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ردیف | عنوان | نوع |
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1 |
Quantum Kernels for Real-World Predictions Based on Electronic Health Records
هستههای کوانتومی برای پیشبینیهای دنیای واقعی بر اساس پروندههای سلامت الکترونیکی-2022 Research on near-term quantum machine learning has explored how classical machine learning
algorithms endowed with access to quantum kernels (similarity measures) can outperform their purely
classical counterparts. Although theoretical work has shown a provable advantage on synthetic data sets,
no work done to date has studied empirically whether the quantum advantage is attainable and with what
data. In this article, we report the first systematic investigation of empirical quantum advantage (EQA) in
healthcare and life sciences and propose an end-to-end framework to study EQA. We selected electronic
health records data subsets and created a configuration space of 5–20 features and 200–300 training samples.
For each configuration coordinate, we trained classical support vector machine models based on radial basis
function kernels and quantum models with custom kernels using an IBM quantum computer, making this
one of the largest quantum machine learning experiments to date. We empirically identified regimes where
quantum kernels could provide an advantage and introduced a terrain ruggedness index, a metric to help
quantitatively estimate how the accuracy of a given model will perform. The generalizable framework introduced here represents a key step toward a priori identification of data sets where quantum advantage could
exist.
INDEX TERMS: Artificial intelligence | digital health | electronic health records (EHR) | empirical quantum advantage (EQA) | machine learning | quantum kernels | real-world data | small data sets | support vector machines (SVM). |
مقاله انگلیسی |
2 |
Vision-assisted recognition of stereotype behaviors for early diagnosis of Autism Spectrum Disorders
تشخیص رفتارهای کلیشه ای برای تشخیص زودهنگام اختلالات طیف اوتیسم با کمک بینایی ماشین-2021 Medical diagnosis supported by computer-assisted technologies is getting more popularity and acceptance among medical society. In this paper, we propose a non-intrusive vision-assisted method based on human action recognition to facilitate the diagnosis of Autism Spectrum Disorder (ASD). We collected a novel and comprehensive video dataset f the most distinctive Stereotype actions of this disorder with the assistance of professional clinicians. Several frameworks as a function of different input modalities were developed and used to produce extensive baseline results. Various local descriptors, which are commonly used within the Bag-of-Visual-Words approach, were tested with Multi-layer Perceptron (MLP), Gaussian Naive Bayes (GNB), and Support Vector Machines (SVM) classifiers for recognizing ASD associated behaviors. Additionally, we developed a framework that first receives articulated pose-based skeleton sequences as input and follows an LSTM network to learn the temporal evolution of the poses. Finally, obtained results were compared with two fine-tuned deep neural networks: ConvLSTM and 3DCNN. The results revealed that the Histogram of Optical Flow (HOF) descriptor achieves the best results when used with MLP classifier. The promising baseline results also confirmed that an action-recognition-based system can be potentially used to assist clinicians to provide a reliable, accurate, and timely diagnosis of ASD disorder.© 2021 Elsevier B.V. All rights reserved. Keywords: Action recognition | Autism Spectrum Disorder | Patient monitoring | Bag-of-visual-words | Convolutional neural networks |
مقاله انگلیسی |
3 |
Biometric keystroke barcoding: A next-gen authentication framework
بارکد ضربه زدن به کلید بیومتریک: یک چارچوب احراز هویت نسل بعدی-2021 Investigation of new intelligent solutions for user identification and authentication is and will be essential for enhancing the security of the alphanumeric passwords entered on touchscreen and traditional keyboards. Extraction of the keystrokes has been very beneficial given the intelligent authentication protocols operating in time-domain; while the time-domain solutions drastically lose their efficiency over time due to converging inter- key times. Realistically reflecting the habitual traits, the frequency-domain solutions, however, reveal unique biometric characteristics better, without any risk of convergence. On the contrary, the existing frequency-based frameworks don’t provide storable biometric data for further classification of the attempts. Therefore, we pro-pose a novel barcoding framework converting habitual biometric information into storable barcodes as very low- size barcode images. The key-press times are extracted and turned into pseudo-signals exhibiting binary-train characteristics for continuous wavelet transformation (CWT). The transformed signals are primarily catego- rized with 4-scale scalograms by various complex frequency B-spline wavelets and subsequently superposed to create the unique barcodes. One-class support vector machines (SVM) is employed as the main classifier for training and testing the barcodes and very promising results are achieved given the lowest equal error rate (EER) of 1.83%. Keywords: Keystroke authentication | Biometrics | Complex wavelets | Scalogram | Barcode | CWT | SVM |
مقاله انگلیسی |
4 |
Using machine learning and computer vision to estimate the angular velocity of wind turbines in smart grids remotely
استفاده از یادگیری ماشین و بینایی ماشین برای برآورد سرعت زاویه ای توربین های بادی در شبکه های هوشمند از راه دور-2021 Today, power generation from clean and renewable resources such as wind and solar is of great salience. Smart grid technology efficiently responds to the increasing demand for electric power. Intelligent monitoring, control, and maintenance of wind energy facilities are indispensable to increase the performance and efficiency of smart grids (SGs). Integration of state-of-the-art machine learning algorithms and vision sensor networks approaches pave the way toward enhancing the wind farms’ performance. The generating power in a wind turbine farm is the most critical parameter that should be measured accurately. Produced power is highly related to weather patterns, and a new farm in a near area is also likely to have similar energy generation. Therefore, accurate and perpetual prediction models of the existing wind farms can be led to develop new stations with lower costs. The paper aims to estimate the angular velocity of turbine blades using vision sensors and signal processing. The high wind in the wind farm can cause the camera to vibrate in successive frames, and the noise in the input images can also strengthen the problem. Thanks to couples of solid computer vision algorithms, including FAST (Features from Accelerated Segment Test), SIFT (Scale-Invariant Feature Transform), SURF (Speeded Up Robust Features), BF (Brute-Force), FLANN (Fast Library for Approximate Nearest Neighbors), AE (Autoencoder), and SVM (support vector machines), this paper accurately localizes the Hub and track the presence of the Blade in consecutive frames of a video stream. The simulation results show that determining the hub location and the blade presence in sequential frames results in an accurate estimation of wind turbine angular velocity with 95.36% accuracy.© 2021 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/). Keywords: Machine vision | Blade detection | Image classification | Signal processing | Wind turbine | Smart grids |
مقاله انگلیسی |
5 |
AI-based Framework for Deep Learning Applications in Grinding
چارچوبی مبتنی بر هوش مصنوعی برای کاربردهای یادگیری عمیق در شبکه سازی-2020 Rejection costs for a finish-machined
gearwheel with grinding burn can rise to the order of 10,000 euros
each. A reduction in costs by reducing rejection rate by only 5-10
pieces per year already amortizes costs for data-acquisition
hardware for online process monitoring. The grinding wheel wear,
one of the major influencing factors responsible for the grinding
burn, depends on a large number of influencing variables like
cooling lubricant, feed rate, circumferential wheel speed and wheel
topography. In the past, machine learning algorithms such as
Support Vector Machines (SVM), Hidden Markov Models (HMM)
and Artificial Neural Networks (ANN) have proven effective for the
predictive analysis of process quality. In addition to predictive
analysis, AI-based applications for process control may raise the
resilience of machining processes. Using machine learning methods
may also lead to a heavy reduction of cost amassed due to a physical
inspection of each workpiece. With this contribution, information
from previous works is leveraged and an AI-based framework for
adaptive process control of a cylindrical grinding process is
introduced. For the development of such a framework, three
research objectives have been derived: First, the dynamic wheel
wear needs to be modelled and measured, because of its strong
impact on the resulting workpiece quality. Second, models to predict
the quality features of the produced workpieces depending on
process setup parameters and materials used have to be established.
Here, special focus is set on deriving models that are independent
of a specific wheel-workpiece-pair. The opportunity to use such a
model in a variety of grinding configurations gives the production
line consistent process support. Third, the resilience of analytical
models regarding graceful degradation of sensors needs to be
tackled, since the stability of such systems has to be guaranteed to
be used in productive environments. Process resilience against
human errors and sensor failures leads to a minimization of
rejection costs in production. To do so, a framework is presented,
where virtual sensors, upon the failure or detection of an erroneous
signal from physical sensors, will be activated and provide signals
to the downstream smart systems until the process is completed or
the physical sensor is changed. Keywords: Cylindrical Grinding | Wheel Wear | Virtual Sensors | Process Resilience | Artificial Intelligence |
مقاله انگلیسی |
6 |
Identifying influential factors distinguishing recidivists among offender patients with a diagnosis of schizophrenia via machine learning algorithms
شناسایی عوامل موثر در تشخیص تکرار مجدد در بین بیماران مجرم با تشخیص اسکیزوفرنی از طریق الگوریتم های یادگیری ماشین-2020 Purpose: There is a lack of research on predictors of criminal recidivism of offender patients diagnosed
with schizophrenia.
Methods: 653 potential predictor variables were anlyzed in a set of 344 offender patients with a diagnosis
of schizophrenia (209 reconvicted) using machine learning algorithms. As a novel methodological
approach, null hypothesis significance testing (NHST), backward selection, logistic regression, trees,
support vector machines (SVM), and naive bayes were used for preselecting variables. Subsequently the
variables identified as most influential were used for machine learning algorithm building and
evaluation.
Results: The two final models (with/without imputation) predicted criminal recidivism with an accuracy
of 81.7 % and 70.6 % and a predictive power (area under the curve, AUC) of 0.89 and 0.76 based on the
following predictors: prescription of amisulpride prior to reoffending, suspended sentencing to
imprisonment, legal complaints
filed by relatives/therapists/public authorities, recent legal issues, number of offences leading to forensic treatment, anxiety upon discharge, being single, violence toward care team and constant breaking of rules during treatment, illegal opioid use, middle east as place of
birth, and time span since the last psychiatric inpatient treatment.
Conclusion: Results provide new insight on possible factors influencing persistent offending in a specific
subgroup of patients with a schizophrenic spectrum disorder. Keywords: Criminal justice | Criminal recidivism | Machine learning | Offender | Schizophrenia |
مقاله انگلیسی |
7 |
Predicting and explaining corruption across countries: A machine learning approach
پیش بینی و توضیح فساد در سراسر کشور: رویکرد یادگیری ماشینی-2020 In the era of Big Data, Analytics, and Data Science, corruption is still ubiquitous and is perceived as one of the
major challenges of modern societies. A large body of academic studies has attempted to identify and explain the
potential causes and consequences of corruption, at varying levels of granularity, mostly through theoretical
lenses by using correlations and regression-based statistical analyses. The present study approaches the phenomenon
from the predictive analytics perspective by employing contemporary machine learning techniques to
discover the most important corruption perception predictors based on enriched/enhanced nonlinear models
with a high level of predictive accuracy. Specifically, within the multiclass classification modeling setting that is
employed herein, the Random Forest (an ensemble-type machine learning algorithm) is found to be the most
accurate prediction/classification model, followed by Support Vector Machines and Artificial Neural Networks.
From the practical standpoint, the enhanced predictive power of machine learning algorithms coupled with a
multi-source database revealed the most relevant corruption-related information, contributing to the related
body of knowledge, generating actionable insights for administrator, scholars, citizens, and politicians. The
variable importance results indicated that government integrity, property rights, judicial effectiveness, and
education index are the most influential factors in defining the corruption level of significance Keywords: Corruption perception | Machine learning | Predictive modeling | Random forest | Society policies and regulations |Government integrity | Social development |
مقاله انگلیسی |
8 |
The impact of entrepreneurship orientation on project performance: A machine learning approach
تأثیر گرایش کارآفرینی بر عملکرد پروژه: یک رویکرد یادگیری ماشینی-2020 Recent studies in project management have shown the important role of entrepreneurship orientation of the
individuals in project performance. Although identifying the role of entrepreneurship orientation as a critical
success factor in project performance has been considered as an important issue, it is also important to develop a
measurement system for predicting performance based on the degree of an individual’s entrepreneurial orientation.
In this study, we use predictive analytics by proposing a machine learning approach to predict individuals’
project performance based on measures of several aspects of entrepreneurial orientation and
entrepreneurial attitude of the individuals. We investigated this relationship using a sample of 185 observations
and a range of machine learning algorithms including lasso, ridge, support vector machines, neural networks,
and random forest. Our results showed that the best method for predicting project performance is lasso. After
identifying the best predictive model, we then used the Bayesian Information Criterion and the Akaike Information
Criterion to identify the most significant factors. Our results identify all three aspects of entrepreneurial
attitude (social self-efficacy, appearance self-efficacy, and comparativeness) and one aspect of entrepreneurial
orientation (proactiveness) as the most important factors. This study contributes to the relationship between
entrepreneurship skills and project performance and provides insights into the application of emerging tools in
data science and machine learning in operations management and project management research. Keywords: Project performance | Entrepreneurship orientation | Machine learning | Supervised learning | Predictive analytics |
مقاله انگلیسی |
9 |
AI-based prediction of independent construction safety outcomes from universal attributes
پیش بینی مبتنی بر هوش مصنوعی از نتایج ایمنی ساخت مستقل از ویژگی های جهانی-2020 This paper significantly improves on, and finishes to validate, an approach proposed in previous research in
which safety outcomes were predicted from attributes with machine learning. Like in the original study, we use
Natural Language Processing (NLP) to extract fundamental attributes from raw incident reports and machine
learning models are trained to predict safety outcomes. The outcomes predicted here are injury severity, injury
type, body part impacted, and incident type. However, unlike in the original study, safety outcomes were not
extracted via NLP but were provided by independent human annotations, eliminating any potential source of
artificial correlation between predictors and predictands. Results show that attributes are still highly predictive,
confirming the validity of the original approach. Other improvements brought by the current study include the
use of (1) a much larger dataset featuring more than 90,000 reports, (2) two new models, XGBoost and linear
SVM (Support Vector Machines), (3) model stacking, (4) a more straightforward experimental setup with more
appropriate performance metrics, and (5) an analysis of per-category attribute importance scores. Finally, the
injury severity outcome is well predicted, which was not the case in the original study. This is a significant
advancement. Keywords: Artificial intelligence | Machine learning | Supervised learning | NLP | Reports | Construction | Safety |
مقاله انگلیسی |
10 |
Use of support vector machines with a parallel local search algorithm for data classification and feature selection
استفاده از ماشینهای بردار پشتیبانی با الگوریتم جستجوی محلی موازی برای طبقه بندی داده ها و انتخاب ویژگی ها-2020 Over the last decade, the number of studies on machine learning has significantly increased. One of the most widely researched areas of machine learning is data classification. Most big data systems require a large amount of information storage for analytic purposes; however, this involves some disadvantages, such as the costs of processing and collecting data. Thus, many researchers and practitioners are working on effectively reducing the number of features used in classification. This paper proposes a method which jointly optimizes both feature selection and classification. A survey of the relevant literature shows that the vast majority of studies focus on either feature selection or classification. In this study, the proposed parallel local search algorithm both selects features and finds a classifier with high rates of accuracy. Moreover, the proposed method is capable of finding solutions for problems that have extremely high numbers of features within a reasonable computation time. Keywords: Support vector machines | Feature selection | Classification | Heuristic | Machine learning |
مقاله انگلیسی |