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1 |
Performance analysis of machine learning algorithm of detection and classification of brain tumor using computer vision
تحلیل عملکرد الگوریتم یادگیری ماشین تشخیص و طبقه بندی تومور مغزی با استفاده از بینایی کامپیوتر-2022 Brain tumor is one of the undesirables, uncontrolled growth of cells in all age groups. Classification of tumors
depends no its origin and degree of its aggressiveness, it also helps the physician for proper diagnosis and
treatment plan. This research demonstrates the analysis of various state-of-art techniques in Machine Learning
such as Logistic, Multilayer Perceptron, Decision Tree, Naive Bayes classifier and Support Vector Machine for
classification of tumors as Benign and Malignant and the Discreet wavelet transform for feature extraction on the
synthetic data that is available data on the internet source OASIS and ADNI. The research also reveals that the
Logistic Regression and the Multilayer Perceptron gives the highest accuracy of 90%. It mimics the human
reasoning that learns, memorizes and is capable of reasoning and performing parallel computations. In future
many more AI techniques can be trained to classify the multimodal MRI Brain scan to more than two classes of
tumors. keywords: هوش مصنوعی | ام آر آی | رگرسیون لجستیک | پرسپترون چند لایه | Artificial Intelligence | MRI | Logistic regression | OASIS | Multilayer Perceptron |
مقاله انگلیسی |
2 |
Data Driven Robust Optimization for Handling Uncertainty in Supply Chain Planning Models
بهینه سازی قوی مبتنی بر داده ها برای مدیریت عدم قطعیت در مدل های برنامه ریزی زنجیره تامین-2021 While addressing supply chain planning under uncertainty, Robust Optimization (RO) is regarded as an efficient and tractable method. As RO involves calculation of several statistical moments or maximum / minimum values involving the objective functions under realizations of these uncertain parameters, the accuracy of this method significantly depends on the efficient techniques to sample the uncertainty parameter space with limited amount of data. Conventional sampling techniques, e.g. box/budget/ellipsoidal, work by sampling the uncertain parameter space inefficiently, often leading to inaccuracies in such estimations. This paper proposes a methodology to amalgamate machine learning and data analytics with RO, thereby making it data-driven. A novel neuro fuzzy clustering mechanism is implemented to cluster the uncertain space such that the exact regions of uncertainty are optimally identified. Subsequently, local density based boundary point detection and Delaunay triangulation based boundary construction enable intelligent Sobol based sampling to sample the uncertain parameter space more accurately. The proposed technique is utilized to explore the merits of RO towards addressing the uncertainty issues of product demand, machine uptime and production cost associated with a multiproduct, and multisite supply chain planning model. The uncertainty in supply chain model is thoroughly analysed by carefully constructing examples and its case studies leading to large scale mixed integer linear and nonlinear programming problems which were efficiently solved in GAMS framework. Demonstration of efficacy of the proposed method over the box, budget and ellipsoidal sampling method through comprehensive analysis adds to other highlights of the current work. Keywords: Uncertainty Modelling | Supply chain Management | Data driven Robust Optimization | Neuro Fuzzy Clustering | Multi-Layered Perceptron |
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 |
Hybrid neural networks for big data classification
شبکه های عصبی ترکیبی برای طبقه بندی داده های بزرگ-2020 Two new hybrid neural architectures combining morphological neurons and perceptrons are introduced in this paper. The first architecture, called Morphological - Linear Neural Network (MLNN) consists of a hidden layer of morphological neurons and an output layer of classical perceptrons has the capability of extracting features. The second architecture, called Linear-Morphological Neural Network (LMNN) is com- posed of one or several perceptron layers as a feature extractor, it is then followed by an output layer of morphological neurons for non-linear classification. Both architectures are trained by stochastic gradient descent. One of the main contributions of this paper is to show that the morphological layer offers a greater capacity to extract features than the perceptron layer. This claim is supported both theoretically and experimentally. We prove that the morphological layer possesses a greater capacity per computation unit to segment the 2D input space than the perceptron layer. In other words, adding more hyper-boxes produces more response regions than adding hyperplanes. From an empirical point of view, we test the two new models on 25 standard datasets at low dimensionality and one big data dataset. The result is that MLNN requires a lesser number of learning parameters than the other tested architectures while achieving better accuracies. Keywords: Morphological neurons | Dendrite processing | Neural networks | Multilayer perceptron | Big data |
مقاله انگلیسی |
5 |
TDP: Two-dimensional perceptron for image recognition
TDP: پرسپترون دو بعدی برای تشخیص تصویر-2020 Convolutional neural network (CNN) is widely applied to different areas due to good recognition
performance. However, convolution operation is a complex computation and consumes the bulk of
processing time for CNN. It is still a hot problem how to develop a novel model with good recognition
performance for deep learning. Here, we propose a novel model, namely, two-dimensional perceptron
(TDP), to get direct input of two-dimensional data for further processing. A TDP has a new network
architecture and an innovative computation process of hidden neurons. In cases with the same
number of hidden neurons, compared with multilayer perceptron (MLP), TDP achieves good recognition
performance with 1×-36× speedup and a decrease of parameters by exceeding 97% on MNIST and
COIL-20 datasets. Meanwhile, TDP obtains 1%–32% improvement of recognition accuracy in comparison
to CNN on CIFAR-10 and SVHN datasets. Furthermore, on INFUSE dataset, TDP has an increase of
F1 score by up to almost 11% in comparison with MLP and CNN. The results indicate that TDP is a
promising and novel model with excellent recognition performance. Keywords: Convolutional neural network | Multilayer perceptron | Two-dimensional perceptron | Recognition performance | F1 score |
مقاله انگلیسی |
6 |
Deep belief network and linear perceptron based cognitive computing for collaborative robots
شبکه باور عمیق و محاسبات شناختی مبتنی بر پرسپترون خطی برای روبات های مشترک-2020 Objective: This paper is to analyze the performance of the control system of collaborative robots
based on cognitive computing technology. Methods: This study combines cognitive computing and
deep belief network algorithms with collaborative robots to construct a cognitive computing system
model based on deep belief networks, which is applied to the control system of collaborative robots.
Further, the simulation is used to compare and analyze the algorithm performance of deep belief
network (DBN), multilayer perceptron (MLP) and the cognitive computing system model of deep
belief network and linear perceptron (DBNLP) proposed in this study. Results: The results show that
compared with the DBN and MLP algorithms, the DBNLP algorithm model has a significantly lower
error rate in the number of repetitions of the training set, the number of hidden neurons, and the
number of network layers. And the number of task backlog, the number of resources to be allocated
and the time consumption are less, as well as the accuracy is high. After comparing and analyzing
the changes in the estimated value of Ex (expected value), En (entropy value) and He (hyper entropy
value), it is found that the estimated value of the DBNLP algorithm model is closer to the true value
than that of the DBN and MLP algorithms. Conclusion: The application of the DBNLP algorithm model
to collaborative robots can significantly improve its accuracy and safety, providing an experimental
basis for the performance improvement of later collaborative robots. Keywords: Collaborative robot | Cognitive computing | Deep belief network | Simulation | Multilayer perceptron |
مقاله انگلیسی |
7 |
Multilayer perception based reinforcement learning supervisory control of energy systems with application to a nuclear steam supply system
کنترل نظارت یادگیری تقویتی مبتنی بر پرسپترون چند لایه بر روی سیستم های انرژی با استفاده از سیستم تأمین بخار هسته ای-2020 Energy system optimization is important in strengthening stability, reliability and economy, which is usually
given by static linear or nonlinear programming. However, the challenge faced in real-life currently is how to
give the optimization by taking naturally existed energy system dynamics into account. To face this challenge, a
multi-layer perception (MLP) based reinforcement learning control (RLC) method is proposed for the nonlinear
dissipative system coupled by an arbitrary energy system and its local controllers, which can be able to optimize
a given performance index dynamically and effectively without the accurate knowledge of system dynamics.
This MLP-based RLC is composed of a MLP-based state-observer and an approximated optimal controller. The
MLP-based state-observer is given for identification, which converges to a bounded neighborhood of the system
dynamics asymptotically. The approximated optimal controller is determined by solving an algebraic Riccati
equation with parameters given by the MLP-based state-observer. Based on Lyapunov direct method, it is further
proven that the closed-loop is uniformly ultimately bounded stable. Finally, this newly-built MLP-based RLC is
applied to the supervisory optimization of thermal power response for a nuclear steam supply system, and
simulation results show not only the satisfactory performance but also the influences from the controller
parameters to closed-loop responses. Keywords: Energy system optimization | Reinforcement learning control | Neural network |
مقاله انگلیسی |
8 |
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 |
9 |
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 |
10 |
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 |