Advances in computational intelligence最新文献_第4页

Differentially private transferrable deep learning with membership-mappings 具有成员映射的差分私有可转移深度学习

Advances in computational intelligence Pub Date : 2022-12-15 DOI: 10.1007/s43674-022-00049-5

Mohit Kumar

引用次数: 9

A novel hybrid dimension reduction and deep learning-based classification for neuromuscular disorder 一种新的基于降维和深度学习的神经肌肉疾病分类方法

Advances in computational intelligence Pub Date : 2022-11-21 DOI: 10.1007/s43674-022-00047-7

Babita Pandey, Devendra Kumar Pandey, Aditya Khamparia, Seema Shukla

{"title":"A novel hybrid dimension reduction and deep learning-based classification for neuromuscular disorder","authors":"Babita Pandey, Devendra Kumar Pandey, Aditya Khamparia, Seema Shukla","doi":"10.1007/s43674-022-00047-7","DOIUrl":"10.1007/s43674-022-00047-7","url":null,"abstract":"<div><p>Correct classification of neuromuscular disorders is essential to provide accurate diagnosis. Presently, gene microarray technology is a widely accepted technology to monitor the expression level of a large number of genes simultaneously. The gene microarray data are a high dimensional data, which usually contains small samples having a large number of genes. Therefore, dimension reduction is a crucial task for correct classification of diseases. Dimension reduction eliminates those genes which are less expressive and enhances the efficiency of the classification model. In the present paper, we developed a novel hybrid dimension reduction method and a deep learning-based classification model for neuromuscular disorders. The hybrid dimension reduction method is deployed in three phase: in the first phase, the expressive genes are selected using <i>F</i> test method, and the mutual information method and the best one among them are selected for further processing. In second phase, the gene selected by the best model is further transformed to low dimension by PCA. In third phase, the deep learning-based classification model is deployed. For experimentation, two diseased and multi-diseased micro array data sets, which is publicly available, is used. The best accuracy by 50-100-50-25-13 deep learning architecture with hybrid dimension reduction, where 100 genes select by <i>F</i> test and PCA with 50 principal components is 89% for NMD data set. The best accuracy by 50-100-2 deep learning architecture with hybrid dimension reduction, where 100 genes select by <i>F</i> test and PCA with 50 principal components is 97% for FSHD data set. The proposed hybrid method gives better classification accuracy result and reduces the search space and time complexity as well for both two diseased and multi-diseased micro array data sets.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"2 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50503646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An effective Reinforcement Learning method for preventing the overfitting of Convolutional Neural Networks 一种防止卷积神经网络过拟合的有效强化学习方法

Advances in computational intelligence Pub Date : 2022-09-29 DOI: 10.1007/s43674-022-00046-8

Ali Mahdavi-Hormat, Mohammad Bagher Menhaj, Ashkan Shakarami

{"title":"An effective Reinforcement Learning method for preventing the overfitting of Convolutional Neural Networks","authors":"Ali Mahdavi-Hormat, Mohammad Bagher Menhaj, Ashkan Shakarami","doi":"10.1007/s43674-022-00046-8","DOIUrl":"10.1007/s43674-022-00046-8","url":null,"abstract":"<div><p>Convolutional Neural Networks are machine learning models that have proven abilities in many variants of tasks. This powerful machine learning model sometimes suffers from overfitting. This paper proposes a method based on Reinforcement Learning for addressing this problem. In this research, the parameters of a target layer in the Convolutional Neural Network take as a state for the Agent of the Reinforcement Learning section. Then the Agent gives some actions as forming parameters of a hyperbolic secant function. This function’s form is changed gradually and implicitly by the proposed method. The inputs of the function are the weights of the layer, and its outputs multiply by the same weights to updating them. In this study, the proposed method is inspired by the Deep Deterministic Policy Gradient model because the actions of the Agent are into a continuous domain. To show the proposed method’s effectiveness, the classification task is considered using Convolutional Neural Networks. In this study, 7 datasets have been used for evaluating the model; MNIST, Extended MNIST, small-notMNIST, Fashion-MNIST, sign language MNIST, CIFAR-10, and CIFAR-100.\u0000</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"2 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50524501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Towards edge devices implementation: deep learning model with visualization for COVID-19 prediction from chest X-ray 面向边缘设备的实施：基于胸部X光的新冠肺炎预测可视化深度学习模型

Advances in computational intelligence Pub Date : 2022-09-28 DOI: 10.1007/s43674-022-00044-w

Shaline Jia Thean Koh, Marwan Nafea, Hermawan Nugroho

{"title":"Towards edge devices implementation: deep learning model with visualization for COVID-19 prediction from chest X-ray","authors":"Shaline Jia Thean Koh, Marwan Nafea, Hermawan Nugroho","doi":"10.1007/s43674-022-00044-w","DOIUrl":"10.1007/s43674-022-00044-w","url":null,"abstract":"<div><p>Due to the outbreak of COVID-19 disease globally, countries around the world are facing shortages of resources (i.e. testing kits, medicine). A quick diagnosis of COVID-19 and isolating patients are crucial in curbing the pandemic, especially in rural areas. This is because the disease is highly contagious and can spread easily. To assist doctors, several studies have proposed an initial detection of COVID-19 cases using radiological images. In this paper, we propose an alternative method for analyzing chest X-ray images to provide an efficient and accurate diagnosis of COVID-19 which can run on edge devices. The approach acts as an enabler for the deep learning model to be deployed in practical application. Here, the convolutional neural network models which are fine-tuned to predict COVID-19 and pneumonia infection from chest X-ray images are developed by adopting transfer learning techniques. The developed model yielded an accuracy of 98.13%, sensitivity of 97.7%, and specificity of 99.1%. To highlight the important regions in the X-ray images which directs the model to its decision/prediction, we adopted the Gradient Class Activation Map (Grad-CAM). The generated heat maps from the Grad-CAM were then compared with the annotated X-ray images by board-certified radiologists. Results showed that the findings strongly correlate with clinical evidence. For practical deployment, we implemented the trained model in edge devices (NCS2) and this has achieved an improvement of 90% in inference speed compared to CPU. This shows that the developed model has the potential to be implemented on the edge, for example in primary care clinics and rural areas which are not well-equipped or do not have access to stable internet connections.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"2 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43674-022-00044-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40391902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Canola and soybean oil price forecasts via neural networks 基于神经网络的油菜和豆油价格预测

Advances in computational intelligence Pub Date : 2022-09-15 DOI: 10.1007/s43674-022-00045-9

Xiaojie Xu, Yun Zhang

{"title":"Canola and soybean oil price forecasts via neural networks","authors":"Xiaojie Xu, Yun Zhang","doi":"10.1007/s43674-022-00045-9","DOIUrl":"10.1007/s43674-022-00045-9","url":null,"abstract":"<div><p>Forecasts of commodity prices are vital issues to market participants and policy-makers. Those of cooking section oil are of no exception, considering its importance as one of main food resources. In the present study, we assess the forecast problem using weekly wholesale price indices of canola and soybean oil in China during January 1, 2010–January 3, 2020, by employing the non-linear auto-regressive neural network as the forecast tool. We evaluate forecast performance of different model settings over algorithms, delays, hidden neurons, and data splitting ratios in arriving at the final models for the two commodities, which are relatively simple and lead to accurate and stable results. Particularly, the model for the price index of canola oil generates relative root mean square errors of 2.66, 1.46, and 2.17% for training, validation, and testing, respectively, and the model for the price index of soybean oil generates relative root mean square errors of 2.33, 1.96, and 1.98% for training, validation, and testing, respectively. Through the analysis, we show usefulness of the neural network technique for commodity price forecasts. Our results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"2 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s43674-022-00045-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50485510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

Attributed community search based on seed replacement and joint random walk 基于种子置换和联合随机游走的属性社区搜索

Advances in computational intelligence Pub Date : 2022-09-01 DOI: 10.1007/s43674-022-00041-z

Ju Li, Huifang Ma

{"title":"Attributed community search based on seed replacement and joint random walk","authors":"Ju Li, Huifang Ma","doi":"10.1007/s43674-022-00041-z","DOIUrl":"10.1007/s43674-022-00041-z","url":null,"abstract":"<div><p>Community search enables personalized community discovery and has wide applications in real-life scenarios. Existing attributed community search algorithms use personalized information provided by attributes to locate desired community. Though achieved promising results, existing works suffer from two major limitations: (i) the precision of the algorithm decreases significantly when the seed comes from the boundary regions of the community. (ii) Most attributed community search methods mainly take the attribute information as edge weights to reveal semantic strength (e.g., attribute similarity, attribute distance, etc.), but largely ignore that attribute may serve as heterogeneous vertex. To make up for these deficiencies, in this paper, we propose a novel two-stage attributed community search method with seed replacement and joint random walk (SRRW). Specifically, in the seed replacement stage, we replace the initial query node with a core node; in the random walk stage, attributes are taken as heterogeneous nodes and the augmented graph is modeled based on the affiliation of the attributes via an overlapping clustering algorithm. And finally, a joint random walk is performed on the augmented graph to explore the desired local community. We conduct extensive experiments on both synthetic and real-world benchmarks, demonstrating its effectiveness for attributed community search.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"2 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50437271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection of cyber attacks on smart grids 智能电网网络攻击检测

Advances in computational intelligence Pub Date : 2022-08-31 DOI: 10.1007/s43674-022-00042-y

Aditi Kar Gangopadhyay, Tanay Sheth, Tanmoy Kanti Das, Sneha Chauhan

引用次数: 0

New models of classifier learning curves 分类器学习曲线的新模型

Advances in computational intelligence Pub Date : 2022-07-16 DOI: 10.1007/s43674-022-00040-0

Vincent Berthiaume

引用次数: 0

Resolvent and new activation functions for linear programming kernel sparse learning 线性规划核稀疏学习的分解函数和新的激活函数

Advances in computational intelligence Pub Date : 2022-06-29 DOI: 10.1007/s43674-022-00038-8

Zhao Lu, Haoda Fu, William R. Prucka

{"title":"Resolvent and new activation functions for linear programming kernel sparse learning","authors":"Zhao Lu, Haoda Fu, William R. Prucka","doi":"10.1007/s43674-022-00038-8","DOIUrl":"10.1007/s43674-022-00038-8","url":null,"abstract":"<div><p>The resolvent operator and the corresponding Green’s function occupy a central position in the realms of differential and integral equations, operator theory, and in particular the modern physics. However, in the field of machine learning, when confronted with the complex and highly challenging learning tasks from the real world, the prowess of Green’s function of resolvent is rarely explored and exploited. This paper aims at innovating the conventional translation-invariant kernels and rotation-invariant kernels, through theoretical investigation into a new view of constructing kernel functions by means of the resolvent operator and its Green’s function. From the practical perspective, the newly developed kernel functions are applied for robust signal recovery from noise corrupted data in the scenario of linear programming support vector learning. In particular, the monotonic and non-monotonic activation functions are used for kernel design to improve the representation capability. In this manner, a new dimension is given for kernel-based robust sparse learning from the following two aspects: firstly, a new theoretical framework by bridging the gap between the mathematical subtleties of resolvent operator and Green’s function theory and kernel construction; secondly, a concretization for the fusion between activation functions design in neural networks and nonlinear kernels design. Finally, the experimental study demonstrates the potential and superiority of the newly developed kernel functions in robust signal recovery and multiscale sparse modeling, as one step towards removing the apparent boundaries between the realms of modern signal processing and computational intelligence.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50523930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-agent-based dynamic railway scheduling and optimization: a coloured petri-net model 基于多智能体的铁路动态调度与优化：一个有色petri网模型

Advances in computational intelligence Pub Date : 2022-06-16 DOI: 10.1007/s43674-022-00039-7

Poulami Dalapati, Kaushik Paul

引用次数: 1