Rapid evaluation method for anisotropic growth of WS2 monolayers by combining machine learning algorithms and kinetic Monte Carlo simulation data

The memristor-based arbiter PUF (APUF) has great potential to be used for hardware security purposes. Its advantage is in its challenge-dependent delays, which cannot be modeled by machine learning algorithms. In this paper, further improvement is proposed, which are circuit configurations to the memristor-based APUF. Two configuration aspects were introduced namely varying the number of memristor per transistor, and the number of challenge and response bits. The purpose of the configurations is to introduce additional variation to the PUF, thereby improve PUF performance in terms of uniqueness, uniformity, and bit-aliasing; as well as resistance against support vector machine (SVM). Monte Carlo simulations were carried out on 180 nm and 130 nm, where both CMOS technologies have produced uniqueness, uniformity, and bit-aliasing values close to the ideal 50%; as well as SVM prediction accuracies no higher than 52.3%, therefore indicating excellent PUF performance.

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A Spasticity Assessment Method for Voluntary Movement using Data Fusion and Machine Learning

10.21203/rs.3.rs-19134/v1 ◽

2020 ◽

Author(s):

yan chen ◽

Song Yu ◽

Qing Cai ◽

Shuangyuan Huang ◽

Ke Ma ◽

...

Keyword(s):

Machine Learning ◽

Classification Accuracy ◽

Voluntary Movement ◽

Muscle Activation ◽

Evaluation Method ◽

Rating Scale ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Measurement Unit ◽

Stroke Patients

Abstract Background: Spasticity is a common complication of stroke. Effective spasticity management can improve patients' recovery efficiency and reduce patients' pain. The present clinical spasticity rating scale exhibits subjectivity and a ceiling effect, which makes it difficult to evaluate spasm objectively and to clinically analyze the pathological mechanism of spasticity. The sensor-based quantitative evaluation method is an effective substitute for the clinical spasm rating scale, but currently, it mainly focuses on the spasm evaluation of passive motion. The study of spasmodic state under active exercise can provide a basis for treatment and rehabilitation training, but the evaluation method of spasmodic state under active exercise has not yet been established. Therefore, we combine inertial measurement unit (IMU) and surface electromyography (sEMG) to test the feasibility of assessing spasticity patterns in stroke patients during voluntary movement. Methods: Nine stroke patients with varying degrees of spasticity and four healthy subjects performed isometric elbow exercises. sEMG and kinematics signals were recorded for all participants. The Empirical Mode Decomposition (EMD) algorithm and double threshold algorithms were used to separate sEMG of involuntary muscle activation from voluntary activation. Then, feature extraction and feature fusion were performed. Four common machine learning algorithms are used to monitor and evaluate spasticity patterns. The validity of the proposed method is verified by comparing the classification accuracy of four machine learning models. Results: Cross-validation yielded high classification accuracies (F1-score>0.88) for all four machine learning classifiers in assessing spasticity patterns. The highest detection performance was obtained using the Random Forest algorithm (average accuracy = 0.979; macro-F1 = 0.976). Conclusions: We present a novel method for assessing post-stroke spasticity based on voluntary movement and machine learning. Good classification performance verifies the feasibility of evaluating spasticity patterns by our method. Reliable classification accuracy achieved by the machine learning algorithms indicated the potential to evaluate spasticity patterns using IMU and sEMG when stroke survivors perform voluntary movements.

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Training Data Selection for Machine Learning-Enhanced Monte Carlo Simulations in Structural Dynamics

Applied Sciences ◽

10.3390/app12020581 ◽

2022 ◽

Vol 12 (2) ◽

pp. 581

Author(s):

Denny Thaler ◽

Leonard Elezaj ◽

Franz Bamer ◽

Bernd Markert

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Supervised Learning ◽

Selection Process ◽

Structural Response ◽

Machine Learning Algorithms ◽

Training Data ◽

Training Data Selection

The evaluation of structural response constitutes a fundamental task in the design of ground-excited structures. In this context, the Monte Carlo simulation is a powerful tool to estimate the response statistics of nonlinear systems, which cannot be represented analytically. Unfortunately, the number of samples which is required for estimations with high confidence increases disproportionally to obtain a reliable estimation of low-probability events. As a consequence, the Monte Carlo simulation becomes a non-realizable task from a computational perspective. We show that the application of machine learning algorithms significantly lowers the computational burden of the Monte Carlo method. We use artificial neural networks to predict structural response behavior using supervised learning. However, one shortcoming of supervised learning is the inability of a sufficiently accurate prediction when extrapolating to data the neural network has not seen yet. In this paper, neural networks predict the response of structures subjected to non-stationary ground excitations. In doing so, we propose a novel selection process for the training data to provide the required samples to reliably predict rare events. We, finally, prove that the new strategy results in a significant improvement of the prediction of the response statistics in the tail end of the distribution.

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Supplemental Material for One Model to Rule Them All? Using Machine Learning Algorithms to Determine the Number of Factors in Exploratory Factor Analysis

Psychological Methods ◽

10.1037/met0000262.supp ◽

2020 ◽

Keyword(s):

Machine Learning ◽

Factor Analysis ◽

Exploratory Factor Analysis ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Number Of Factors

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Forecasting US movies box office performances in Turkey using machine learning algorithms

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189120 ◽

2020 ◽

Vol 39 (5) ◽

pp. 6579-6590

Author(s):

Sandy Çağlıyor ◽

Başar Öztayşi ◽

Selime Sezgin

Keyword(s):

Machine Learning ◽

Global Economy ◽

Learning Algorithms ◽

Forecast Model ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

High Stakes ◽

Box Office ◽

Industry Forecast ◽

The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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Intelligent system of English composition scoring model based on improved machine learning algorithm

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189235 ◽

2020 ◽

pp. 1-11

Author(s):

Jie Liu ◽

Lin Lin ◽

Xiufang Liang

Keyword(s):

Machine Learning ◽

Evaluation System ◽

Intelligent System ◽

Learning Algorithm ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Assessment System ◽

English Composition ◽

Region Extraction ◽

Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

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The Unlearnable Checkerboard Pattern

Communications of the Blyth Institute ◽

10.33014/issn.2640-5652.1.2.holloway.1 ◽

2019 ◽

Vol 1 (2) ◽

pp. 78-80

Author(s):

Eric Holloway

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Checkerboard Pattern ◽

Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms. Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

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