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Online Identification of Nonlinear Systems With Separable Structure

IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2024年第6期35卷 8695-8701页

作者： Chen, Guang-Yong Gan, Min Chen, Long Chen, C. L. Philip Fuzhou Univ Coll Comp & Data Sci Fuzhou 350116 Peoples R China Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Peoples R China Univ Macau Fac Sci & Technol Macau Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

Separable nonlinear models (SNLMs) are of great importance in system modeling, signal processing, and machine learning because of their flexible structure and excellent description of nonlinear behaviors. The online identification of such models is quite challenging, and previous related work usually ignores the special structure where the estimated parameters can be partitioned into a linear and a nonlinear part. In this brief, we propose an efficient first-order recursive algorithm for SNLMs by introducing the variable projection (VP) step. The proposed algorithm utilizes the recursive least-squares method to eliminate the linear parameters, resulting in a reduced function. Then, the stochastic gradient descent (SGD) algorithm is employed to update the parameters of the reduced function. By considering the tight coupling relationship between linear parameters and nonlinear parameters, the proposed first-order VP algorithm is more efficient and robust than the traditional SGD algorithm and alternating optimization algorithm. More importantly, since the proposed algorithm just uses the first-order information, it is easier to apply it to large-scale models. Numerical results on examples of different sizes confirm the effectiveness and efficiency of the proposed algorithm.

关键词： Signal processing algorithms Optimization Numerical models Jacobian matrices Stochastic processes machine learning algorithms Couplings Feedforward neural networks (FNNs) online identification separable nonlinear models (SNLMs) stochastic gradient descent (SGD) method variable projection (VP) method

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XGBoost Model and Its Application to Personal Credit Evaluation

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IEEE INTELLIGENT SYSTEMS 2020年第3期35卷 52-61页

作者： Li, Hua Cao, Yumeng Li, Siwen Zhao, Jianbin Sun, Yutong Zhengzhou Univ Sch Math & Stat Zhengzhou Peoples R China Henan Acad Big Data Zhengzhou Peoples R China Henan Key Lab Financial Engn Zhengzhou Peoples R China Macau Univ Sci & Technol Sch Business Macau Peoples R China

This article investigates the application of the eXtreme Gradient Boosting (XGB) method to the credit evaluation problem based on big data. We first study the theoretical modeling of the credit classification problem using XGB algorithm, and then we apply the XGB model to the personal loan scenario based on the open data set from Lending Club Platform in USA. The empirical study shows that the XGB model has obvious advantages in both feature selection and classification performance compared to the logistic regression and the other three tree-based models.

关键词： Predictive models Decision trees Data models Analytical models Prediction algorithms machine learning algorithms Context modeling XGB Ensemble learning Feature Engineering Credit Evaluation

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Breaking Boundaries in Diagnosis: Non-Invasive Anemia Detection Empowered by AI

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IEEE ACCESS 2024年 12卷 9292-9307页

作者： Muljono Wulandari, Sari Ayu Al Azies, Harun Naufal, Muhammad Prasetyanto, Wisnu Adi Zahra, Fatima Az Univ Dian Nuswantoro Fac Comp Sci Semarang 50131 Central Java Indonesia Univ Dian Nuswantoro Fac Engn Semarang 50131 Central Java Indonesia Univ Diponegoro Fac Med Semarang 50131 Central Java Indonesia

This article evolved because several instances of anemia are still discovered too late, especially in communities with limited medical resources and access to laboratory tests. Invasive diagnostic technologies and expensive expenses are additional impediments to early diagnosis. An effective, accurate, and non-invasive method is required to detect anemia. In this study, the conjunctival image of the eye is analyzed as a non-invasive method of detecting anemia. Various model approaches were tested in an endeavor to categorize anemic and healthy patients as accurately as possible. The Support Vector machine (SVM) algorithm-integrated MobileNetV2 method was determined to be the most effective plan. With this combination, the accuracy of 93%, sensitivity of 91%, and specificity of 94%. These findings show that the model can successfully identify healthy patients while accurately identifying anemic patients. This method offers a non-invasive means of detecting anemia early on, making it promising for use in clinical settings. The SVM+MobileNetV2 technique relies on images of the eye's conjunctiva and can potentially improve healthcare by identifying people who may have had earlier anemia. This technique stands out as a solid option for the efficient and precise diagnosis of anemia when accuracy, sensitivity, and specificity are balanced.

关键词： Support vector machines Blood Feature extraction machine learning algorithms Image color analysis Convolutional neural networks Eye diseases Anemia detection eye conjunctival images MobileNetV2 non-invasive SVM

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A Pseudorange-Based GPS Spoofing Detection Using Hyperbola Equations

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第8期72卷 10770-10783页

作者： Demir, Mehmet Ozgun Kurt, Gunes Karabulut Pusane, Ali Emre Bogazici Univ Dept Elect & Elect Engn TR-34342 Istanbul Turkiye Polytech Montreal Dept Elect Engn Montreal PQ H3T 1J4 Canada

Spoofing attacks are one of the most critical threats against secure global navigation satellite system (GNSS) positioning. Since correct positioning is a must in many vehicle-to-everything (V2X) communication systems, the detection of these attacks is vital. In the literature on the detection strategies of spoofing attacks, the majority of the solutions are based on the assumption that all available signals are spoofed. In this paper, we focus on detecting spoofing attacks in which authentic and spoofing positioning signals coexist in a V2X system. The observable pseudorange values are utilized with the help of derived hyperbola equations during the design of the spoofing detection algorithms. We propose an algorithm, which is named as the sub-optimal search-based spoofing detection algorithm (Algorithm 1), and it considers all possible numbers of spoofing attacking signals, but not all spoofing scenarios with the same amount of spoofing signals. To address the complexity problems based on the increased number of search scenarios of this approach, we propose another algorithm, which is called subset selection-based spoofing detection algorithm (Algorithm 2), with a smart selection of the search subsets. Both of these algorithms are first compared with fixed detection thresholds, which are determined with the Pareto front approach. Then, the performance of the algorithms is investigated when vehicle mobility and spoofing imperfection are considered. Finally, a supervised learning-based decision tree machine learning (ML) algorithm is run without specifying any detection threshold. The results indicate that Algorithm 1 provides higher detection rates than the subset selection-based algorithm;however, the false alarm ratios of Algorithm 2 are much lower than its original performance.

关键词： Vehicle-to-everything Global navigation satellite system Global Positioning System Detection algorithms Mathematical models machine learning algorithms Satellites Global positioning system satellite navigation systems spoofing communication system security detection algorithms

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RC-RNN: Reconfigurable Cache Architecture for Storage Systems Using Recurrent Neural Networks

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2022年第3期10卷 1492-1506页

作者： Ebrahimi, Shahriar Salkhordeh, Reza Osia, Seyed Ali Taheri, Ali Rabiee, Hamid R. Asadi, Hossen Sharif Univ Technol Dept Comp Engn Tehran *** Iran

Solid-State Drives (SSDs) have significant performance advantages over traditional Hard Disk Drives (HDDs) such as lower latency and higher throughput. Significantly higher price per capacity and limited lifetime, however, prevents designers to completely substitute HDDs by SSDs in enterprise storage systems. SSD-based caching has recently been suggested for storage systems to benefit from higher performance of SSDs while minimizing the overall cost. While conventional caching algorithms such as Least Recently Used (LRU) provide high hit ratio in processors, due to the highly random behavior of Input/Output (I/O) workloads, they hardly provide the required performance level for storage systems. In addition to poor performance, inefficient algorithms also shorten SSD lifetime with unnecessary cache replacements. Such shortcomings motivate us to benefit from more complex non-linear algorithms to achieve higher cache performance and extend SSD lifetime. In this article, we propose RC-RNN, the first reconfigurable SSD-based cache architecture for storage systems that utilizes machine learning to identify performance-critical data pages for I/O caching. The proposed architecture uses Recurrent Neural Networks (RNN) to characterize ongoing workloads and optimize itself towards higher cache performance while improving SSD lifetime. RC-RNN attempts to learn characteristics of the running workload to predict its behavior and then uses the collected information to identify performance-critical data pages to fetch into the cache. We implement the proposed architecture on a physical server equipped with a Core-i7 CPU, 256GB SSD, and a 2TB HDD running Linux kernel 4.4.0. Experimental results show that RC-RNN characterizes workloads with an accuracy up to 94.6 percent for SNIA I/O workloads. RC-RNN can perform similarly to the optimal cache algorithm by an accuracy of 95 percent on average, and outperforms previous SSD caching architectures by providing up to 7x higher hit ratio

关键词： Computer architecture machine learning algorithms machine learning Performance evaluation Time factors Recurrent neural networks Predictive models Data storage systems I O caching solid-state drives recurrent neural networks applied machine learning I O workload characterization

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RFSMMA: A New Computational Model to Identify and Prioritize Potential Small Molecule-MiRNA Associations

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JOURNAL OF CHEMICAL INFORMATION AND MODELING 2019年第4期59卷 1668-1679页

作者： Wang, Chun-Chun Chen, Xing Qu, Jia Sun, Ya-Zhou Li, Jian-Qiang China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China

More and more studies found that many complex human diseases occur accompanied by aberrant expression of microRNAs (miRNAs). Small molecule (SM) drugs have been utilized to treat complex human diseases by affecting the expression of miRNAs. Several computational methods were proposed to infer underlying associations between SMs and miRNAs. In our study, we proposed a new calculation model of random forest based small molecule-miRNA association prediction (RFSMMA) which was based on the known SM-miRNA associations in the SM2miR database. RFSMMA utilized the similarity of SMs and miRNAs as features to represent SM-miRNA pairs and further implemented the machine learning algorithm of random forest to train training samples and obtain a prediction model. In RFSMMA, integrating multiple kinds of similarity can avoid the bias of single similarity and choosing more reliable features from original features can represent SM-miRNA pairs more accurately. We carried out cross validations to assess predictive accuracy of RFSMMA. As a result, RFSMMA acquired AUCs of 0.9854, 0.9839, 0.7052, and 0.9917 +/- 0.0008 under global leave-one-out cross validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV, and 5-fold cross validation, respectively, under data set 1. Based on data set 2, RFSMMA obtained AUCs of 0.8456, 0.8463, 0.6653, and 0.8389 +/- 0.0033 under four cross validations according to the order mentioned above. In addition, we implemented a case study on three common SMs, namely, 5-fluorouracil, 17 beta-estradiol, and 5-aza-2'-deoxycytidine. Among the top 50 associated miRNAs of these three SMs predicted by RFSMMA, 31, 32, and 28 miRNAs were verified, respectively. Therefore, RFSMMA is shown to be an effective and reliable tool for identifying underlying SM-miRNA associations.

关键词： associations cross-validation Case studies Prediction models decitabine Association (Psychology) machine learning algorithms MicroRNAs maladjustment Computational modeling

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Averaged dependence estimators for DoS attack detection in IoT networks

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 102卷 198-209页

作者： Baig, Zubair A. Sanguanpong, Surasak Firdous, Syed Naeem Van Nhan Vo Tri Gia Nguyen So-In, Chakchai Deakin Univ Sch Informat Technol Geelong Vic 3220 Australia Kasetsart Univ Fac Engn Dept Comp Engn Bangkok 10900 Thailand Edith Cowan Univ Perth WA 6000 Australia Khon Kaen Univ Fac Sci Dept Comp Sci Appl Network Technol ANT Lab Khon Kaen 40002 Thailand Duy Tan Univ Fac Informat Technol Da Nang 550000 Vietnam

Wireless sensor networks (WSNs) have evolved to become an integral part of the contemporary Internet of Things (IoT) paradigm. The sensor node activities of both sensing phenomena in their immediate environments and reporting their findings to a centralized base station (BS) have remained a core platform to sustain heterogeneous service-centric applications. However, the adversarial threat to the sensors of the IoT paradigm remains significant. Denial of service (DoS) attacks, comprising a large volume of network packets, targeting a given sensor node(s) of the network, may cripple routine operations and cause catastrophic losses to emergency services. This paper presents an intelligent DoS detection framework comprising modules for data generation, feature ranking and generation, and training and testing. The proposed framework is experimentally tested under actual IoT attack scenarios, and the accuracy of the results is greater than that of traditional classification techniques. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Internet of Things Communication system security machine learning algorithms

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Prediction of Intrinsically Disordered Proteins with a Low Computational Complexity Method

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Computer Modeling in Engineering & Sciences 2020年第10期125卷 111-123页

作者： Jia Yang Haiyuan Liu Hao He Nankai University School of Electronic Information and Optical EngineeringTianjin Key Laboratory of Optoelectronic Sensor and Sensing Network TechnologyTianjin300350China Hebei University of Technology Tianjin300400China

The prediction of intrinsically disordered proteins is a hot research area in *** to the high cost of experimental methods to evaluate disordered regions of protein sequences,it is becoming increasingly important to predict those regions through computational *** this paper,we developed a novel scheme by employing sequence complexity to calculate six features for each residue of a protein sequence,which includes the Shannon entropy,the topological entropy,the sample entropy and three amino acid preferences including Remark 465,Deleage/Roux,and Bfactor(2STD).Particularly,we introduced the sample entropy for calculating time series complexity by mapping the amino acid sequence to a time series of *** our knowledge,the sample entropy has not been previously used for predicting IDPs and hence is being used for the first time in our *** addition,the scheme used a properly sized sliding window in every protein sequence which greatly improved the prediction ***,we used seven machine learning algorithms and tested with 10-fold cross-validation to get the results on the dataset R80 collected by Yang et *** of the dataset DIS1556 from the Database of Protein Disorder(DisProt)(https://***)containing experimentally determined intrinsically disordered proteins(IDPs).The results showed that k-Nearest Neighbor was more appropriate and an overall prediction accuracy of 92%.Furthermore,our method just used six features and hence required lower computational complexity.

关键词： Bioinformatics intrinsically disordered proteins machine learning algorithms sequences computational complexity

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IoT Cybersecurity: On the Use of Boosting-Based Approaches for Botnet Detection

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IT PROFESSIONAL 2024年第6期26卷 45-54页

作者： Saied, Mohamed Guirguis, Shawkat Madbouly, Magda Alexandria Univ Inst Grad Studies & Res Dept Informat Technol Comp Sci & Informat Technol Alexandria 21123 Egypt Ezz Steel Grp Garden City 1089 Cairo Egypt Inst Grad Studies & Res Dept Informat Technol Comp Sci & Informat Alexandria 21123 Egypt Inst Grad Studies & Res Fac Comp & Data Sci Alexandria 21123 Egypt

The rapid expansion of Internet of Things (IoT) adoption has brought about significant cybersecurity challenges, with botnet attacks being a critical concern. To address this issue, machine learning algorithms, particularly boosting-based approaches, have shown promise in detecting and mitigating botnet intrusions. However, the selection of an appropriate algorithm plays a crucial role in achieving accurate detection and reducing the probability of infection. This article focuses on the utilization of boosting-based algorithms for botnet detection in IoT environments. It evaluates the performance of five boosting-based machine learning algorithms in botnet binary detection. The empirical findings underscored the significant potential of boosting-based algorithms in effectively detecting botnet attacks within IoT environments. The histogram gradient boosting algorithm achieved the best performance for binary detection with an accuracy rate of 0.999977. In addition, a temporal evaluation is presented to evaluate the computational requirements of each algorithm to cope with the resources constrained nature of IoT.

关键词： Histograms machine learning algorithms Accuracy Botnet Boosting Internet of Things Computer security Histograms machine learning algorithms Accuracy Botnet Boosting Internet of Things Computer security

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Analysis and Accurate Prediction of User's response Behavior in Incentive-Based Demand Response

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IEEE ACCESS 2019年 7卷 3170-3180页

作者： Liu, Di Sun, Yi Qu, Yao Li, Bin Xu, Yonghai North China Elect Power Univ Sch Elect & Elect Engn Beijing 102206 Peoples R China

Incentive-based demand response can fully mobilize a variety of demand-side resources to participate in the electricity market, but the uncertainty of user response behavior greatly limits the development of demand response services. This paper first constructed an implementation framework for incentive-based demand response and clarified how load-serving entity aggregates demand-side resources to participate in the power market business. Then, the characteristics of the user's response behavior were analyzed;it is found that the user's response behavior is variable, and it has a strong correlation on the timeline. Based on this, a prediction method of user response behavior based on long short-term memory (LSTM) is proposed after the analysis of the characteristics of the LSTM algorithm. The proposed prediction method was verified by simulation under the simulation environment setup by TensorFlow. The simulation results showed that, compared with the traditional linear or nonlinear regression methods, the proposed method can significantly improve the accuracy of the prediction. At the same time, it is verified by further experiments that the proposed algorithm has good performance in various environments and has strong robustness.

关键词： Artificial neural networks machine learning algorithms state estimation power demand activity recognition consumer behavior

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