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نتیجه جستجو - پرسپترون چند لایه

تعداد مقالات یافته شده: 15
ردیف عنوان نوع
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
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