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Optimized stacked autoencoder for IoT Enabled Financial Crisis Prediction Model

Computers, Materials & Continua 2022年第4期71卷 1079-1094页

作者： Mesfer Al Duhayyim Hadeel Alsolai Fahd N.Al-Wesabi Nadhem Nemri Hany Mahgoub Anwer Mustafa Hilal Manar Ahmed Hamza Mohammed Rizwanullah Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recently,Financial Technology(FinTech)has received more attention among financial sectors and researchers to derive effective solutions for any financial institution or *** crisis prediction(FCP)is an essential topic in business sector that finds it useful to identify the financial condition of a financial *** the same time,the development of the internet of things(IoT)has altered the mode of human interaction with the physical *** IoT can be combined with the FCP model to examine the financial data from the users and perform decision making *** paper presents a novel multi-objective squirrel search optimization algorithm with stacked autoencoder(MOSSA-SAE)model for FCP in IoT *** MOSSA-SAE model encompasses different subprocesses namely preprocessing,class imbalance handling,parameter tuning,and ***,the MOSSA-SAE model allows the IoT devices such as smartphones,laptops,etc.,to collect the financial details of the users which are then transmitted to the cloud for further *** addition,SMOTE technique is employed to handle class imbalance *** goal of MOSSA in SMOTE is to determine the oversampling rate and area of nearest neighbors of ***,SAE model is utilized as a classification technique to determine the class label of the financial *** the same time,the MOSSA is applied to appropriately select the‘weights’and‘bias’values of the *** extensive experimental validation process is performed on the benchmark financial dataset and the results are examined under distinct *** experimental values ensured the superior performance of the MOSSA-SAE model on the applied dataset.

关键词： Financial data financial crisis prediction class imbalance problem internet of things stacked autoencoder

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Hybrid Image Compression-Encryption Scheme Based on Multilayer stacked autoencoder and Logistic Map

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China Communications 2022年第1期19卷 238-252页

作者： Neetu Gupta Ritu Vijay Computer Science&Engineering Department Banasthali VidyapithBanasthaliIndia Department of Electronics Engineering Banasthali VidyapithBanasthaliIndia.

Secure transmission of images over a communication channel, with limited data transfer capacity, possesses compression and encryption schemes. A deep learning based hybrid image compression-encryption scheme is proposed by combining stacked auto-encoder with the logistic map. The proposed structure of stacked autoencoder has seven multiple layers, and back propagation algorithm is intended to extend vector portrayal of information into lower vector space. The randomly generated key is used to set initial conditions and control parameters of logistic map. Subsequently, compressed image is encrypted by substituting and scrambling of pixel sequences using key stream sequences generated from logistic *** proposed algorithms are experimentally tested over five standard grayscale images. Compression and encryption efficiency of proposed algorithms are evaluated and analyzed based on peak signal to noise ratio(PSNR), mean square error(MSE), structural similarity index metrics(SSIM) and statistical,differential, entropy analysis respectively. Simulation results show that proposed algorithms provide high quality reconstructed images with excellent levels of security during transmission..

关键词： compression-encryption stacked autoencoder chaotic system back propagation algorithm logistic map

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Iterative learning-based many-objective history matching using deep neural network with stacked autoencoder

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Petroleum Science 2021年第5期18卷 1465-1482页

作者： Jaejun Kim Changhyup Park Seongin Ahn Byeongcheol Kang Hyungsik Jung Ilsik Jang Department of Energy Systems Engineering Seoul National UniversitySeoul08826Republic of Korea Department of Energy and Resources Engineering Kangwon National UniversityChuncheonKangwon24341Republic of Korea Geo-ICT Convergence Research Team Korea Institute of Geoscience and Mineral ResourcesDaejeon34132Republic of Korea Department of Energy and Resources Engineering Chosun UniversityGwangju61452Republic of Korea

This paper presents an innovative data-integration that uses an iterative-learning method,a deep neural network(DNN)coupled with a stacked autoencoder(SAE)to solve issues encountered with many-objective history *** proposed method consists of a DNN-based inverse model with SAE-encoded static data and iterative updates of supervised-learning data are based on distance-based clustering *** functions as an inverse model and results in encoded flattened data,while SAE,as a pre-trained neural network,successfully reduces dimensionality and reliably reconstructs *** iterative-learning method can improve the training data for DNN by showing the error reduction achieved with each iteration *** proposed workflow shows the small mean absolute percentage error below 4%for all objective functions,while a typical multi-objective evolutionary algorithm fails to significantly reduce the initial population *** learning-based manyobjective history matching estimates the trends in water cuts that are not reliably included in dynamicdata *** confirms the proposed workflow constructs more plausible *** workflow would be a reliable alternative to overcome the less-convergent Pareto-based multi-objective evolutionary algorithm in the presence of geological uncertainty and varying objective functions.

关键词： Deep neural network stacked autoencoder History matching Iterative learning Clustering Many-objective

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Batch Process Monitoring Based on Multi-stage Fourth Order Moment stacked autoencoder

Batch Process Monitoring Based on Multi-stage Fourth Order M...

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第32届中国控制与决策会议

作者： Jin Chen Wang Pu Wang Kai Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

Fault monitoring can find out-of-control conditions of equipment operation in a timely manner, which is essential for eliminating faults and for stable operation of industrial systems in batch processes. Many conventional data-driven fault detection methods focus less on the non-Gaussian and Multi-stage characteristics of batch process data, which may result in degradation of monitoring performance. In this paper, a Multi-stage Fourth Order Moment Staked autoencoder（M-FOM-SAE） is designed to solve the above problems. The proposed method firstly automatically determines the number of clusters and divides the batch process into multiple stages. After that, the FOM-SAE model is established in each sub-stage, which can not only effectively learn the nonlinear features of process data, but also extract the non-Gaussian information. The proposed strategy is applied to real-world industrial processes. Experimental results indicate that it can better capture the non-Gaussian and Multi-stage characteristics of process data, and improve the ability to monitor abnormalities.

关键词： Batch process Fault monitoring non-Gaussian Multi-stage stacked autoencoder

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Automatic Detection of COVID-19 Using a stacked Denoising Convolutional autoencoder

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Computers, Materials & Continua 2021年第12期69卷 3259-3274页

作者： Habib Dhahri Besma Rabhi Slaheddine Chelbi Omar Almutiry Awais Mahmood Adel M.Alimi College of Applied Computer Sciences(CACS) King Saud UniversityRiyadh11543Saudi Arabia Faculty of Sciences and Technology of Sidi Bouzid University of KairouanKairouanTunisia National School of Engineers(ENIS) University of SfaxTunisia Department of Computer Sciences College of Sciences and Arts in UnaizahQassim UniversityAl-QassimKingdom of Saudi Arabia

The exponential increase in new coronavirus disease 2019(COVID-19)cases and deaths has made COVID-19 the leading cause of death in many ***,in this study,we propose an efficient technique for the automatic detection of COVID-19 and pneumonia based on X-ray images.A stacked denoising convolutional autoencoder(SDCA)model was proposed to classify X-ray images into three classes:normal,pneumonia,and *** SDCA model was used to obtain a good representation of the input data and extract the relevant features from noisy *** proposed model’s architecture mainly composed of eight autoencoders,which were fed to two dense layers and SoftMax *** proposed model was evaluated with 6356 images from the datasets from different *** experiments and evaluation of the proposed model were applied to an 80/20 training/validation split and for five cross-validation data splitting,*** metrics used for the SDCA model were the classification accuracy,precision,sensitivity,and specificity for both *** results demonstrated the superiority of the proposed model in classifying X-ray images with high accuracy of 96.8%.Therefore,this model can help physicians accelerate COVID-19 diagnosis.

关键词： stacked autoencoder augmentation multiclassification COVID-19 convolutional neural network

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Energy Aware Secure Cyber-Physical Systems with Clustered Wireless Sensor Networks

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Computers, Materials & Continua 2022年第9期72卷 5499-5513页

作者： Masoud Alajmi Mohamed K.Nour Siwar Ben Haj Hassine Mimouna Abdullah Alkhonaini Manar Ahmed Hamza Ishfaq Yaseen Abu Sarwar Zamani Mohammed Rizwanullah Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityAbha62529Saudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recently,cyber physical system(CPS)has gained significant attention which mainly depends upon an effective collaboration with computation and physical *** greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems(CPES).At the same time,the rising ubiquity of wireless sensor networks(WSN)in several application areas makes it a vital part of the design of *** security and energy efficiency are the major challenging issues in CPES,this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms(EASCPSMA).The presented EASCPS-MA technique intends to attain lower energy utilization via clustering and security using intrusion *** EASCPSMA technique encompasses two main stages namely improved fruit fly optimization algorithm(IFFOA)based clustering and optimal deep stacked autoencoder(OSAE)based intrusion ***,the optimal selection of stacked autoencoder(SAE)parameters takes place using root mean square propagation(RMSProp)*** extensive performance validation of the EASCPS-MA technique takes place and the results are inspected under varying *** simulation results reported the improved effectiveness of the EASCPS-MA technique over other recent approaches interms of several measures.

关键词： Intrusion detection system metaheuristics stacked autoencoder deep learning cyber physical energy systems clustering wsn

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UCAV situation assessment method based on C-LSHADE-Means and SAE-LVQ

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系统工程与电子技术:英文版 2023年第5期34卷 1235-1251页

作者： XIE Lei TANG Shangqin WEI Zhenglei XUAN Yongbo WANG Xiaofei Institute of Aeronautics Engineering Air Force Engineering UniversityXi’an 710038China China Aerodynamics Research&Development Center Mianyang 621000China Blue Sky Innovation Center for Frontier Science Beijing 100000China

The unmanned combat aerial vehicle(UCAV)is a research hot issue in the world,and the situation assessment is an important part of *** overcome shortcomings of the existing situation assessment methods,such as low accuracy and strong dependence on prior knowledge,a datadriven situation assessment method is *** clustering and classification are combined,the former is used to mine situational knowledge,and the latter is used to realize rapid *** evaluation factor and distance evaluation factor are proposed to transform multi-dimensional air combat information into two-dimensional features.A convolution success-history based adaptive differential evolution with linear population size reduc-tion-means(C-LSHADE-Means)algorithm is *** convolutional pooling layer is used to compress the size of data and preserve the distribution *** LSHADE algorithm is used to initialize the center of the mean clustering,which over-comes the defect of initialization *** experi-ment with the seven clustering algorithms is done on the UCI data set,through four clustering indexes,and it proves that the method proposed in this paper has better clustering performance.A situation assessment model based on stacked autoen-coder and learning vector quantization(SAE-LVQ)network is constructed,and it uses SAE to reconstruct air combat data fea-tures,and uses the self-competition layer of the LVQ to achieve efficient *** with the five kinds of assess-ments models,the SAE-LVQ model has the highest ***,three kinds of confrontation processes from air combat maneuvering instrumentation(ACMI)are selected,and the model in this paper is used for situation *** assessment results are in line with the actual situation.

关键词： unmanned combat aerial vehicle(UCAV) situation assessment clustering K-means stacked autoencoder learn-ing vector quantization

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Research on Deep Knowledge Tracking Incorporating Rich Features and Forgetting Behaviors

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Journal of Harbin Institute of Technology(New Series) 2022年第4期29卷 1-6页

作者： Lasheng Yu Xiaopeng Zheng School of Computer Science and Engineering Central South UniversityChangsha 410083China

The individualization of education and teaching through the computer⁃aided education system provides students with personalized learning,so that each student can obtain the knowledge they *** this stage,there are a lot of intelligent tutoring *** these systems,students􀆳learning actions are tracked in real⁃time,and there are a lot of available *** these data,personalized education that suits each student can be *** improve the quality of education,some models for predicting students􀆳next practice have been produced,such as Bayesian Knowledge Tracing(BKT),Performance Factor Analysis(PFA),and Deep Knowledge Tracing(DKT)with the development of deep ***,the model only considers the knowledge component and correctness of the problem,ignoring the breadth of other characteristics of the information collected by the intelligent tutoring system,the lag time of the previous interaction,the number of past attempts to a problem,and situations that students have forgotten the *** some studies consider forgetting and rich information when modeling student knowledge,they often ignore student learning *** main contribution of this paper is in two *** is to transform the input into a position feature vector by introducing an auto⁃encoding network layer and to carry out multiple sets of bad political *** other is to consider repeated time intervals,sequence time intervals,and the number of attempts to simulate forgetting *** paper proposes an adaptive algorithm for the original DKT *** using the stacked auto⁃encoder network,the input dimension is reduced to half of the original and the original features are retained and consider the forgetting memory behavior according to the time sequence of students􀆳*** model proposed in this paper has been experimented on two public data sets to improve the original accuracy.

关键词： LSTM knowledge of tracking DKT stacked autoencoder forgetting behavior feature information

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Deep learning technique for process fault detection and diagnosis in the presence of incomplete data

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Chinese Journal of Chemical Engineering 2020年第9期28卷 2358-2367页

作者： Cen Guo Wenkai Hu Fan Yang Dexian Huang Department of Automation Tsinghua UniversityBeijing 10084China Cornell University NY 14850United States of America University of Alberta EdmontonAB T6G 1H9Canada

In modern industrial processes, timely detection and diagnosis of process abnormalities are critical for monitoring process operations. Various fault detection and diagnosis(FDD) methods have been proposed and implemented, the performance of which, however, could be drastically influenced by the common presence of incomplete or missing data in real industrial scenarios. This paper presents a new FDD approach based on an incomplete data imputation technique for process fault recognition. It employs the modified stacked autoencoder,a deep learning structure, in the phase of incomplete data treatment, and classifies data representations rather than the imputed complete data in the phase of fault identification. A benchmark process, the Tennessee Eastman process, is employed to illustrate the effectiveness and applicability of the proposed method.

关键词： Alarm configuration Deep learning Fault detection and diagnosis Incomplete data stacked autoencoder

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Enhanced Deep autoencoder Based Feature Representation Learning for Intelligent Intrusion Detection System

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Computers, Materials & Continua 2021年第9期68卷 3271-3288页

作者： Thavavel Vaiyapuri Adel Binbusayyis College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

In the era of Big data,learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system(IDS).Owing to the lack of accurately labeled network traffic data,many unsupervised feature representation learning models have been proposed with state-of-theart ***,these models fail to consider the classification error while learning the feature ***,the learnt feature representation may degrade the performance of the classification *** the first time in the field of intrusion detection,this paper proposes an unsupervised IDS model leveraging the benefits of deep autoencoder(DAE)for learning the robust feature representation and one-class support vector machine(OCSVM)for finding the more compact decision hyperplane for intrusion ***,the proposed model defines a new unified objective function to minimize the reconstruction and classification error *** unique contribution not only enables the model to support joint learning for feature representation and classifier training but also guides to learn the robust feature representation which can improve the discrimination ability of the classifier for intrusion *** set of evaluation experiments are conducted to demonstrate the potential of the proposed ***,the ablation evaluation on benchmark dataset,NSL-KDD validates the design decision of the proposed ***,the performance evaluation on recent intrusion dataset,UNSW-NB15 signifies the stable performance of the proposed ***,the comparative evaluation verifies the efficacy of the proposed model against recently published state-of-the-art methods.

关键词： Cybersecurity network intrusion detection deep learning autoencoder stacked autoencoder feature representational learning joint learning one-class classifier OCSVM

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