Microarray gene expression profile shall be exploited for the efficient and effective classification of cancers. This is a computationally challenging task because of large quantity of genes and relatively small amount of experiments in gene expression data. The repercussion of this work is to devise a framework of techniques based on supervised machine learning for discrimination of acute lymphoblastic leukemia and acute myeloid leukemia using microarray gene expression profiles. Artificial neural network (ANN) technique was employed for this classification. Moreover, ANN was compared with other five machine learning techniques. These methods were assessed on eight different classification performance measures. This article reports a significant classification accuracy of 98% using ANN with no error in identification of acute lymphoblastic leukemia and only one error in identification of acute myeloid leukemia on tenfold cross-validation and leave-one-out approach. Furthermore, models were validated on independent test data, and all samples were correctly classified.
相似文献The detection of manmade disasters particularly fire is valuable because it causes many damages in terms of human lives. Research on fire detection using wireless sensor network and video-based methods is a very hot research topic. However, the WSN based detection model need fire happens and a lot of smoke and fire for detection. Similarly, video-based models also have some drawbacks because conventional algorithms need feature vectors and high rule-based models for detection. In this paper, we proposed a fire detection method which is based on powerful machine learning and deep learning algorithms. We used both sensors data as well as images data for fire prevention. Our proposed model has three main deep neural networks i.e. a hybrid model which consists of Adaboost and many MLP neural networks, Adaboost-LBP model and finally convolutional neural network. We used Adaboost-MLP model to predict the fire. After the prediction, we proposed two neural networks i.e. Adaboost-LBP model and convolutional neural network for detection of fire using the videos and images taken from the cameras installed for the surveillance. Adaboost-LBP model is to generate the ROIs from the image where emergencies exist Our proposed model results are quite good, and the accuracy is almost 99%. The false alarming rate is very low and can be reduced more using further training.
相似文献In the present article, delay and system of delay differential equations are treated using feed-forward artificial neural networks. We have solved multiple problems using neural network architectures with different depths. The neural networks are trained using the extreme learning machine algorithm for the satisfaction of delay differential equations and associated initial/boundary conditions. Further, numerical rates of convergence of the proposed algorithm are reported based on variation of error in the obtained solution for different number of training points. Emphasis is on analysing whether deeper network architectures trained with extreme learning machine algorithm can perform better than shallow network architectures for approximating the solutions of delay differential equations.
相似文献Rapid and exponential development of textual data in recent years has yielded to the need for automatic text summarization models which aim to automatically condense a piece of text into a shorter version. Although various unsupervised and machine learning-based approaches have been introduced for text summarization during the last decades, the emergence of deep learning has made remarkable progress in this field. However, deep learning-based text summarization models are still in their early steps of development and their potential has yet to be fully explored. Accordingly, a novel abstractive summarization model is proposed in this paper which utilized the combination of convolutional neural network and long short-term memory integrated with auxiliary attention in its encoder to increase the saliency and coherency of generated summaries. The proposed model was validated on CNN\Daily Mail and DUC-2004 datasets and empirical results indicated that not only the proposed model outperformed existing models in terms of ROUGE metric but also its generated summaries had higher saliency and readability compared to the baseline model according to human evaluation.
相似文献Tailoring the muckpile shape and its fragmentation to the requirements of the excavating equipment in surface mines can significantly improve the efficiency and savings through increased production, machine life and reduced maintenance. Considering the various blast parameters together to predict the throw is subtle and can lead to wrong conclusions. In this paper, a different approach was followed to combine the representational power of multilayer neural networks and various machine learning techniques to predict the throw of a bench blast using the data from a limestone mine located in central India. Then, using various analysis techniques, the training parameters have been adjusted to reduce the cross-validation error and increase the accuracy. Here, four different architectures of neural networks have been trained by different techniques, and the best model has been selected. The different machine learning techniques have been implemented on the basis of accuracy of the output. The sensitivity analysis has been done to get the relative importance of the variables in prediction of the output.
相似文献The subject of content-based cybercrime has put on substantial coverage in recent past. It is the need of the time for web-based social media providers to have the capability to distinguish oppressive substance both precisely and proficiently to secure their clients. Support vector machine (SVM) is usually acknowledged as an efficient supervised learning model for various classification problems. Nevertheless, the success of an SVM model relies upon the ideal selection of its parameters as well as the structure of the data. Thus, this research work aims to concurrently optimize the parameters and feature selection with a target to build the quality of SVM. This paper proposes a novel hybrid model that is the integration of cuckoo search and SVM, for feature selection and parameter optimization for efficiently solving the problem of content-based cybercrime detection. The proposed model is tested on four different datasets obtained from Twitter, ASKfm and FormSpring to identify bully terms using Scikit-Learn library and LIBSVM of Python. The results of the proposed model demonstrate significant improvement in the performance of classification on all the datasets in comparison to recent existing models. The success rate of the SVM classifier with the excellent recall is 0.971 via tenfold cross-validation, which demonstrates the high efficiency and effectiveness of the proposed model.
相似文献Current work introduces a fast converging neural network-based approach for solution of ordinary and partial differential equations. Proposed technique eliminates the need of time-consuming optimization procedure for training of neural network. Rather, it uses the extreme learning machine algorithm for calculating the neural network parameters so as to make it satisfy the differential equation and associated boundary conditions. Various ordinary and partial differential equations are treated using this technique, and accuracy and convergence aspects of the procedure are discussed.
相似文献Mobile phones are rapidly becoming the most widespread and popular form of communication; thus, they are also the most important attack target of malware. The amount of malware in mobile phones is increasing exponentially and poses a serious security threat. Google’s Android is the most popular smart phone platforms in the world and the mechanisms of permission declaration access control cannot identify the malware. In this paper, we proposed an ensemble machine learning system for the detection of malware on Android devices. More specifically, four groups of features including permissions, monitoring system events, sensitive API and permission rate are extracted to characterize each Android application (app). Then an ensemble random forest classifier is learned to detect whether an app is potentially malicious or not. The performance of our proposed method is evaluated on the actual data set using tenfold cross-validation. The experimental results demonstrate that the proposed method can achieve a highly accuracy of 89.91%. For further assessing the performance of our method, we compared it with the state-of-the-art support vector machine classifier. Comparison results demonstrate that the proposed method is extremely promising and could provide a cost-effective alternative for Android malware detection.
相似文献Classification is a hot topic in hyperspectral remote sensing community. In the last decades, numerous effort has been concentrate on the classification problem. However, most of the methods accuracy is not high enough due to the fact that they do not extract features in a deep manner. In this paper, a new hyperspectral data classification skeleton based on exponential flexible momentum deep convolution neural network (EFM-CNN) is proposed. First, the fitness of convolution neural network is substantiated by following classical spectral information-based classification. Then, a novel deep architecture is proposed, which is a hybrid of principle component analysis (PCA), improved convolution neural network based on exponential flexible momentum and support vector machine (SVM). Experimental results indicate that the classifier can effectively improve the accuracy with the state-of-the-art algorithms. And compared with homologous parameters momentum updating methods such as adaptive momentum method, standard momentum gradient method and elastic momentum method, on LeNet5 net and multiple neural network, the accuracy obtained of proposed algorithm increases by 2.6% and 6.5% on average respectively.
相似文献Network attack may have a serious impact on network security. With the rapid development of quantum machine learning, variational quantum neural network (VQNN) has demonstrated quantum advantages in classification problems. The intrusion detection system (IDS) based on quantum machine learning has higher accuracy and efficiency than the IDS based on traditional machine learning. In this work, we propose a intrusion detection scheme based on VQNN, which is composed of variational quantum circuit (VQC) and classical machine learning (ML) strategy. In order to verify the effectiveness of the scheme, we used the VQNN model and some classic ML models (Such as artificial neural network, support vector machines, K-Nearest Neighbors, Naive Bayes, decision tree) to conduct comparative experiments. The results indicate that the proposed IDS model based on VQNN has a 97.21% precision, which is higher than other classic IDS models. Furthermore, our VQC can be deployed on the overwhelming majority of recent noisy intermediate-scale quantum machines (such as IBM). This research will contribute to the construction of general variational quantum framework and experimental design and highlight the potential hopes and challenges of hybrid quantum classical learning schemes.
相似文献A method for checking the accuracy of prognostic models in the absence of experimental data for comparing the modeling results is presented. The developed neural network determines a technology class, which is compared with the results obtained using the fuzzy logic model. The model accuracy is determined by computing the root-mean-square error of the modeling and the correlation between the results obtained using the fuzzy logic model and the neural network.
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