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1 |
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 |
2 |
Fixed-Wing UAVs flocking in continuous spaces: A deep reinforcement learning approach
پهپادهای ثابت بال در فضاهای مداوم هجوم می آورند: یک رویکرد یادگیری تقویتی عمیق-2020 Fixed-Wing UAVs (Unmanned Aerial Vehicles) flocking is still a challenging problem due to the
kinematics complexity and environmental dynamics. In this paper, we solve the leader–followers
flocking problem using a novel deep reinforcement learning algorithm that can generate roll angle
and velocity commands by training an end-to-end controller in continuous state and action spaces.
Specifically, we choose CACLA (Continuous Actor–Critic Learning Automation) as the base algorithm
and we use the multi-layer perceptron to represent both the actor and the critic. Besides, we further
improve the learning efficiency by using the experience replay technique that stores the training
data in the experience memory and samples from the memory as needed. We have compared the
performance of the proposed CACER (Continuous Actor–Critic with Experience Replay) algorithm
with benchmark algorithms such as DDPG and double DQN in numerical simulation, and we have
demonstrated the performance of the learned optimal policy in semi-physical simulation without any
parameter tuning. Keywords: Fixed-wing UAV | Flocking | Reinforcement learning | Actor–critic |
مقاله انگلیسی |
3 |
Secondary User Experience-oriented Resource Allocation in AI-empowered Cognitive Radio Networks Using Deep Neuroevolution
تخصیص منابع کاربر گرا ثانویه در شبکه های رادیویی شناختی دارای هوش مصنوعی با استفاده از تکامل عصبی عمیق-2020 Secondary user (SU)-experience-oriented resource
allocation (RA) will become increasingly important in cognitive
radio networks (CRNs) in future wireless networks. For efficient
real-time processes, cognitive radios (CRs) are usually combined
with artificial intelligence (AI) to improve better adaptation and
intelligent RA. However, deep learning (DL), which is a key
AI strategy with remarkable capabilities towards advancing this
vision, has several built-in limitations. Firstly, the most successful
DL applications require training with large amounts of data;
secondly, they assume that the data samples to be independent,
while in CRNs one typically encounters sequences of highly
correlated states. To circumvent this issue, this paper introduces
a deep neuroevolution (DNE) technique for dynamic RA. Using
this technique, a stable learning framework was achieved by
introducing the phenotypic plasticity of transmission rates and
delay constraints inside a multi-layer perceptron (MLP). The
stability of SU satisfaction as they increased in number was
achieved at 36 SUs, which is a 13.3% decrease from when they
were only 6 SUs in the CRN for all learning mechanisms. Keywords: Secondary user | Artificial intelligence| Cognitive radio networks | Ant colony optimization | Logistic regression | Multilayer perceptron | Deep Q-network | Deep neuroevolution |
مقاله انگلیسی |
4 |
Correlation and prediction of surface tension of highly non-ideal hydrous binary mixtures using artificial neural network
همبستگی و پیش بینی تنش سطحی مخلوطهای باینری هیدروژن غیر ایده آل با استفاده از شبکه عصبی مصنوعی-2020 Prediction of surface tension of highly non-ideal binary aqueous–organic mixtures is crucial for interpreting the
interaction between the molecules. In this regard, a multi-layer perceptron (MLP) artificial neural network
(ANN) model is developed to predict the binary aqueous–organic surface tension as a function of mixture
composition and temperature while the organic compounds are very dissimilar in size and type. To correlate the
binary surface tension, gathered experimental surface tension data consisted of 30 binary mixtures containing
2271 data points in the wide temperature range of 273–471.15 K are randomly divided into three different
subsets namely training (70 % of total data), validation (15 % of total data) and testing (15 % of total data)
subsets. Different input variables are examined and the number of hidden neurons is optimized. The obtained
results revealed that it is possible to correlate the binary surface tension with the best MLP network with 27
neurons in the hidden layer and inputs variables of temperature, mole fraction, molecular weight and critical
pressure of non-water component with the average absolute relative deviation (AARD %) of lower than 1.43 %.
Comparison of accuracy of the MLP model with several common models such as Jouyban-Acree model, Wilson
equation, Paquette and Rasmussen areas and several equations of state including SRK, PR and CPA revealed
more accuracy of the proposed MLP based model. Keywords: Surface tension | Modeling | Binary mixture | Non-ideal | ANN |
مقاله انگلیسی |
5 |
Machine learning modelling for the ultrasonication-mediated disruption of recombinant E: coli for the efficient release of nitrilase
مدل سازی یادگیری ماشین برای اختلال امواج فراصوت واسطه نوترکیب E: coli برای انتشار کارآمد نیتریلاز-2019 The ultrasonication-mediated cell disruption of recombinant E. coli was modeled using three machine learning
techniques namely Multiple linear regression (MLR), Multi-layer perceptron (MLP) and Sequential minimal
optimization (SMO). The four attributes were cellmass concentration (g/L), acoustic power (A), duty cycle (%)
and treatment time of sonication (min). For the three responses (nitrilase, total protein release and cell disruption)
MLP model was found to be at par with RSM model in terms of generalization as well as prediction
capability. Nitrilase release was significantly influenced by the cellmass concentration so was in case of total
protein release. Fraction of cells disrupted was heavily influenced by acoustic power and sonication time. Almost
32 U/mL nitrilase could be released for 300 g/L cellmass concentration when sonicated at 225W for 1 min with
20% duty cycle. Keywords: Machine learning model | Escherichia coli | Ultrasonication | Multi-layer perceptron | Nitrilase |
مقاله انگلیسی |
6 |
Prediction of bioconcentration factors in fish and invertebrates using machine learning
پیش بینی عوامل بیوکنترل در ماهی و بی مهرگان با استفاده از یادگیری ماشین-2019 The application of machine learning has recently gained interest from ecotoxicological fields for its ability to
model and predict chemical and/or biological processes, such as the prediction of bioconcentration. However,
comparison of different models and the prediction of bioconcentration in invertebrates has not been previously
evaluated. A comparison of 24 linear and machine learning models is presented herein for the prediction of
bioconcentration in fish and important factors that influenced accumulation identified. R2 and root mean square
error (RMSE) for the test data (n=110 cases) ranged from 0.23–0.73 and 0.34–1.20, respectively. Model performance
was critically assessed with neural networks and tree-based learners showing the best performance. An
optimised 4-layer multi-layer perceptron (14 descriptors) was selected for further testing. The model was applied
for cross-species prediction of bioconcentration in a freshwater invertebrate, Gammarus pulex. The model
for G. pulex showed good performancewith R2 of 0.99 and 0.93 for the verification and test data, respectively. Important
molecular descriptors determined to influence bioconcentration were molecular mass (MW), octanolwater
distribution coefficient (logD), topological polar surface area (TPSA) and number of nitrogen atoms (nN)
among others. Modelling of hazard criteria such as PBT, showed potential to replace the need for animal testing.
However, the use of machine learning models in the regulatory context has been minimal to date and is critically
discussed herein. The movement away from experimental estimations of accumulation to in silico modelling
would enable rapid prioritisation of contaminants that may pose a risk to environmental health and the food
chain. Keywords: Modelling | PBT | Pharmaceutical | Bioconcentration | BCF | Machine learning |
مقاله انگلیسی |
7 |
Acoustic emission pattern recognition in CFRP retrofitted RC beams for failure mode identification
شناسایی الگوی انتشار صوتی در پرتوهای RC با استفاده از CFRP جهت شناسایی حالت خرابی-2019 The application of fiber reinforced polymer (FRP) composites to repair reinforcement concrete (RC) structures
has emerged as a new and viable choice. However, the understanding of the durability and long-term performance
of this combined system still remains elusive. Adopting non-destructive techniques such as acoustic
emission (AE) will raise confidence in exploiting the full potential of this material. The objective of the current
study is to identify failure mechanisms in CFRP-retrofitted RC beams by applying advanced pattern recognition
techniques on the collected AE data. Six RC beams with artificially induced damage repaired with CFRP sheets
are tested with flexural loads and monitored with AE sensors. Since damage mechanisms in the retrofitted RC
beams are unknown a priori, a pattern recognition methodology is developed. After preprocessing the AE data
using the principal component analysis (PCA), the unsupervised k-means clustering method is applied to automatically
cluster and separate the AE patterns. The neural networks based on multi-layer perceptron (MLP) or
support vector machine (SVM) algorithm are then developed to better understand the trends in the AE data and
their association with the observed damage mechanism. Finally, the trained models are used to successfully
identify damage modes in other similar samples. Keywords: Failure mode identification | CFRP retrofitted RC beams | Acoustic emission | Pattern recognition | K-means clustering | Multilayer perceptron | Support vector machine |
مقاله انگلیسی |
8 |
Implementation of nature-inspired optimization algorithms in some data mining tasks
اجرای الگوریتم های بهینه سازی با الهام از طبیعت در برخی از کارهای داده کاوی-2019 Data mining optimization received much attention in the last decades due to introducing new optimization
techniques, which were applied successfully to solve such stochastic mining problems. This paper
addresses implementation of evolutionary optimization algorithms (EOAs) for mining two famous data
sets in machine learning by implementing four different optimization techniques. The selected data sets
used for evaluating the proposed optimization algorithms are Iris dataset and Breast Cancer dataset. In
the classification problem of this paper, the neural network (NN) is used with four optimization techniques,
which are whale optimization algorithm (WOA), dragonfly algorithm (DA), multiverse optimization
(MVA), and grey wolf optimization (GWO). Different control parameters were considered for
accurate judgments of the suggested optimization techniques. The comparitive study proves that, the
GWO, and MVO provide accurate results over both WO, and DA in terms of convergence, runtime, classification
rate, and MSE. Keywords: Data mining | Optimization | Evolutionary computation | Multi-layer perceptron | Metaheuristics |
مقاله انگلیسی |
9 |
Automatic classification of hydrocarbon ‘‘leads’’ in seismic images through artificial and convolutional neural networks
طبقه بندی خودکار هیدروکربن "leads" در تصاویر لرزه ای از طریق شبکه های عصبی مصنوعی و درهم تنیده-2019 This paper aims to provide alternative approaches for automatic classification of subsurface hydrocarbonbearing
regions from 2D seismic images driven by multi-layer perceptron neural networks (MLPs) (a kind
of artificial neural network) and convolutional neural networks (CNNs). The first approach is based on a
standard MLP whose features are controlled by Haralick’s textural descriptors; the second one is developed
with a multiple-layer CNN. Both techniques are studied to identify geologic ‘‘leads’’, instead of delineating
other structures of the porous medium, such as salt bodies or seismic faults. The outcomes obtained from
each approach are evaluated for a dataset of seismic images corresponding to the offshore SEAL Basin in
Brazil’s northeastern. Performance indicators (accuracy, recall, precision, F-measure and loss) are computed
to verify training and validation of the network learning capabilities. It is shown that for both MLP and CNN
configurations, good agreement is achieved in blind testing qualitatively and quantitatively. Keywords: Pattern recognition | Seismic imaging | Reservoir characterization | Machine learning |
مقاله انگلیسی |
10 |
Big Data Forecasting Using Evolving Multi-layer Perceptron
پیش بینی داده های بزرگ با استفاده از تکامل چند لایه ای Perceptron-2016 One of the mostly used commodities in investment
is gold. However, gold price tends to have fluctuation. This
paper proposed an Evolving Multi-Layer Perceptron (eMLP) to
forecast accurately the gold price by considering its daily
fluctuate price and utilizing information from a big data of actual
dataset. The proposed eMLP algorithm combines the concept of
evolving connectionist system and multi-layer perceptron in
neural network. This algorithm can expand its own structure
based on the incoming input. An experiment was conducted using
actual dataset from January 3rd, 2011 to April 26th, 2013 for
training purpose and dataset from April 29th, 2013 to April 25th,
2014 for testing. Experiment results showed that the proposed
eMLP gives excellent accuracy with the Mean Absolute
Percentage Error (MAPE) up to 0.769% for the selected
parameters: sensitivity threshold 0.9, error threshold 0.1,
learning rate1 0.9, and learning rate2 0.9.
Keywords: evolving multi-layer perceptron | evolving connectionist system | forecasting | gold price |
مقاله انگلیسی |