Investigating Group-Specific Models of Hospital Workers’ Well-Being: Implications for Algorithmic Bias

2020 ◽  
Vol 14 (04) ◽  
pp. 477-499
Author(s):  
Vinesh Ravuri ◽  
Projna Paromita ◽  
Karel Mundnich ◽  
Amrutha Nadarajan ◽  
Brandon M. Booth ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ambulatory Monitoring ◽  
Real Life ◽  
Well Being ◽  
Machine Learning Algorithms ◽  
Prior Work ◽  
Hospital Workers ◽  
Job Responsibilities ◽  
Occupational Role ◽  
Algorithmic Bias

Hospital workers often experience burnout due to the demanding job responsibilities and long work hours. Data yielding from ambulatory monitoring combined with machine learning algorithms can afford us a better understanding of the naturalistic processes that contribute to this burnout. Motivated by the challenges related to the accurate tracking of well-being in real-life, prior work has investigated group-specific machine learning (GS-ML) models that are tailored to groups of participants. We examine a novel GS-ML for estimating well-being from real-life multimodal measures collected in situ from hospital workers. In contrast to the majority of prior work that uses pre-determined clustering criteria, we propose an iterative procedure that refines participant clusters based on the representations learned by the GS-ML models. Motivated by prior work that highlights the differential impact of job demands on well-being, we further explore the participant clusters in terms of demography and job-related attributes. Results indicate that the GS-ML models mostly outperform general models in estimating well-being constructs. The GS-ML models further depict different degrees of predictive power for each participant cluster, as distinguished upon age, education, occupational role, and number of supervisees. The observed discrepancies with respect to the GS-ML model decisions are discussed in association with algorithmic bias.

A new classification system for autism based on machine learning of artificial intelligence

2021 ◽  
pp. 1-18
Author(s):  
Seyed Reza Shahamiri ◽  
Fadi Thabtah ◽  
Neda Abdelhamid
Keyword(s):  
Machine Learning ◽  
Scoring Function ◽  
Autistic Traits ◽  
Well Being ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
The Social ◽  
Hidden Patterns ◽  
The Individual ◽  
Fold Cross Validation

BACKGROUND: Autistic Spectrum Disorder (ASD) is a neurodevelopment condition that is normally linked with substantial healthcare costs. Typical ASD screening techniques are time consuming, so the early detection of ASD could reduce such costs and help limit the development of the condition. OBJECTIVE: We propose an automated approach to detect autistic traits that replaces the scoring function used in current ASD screening with a more intelligent and less subjective approach. METHODS: The proposed approach employs deep neural networks (DNNs) to detect hidden patterns from previously labelled cases and controls, then applies the knowledge derived to classify the individual being screened. Specificity, sensitivity, and accuracy of the proposed approach are evaluated using ten-fold cross-validation. A comparative analysis has also been conducted to compare the DNNs’ performance with other prominent machine learning algorithms. RESULTS: Results indicate that deep learning technologies can be embedded within existing ASD screening to assist the stakeholders in the early identification of ASD traits. CONCLUSION: The proposed system will facilitate access to needed support for the social, physical, and educational well-being of the patient and family by making ASD screening more intelligent and accurate.

Measurement of Users’ Well-Being Through Domotic Sensors and Machine Learning Algorithms

2020 ◽  
Vol 20 (14) ◽  
pp. 8029-8038 ◽  
Author(s):  
Sara Casaccia ◽  
Luca Romeo ◽  
Andrea Calvaresi ◽  
Nicole Morresi ◽  
Andrea Monteriu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Well Being ◽  
Machine Learning Algorithms

scholarly journals Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization

2019 ◽  
Vol 9 (18) ◽  
pp. 3665 ◽  
Author(s):  
Ahmet Çağdaş Seçkin ◽  
Aysun Coşkun
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Deductive Reasoning ◽  
Real Life ◽  
Indoor Positioning ◽  
Machine Learning Algorithms ◽  
Learning Models ◽  
Positioning Systems ◽  
Common Solution ◽  
Variable Reduction

Wi-Fi-based indoor positioning offers significant opportunities for numerous applications. Examining the Wi-Fi positioning systems, it was observed that hundreds of variables were used even when variable reduction was applied. This reveals a structure that is difficult to repeat and is far from producing a common solution for real-life applications. It aims to create a common and standardized dataset for indoor positioning and localization and present a system that can perform estimations using this dataset. To that end, machine learning (ML) methods are compared and the results of successful methods with hierarchical inclusion are then investigated. Further, new features are generated according to the measurement point obtained from the dataset. Subsequently, learning models are selected according to the performance metrics for the estimation of location and position. These learning models are then fused hierarchically using deductive reasoning. Using the proposed method, estimation of location and position has proved to be more successful by using fewer variables than the current studies. This paper, thus, identifies a lack of applicability present in the research community and solves it using the proposed method. It suggests that the proposed method results in a significant improvement for the estimation of floor and longitude.

An Economic Perspective on Algorithmic Fairness

2020 ◽  
Vol 110 ◽  
pp. 91-95 ◽  
Author(s):  
Ashesh Rambachan ◽  
Jon Kleinberg ◽  
Jens Ludwig ◽  
Sendhil Mullainathan
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Decision Making Under Uncertainty ◽  
Economic Perspective ◽  
Optimal Regulation ◽  
Economic Framework ◽  
Computer Scientists ◽  
Algorithmic Bias

There are widespread concerns that the growing use of machine learning algorithms in important decisions may reproduce and reinforce existing discrimination against legally protected groups. Most of the attention to date on issues of “algorithmic bias” or “algorithmic fairness” has come from computer scientists and machine learning researchers. We argue that concerns about algorithmic fairness are at least as much about questions of how discrimination manifests itself in data, decision-making under uncertainty, and optimal regulation. To fully answer these questions, an economic framework is necessary--and as a result, economists have much to contribute.

scholarly journals Recognition and Digitization of Handwritten Text using Histogram of Gradients and Artificial Neural Network

2021 ◽  
Vol 12 (6) ◽  
pp. 2555-2564
Author(s):  
Dr.S.K.Nivetha Et al.
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Real Life ◽  
Machine Learning Algorithms ◽  
Postal Code ◽  
Training Costs ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Artificial Neural

Handwriting recognition is one of the most persuasive and interesting projects as it is required in many real-life applications such as bank-check processing, postal-code recognition, handwritten notes or question paper digitization etc. Machine learning and deep learning methods are being used by developers to make computers more intelligent. A person learns how to execute a task by learning and repeating it over and over before it memorises the steps. The neurons in his brain will then be able to easily execute the task that he has mastered. This is also very close to machine learning. It employs a variety of architectures to solve various problems. Handwritten text recognition systems are models that capture and interpret handwritten numeric and character data from sources such as paper documents and photographs. For this application, a variety of machine learning algorithms were used. However, several limitations have been found, such as a large number of iterations, high training costs, and so on. Even though the other models have given impressive accuracy, it still has some drawbacks. In an unsupervised way, the Artificial Neural Network is used to learn effective data coding. For recognising real-world data, we built a model using Histogram of Oriented Gradients (HOG) and Artificial Neural Networks (ANN).

scholarly journals A Hybrid Meta-Learner Technique for Credit Scoring of Banks’ Customers

2017 ◽  
Vol 7 (5) ◽  
pp. 2073-2082 ◽  
Author(s):  
A. G. Armaki ◽  
M. F. Fallah ◽  
M. Alborzi ◽  
A. Mohammadzadeh
Keyword(s):  
Machine Learning ◽  
Hybrid Model ◽  
Credit Scoring ◽  
Clustering Algorithms ◽  
Real Life ◽  
Ensemble Methods ◽  
Scoring Systems ◽  
Error Rates ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques

Financial institutions are exposed to credit risk due to issuance of consumer loans. Thus, developing reliable credit scoring systems is very crucial for them. Since, machine learning techniques have demonstrated their applicability and merit, they have been extensively used in credit scoring literature. Recent studies concentrating on hybrid models through merging various machine learning algorithms have revealed compelling results. There are two types of hybridization methods namely traditional and ensemble methods. This study combines both of them and comes up with a hybrid meta-learner model. The structure of the model is based on the traditional hybrid model of ‘classification + clustering’ in which the stacking ensemble method is employed in the classification part. Moreover, this paper compares several versions of the proposed hybrid model by using various combinations of classification and clustering algorithms. Hence, it helps us to identify which hybrid model can achieve the best performance for credit scoring purposes. Using four real-life credit datasets, the experimental results show that the model of (KNN-NN-SVMPSO)-(DL)-(DBSCAN) delivers the highest prediction accuracy and the lowest error rates.

scholarly journals SEO Ministration : Content Related Tag Suggestion Using One vs Rest

2021 ◽  
pp. 245-253
Author(s):  
Dyapa Sravan Reddy ◽  
Lakshmi Prasanna Reddy ◽  
Kandibanda Sai Santhosh ◽  
Virrat Devaser
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Learning Algorithms ◽  
Real Life ◽  
Machine Learning Algorithms ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

SEO Analyst pays a lot of time finding relevant tags for their articles and in some cases, they are unaware of the content topics. The current proposed ML model will recommend content-related tags so that the Content writers/SEO analyst will be having an overview regarding the content and minimizes their time spent on unknown articles. Machine Learning algorithms have a plethora of applications and the extent of their real-life implementations cannot be estimated. Using algorithms like One vs Rest (OVR), Long Short-Term Memory (LSTM), this study has analyzed how Machine Learning can be useful for tag suggestions for a topic. The training of the model with One vs Rest turned out to deliver more accurate results than others. This Study certainly answers how One vs Rest is used for tag suggestions that are needed to promote a website and further studies are required to suggest keywords required.

scholarly journals Automatic emotion recognition in healthcare data using supervised machine learning

2021 ◽  
Vol 7 ◽  
pp. e751
Author(s):  
Nazish Azam ◽  
Tauqir Ahmad ◽  
Nazeef Ul Haq
Keyword(s):  
Machine Learning ◽  
Psychological Health ◽  
Emotional State ◽  
Negative Emotion ◽  
Emotional Health ◽  
Well Being ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Health Issues ◽  
Healthcare Data

Human feelings are fundamental to perceive the conduct and state of mind of an individual. A healthy emotional state is one significant highlight to improve personal satisfaction. On the other hand, bad emotional health can prompt social or psychological well-being issues. Recognizing or detecting feelings in online health care data gives important and helpful information regarding the emotional state of patients. To recognize or detection of patient’s emotion against a specific disease using text from online sources is a challenging task. In this paper, we propose a method for the automatic detection of patient’s emotions in healthcare data using supervised machine learning approaches. For this purpose, we created a new dataset named EmoHD, comprising of 4,202 text samples against eight disease classes and six emotion classes, gathered from different online resources. We used six different supervised machine learning models based on different feature engineering techniques. We also performed a detailed comparison of the chosen six machine learning algorithms using different feature vectors on our dataset. We achieved the highest 87% accuracy using MultiLayer Perceptron as compared to other state of the art models. Moreover, we use the emotional guidance scale to show that there is a link between negative emotion and psychological health issues. Our proposed work will be helpful to automatically detect a patient’s emotion during disease and to avoid extreme acts like suicide, mental disorders, or psychological health issues. The implementation details are made publicly available at the given link: https://bit.ly/2NQeGET.

scholarly journals Data mining methods of healthy indoor climate coefficients for comfortable well-being

2018 ◽  
Vol 29 (3) ◽  
pp. 7-12
Author(s):  
Grit Behrens ◽  
Klaus Schlender ◽  
Florian Fehring
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Indoor Air ◽  
Well Being ◽  
Machine Learning Algorithms ◽  
Indoor Climate ◽  
Measurement And Analysis ◽  
Scientific Project ◽  
Mining Methods ◽  
Analysis System

Abstract This article provides information about a currently developed measurement and analysis system ‘Smart Monitoring’, which is used on scientific project in terms of healthy indoor air coefficients, as well as the processing of the collected data for machine learning algorithms. The target is to reduce CO2 emissions caused by wrong ventilation habits in building sector after renovation process in older buildings.

scholarly journals Production fault simulation and forecasting from time series data with machine learning in glove textile industry

2019 ◽  
Vol 14 ◽  
pp. 155892501988346 ◽  
Author(s):  
Mine Seçkin ◽  
Ahmet Çağdaş Seçkin ◽  
Aysun Coşkun
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Production Process ◽  
Time Series Data ◽  
Real Life ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Random Parameters ◽  
Data Set ◽  
Textile Sector

Although textile production is heavily automation-based, it is viewed as a virgin area with regard to Industry 4.0. When the developments are integrated into the textile sector, efficiency is expected to increase. When data mining and machine learning studies are examined in textile sector, it is seen that there is a lack of data sharing related to production process in enterprises because of commercial concerns and confidentiality. In this study, a method is presented about how to simulate a production process and how to make regression from the time series data with machine learning. The simulation has been prepared for the annual production plan, and the corresponding faults based on the information received from textile glove enterprise and production data have been obtained. Data set has been applied to various machine learning methods within the scope of supervised learning to compare the learning performances. The errors that occur in the production process have been created using random parameters in the simulation. In order to verify the hypothesis that the errors may be forecast, various machine learning algorithms have been trained using data set in the form of time series. The variable showing the number of faulty products could be forecast very successfully. When forecasting the faulty product parameter, the random forest algorithm has demonstrated the highest success. As these error values have given high accuracy even in a simulation that works with uniformly distributed random parameters, highly accurate forecasts can be made in real-life applications as well.

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