, and algorithm selection. Metrics and results can be conveniently visualized using the PHOTONAI Explorer and predictive models are shareable in a standardized format for further external validation or application. A growing add-on ecosystem allows researchers to offer data modality specific algorithms to the community and enhance machine learning in the areas of the life sciences. Its practical utility is demonstrated on an exemplary medical machine learning problem, achieving a state-of-the-art solution in few lines of code. Source code is publicly available on Github, while examples and documentation can be found at www.photon-ai.com.

Abstract P330: Optimizing Predictions of Infarct Core Using Machine Learning

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Rania Abdelkhaleq ◽  
Victor Lopez-Rivera ◽  
Sergio Salazar-Marioni ◽  
Songmi Lee ◽  
Youngran Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Infarct Volume ◽  
Learning Model ◽  
Superior Performance ◽  
Infarct Core ◽  
Vessel Occlusion ◽  
Machine Learning Model ◽  
Selection For ◽  
Learning Analysis

Introduction: Evaluation of infarct core by advanced neuroimaging has facilitated patient selection for endovascular stroke therapy (EST), however the accuracy of machine-learning analysis compared to these modalities remains unexplored. We test the performance of computed tomography-Alberta Stroke Program Early Computed Tomography Score (CT- ASPECTS) vs. Computed Tomography Perfusion (CTP)-RAPID, vs. an extension of our novel machine-learning model, Deep Symmetry-sensitive Network (DeepSymNet [ref]), using the final infarct volume (FIV) in patients with rapid and successful endovascular reperfusion as the gold standard. Methods and Materials: We identified consecutive patients with large vessel occlusion acute ischemic stroke that underwent EST with TICI 2b/3 reperfusion. FIV was determined by volumetric measurements on 24-48h DWI MRI. The DeepSymNet algorithm combines symmetric and absolute brain representations and had been trained to predict CTP-RAPID core size from CTA source images acquired at presentation. Performance at predicting FIV was determined by Pearson’s correlation for CT- ASPECTS, CTP-RAPID, and DeepSymNet. Data are presented as median [IQR]. Results: Among the 76 patients that met inclusion criteria, 55.2% were male, the median age was 68 years [54-77], and 32.8% were White. 71% of the patients demonstrated an MCA occlusion, and 55% of all occlusions were left-sided. Median ASPECTS on presentation was 8 [7-8.5] and the median FIV was 10 mL [2-37]. ASPECTS, CTP-RAPID and DeepSymNet all correlated with FIV, with comparable performances from ASPECTS (R 2 =-0.398) and CTP-RAPID (R 2 =0.403) and superior performance by DeepSymNet (R 2 =-0.606)(Table). Conclusions: The DeepSymNet machine learning model analyzing CTA source images demonstrated superior performance to ASPECTS and CTP-RAPID in FIV prediction. These findings suggest machine learning models may provide improved predictions of infarct core and selection for EST.

Declarative data serving

2021 ◽  
Vol 14 (11) ◽  
pp. 2555-2562
Author(s):  
Ted Shaowang ◽  
Nilesh Jain ◽  
Dennis D. Matthews ◽  
Sanjay Krishnan
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Computer Architecture ◽  
Edge Computing ◽  
Low Latency ◽  
Complex Task ◽  
Machine Learning Model ◽  
Data Flows ◽  
The Right ◽  
High Level

Recent advances in computer architecture and networking have ushered in a new age of edge computing, where computation is placed close to the point of data collection to facilitate low-latency decision making. As the complexity of such deployments grow into networks of interconnected edge devices, getting the necessary data to be in "the right place at the right time" can become a challenge. We envision a future of edge analytics where data flows between edge nodes are declaratively configured through high-level constraints. Using machine learning model-serving as a prototypical task, we illustrate how the heterogeneity and specialization of edge devices can lead to complex, task-specific communication patterns even in relatively simple situations. Without a declarative framework, managing this complexity will be challenging for developers and will lead to brittle systems. We conclude with a research vision for database community that brings our perspective to the emergent area of edge computing.

Sign in / Sign up

Export Citation Format

Share Document