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Using Cross Entropy as a Performance Metric for Quantifying Uncertainty in DNN Image Classifiers: An Application to Classification of Lung Cancer on CT Images
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作者 Eri Matsuyama Masayuki Nishiki +1 位作者 Noriyuki Takahashi Haruyuki Watanabe 《Journal of Biomedical Science and Engineering》 2024年第1期1-12,共12页
Cross entropy is a measure in machine learning and deep learning that assesses the difference between predicted and actual probability distributions. In this study, we propose cross entropy as a performance evaluation... Cross entropy is a measure in machine learning and deep learning that assesses the difference between predicted and actual probability distributions. In this study, we propose cross entropy as a performance evaluation metric for image classifier models and apply it to the CT image classification of lung cancer. A convolutional neural network is employed as the deep neural network (DNN) image classifier, with the residual network (ResNet) 50 chosen as the DNN archi-tecture. The image data used comprise a lung CT image set. Two classification models are built from datasets with varying amounts of data, and lung cancer is categorized into four classes using 10-fold cross-validation. Furthermore, we employ t-distributed stochastic neighbor embedding to visually explain the data distribution after classification. Experimental results demonstrate that cross en-tropy is a highly useful metric for evaluating the reliability of image classifier models. It is noted that for a more comprehensive evaluation of model perfor-mance, combining with other evaluation metrics is considered essential. . 展开更多
关键词 Cross Entropy Performance Metrics DNN Image classifiers Lung Cancer Prediction Uncertainty
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Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection
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作者 Islam Zada Mohammed Naif Alatawi +4 位作者 Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi 《Computers, Materials & Continua》 SCIE EI 2024年第8期2917-2939,共23页
Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention mechanisms.Supervised machine learning classifiers have emerged as promising tools for malwar... Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention mechanisms.Supervised machine learning classifiers have emerged as promising tools for malware detection.However,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection.Addressing this gap can provide valuable insights for enhancing cybersecurity strategies.While numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware detection.Understanding the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security measures.This study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows systems.The objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows malware.Evaluating the accuracy,efficiency,and suitability of each classifier for real-world malware detection scenarios.Identifying the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and researchers.Offering recommendations for selecting the most effective classifier for Windows malware detection based on empirical evidence.The study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and evaluation.Exploratory data analysis involves understanding the dataset’s characteristics and identifying preprocessing requirements.Data preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for training.Model training utilizes various supervised classifiers,and their performance is evaluated using metrics such as accuracy,precision,recall,and F1 score.The study’s outcomes comprise a comparative analysis of supervised machine learning classifiers for Windows malware detection.Results reveal the effectiveness and efficiency of each classifier in detecting different types of malware.Additionally,insights into their strengths and limitations provide practical guidance for enhancing cybersecurity defenses.Overall,this research contributes to advancing malware detection techniques and bolstering the security posture of Windows systems against evolving cyber threats. 展开更多
关键词 Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent classifier Decision Tree
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Knowledge discovery method for feature-decision level fusion of multiple classifiers 被引量:1
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作者 孙亮 韩崇昭 《Journal of Southeast University(English Edition)》 EI CAS 2006年第2期222-227,共6页
To improve the performance of the multiple classifier system, a new method of feature-decision level fusion is proposed based on knowledge discovery. In the new method, the base classifiers operate on different featur... To improve the performance of the multiple classifier system, a new method of feature-decision level fusion is proposed based on knowledge discovery. In the new method, the base classifiers operate on different feature spaces and their types depend on different measures of between-class separability. The uncertainty measures corresponding to each output of each base classifier are induced from the established decision tables (DTs) in the form of mass function in the Dempster-Shafer theory (DST). Furthermore, an effective fusion framework is built at the feature-decision level on the basis of a generalized rough set model and the DST. The experiment for the classification of hyperspectral remote sensing images shows that the performance of the classification can be improved by the proposed method compared with that of plurality voting (PV). 展开更多
关键词 multiple classifier fusion knowledge discovery Dempster-Shafer theory generalized rough set HYPERSPECTRAL
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Flow Field Characteristics of the Rotor Cage in Turbo Air Classifiers 被引量:2
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作者 GUO Lijie LIU Jiaxiang LIU Shengzhao 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2009年第3期426-432,共7页
The turbo air classifier is widely used powder classification equipment in a variety of fields. The flow field characteristics of the turbo air classifier are important basis for the improvement of the turbo air class... The turbo air classifier is widely used powder classification equipment in a variety of fields. The flow field characteristics of the turbo air classifier are important basis for the improvement of the turbo air classifier's structural design. The flow field characteristics of the rotor cage in turbo air classifiers were investigated trader different operating conditions by laser Doppler velocimeter(LDV), and a measure diminishing the axial velocity is proposed. The investigation results show that the tangential velocity of the air flow inside the rotor cage is different from the rotary speed of the rotor cage on the same measurement point due to the influences of both the negative pressure at the exit and the rotation of the rotor cage. The tangential velocity of the air flow likewise decreases as the radius decreases in the case of the rotor cage's low rotary speed. In contrast, the tangential velocity of the air flow increases as the radius decreases in the case of the rotor cage's high rotary speed. Meanwhile, the vortex inside the rotor cage is found to occur near the pressure side of the blade when the rotor cage's rotary speed is less than the tangential velocity of air flow. On the contrary, the vortex is found to occur near the blade suction side once the rotor cage's rotary speed is higher than the tangential velocity of air flow. Inside the rotor cage, the axial velocity could not be disregarded and is largely determined by the distances between the measurement point and the exit. 展开更多
关键词 turbo air classifier rotor cage flow field characteristic laser Doppler velocimeter(LDV)
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Predicting Stock Prices Using Polynomial Classifiers: The Case of Dubai Financial Market 被引量:4
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作者 Khaled Assaleh Hazim El-Baz Saeed Al-Salkhadi 《Journal of Intelligent Learning Systems and Applications》 2011年第2期82-89,共8页
Predicting stock price movements is a challenging task for academicians and practitioners. In particular, forecasting price movements in emerging markets seems to be more elusive because they are usually more volatile... Predicting stock price movements is a challenging task for academicians and practitioners. In particular, forecasting price movements in emerging markets seems to be more elusive because they are usually more volatile often accompa-nied by thin trading-volumes and they are susceptible to more manipulation compared to mature markets. Technical analysis of stocks and commodities has become a science on its own;quantitative methods and techniques have been applied by many practitioners to forecast price movements. Lagging and sometimes leading technical indicators pro-vide rich quantitative tools for traders and investors in their attempt to gain advantage when making investment or trading decisions. Artificial Neural Networks (ANN) have been used widely in predicting stock prices because of their capability in capturing the non-linearity that often exists in price movements. Recently, Polynomial Classifiers (PC) have been applied to various recognition and classification application and showed favorable results in terms of recog-nition rates and computational complexity as compared to ANN. In this paper, we present two prediction models for predicting securities’ prices. The first model was developed using back propagation feed forward neural networks. The second model was developed using polynomial classifiers (PC), as a first time application for PC to be used in stock prices prediction. The inputs to both models were identical, and both models were trained and tested on the same data. The study was conducted on Dubai Financial Market as an emerging market and applied to two of the market’s leading stocks. In general, both models achieved very good results in terms of mean absolute error percentage. Both models show an average error around 1.5% predicting the next day price, an average error of 2.5% when predicting second day price, and an average error of 4% when predicted the third day price. 展开更多
关键词 DUBAI FINANCIAL MARKET POLYNOMIAL classifiers STOCK MARKET Neural Networks
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An Alignment-Based Approach to L2 Learning of Chinese Numeral Classifiers 被引量:4
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作者 Chuming WANG Wei HONG 《Chinese Journal of Applied Linguistics》 2021年第3期335-350,431,共17页
This study investigated the efficiency of learning the Chinese numeral classifiers by L2 Chinese learners by means of an alignment-oriented task. Participants were a total of 96 intermediate learners of L2 Chinese, wh... This study investigated the efficiency of learning the Chinese numeral classifiers by L2 Chinese learners by means of an alignment-oriented task. Participants were a total of 96 intermediate learners of L2 Chinese, who were randomly assigned to two experimental groups and one control group, with each group consisting of 32 participants. The continuation task used in this study consisted of a picture-based Chinese text depicting a room with an array of objects, which necessitates the use of classifiers. The two experimental groups were both required to first read the text and then write to describe their own rooms in comparison with the one in the text. One group was instructed to use the classifiers from the text as much as possible in their writing, whereas the other was not required to do so. Participants in the control group were first given the picture to look at in the absence of the text and then asked to describe their own rooms. The results showed that the continuation task significantly enhanced participants’ retention of the Chinese numeral classifiers, suggesting that the alignment-based approach is an effective way to learn difficult linguistic categories such as the Chinese classifiers. 展开更多
关键词 ALIGNMENT interaction continuation task learn-together-use-together(LTUT)principle classifiers
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Classification performance of model coal mill classifiers with swirling and non-swirling inlets 被引量:3
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作者 Lele Feng Hai Zhang +4 位作者 Lilin Hu Yang Zhang Yuxin Wu Yuzhao Wang Hairui Yang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2020年第3期777-784,共8页
The classification performance of model coal mill classifiers with different bottom incoming flow inlets was experimentally and numerically studied.The flow field adjacent to two neighboring impeller blades was measur... The classification performance of model coal mill classifiers with different bottom incoming flow inlets was experimentally and numerically studied.The flow field adjacent to two neighboring impeller blades was measured using the particle image velocimetry technique.The results showed that the flow field adjacent to two neighboring blades with the swirling inlet was significantly different from that with the non-swirling inlet.With the swirling inlet,there was a vortex located between two neighboring blades,while with the nonswirling inlet,the vortex was attached to the blade tip.The vorticity of the vortex with the non-swirling inlet was much lower than that with the swirling inlet.The classifier with the non-swirling inlet demonstrated a larger cut size than that with the swirling inlet when the impeller was stationary(~0 r·min-1).As the impeller rotational speed increased,the cut size of the cases with non-swirling and swirling inlets both decreased,and the one with the non-swirling inlet decreased more dramatically.The values of the cut size of the two classifiers were close to each other at a high impeller rotational speed(≥120 r·min-1).The overall separation efficiency of the classifier with the non-swirling inlet was lower than that with the swirling inlet,and monotonically increased as the impeller rotational speed increased.With the swirling inlet,the overall separation efficiency first increased with the impeller rotational speed and then decreased when the rotational speed was above 120 r·min-1,and the variation trend of the separation efficiency was more moderate.As the initial particle concentration increased,the cut sizes of both swirling and non-swirling inlet cases decreased first and then barely changed.At a low initial particle concentration(b 0.04 kg·m-3),the classifier with the swirling inlet had a larger cut size than that with the non-swirling inlet. 展开更多
关键词 Coal mill classifier Cut size Non-swirling inlet Particle image velocimetry Impeller rotational speed
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Video Concept Detection Based on Multiple Features and Classifiers Fusion 被引量:1
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作者 Dong Yuan Zhang Jiwei +2 位作者 Zhao Nan Chang Xiaofu Liu Wei 《China Communications》 SCIE CSCD 2012年第8期105-121,共17页
The rapid growth of multimedia content necessitates powerful technologies to filter, classify, index and retrieve video documents more efficiently. However, the essential bottleneck of image and video analysis is the ... The rapid growth of multimedia content necessitates powerful technologies to filter, classify, index and retrieve video documents more efficiently. However, the essential bottleneck of image and video analysis is the problem of semantic gap that low level features extracted by computers always fail to coincide with high-level concepts interpreted by humans. In this paper, we present a generic scheme for the detection video semantic concepts based on multiple visual features machine learning. Various global and local low-level visual features are systelrtically investigated, and kernelbased learning method equips the concept detection system to explore the potential of these features. Then we combine the different features and sub-systen on both classifier-level and kernel-level fusion that contribute to a more robust system Our proposed system is tested on the TRECVID dataset. The resulted Mean Average Precision (MAP) score is rmch better than the benchmark perforrmnce, which proves that our concepts detection engine develops a generic model and perforrrs well on both object and scene type concepts. 展开更多
关键词 concept detection visual feature extraction kemel-based learning classifier fusion
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Integrating RFID Technology with Intelligent Classifiers for Meaningful Prediction Knowledge 被引量:1
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作者 Peter Darcy Steven Tucker Bela Stantic 《Advances in Internet of Things》 2013年第2期27-33,共7页
Radio Frequency Identification (RFID) is wireless technology that has been designed to automatically identify tagged objects using a reader. Several applications of this technology have been introduced in past literat... Radio Frequency Identification (RFID) is wireless technology that has been designed to automatically identify tagged objects using a reader. Several applications of this technology have been introduced in past literature such as pet identification and luggage tracking which have increased the efficiency and effectiveness of each environment into which it was integrated. However, due to the ambiguous nature of the captured information with the existence of missing, wrong and duplicate readings, the wide-scale adoption of the architecture is limited to commercial sectors where the integrity of the observations can tolerate ambiguity. In this work, we propose an application of RFID to take the reporting of class attendance and to integrate a predictive classifier to extract high level meaningful information that can be used in diverse areas such as scheduling and low student retention. We conclude by providing an analysis of the core strengths and opportunities that exist for this concept and how we might extend it in future research. 展开更多
关键词 RADIO Frequency Identification classifiER PREDICTION NEURAL NETWORK BAYESIAN NETWORK
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Modeling the effects of mechanical parameters on the hydrodynamic behavior of vertical current classifiers 被引量:3
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作者 Arabzadeh Jarkani Soroush Khoshdast Hamid +1 位作者 Shariat Elaheh Sam Abbas 《International Journal of Mining Science and Technology》 SCIE EI 2014年第1期123-127,共5页
This study modeled the effects of structural and dimensional manipulations on hydrodynamic behavior of a bench vertical current classifier. Computational fluid dynamics (CFD) approach was used as modeling method, an... This study modeled the effects of structural and dimensional manipulations on hydrodynamic behavior of a bench vertical current classifier. Computational fluid dynamics (CFD) approach was used as modeling method, and turbulent intensity and fluid velocity were applied as system responses to predict the over- flow cut size variations. These investigations showed that cut size would decrease by increasing diameter and height of the separation column and cone section depth, due to the decrease of turbulent intensity and fluid velocity. As the size of discharge gate increases, the overflow cut-size would decrease due to freely fluid stream out of the column. The overflow cut-size was significantly increased in downward fed classifier compared to that fed by upward fluid stream. In addition, reforming the shape of angular overflow outlet's weir into the curved form prevented stream inside returning and consequently unselec- tire cut-size decreasing. 展开更多
关键词 Hydraulic classifier Modeling Computational fluid dynamic Cut size
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Comparative Study on Tree Classifiers for Application to Condition Monitoring ofWind Turbine Blade through Histogram Features Using Vibration Signals: A Data-Mining Approach 被引量:1
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作者 A.Joshuva V.Sugumaran 《Structural Durability & Health Monitoring》 EI 2019年第4期399-416,共18页
Wind energy is considered as a alternative renewable energy source due to its low operating cost when compared with other sources.The wind turbine is an essential system used to change kinetic energy into electrical e... Wind energy is considered as a alternative renewable energy source due to its low operating cost when compared with other sources.The wind turbine is an essential system used to change kinetic energy into electrical energy.Wind turbine blades,in particular,require a competitive condition inspection approach as it is a significant component of the wind turbine system that costs around 20-25 percent of the total turbine cost.The main objective of this study is to differentiate between various blade faults which affect the wind turbine blade under operating conditions using a machine learning approach through histogram features.In this study,blade bend,hub-blade loose connection,blade erosion,pitch angle twist,and blade cracks were simulated on the blade.This problem is formulated as a machine learning problem which consists of three phases,namely feature extraction,feature selection and feature classification.Histogram features are extracted from vibration signals and feature selection was carried out using the J48 decision tree algorithm.Feature classification was performed using 15 tree classifiers.The results of the machine learning classifiers were compared with respect to their accuracy percentage and a better model is suggested for real-time monitoring of a wind turbine blade. 展开更多
关键词 Condition monitoring fault diagnosis wind turbine blade machine learning histogram features tree classifiers
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WORD SENSE DISAMBIGUATION BASED ON IMPROVED BAYESIAN CLASSIFIERS 被引量:1
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作者 Liu Ting Lu Zhimao Li Sheng 《Journal of Electronics(China)》 2006年第3期394-398,共5页
Word Sense Disambiguation (WSD) is to decide the sense of an ambiguous word on particular context. Most of current studies on WSD only use several ambiguous words as test samples, thus leads to some limitation in prac... Word Sense Disambiguation (WSD) is to decide the sense of an ambiguous word on particular context. Most of current studies on WSD only use several ambiguous words as test samples, thus leads to some limitation in practical application. In this paper, we perform WSD study based on large scale real-world corpus using two unsupervised learning algorithms based on ±n-improved Bayesian model and Dependency Grammar (DG)-improved Bayesian model. ±n-improved classifiers reduce the window size of context of ambiguous words with close-distance feature extraction method, and decrease the jamming of useless features, thus obviously improve the accuracy, reaching 83.18% (in open test). DG-improved classifier can more effectively conquer the noise effect existing in Naive-Bayesian classifier. Experimental results show that this approach does better on Chinese WSD, and the open test achieved an accuracy of 86.27%. 展开更多
关键词 Word Sense Disambiguation (WSD) Natural Language Processing (NLP) Unsupervised learning algorithm Dependency Grammar (DG) Bayesian classifier
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The Diversity of Classifiers and Its Applications to Combination
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作者 林磊 Wang Xiaolong Liu Jiafeng 《High Technology Letters》 EI CAS 2002年第4期33-36,共4页
In various application areas of pattern recognition, combing multiple classifiers is regarded as a new method for achieving a substantial gain in performance of systems. This paper discusses the properties of the dive... In various application areas of pattern recognition, combing multiple classifiers is regarded as a new method for achieving a substantial gain in performance of systems. This paper discusses the properties of the diversity of classifiers and its applications. At the same time, the paper presents a novel method for combining multiple classifiers based on the diversity. Fusion strategies are discussed for providing a basis for combing classifiers. These combination strategies are experimentally tested on online handwritten Chinese character recognition system and their effectiveness is considered. 展开更多
关键词 COMBING multiple classifiers diversity FUSION strategies handwritten Chinese CHARACTER recognition
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Performance Evaluation of Multiple Classifiers for Predicting Fake News
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作者 Arzina Tasnim Md. Saiduzzaman +2 位作者 Mohammad Arafat Rahman Jesmin Akhter Abu Sayed Md. Mostafizur Rahaman 《Journal of Computer and Communications》 2022年第9期1-21,共21页
The rise of fake news on social media has had a detrimental effect on society. Numerous performance evaluations on classifiers that can detect fake news have previously been undertaken by researchers in this area. To ... The rise of fake news on social media has had a detrimental effect on society. Numerous performance evaluations on classifiers that can detect fake news have previously been undertaken by researchers in this area. To assess their performance, we used 14 different classifiers in this study. Secondly, we looked at how soft voting and hard voting classifiers performed in a mixture of distinct individual classifiers. Finally, heuristics are used to create 9 models of stacking classifiers. The F1 score, prediction, recall, and accuracy have all been used to assess performance. Models 6 and 7 achieved the best accuracy of 96.13 while having a larger computational complexity. For benchmarking purposes, other individual classifiers are also tested. 展开更多
关键词 Fake News Machine Learning TF-IDF classifiER Estimator F1 Score RECALL Precision Voting classifiers Stacking classifier Soft Voting Hard Voting
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Detection of Turbulence Anomalies Using a Symbolic Classifier Algorithm in Airborne Quick Access Record(QAR)Data Analysis 被引量:1
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作者 Zibo ZHUANG Kunyun LIN +1 位作者 Hongying ZHANG Pak-Wai CHAN 《Advances in Atmospheric Sciences》 SCIE CAS CSCD 2024年第7期1438-1449,共12页
As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The ... As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards. 展开更多
关键词 turbulence detection symbolic classifier quick access recorder data
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Real and Altered Fingerprint Classification Based on Various Features and Classifiers
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作者 Saif Saad Hameed Ismail Taha Ahmed Omar Munthir Al Okashi 《Computers, Materials & Continua》 SCIE EI 2023年第1期327-340,共14页
Biometric recognition refers to the identification of individuals through their unique behavioral features(e.g.,fingerprint,face,and iris).We need distinguishing characteristics to identify people,such as fingerprints... Biometric recognition refers to the identification of individuals through their unique behavioral features(e.g.,fingerprint,face,and iris).We need distinguishing characteristics to identify people,such as fingerprints,which are world-renowned as the most reliablemethod to identify people.The recognition of fingerprints has become a standard procedure in forensics,and different techniques are available for this purpose.Most current techniques lack interest in image enhancement and rely on high-dimensional features to generate classification models.Therefore,we proposed an effective fingerprint classification method for classifying the fingerprint image as authentic or altered since criminals and hackers routinely change their fingerprints to generate fake ones.In order to improve fingerprint classification accuracy,our proposed method used the most effective texture features and classifiers.Discriminant Analysis(DCA)and Gaussian Discriminant Analysis(GDA)are employed as classifiers,along with Histogram of Oriented Gradient(HOG)and Segmentation-based Feature Texture Analysis(SFTA)feature vectors as inputs.The performance of the classifiers is determined by assessing a range of feature sets,and the most accurate results are obtained.The proposed method is tested using a Sokoto Coventry Fingerprint Dataset(SOCOFing).The SOCOFing project includes 6,000 fingerprint images collected from 600 African people whose fingerprints were taken ten times.Three distinct degrees of obliteration,central rotation,and z-cut have been performed to obtain synthetically altered replicas of the genuine fingerprints.The proposal achieved massive success with a classification accuracy reaching 99%.The experimental results indicate that the proposed method for fingerprint classification is feasible and effective.The experiments also showed that the proposed SFTA-based GDA method outperformed state-of-art approaches in feature dimension and classification accuracy. 展开更多
关键词 Fingerprint classification HOG SFTA discriminant analysis(DCA)classifier gaussian discriminant analysis(GDA)classifier SOCOFing
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Belief Combination of Classifiers for Incomplete Data
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作者 Zuowei Zhang Songtao Ye +2 位作者 Yiru Zhang Weiping Ding Hao Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2022年第4期652-667,共16页
Data with missing values,or incomplete information,brings some challenges to the development of classification,as the incompleteness may significantly affect the performance of classifiers.In this paper,we handle miss... Data with missing values,or incomplete information,brings some challenges to the development of classification,as the incompleteness may significantly affect the performance of classifiers.In this paper,we handle missing values in both training and test sets with uncertainty and imprecision reasoning by proposing a new belief combination of classifier(BCC)method based on the evidence theory.The proposed BCC method aims to improve the classification performance of incomplete data by characterizing the uncertainty and imprecision brought by incompleteness.In BCC,different attributes are regarded as independent sources,and the collection of each attribute is considered as a subset.Then,multiple classifiers are trained with each subset independently and allow each observed attribute to provide a sub-classification result for the query pattern.Finally,these sub-classification results with different weights(discounting factors)are used to provide supplementary information to jointly determine the final classes of query patterns.The weights consist of two aspects:global and local.The global weight calculated by an optimization function is employed to represent the reliability of each classifier,and the local weight obtained by mining attribute distribution characteristics is used to quantify the importance of observed attributes to the pattern classification.Abundant comparative experiments including seven methods on twelve datasets are executed,demonstrating the out-performance of BCC over all baseline methods in terms of accuracy,precision,recall,F1 measure,with pertinent computational costs. 展开更多
关键词 classifier fusion classifiCATION evidence theory incomplete data missing values
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Fault Detection of Fuel Injectors Based on One-Class Classifiers
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作者 Dimitrios Moshou Athanasios Natsis +3 位作者 Dimitrios Kateris Xanthoula-Eirini Pantazi Ioannis Kalimanis Ioannis Gravalos 《Modern Mechanical Engineering》 2014年第1期19-27,共9页
Fuel injectors are considered as an important component of combustion engines. Operational weakness can possibly lead to the complete machine malfunction, decreasing reliability and leading to loss of production. To o... Fuel injectors are considered as an important component of combustion engines. Operational weakness can possibly lead to the complete machine malfunction, decreasing reliability and leading to loss of production. To overcome these circumstances, various condition monitoring techniques can be applied. The application of acoustic signals is common in the field of fault diagnosis of rotating machinery. Advanced signal processing is utilized for the construction of features that are specialized in detecting fuel injector faults. A performance comparison between novelty detection algorithms in the form of one-class classifiers is presented. The one-class classifiers that were tested included One-Class Support Vector Machine (OCSVM) and One-Class Self Organizing Map (OCSOM). The acoustic signals of fuel injectors in different operational conditions were processed for feature extraction. Features from all the signals were used as input to the one-class classifiers. The one-class classifiers were trained only with healthy fuel injector conditions and compared with new experimental data which belonged to different operational conditions that were not included in the training set so as to contribute to generalization. The results present the effectiveness of one-class classifiers for detecting faults in fuel injectors. 展开更多
关键词 Fuel Injectors FAULT Detection ACOUSTICS NEURAL Networks ONE-CLASS classifiers
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Clustering-Aided Supervised Malware Detection with Specialized Classifiers and Early Consensus
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作者 Murat Dener Sercan Gulburun 《Computers, Materials & Continua》 SCIE EI 2023年第4期1235-1251,共17页
One of the most common types of threats to the digital world is malicious software.It is of great importance to detect and prevent existing and new malware before it damages information assets.Machine learning approac... One of the most common types of threats to the digital world is malicious software.It is of great importance to detect and prevent existing and new malware before it damages information assets.Machine learning approaches are used effectively for this purpose.In this study,we present a model in which supervised and unsupervised learning algorithms are used together.Clustering is used to enhance the prediction performance of the supervised classifiers.The aim of the proposed model is to make predictions in the shortest possible time with high accuracy and f1 score.In the first stage of the model,the data are clustered with the k-means algorithm.In the second stage,the prediction is made with the combination of the classifier with the best prediction performance for the related cluster.While choosing the best classifiers for the given clusters,triple combinations of ten machine learning algorithms(kernel support vector machine,k-nearest neighbor,naive Bayes,decision tree,random forest,extra gradient boosting,categorical boosting,adaptive boosting,extra trees,and gradient boosting)are used.The selected triple classifier combination is positioned in two stages.The prediction time of the model is improved by positioning the classifier with the slowest prediction time in the second stage.The selected triple classifier combination is positioned in two tiers.The prediction time of the model is improved by positioning the classifier with the highest prediction time in the second tier.It is seen that clustering before classification improves prediction performance,which is presented using Blue Hexagon Open Dataset for Malware Analysis(BODMAS),Elastic Malware Benchmark for Empowering Researchers(EMBER)2018 and Kaggle malware detection datasets.The model has 99.74%accuracy and 99.77%f1 score for the BODMAS dataset,99.04%accuracy and 98.63%f1 score for the Kaggle malware detection dataset,and 96.77%accuracy and 96.77%f1 score for the EMBER 2018 dataset.In addition,the tiered positioning of classifiers shortened the average prediction time by 76.13%for the BODMAS dataset and 95.95%for the EMBER 2018 dataset.The proposed method’s prediction performance is better than the rest of the studies in the literature in which BODMAS and EMBER 2018 datasets are used. 展开更多
关键词 Malware detection ensemble learning classifiCATION CLUSTERING specialized classifier early consensus
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Video-Based Face Recognition with New Classifiers
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作者 Soniya Singhal Madasu Hanmandlu Shantaram Vasikarla 《Journal of Modern Physics》 2021年第3期361-379,共19页
An exhaustive study has been conducted on face videos from YouTube video dataset for real time face recognition using the features from deep learning architectures and also the information set features. Our objective ... An exhaustive study has been conducted on face videos from YouTube video dataset for real time face recognition using the features from deep learning architectures and also the information set features. Our objective is to cash in on a plethora of deep learning architectures and information set features. The deep learning architectures dig in features from several layers of convolution and max-pooling layers though a placement of these layers is architecture dependent. On the other hand, the information set features depend on the entropy function for the generation of features. A comparative study of deep learning and information set features is made using the well-known classifiers in addition to developing Constrained Hanman Transform (CHT) and Weighted Hanman Transform (WHT) classifiers. It is demonstrated that information set features and deep learning features have comparable performance. However, sigmoid-based information set features using the new classifiers are found to outperform MobileNet features. 展开更多
关键词 Face Recognition on Videos Information Sets Constrained Hanman Transform classifier Weighted Hanman Transform classifier Video Face Dataset MobileNet Vgg-16 Inception Net ResNet
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