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 Comparison of machine learning models based on time domain and frequency domain features for faults diagnosis in rotating machines

2018 ◽  
Vol 211 ◽  
pp. 17009
Author(s):  
Natalia Espinoza Sepulveda ◽  
Jyoti Sinha
Keyword(s):  
Machine Learning ◽  
Frequency Domain ◽  
Time Domain ◽  
Intelligent Systems ◽  
Learning Models ◽  
Machine Vibration ◽  
Vibration Data ◽  
Machine Learning Model ◽  
The Time Domain ◽  
Machine Learning Models

The development of technologies for the maintenance industry has taken an important role to meet the demanding challenges. One of the important challenges is to predict the defects, if any, in machines as early as possible to manage the machines downtime. The vibration-based condition monitoring (VCM) is well-known for this purpose but requires the human experience and expertise. The machine learning models using the intelligent systems and pattern recognition seem to be the future avenue for machine fault detection without the human expertise. Several such studies are published in the literature. This paper is also on the machine learning model for the different machine faults classification and detection. Here the time domain and frequency domain features derived from the measured machine vibration data are used separated in the development of the machine learning models using the artificial neutral network method. The effectiveness of both the time and frequency domain features based models are compared when they are applied to an experimental rig. The paper presents the proposed machine learning models and their performance in terms of the observations and results.

scholarly journals Machine Learning Accurately Predicts Next Season NHL Player Injury Before It Occurs: Validation of 10,449 Player-Years from 2007-17

2020 ◽  
Vol 8 (7_suppl6) ◽  
pp. 2325967120S0036
Author(s):  
Audrey Wright ◽  
Jaret Karnuta ◽  
Bryan Luu ◽  
Heather Haeberle ◽  
Eric Makhni ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Injury Risk ◽  
Medical Personnel ◽  
Operating Characteristics ◽  
K Nearest Neighbors ◽  
Predictive Algorithm ◽  
Preventative Intervention ◽  
Machine Learning Model ◽  
And Performance

Objectives: With the accumulation of big data surrounding National Hockey League (NHL) and the advent of advanced computational processors, machine learning (ML) is ideally suited to develop a predictive algorithm capable of imbibing historical data to accurately project a future player’s availability to play based on prior injury and performance. To the end of leveraging available analytics to permit data-driven injury prevention strategies and informed decisions for NHL franchises beyond static logistic regression (LR) analysis, the objective of this study of NHL players was to (1) characterize the epidemiology of publicly reported NHL injuries from 2007-17, (2) determine the validity of a machine learning model in predicting next season injury risk for both goalies and non-goalies, and (3) compare the performance of modern ML algorithms versus LR analyses. Methods: Hockey player data was compiled for the years 2007 to 2017 from two publicly reported databases in the absence of an official NHL-approved database. Attributes acquired from each NHL player from each professional year included: age, 85 player metrics, and injury history. A total of 5 ML algorithms were created for both non-goalie and goalie data; Random Forest, K-Nearest Neighbors, Naive Bayes, XGBoost, and Top 3 Ensemble. Logistic regression was also performed for both non-goalie and goalie data. Area under the receiver operating characteristics curve (AUC) primarily determined validation. Results: Player data was generated from 2,109 non-goalies and 213 goalies with an average follow-up of 4.5 years. The results are shown below in Table 1.For models predicting following season injury risk for non-goalies, XGBoost performed the best with an AUC of 0.948, compared to an AUC of 0.937 for logistic regression. For models predicting following season injury risk for goalies, XGBoost had the highest AUC with 0.956, compared to an AUC of 0.947 for LR. Conclusion: Advanced ML models such as XGBoost outperformed LR and demonstrated good to excellent capability of predicting whether a publicly reportable injury is likely to occur the next season. As more player-specific data become available, algorithm refinement may be possible to strengthen predictive insights and allow ML to offer quantitative risk management for franchises, present opportunity for targeted preventative intervention by medical personnel, and replace regression analysis as the new gold standard for predictive modeling. [Figure: see text]

scholarly journals A Machine Learning Model for Recommending Restaurants based on User Ratings

2020 ◽  
Vol 9 (1) ◽  
pp. 732-736
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
K Nearest Neighbors ◽  
Processing Pipeline ◽  
Chinese Restaurant ◽  
Registered User ◽  
Machine Learning Model ◽  
History Of ◽  
Multiclass Svm ◽  
User Ratings

However, oftentimes people just search a restaurant by using word “restaurant”, while the word “restaurant” means differently to different individuals. For an Asian, it can mean a “Chinese restaurant” or “Thai restaurant”. How to correctly interpret search requests based on people’s preference is a challenge. Building a machine-learning model based on activity history of a registered user can solve this problem. The activity histories used by this research are reviews and ratings from users. This project introduces a data processing pipeline, which uses reviews from registered users to generate a machine-learning model for each registered user. This project also defines an architecture, which uses the generated machine-learning models to support real-time personalized recommendations for restaurant searching and type of foods good at those recommended restaurants. Finally, this project aims to develop a good machine learning model, different collaborative filtering methodologies are considered to predict restaurants using user ratings. Slope One, k-Nearest Neighbors algorithm and multiclass SVM classification are some of the collaborating methodologies are going to consider in this project.

scholarly journals EEG beta-band spectral entropy predicts the effects of drug treatment on patients with herpes zoster

2019 ◽  
Author(s):  
Mengying Wei ◽  
Yuliang Liao ◽  
Jia Liu ◽  
Linling Li ◽  
Gan Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Herpes Zoster ◽  
Drug Treatment ◽  
Spectral Entropy ◽  
Beta Band ◽  
K Nearest Neighbors ◽  
Power Spectral ◽  
Machine Learning Model ◽  
Eeg Data ◽  
The Relationship

Abstract Background Medication is the main approach for early treatment of herpes zoster (HZ), but it could be ineffective in some patients. It is highly desired to predict the medication responses in order to control the degree of pain for HZ patients. The present study is aimed to elucidate the relationship between medication outcome and neural activity using electroencephalography (EEG) and to establish a machine learning model for early prediction of the medication responses from EEG. Methods We acquired and analyzed eye-closed resting-state EEG data 1-2 days after medication from 70 HZ patients with different drug treatment outcomes (measured 5-6 days after medicaiton): 45 medication-sensitive pain (MSP) patients and 25 medication-resistant pain (MRP) patients. EEG power spectral entropy (PSE) of each frequency band was compared at each channel between MSP and MRP patients, and those features showing sigificant difference between two groups were used to predict medication outcome with different machine learning methods. Results MSP patients showed significantly weaker beta-band PSE in the central-parietal regions than MRP patients. Based on these EEG PSE features and a k-nearest neighbors (k-NN) classifier, we can predicate the medication outcome with 80% ± 11.7% accuracy, 82.5% ± 14.7% sensitivity, 77.7% ± 27.3% specificity and an AUC of 0.85. Conclusion EEG beta-band PSE in the central-parietal region is predictive of the effectiveness of drug treatment on HZ patients, and it could potentially be used for early pain management and therapeutic prognosis.

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 MACHINE LEARNING FOR DATA CLASSIFICATION IN INDONESIA REGIONAL ELECTIONS BASED ON POLITICAL PARTIES SUPPORT

2020 ◽  
Vol 13 (2) ◽  
pp. 89-96
Author(s):  
Muhammad Fachrie
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Political Parties ◽  
Multilayer Perceptron ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
K Nearest Neighbors ◽  
Machine Learning Model ◽  
Regional Elections ◽  
Election Commission

In this paper, we discuss the implementation of Machine Learning (ML) to predict the victory of candidates in Regional Elections in Indonesia based on data taken from General Election Commission (KPU). The data consist of composition of political parties that support each candidate. The purpose of this research is to develop a Machine Learning model based on verified data provided by official institution to predict the victory of each candidate in a Regional Election instead of using social media data as in previous studies. The prediction itself simply a classification task between two classes, i.e. ‘win’ and ‘lose’. Several Machine Learning algorithms were applied to find the best model, i.e. k-Nearest Neighbors, Naïve Bayes Classifier, Decision Tree (C4.5), and Neural Networks (Multilayer Perceptron) where each of them was validated using 10-fold Cross Validation techniques. The selection of these algorithms aims to observe how the data works on different Machine Learning approaches. Besides, this research also aims to find the best combination of features that can lead to gain the highest performance. We found in this research that Neural Networks with Multilayer Perceptron is the best model with 74.20% of accuracy.

scholarly journals Machine Learning Outperforms Logistic Regression Analysis to Predict Next-Season NHL Player Injury: An Analysis of 2322 Players From 2007 to 2017

2020 ◽  
Vol 8 (9) ◽  
pp. 232596712095340
Author(s):  
Bryan C. Luu ◽  
Audrey L. Wright ◽  
Heather S. Haeberle ◽  
Jaret M. Karnuta ◽  
Mark S. Schickendantz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Injury Risk ◽  
Operating Characteristic ◽  
Performance Metrics ◽  
Historical Data ◽  
Characteristic Curve ◽  
National Hockey League ◽  
K Nearest Neighbors ◽  
Machine Learning Model

Background: The opportunity to quantitatively predict next-season injury risk in the National Hockey League (NHL) has become a reality with the advent of advanced computational processors and machine learning (ML) architecture. Unlike static regression analyses that provide a momentary prediction, ML algorithms are dynamic in that they are readily capable of imbibing historical data to build a framework that improves with additive data. Purpose: To (1) characterize the epidemiology of publicly reported NHL injuries from 2007 to 2017, (2) determine the validity of a machine learning model in predicting next-season injury risk for both goalies and position players, and (3) compare the performance of modern ML algorithms versus logistic regression (LR) analyses. Study Design: Descriptive epidemiology study. Methods: Professional NHL player data were compiled for the years 2007 to 2017 from 2 publicly reported databases in the absence of an official NHL-approved database. Attributes acquired from each NHL player from each professional year included age, 85 performance metrics, and injury history. A total of 5 ML algorithms were created for both position player and goalie data: random forest, K Nearest Neighbors, Naïve Bayes, XGBoost, and Top 3 Ensemble. LR was also performed for both position player and goalie data. Area under the receiver operating characteristic curve (AUC) primarily determined validation. Results: Player data were generated from 2109 position players and 213 goalies. For models predicting next-season injury risk for position players, XGBoost performed the best with an AUC of 0.948, compared with an AUC of 0.937 for LR ( P < .0001). For models predicting next-season injury risk for goalies, XGBoost had the highest AUC with 0.956, compared with an AUC of 0.947 for LR ( P < .0001). Conclusion: Advanced ML models such as XGBoost outperformed LR and demonstrated good to excellent capability of predicting whether a publicly reportable injury is likely to occur the next season.

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.

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.

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.

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