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Multifaceted radiomics for distant metastasis prediction in head & neck cancer

PHYSICS IN MEDICINE AND BIOLOGY 2020年第15期65卷 155009-155009页

作者： Zhou, Zhiguo Wang, Kai Folkert, Michael Liu, Hui Jiang, Steve Sher, David Wang, Jing Univ Texas Southwestern Med Ctr Dallas Dept Radiat Oncol Dallas TX 75390 USA Univ Cent Missouri Sch Comp Sci & Math Warrensburg MO USA

Accurately predicting distant metastasis in head & neck cancer has the potential to improve patient survival by allowing early treatment intensification with systemic therapy for high-risk patients. By extracting large amounts of quantitative features and mining them, radiomics has achieved success in predicting treatment outcomes for various diseases. However, there are several challenges associated with conventional radiomic approaches, including: (1) how to optimally combine information extracted from multiple modalities;(2) how to construct models emphasizing different objectives for different clinical applications;and (3) how to utilize and fuse output obtained by multiple classifiers. To overcome these challenges, we propose a unified model termed as multifaceted radiomics (M-radiomics). In M-radiomics, a deep learning with stacked sparse autoencoder is first utilized to fuse features extracted from different modalities into one representation feature set. A multi-objective optimization model is then introduced into M-radiomics where probability-based objective functions are designed to maximize the similarity between the probability output and the true label vector. Finally, M-radiomics employs multiple base classifiers to get a diverse Pareto-optimal model set and then fuses the output probabilities of all the Pareto-optimal models through an evidential reasoning rule fusion (ERRF) strategy in the testing stage to obtain the final output probability. Experimental results show that M-radiomics with the stacked autoencoder outperforms the model without the autoencoder. M-radiomics obtained more accurate results with a better balance between sensitivity and specificity than other single-objective or single-classifier-based models.

关键词： distant metastasis prediction head & neck cancer radiomics stacked autoencoder multi-objective optimization evidential reasoning rule

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Semi-Supervised Learning Algorithm for Identifying High-Priority Drug-Drug Interactions Through Adverse Event Reports

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2020年第1期24卷 57-68页

作者： Liu, Ning Chen, Cheng-Bang Kumara, Soundar Penn State Univ Dept Ind & Mfg Engn University Pk PA 16802 USA

Identifying drug-drug interactions (DDIs) is a critical enabler for reducing adverse drug events and improving patient safety. Generating proper DDI alerts during prescribing workflow has the potential to prevent DDI-related adverse events. However, the implementation of DDI alerting system remains a challenge as users are experiencing alert overload which causes alert fatigue. One strategy to optimize the current system is to establish a list of high-priority DDIs for alerting purposes, though it is a resource-intensive task. In this study, we propose a machine learning framework to extract useful features from the FDA adverse event reports and then identify potential high-priority DDIs using an autoencoder-based semi-supervised learning algorithm. The experimental results demonstrate the effectiveness of using adverse event feature representations in differentiating high- and low-priority DDIs. Additionally, the proposed algorithm utilizes stacked autoencoders and weighted support vector machine for boosting classification performance, which outperforms other competing methods in terms of F-measure and AUC score. This framework integrates multiple information sources, leverages domain knowledge and clinical evidence, and provides a practical approach for pre-screening high-priority DDI candidates for medication alerts.

关键词： Drugs Machine learning algorithms Machine learning Semisupervised learning Prediction algorithms Feature extraction Support vector machines Semi-supervised learning stacked autoencoder classification drug-drug interactions clinical decision support adverse event reports

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Date-Driven Soft-Sensor Design for Biological Wastewater Treatment Using Deep Neural Networks and Genetic Algorithms

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JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 2016年第10期49卷 925-936页

作者： Qiu, Yu Liu, Yiqi Huang, Daoping South China Univ Technol Sch Automat Sci & Engn Wushan Rd Guangzhou 510640 Guangdong Peoples R China

In wastewater treatment plants (WWTPs), some variables such as BOD5 and COD that are related to effuent quality, are difficult to measure directly online due to technical or economic limitations. To deal with this problem, a soft sensor that is based on a deep neural network with a named stacked autoencoder (SAE) is developed for WWTPs. Neural networks with deep structure are superior to shallow ones when facing complex problems in modern applications, which makes them suitable for wastewater treatment processes. However, deep structures are difficult to train when using traditional learning algorithms, and there are no general guidelines for identifying the proper network structure for a specific application. In the present work, a deep learning technique called the greedy layer-wise training algorithm is employed to train a deep neural network, and a genetic-algorithm strategy is developed for identifying the number of neurons in each hidden layer. In order to demonstrate its usefulness, the proposed soft sensor is tested through the test-bed Benchmark Simulation Model No. 1 (BSM1) with different weather conditions. The results indicate that the proposed soft sensor based on a deep-structure neural network can achieve better prediction and generalization performance in comparison with commonly used methodologies.

关键词： Wastewater stacked autoencoder Soft Sensor Genetic Algorithm

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An Abnormal Data Processing Method Based on An Ensemble Algorithm for Early Warning of Wind Turbine Failure

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IEEE ACCESS 2021年 9卷 69044-69060页

作者： Zhao, Qiang Bao, Kunkun Wei, Zhenfan Han, Yinghua Wang, Jinkuan Northeastern Univ Qinhuangdao Sch Control Engn Qinhuangdao 066004 Hebei Peoples R China Northeastern Univ Qinhuangdao Sch Comp & Commun Engn Qinhuangdao 066004 Hebei Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

In the modeling process of fault warning models, modeling data plays an important role, the quality of which affects the performance of the model. The manual selection of modeling data according to fault records is time-consuming and makes it difficult to guarantee the high quality of the data because of inconsistencies, errors, and losses of records in the fault log file. For this reason, the present study proposes a framework of abnormal data processing based on an unsupervised serialization ensemble algorithm, which considers the high dimensional characteristics of operational data and the relationship between known low dimensional variables. Meanwhile, the influence of modeling data using different data processing methods on fault prediction performance is studied. The improved stacked autoencoder (I-SAE) based on the idea of partial data reconstruction is proposed to learn the high dimensional characteristics of operational data, which can enhance the separability of normal and abnormal operational data. The improved density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm based on the density ratio is developed to handle sparse abnormal data with data imbalance characteristics. Finally, the case analysis results demonstrate that the abnormal data processing model proposed in this article has better performance than other methods, and the performance of the fault prediction model can be effectively enhanced by improving the quality of modeling data.

关键词： Data models Wind turbines Predictive models Data processing Mathematical model Analytical models Training Density ratio early warning serialization ensemble stacked autoencoder

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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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Performance improvement of deep neural network classifiers by a simple training strategy

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2018年 67卷 14-23页

作者： Caliskan, Abdullah Yuksel, Mehmet Emin Badem, Hasan Basturk, Alper Erciyes Univ Dept Biomed Engn Kayseri Turkey Erciyes Univ Dept Comp Engn Kayseri Turkey Kahramanmaras Sutcu Imam Univ Dept Comp Engn Kahramanmaras Turkey

Improving the classification performance of Deep Neural Networks (DNN) is of primary interest in many different areas of science and technology involving the use of DNN classifiers. In this study, we present a simple training strategy to improve the classification performance of a DNN. In order to attain our goal, we propose to divide the internal parameter space of the DNN into partitions and optimize these partitions individually. We apply our proposed strategy with the popular L-BFGS optimization algorithm even though it can be applied with any optimization algorithm. We evaluate the performance improvement obtained by using our proposed method by testing it on a number of well-known classification benchmark data sets and by performing statistical analysis procedures on classification results. The DNN classifier trained with the proposed strategy is also compared with the state-of-the-art classifiers to demonstrate its effectiveness. Our classification experiments show that the proposed method significantly enhances the training process of the DNN classifier and yields considerable improvements in the accuracy of the classification results. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： autoencoder Deep neural network Deep learning Limited memory BFGS Softmax classifier stacked autoencoder

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Predicting soluble solids content in "Fuji" apples of different ripening stages based on multiple information fusion

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PATTERN RECOGNITION LETTERS 2021年 151卷 76-84页

作者： Huang, Yirui Wang, Juan Li, Na Yang, Jing Ren, Zhenhui Hebei Agr Univ Coll Mech & Elect Engn Baoding Peoples R China

Soluble solids content (SSC) is one of the important components for the assessment of fruit quality. Near infrared (NIR) spectroscopy, multispectral and hyperspectral imaging techniques are widely used methods for the SSC estimation. However, the high prices are unaffordable f or small commercial orchards. The objective of this work is to present a computer vision system for the SSC estimation of "Fuji" apples in different ripening stages. Multiple information of the color channels (channel a * and channel b *) in L*a*b* color space translated from RGB images were applied to extract color features. stacked autoencoder (SAE) algorithm was used to extract color features in pixel-level, and the trained parameters were used as the initial parameters of the prediction model by using back propagation neural network (BPNN) algorithm. The results show that the prediction accuracy of the SAE-BPNN model based on color features in pixel level is higher than that of the BPNN model based on pure color features in feature-level. Moreover, the model with fusion information of both color channels as inputs can get better prediction results than that of single channel. The SAE-BPNN model with SAE structure of 270-120-30 yielded the best result with R-p(2) = 0.5953 and RMSEP = 0.8856%. This research indicates that RGB imaging technology based on multiple information has potential for the SSC prediction, which provides reference values for fruit harvesting and non-destructive evaluation of fruit quality. (C) 2021 Elsevier B.V. All rights reserved.

关键词： RGB imaging Soluble solids content L*a*b* color space stacked autoencoder Back propagation neural network

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Modified genetic algorithm-based feature selection combined with pre-trained deep neural network for demand forecasting in outpatient department

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 82卷 216-230页

作者： Jiang, Shancheng Chin, Kwai-Sang Wang, Long Qu, Gang Tsui, Kwok L. City Univ Hong Kong Dept Syst Engn & Engn Management 83 Tat Chee Ave Kowloon Tong Hong Kong Peoples R China Dalian Univ Affiliated Xinhua Hosp 146 Wansui St Dalian Liaoning Peoples R China

A well-performed demand forecasting can provide outpatient department (OPD) managers with essential information for staff scheduling and rostering, considering the non-reservation policy of OPD in China. Based on the results reported by relevant studies, most approaches have focused on forecasting the overall amount of patient flow and ignored the demand for other key resources in OPD or similar department. Moreover, few studies have conducted feature selection before training a forecast model, which is a significant pre-processing operation of data mining and widely applied for knowledge discovery in expert and intelligent system. This study develops a novel hybrid methodology to forecast the patients' demand for different key resources in OPD, by combining a new feature selection method and a deep learning approach. A modified version of genetic algorithm (MGA) is proposed for feature selection. The key operators of normal genetic algorithm are redesigned to utilize useful information provided by filter-based feature selection and feature combinations. A feedforward deep neural network is introduced as the forecast model, and the initial parameter set is generated from a stacked autoencoder-based pre-training process to overcome the optimization challenges in constructing deep architectures. In order to evaluate the performance of our methodology, it is applied to an OPD located at Northeast China. The results are compared with those obtained from combinations of other feature selection methods and demand forecasting models. Compared with GA and PCA, MGA improves the quality and efficiency of feature selection, with less selected features to get higher forecast accuracy. Pre-trained DNN optimally strengthens the advantage of MGA, compared with MLR, ARIMAX and SANN. The combination of MGA and pre-trained DNN possesses strongest predictive power among all involved combinations. Furthermore, the results of proposed methodology are crucial prerequisites for staff sche

关键词： Deep learning Feature selection Modified genetic algorithm stacked autoencoder Demand forecasting in hospital

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Physiological-signal-based mental workload estimation via transfer dynamical autoencoders in a deep learning framework

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NEUROCOMPUTING 2019年 347卷 212-229页

作者： Yin, Zhong Zhao, Mengyuan Zhang, Wei Wang, Yongxiong Wang, Yagang Zhang, Jianhua Univ Shanghai Sci & Technol Engn Res Ctr Opt Instrument & Syst Shanghai Key Lab Modern Opt Syst Minist Educ Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Sch Marxism Shanghai 200093 Peoples R China Oslo Metropolitan Univ Dept Comp Sci OsloMet Artificial Intelligence Lab N-0130 Oslo Norway

Evaluating operator mental workload (MW) in human-machine systems via neurophysiological signals is crucial for preventing unpredicted operator performance degradation. However, the feature of physiological signals is associated with the historical values at the previous time steps and its statistical properties vary across individuals and types of mental tasks. In this study, we propose a new transfer dynamical autoencoder (TDAE) to capture the dynamical properties of electroencephalograph (EEG) features and the individual differences. The TDAE consists of three consecutively-connected modules, which are termed as feature filter, abstraction filter, and transferred MW classifier. The feature and abstraction filters introduce dynamical deep network to abstract the EEG features across adjacent time steps to salient MW indicators. Transferred MW classifier exploits large volume EEG data from an source-domain EEG database recorded under emotional stimuli to improve the model training stability. We tested our algorithms on two target EEG databases. The classification performance shows TDAE significantly outperforms existing shallow and deep MW classification models. We also investigated how to select TDAE hyper-parameters and found its superiority in accuracy can be achieved with proper filter orders. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Mental workload Electroencephalograph Deep learning stacked autoencoder Operator functional state

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Multi-stage intrusion detection system aided by grey wolf optimization algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第3期27卷 3819-3836页

作者： Chatterjee, Somnath Shaw, Vaibhav Das, Ranit Future Inst Engn & Management Comp Sci & Engn Dept Sonarpur Stn Rd Kolkata 700150 West Bengal India

A Network Intrusion Detection System (NIDS) is frequently used for monitoring and detecting malicious activities in network traffic. A typical NIDS has four stages: a data source, data pre-processing, a decision-making technique, and a defense reaction. We have utilized both anomaly and signature based techniques to build a framework which is resilient to identifying both known and unknown attack. The incoming data packet is fed into the stacked autoencoder to identify whether it is a benign or malicious. If found to be malicious we extract the most relevant features from the network packet using grey wolf optimization algorithm. Then these attribute are provided to RandomForest classifier to determine if this malign attack is present in our knowledge base. If it is present we progress to identify the attack type using LightGBM classifier. If not, we term it as zero-day attack. To evaluate the usability of the proposed framework we have assessed it using two publicly available datasets namely UNSW-NB15 and CIC-IDS-2017 dataset. We have obtained an accuracy of 90.94% and 99.67% on the datasets respectively.

关键词： Computer networks Intrusion detection system stacked autoencoder Decison trees

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