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Local Entropy Based Remora Optimization and sparse autoencoders for Cancer Diagnosis Through Microarray Gene Expression Analysis

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IEEE ACCESS 2024年 12卷 39285-39299页

作者： Bharanidharan, N. Chakravarthy, S. R. Sannasi Venkatesan, Vinoth Kumar Abbas, Mohamed Mahesh, T. R. Mohan, E. Venkatesan, Krishnamoorthy Vellore Inst Technol Sch Comp Sci Engn & Informat Syst SCORE Vellore 632014 India Bannari Amman Inst Technol Dept ECE Sathyamangalam 638401 India King Khalid Univ Coll Engn Dept Elect Engn Abha 61421 Saudi Arabia JAIN Deemed Univ Dept Comp Sci & Engn Bengaluru India SIMATS Saveetha Sch Engn Dept Elect & Commun Engn Chennai Tamil Nadu India Arba Minch Univ Dept Math Units Basic Sci Sawla Campus Arba Minch 4400 Ethiopia

Gene expression analysis can be used as a tool to detect cancer and the type of cancer in its early stages. However, the computational complexity for gene expression analysis is quite high due to the large amount of numerical data. Hence, dimensionality reduction techniques are employed to reduce the computational complexity, which in turns reduces the accuracy of cancer diagnosis. This research work focuses on the development of computerized tools with reduced computational complexity and increased accuracy to identify cancer cells in five different regions, namely breast, liver, lung, prostate, and renal. sparse autoencoder is implemented to reduce feature dimensions, and Remora Optimization is used to increase the performance of cancer diagnosis system through transformation of features. The local entropy-based fitness function is utilized to evaluate the appropriateness of population in Remora Optimization algorithm, and it is considered as the important reason for performance improvement in the proposed cancer diagnosis system. 1027 patients are taken into consideration in this analysis of the microarray datasets, which were gathered from the Curated Microarray Database. Six distinct metrics are used to examine the classification performance, and four different supervised classifiers are investigated. When the suggested sparse autoencoder and local entropy-based Remora Optimization are applied, the average balanced accuracy of the examined classifiers is 93.4%. The average dimensions are decreased from 36045 to 80 dimensions using the suggested method. In the absence of the suggested methodology, the vanilla classifiers yield an average balanced accuracy of 82.7%.

关键词： Cancer Optimization Feature extraction Gene expression Classification algorithms Mathematical models Machine learning algorithms Entropy Encoding Deep learning Medical diagnosis entropy sparse autoencoder remora optimization dimensionality reduction deep learning machine learning

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An Effective sparse autoencoders based Deep Learning Framework for fMRI Scans Classification 22

An Effective Sparse Autoencoders based Deep Learning Framewo...

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22nd International Conference on Enterprise Information Systems (ICEIS)

作者： Mahmoud, Abeer M. Karamti, Hanen Alrowais, Fadwa Ain Shams Univ Fac Comp & Informat Sci Cairo Egypt Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Comp Sci Dept POB 84428 Riyadh Saudi Arabia Univ Sfax MIRACL Lab ISIMS BP 242 Sfax 3021 Tunisia

ISBN: (纸本)9789897584237

Deep Learning (DL) identifies features of medical scans automatically in a way very near to expert doctors and sometimes over beats in treatment procedures. In fact, it increases model generalization as it doesn't focus on low level features and reduces difficulties (eg: overfitting) of training high dimensional data. Therefore, DL becomes a prioritized choice in building most recent Computer-Aided Diagnosis (CAD) systems. From other prospective, Autism Spectrum Disorder (ASD) is a brain disorder characterized by social miscommunication and confusing repetitive behaviours. The accurate diagnosis of ASD through analysing brain scans of patients is considered a research challenge. Some appreciated efforts has been reported in literature, however the problem still needs enhancement and examination of different models. A multi-phase learning algorithm combining supervised and unsupervised approaches is proposed in this paper to classify brain scans of individuals as ASD or controlled patients (TC). First, unsupervised learning is adopted using two sparse autoencoders for feature extraction and refinement of optimal network weights using back-propagation error minimization. Then, third autoencoder act as a supervised classifier. The Autism Brain fMRI (ABIDE-I) dataset is used for evaluation and cross-validation is performed. The proposed model recorded effective and promising results compared to literatures.

关键词： sparse autoencoder Autism Medical Image Classification

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Class-Specific Pre-trained sparse autoencoders for Learning Effective Features for Document Classification 8

Class-Specific Pre-trained Sparse Autoencoders for Learning ...

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8th Computer Science and Electronic Engineering (CEEC)

作者： Abdulhussain, Maysa I. Gan, John Q. Univ Essex Sch Comp Sci & Elect Engn Colchester C04 3SQ Essex England Univ Baghdad Dept Comp Sci Coll Sci Baghdad Iraq

ISBN: (纸本)9781509020508

sparse autoencoder is a commonly used deep learning approach for automatically learning features from unlabelled data (unsupervised feature learning). This paper proposes class-specific (supervised) pre-trained approach based on sparse autoencoder to gain low-dimensional interesting structure of features with high performance in document classification. Experimental results have demonstrated the advantages and usefulness of the proposed method in document classification in high-dimensional feature space, in terms of the limited number of features required to achieve good classification accuracy.

关键词： sparse autoencoder deep learning feature learning document categorization

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SPRBF-ABLS: a novel attention-based broad learning systems with sparse polynomial-based radial basis function neural networks

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JOURNAL OF INTELLIGENT MANUFACTURING 2023年第4期34卷 1779-1794页

作者： Wang, Jing Lyu, Shubin Chen, C. L. Philip Zhao, Huimin Lin, Zhengchun Quan, Pingsheng Guangdong Polytech Normal Univ Fac Comp Sci Guangzhou Peoples R China Univ Macau Fac Sci & Technol Macau 99999 Peoples R China South China Univ Technol Guangzhou Peoples R China

Broad learning system (BLS) is a fast and efficient learning model. However, BLS has limited representation capacity in the feature mapping layer. Additionally, BLS lacks local mapping capability. To address these problems, a cascaded neural network framework based on a sparse polynomial-based RBF neural network and an attention-based broad learning system (SPRBF-ABLS) is proposed. We first propose a sparse polynomial weight-based RBF neural network (SPRBF) for feature mapping. Then an attention mechanism for BLS is proposed to enhance the representation capacity of BLS. The proposed model is evaluated on regression, classification, and face recognition datasets. In regression and classification experiments, the nonlinear approximation capability of the proposed model outperforms other BLS models. In face recognition experiments, the proposed model can improve the representation capacity, especially the robustness against noisy images. The experiments demonstrate the effectiveness and robustness of the proposed model.

关键词： Broad learning system Polynomial-based RBF neural network sparse autoencoder Attention mechanism

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sparse auto encoder driven support vector regression based deep learning model for predicting network intrusions

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2021年第4期14卷 2419-2429页

作者： Preethi, D. Khare, Neelu Vellore Inst Technol Sch Informat Technol & Engn Vellore Tamil Nadu India

The Network Intrusion Detection System (NIDS) assumes a prominent aspect in ensuring network security. It serves better than traditional network security mechanisms, such as firewall systems. The result of the NIDS indicates the enhanced and efficient performance of the algorithms. It is utilized to predict intrusions, and it also has better training times for the algorithms. In this paper, a capable deep learning model using sparse Auto Encoder (SAE) is proposed. It is a self-taught learning framework. Such a model is a competent unsupervised learning algorithm in reconstructing new feature representation;thus, it diminishes the dimensionality. The SAE requires minimum training time substantially and efficiently enhances the prediction accuracy of Support Vector Regression (SVR) related to attacks. The experiments are administered using the standard intrusion detection dataset NSL-KDD, and therefore, the implementations are performed using python and tensor flow. The proposed model's effectiveness is estimated with other models viz., the PCA-SVR and SVR models applying prediction metrics such as R2 score, Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), accuracy and also training time. Results validate that the proposed SAE-SVR model has accelerated the training time of SVR and has the edge over the other models weighed in terms of prediction metrics. The model improves the rate of prediction by bringing down the error rates and yields a pioneering research mechanism for predicting the intrusions.

关键词： Network intrusion detection Network security Deep learning sparse autoencoder Support vector regression NSL-KDD Tensor flow

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Anomaly Feature Learning for Unsupervised Change Detection in Heterogeneous Images: A Deep sparse Residual Model

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2020年 13卷 588-600页

作者： Touati, Redha Mignotte, Max Dahmane, Mohamed Univ Montreal Fac Arts & Sci DIRO Vis Lab Montreal PQ H3C 3J7 Canada CRIM R&D Vis Dept Montreal PQ H3N 1M3 Canada

In this article, we propose a novel and simple automatic model based on multimodal anomaly feature learning in a residual space, aiming at solving the binary classification problem of temporal change detection (CD) between pairs of heterogeneous remote sensing images. The model starts by learning from image pairs the normal existing patterns in the before and after images to come up with a suitable representation of the normal (nonchange) class. To achieve this, we employ a stacked sparse autoencoder trained on a large number of temporal image features (training data) in an unsupervised manner. To classify pixels of new unseen image-pairs, the built anomaly detection model reconstructs the input from its representation in the latent space. First, the probe (new) image (i.e., the bitemporal heterogeneous image pair as the input request) is encoded in this compact normal space from a stacked hidden representation. The reconstruction error is computed using the L2 norm in what we call the residual normal space. In which, the nonchange patterns are characterized by small reconstruction errors as a normal class while the change patterns are quantified by high reconstruction errors categorizing the abnormal class. The dichotomic (changed/unchanged) classification map is generated in the residual space by clustering the reconstructed errors using a Gaussian mixture model. Experimental results on different real heterogeneous images, reflecting a mixture of imaging and land surface CD conditions, confirm the robustness of the proposed anomaly detection model.

关键词： Anomalous patterns change detection (CD) deep learning feature space reconstruction heterogeneous remote sensing multimodal anomaly detector reconstruction error sparse autoencoder

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Enhancing the reliability of protection scheme for PV integrated microgrid by discriminating between array faults and symmetrical line faults using sparse auto encoder

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IET RENEWABLE POWER GENERATION 2019年第2期13卷 308-317页

作者： Manohar, Murli Koley, Ebha Ghosh, Subhojit Natl Inst Technol Dept Elect Engn Raipur CG India

The ever increasing power demand and stress on reducing carbon footprint have paved the way for widespread use of photovoltaic (PV) integrated microgrid. However, the development of a reliable protection scheme for PV integrated microgrid is challenging because of the similar voltage-current profile of PV array faults and symmetrical line faults. Conventional protection schemes based on pre-defined threshold setting are not able to distinguish between PV array and symmetrical faults, and hence fail to provide separate controlling actions for the two cases. In this regard, a protection scheme based on sparse autoencoder (SAE) and deep neural network has been proposed to discriminate between array faults and symmetrical line faults in addition to perform mode detection, fault detection, classification and section identification. The voltage-current signals retrieved from relaying buses are converted into grey-scale images and further fed as input to the SAE to perform unsupervised feature learning. The performance of the proposed scheme has been evaluated through reliability analysis and compared with artificial neural network, support vector machine and decision tree based techniques under both islanding and grid-connected mode of the microgrid. The scheme has been also validated for field applications by performing real-time simulations on OPAL-RT digital simulator.

关键词： support vector machines learning (artificial intelligence) decision trees power generation protection power engineering computing neural nets photovoltaic power systems fault diagnosis power generation faults distributed power generation power grids unsupervised learning PV integrated microgrid symmetrical line faults sparse auto encoder photovoltaic integrated microgrid reliable protection scheme similar voltage-current profile PV array faults conventional protection schemes sparse autoencoder fault detection SAE deep neural network approach array faults fault classification fault section identification grey-scale image dataset unsupervised feature learning reliability analysis artificial neural network support vector machine decision tree-based techniques grid-connected mode islanding mode OPAL-RT digital simulator

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Locally weighted embedding topic modeling by markov random walk structure approximation and sparse regularization

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NEUROCOMPUTING 2018年 285卷 35-50页

作者： Wei, Chao Luo, Senlin Pan, Limin Wu, Zhouting Zhang, Ji Safi, Qamas Gul Khan Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Univ Engn & Technol Dept Comp Sci Taxila 47050 Punjab Pakistan

Topic model is a practical method for learning interpretable models of text corpora and have become a key problem of document representation. Some recently proposed topic models incorporate the intrinsic geometrical information of the document manifold and yield a discriminative topic representation. However, the existing manifold-inspired topic models fail to provide the probability weighting information of local geometrical pattern, thus leads to a limitation to estimate intrinsic semantic information of topic representation. In this paper, we consider the problem of topic modeling with intrinsic structure of document manifold and propose an unsupervised autoencoder-based topic modeling framework, named locally weighted embedding topic model (LWE-TM). Different from existing manifold-inspired topic models, LWE-TM defines a group of probability coefficients to uncover the local geometrical pattern by the Markov random walk structure of affinity graph, and regularizes the training of sparse autoencoder (sAE) to explicitly recover such local geometrical pattern with the topics encoding. Under the regularized training framework, the encoding network becomes local-invariant around the neighborhood of the document manifold and enable us to perform a readily topic inference for out-of-sample documents, efficiently improving the generalization and discrimination of topics encoding. The experimental results on two widely-used corpus demonstrate the superiority of LWE-TM to comparative models in document modeling, document clustering and classification tasks. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Topic model sparse autoencoder Markov random walk Affine mapping

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H-BLS: a hierarchical broad learning system with deep and sparse feature learning

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APPLIED INTELLIGENCE 2023年第1期53卷 153-168页

作者： Guo, Wei Chen, Shuangshuang Yuan, Xiaofeng Yancheng Teachers Univ Jiangsu Prov Key Construct Lab Big Data Psychol & Yancheng 224002 Peoples R China Yancheng Teachers Univ Coll Informat Engn Yancheng 224002 Peoples R China

Broad learning system (BLS) is an emerging machine learning algorithm with high efficiency and good approximation capability. It has been proved that BLS can learn hundreds of times faster than traditional deep learning algorithms while providing a comparable or even better generalization performance. Owing to its superb efficiency and powerful learning ability, the BLS is attracting increasing attention from machine learning community and can be considered as an alternative to deep learning in some situations. However, due to its shallow structure, the feature learning of BLS is not sufficient and which may probably limit its learning performance. For this issue, this paper proposes a novel hierarchical broad learning system (H-BLS) with deep and sparse feature learning. Different from the original BLS which conducts feature learning simply using a single-layer function mapping, the H-BLS adopts a hierarchical feature learning framework with multi-layer and multi-group structure to extract high-level and rich feature information from the original input, so as to improve the feature representation capability of the model. Meanwhile, in the hierarchical feature learning process of H-BLS, a new l(1)-constrained sparse autoencoder is employed and embedded in each layer of the framework for feature reconstruction, so as to eliminate redundancy of the input and generate more sparse and compact feature representations, thus further enhancing its learning performance. The learning ability of the proposed H-BLS is firstly evaluated by ten commonly used regression data sets, and the experimental results show that H-BLS performs better compared with several representative learning algorithms such as SVM, LSSVM, ELM, BLS and two recently proposed BLS variants. Moreover, the H-BLS also shows advantages over the state-of-the-art methods in terms of classification accuracy and training time on image classification problems.

关键词： Broad learning system Feature learning Hierarchical framework sparse autoencoder Deep learning

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An automated intrusion detection system in IoT system using Attention based Deep Bidirectional sparse Auto Encoder model

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KNOWLEDGE-BASED SYSTEMS 2024年 305卷

作者： Swathi, K. Bindu, G. Hima GITAM Deemed Univ Dept Comp Sci & Engn Hyderabad 502329 Telangana India GITAM Sch Technol Dept Comp Sci & Engn Hyderabad 502329 Telangana India

Nowadays, the Internet of Things (IoT) is a smart network connected to the Internet for transmitting gathered data with verified protocols. Attackers frequently use communication protocol defects as the basis for their attacks. Better protection measures are required since attacks affect the reputations of service providers. Both machine learning (ML) and deep learning (DL) methods have been developed in a number of research works to detect network intrusions. However, the system's security is limited by the rising number of new threats. Critical problems in IoT platforms, cyber-physical systems, wireless networks, and fog computing are caused by such attacks. The development of various cyber-security attacks reinforces the need for a strong intrusion detection system (IDS) in the IoT platform. The proposed study introduced a robust deep-feature learning mechanism for automatically detecting network intruders in the IoT platform. Initially, input data are gathered from the given dataset. Pre-processing helps reduce any noise in the data and improves the data quality using cleaning, outlier removal, and min-max normalization. The proposed Attention-based Deep Bidirectional sparse Auto Encoder (AD-BiSA) model is the most important feature retrieved using the attention-based deep Bi-LSTM model. The different IoT network threats are categorized using a sparse autoencoder approach. The chaotic Seagull Optimization (CSGO) algorithm decreases the loss and enhances the weight in the proposed DL technique. The UNSW NB15_IDS and NSL-KDD datasets achieve accuracy rates of 99.71% and 98.97%, respectively, for the proposed technique. The proposed method achieves better performance than existing approaches.

关键词： IoT attacks Automated intrusion detection system Attention-based Deep Bi-LSTM sparse autoencoder Chaotic Seagull Optimization

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