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摘要: As per World Health Organization report which was released in the year of 2019, Diabetes claimed the lives of approximately 1.5 million individuals globally in 2019 and around 450 million people are affected by diabetes all over the world. Hence it is inferred that diabetes is rampant across the world with the majority of the world population being affected by it. Among the diabetics, it can be observed that a large number of people had failed to identify their disease in the initial stage itsel关键词: Diabetes; type-1; type-2; feature selection; classification; fuzzy rules; fuzzy cognitive maps; classifier
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摘要: Crop yield has been predicted using environmental, land, water, and crop characteristics in a prospective research design. When it comes to predicting crop production, there are a number of factors to consider, including weather conditions, soil qualities, water levels and the location of the farm. A broad variety of algorithms based on deep learning are used to extract useful crops for forecasting. The combination of data mining and deep learning creates a whole crop yield prediction system tha关键词: Data mining; deep learning; crop production; tweak chick swarm optimization algorithm; discrete deep belief network with VGG Net classifier
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摘要: Nowadays in the medical field, imaging techniques such as Optical Coherence Tomography (OCT) are mainly used to identify retinal diseases. In this paper, the Central Serous Chorio Retinopathy (CSCR) image is analyzed for various stages and then compares the difference between CSCR before as well as after treatment using different application methods. The first approach, which was focused on image quality, improves medical image accuracy. An enhancement algorithm was implemented to improve the OC关键词: OCT; CSCR; macula; segmentation; boosted anisotropic diffusion with unsharp masking filter; two class support vector machine classifier and shallow neural network with powell-beale classifier
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摘要: The number of attacks is growing tremendously in tandem with the growth of internet technologies. As a result, protecting the private data from prying eyes has become a critical and tough undertaking. Many intrusion detection solutions have been offered by researchers in order to decrease the effect of these attacks. For attack detection, the prior system has created an SMSRPF (Stacking Model Significant Rule Power Factor) classifier. To provide creative instance detection, the SMSRPF combines t关键词: Intrusion detection system (IDS); ensemble wrapper filter (EWF); stacking model with significant rule power factor (SMSRPF); classifier
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摘要: In recent years, Peripheral blood smear is a generic analysis to assess the person’s health status. Manual testing of Peripheral blood smear images are difficult, time-consuming and is subject to human intervention and visual error. This method encouraged for researchers to present algorithms and techniques to perform the peripheral blood smear analysis with the help of computer-assisted and decision-making techniques. Existing CAD based methods are lacks in attaining the accurate detection of a关键词: Peripheral blood smear; DCNN classifier; pre-processing; segmentation; feature extraction; salp swarm optimization; classification
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摘要: Worldwide, many elders are suffering from Alzheimer’s disease (AD). The elders with AD exhibit various abnormalities in their activities, such as sleep disturbances, wandering aimlessly, forgetting activities, etc., which are the strong signs and symptoms of AD progression. Recognizing these symptoms in advance could assist to a quicker diagnosis and treatment and to prevent the progression of Disease to the next stage. The proposed method aims to detect the behavioral abnormalities found in Dai关键词: Alzheimer’s disease; abnormal activity detection; classifier chain; multi-headed CNN-LSTM; wearable sensor
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摘要: Automatic Speech Emotion Recognition (SER) is used to recognize emotion from speech automatically. Speech Emotion recognition is working well in a laboratory environment but real-time emotion recognition has been influenced by the variations in gender, age, the cultural and acoustical background of the speaker. The acoustical resemblance between emotional expressions further increases the complexity of recognition. Many recent research works are concentrated to address these effects individually关键词: Speech emotion recognition; hierarchical classifier design; ensemble; emotion speech corpora
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摘要: Background(#br)Modern day's society is engaged in commitment-based and time-bound jobs. This invites tension and mental depression among many people who are not able to cope up with this type of working environment. Cases of mental depression are increasing day by day all over the world. Recently, the onset of the COVID-19 pandemic has added further fuel to the fire. In many countries, the ratio between patients with mental depression and psychiatrists or psychologists is remarkably poor. Under 关键词: Multi-layer perceptron (MLP);Convolution neural network with MLP as a classifier (CNN);Recurrent neural network (RNN);RNN with LSTM (long- and short-term memory);Support vector machine (SVM);Logistic regression (LR);Mental depression tracker
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Sestito Guilherme Serpa;Turcato Afonso Celso;Dias Andre Luis;Ferrari Paolo;da Silva Maíra Martins;
Expert Systems With ApplicationsVolume 206, Issue , 2022, PP
摘要: Reliability and dependability are critical demands of the fourth industrial revolution that Real-time Ethernet (RTE) networks have to meet. The use of anomaly detection and prevention techniques can further enhance existing RTE networks. This work presents a general and efficient anomaly detection strategy based on machine learning techniques. The proposal is of general purpose since only normal (i.e not anomalous) traffic data and statistical features are used during the training phase of the c关键词: OCSVM classifier;Differential evolution;PROFINET;Ethernet/IP;Anomaly detection
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摘要: The paper focuses on the construction of an artificial intelligence-based heart disease detection system using machine learning algorithms. We show how machine learning can help predict whether a person will develop heart disease. In this paper, a python-based application is developed for healthcare research as it is more reliable and helps track and establish different types of health monitoring applications. We present data processing that entails working with categorical variables and convers关键词: Artificial intelligence;Heart disease detection system;Machine learning;Predictive analytics;Random forest classifier algorithm
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摘要: High-dimension and low-sample-size (HDLSS) data sets have posed great challenges to many machine learning methods. To deal with practical HDLSS problems, development of new classification techniques is highly desired. After the cause of the over-fitting phenomenon is identified, a new classification criterion for HDLSS data sets, termed tolerance similarity, is proposed to emphasize maximization of within-class variance on the premise of class separability. Leveraging on this criterion, a novel 关键词: Binary linear classifier;Quadratic programming;Data piling;Covariance matrix
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摘要: Correlative microscopy combines data from different microscopical techniques to gain unique insights about specimens. A key requirement to unlocking the full potential is an advanced classification method that can combine the various analytical signals into physically meaningful phases. The prevalence of highly imbalanced class distributions and high intra-class variability in such real applications makes this a difficult task, yet no study of classifier performance exists in the context of corr关键词: Correlative microscopy;Machine learning;Classification;Multiple classifier systems;Dynamic selection
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摘要: Lately, Convolutional Neural Networks (CNNs) have been introduced to extract features and further enhance the classification performance in different application areas. In this study, a hybrid fuzzy multiple (HFM)-SVM design with the convolution-base (which consists of a series of pooling and convolutional layers) and composite kernel function is proposed. The objective of the proposed classifier is to enhance the nonlinear fitting ability of the classifier and improve the classification perform关键词: SVM classifier;Composite kernel function;Face Recognition dataset;Black plastic wastes sorting;Partial discharge dataset
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Pagès-Zamora Alba;Ochoa Idoia;Cavero Gonzalo Ruiz;Villalvilla-Ornat Pol;
Pattern RecognitionVolume 129, Issue , 2022, PP
摘要: Unsupervised ensemble learning refers to methods devised for a particular task that combine data provided by decision learners taking into account their reliability, which is usually inferred from the data. Here, the variant calling step of the next generation sequencing technologies is formulated as an unsupervised ensemble classification problem. A variant calling algorithm based on the expectation-maximization algorithm is further proposed that estimates the maximum-a-posteriori decision amon关键词: Expectation maximization algorithm;Variant calling;Genome sequencing;Unsupervised multi-class ensemble classifier;GATK
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摘要: This paper proposes heterogeneous based ensemble Classifiers (voting and stacking method) to identify and classify different power system disturbances (power quality (PQ), faults, transients, and wind power variation) in wind integrated microgrid network. In the pre-processing stage of classification, a Discrete wavelet transform (DWT) technique is applied to extract the features from power system disturbance signals. The classification process for the proposed ensemble models involves two level关键词: Power quality (PQ);Discrete wavelet transform (DWT);Ensemble classifier;Logistic regression (LR) classifier;K-Nearest Neighbor (KNN) classifier
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摘要: Façade defect classification based on deep learning has made great progresses in recent years. However, deep learning models commonly need abundant labeled data for training, and it could be impractical and expensive to collect sufficient labeled samples for all classes of defects. Sometimes, there are only a few samples in rare classes, which are not able to support the training process. In addition, common classifiers based on deep learning cannot easily extend their recognition classes and th关键词: Façade defects;Few-shot learning;Extensible classifier;Contrastive learning
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摘要: In the medical diagnosis, expensive costs will increase significantly with the increment of medical information, and they can be reduced by data mining methods. The neighborhood classifier, as one of the extensions of the neighborhood rough set, has become an intuitive and effective classification method in data mining. However, there are still some defects which limit its performance. On the one hand, most existing neighborhood classifiers suffer from high computation complexity to obtain the n关键词: Neighborhood rough set;Neighborhood classifier;Hash bucket;Neighborhood radius;Distance voting rule;Medical diagnosis
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摘要: Healthcare is one of the key areas of prospect for the Internet of Things (IoT). To facilitate better medical services, enormous growth in the field of the Internet of Medical Things (IoMT) is observed recently. Despite the numerous benefits, the cyber threats on connected healthcare devices can compromise privacy and can also cause damage to the health of the concerned patient. The massive demand for IoMT devices with seamless and effective medical facilities for the large-scale population requ关键词: Internet of Medical Things;Intrusion detection;Tree classifier;Security;Estimator
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摘要: Ensembles of classifiers have been receiving much attention lately, they consist of a collection of classifiers that process the same information and their output is combined in some manner. The combination method is probably the most important part in a ensemble of classifiers however, many works found in literature focus mostly on the classification step, using simple approaches on the combination step, such as majority voting. In this paper, we propose a new combination method based on a gene关键词: Classifier ensembles;Choquet integrals;Classification;Quasi-overlap functions
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摘要: Learning Bayesian network classifiers (BNCs) from data is NP-hard. Of numerous BNCs, averaged one-dependence estimator (AODE) performs extremely well against more sophisticated newcomers, and its trade-off between bias and variance can be attributed to the independence assumption and i.i.d. assumption, which respectively address the issues of structure complexity and data complexity. To alleviate these assumptions and improve AODE, we propose to apply double weighting, including attribute weight关键词: Bayesian network classifier;Attribute weighting;Model weighting;Generative learning;Discriminative learning
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摘要: Neighborhood granulation underlies neighborhood rough sets, and it also induces the basic classifier of K-nearest neighbor (KNN). Based on neighborhood granulation and its distance measurement, a relative-quantitative classifier (KNGR) and an absolute-quantitative classifier (KNGA) already promote the KNN classifier, and a double-quantitative classifier (KNGD) makes a further improvement but still has advancement space. This paper devotes to developing more general and robust double-quantitative关键词: Neighborhood rough sets;Granular computing;Statistical distance measurement;Double quantization;Classification learning;K-nearest neighbor classifier
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摘要: Current feature adaptation methods align the joint distributions across domains. But they may be limited because the difference between distributions cannot be completely eliminated. Existing classifier adaptation methods find the shared classifier across domains based on the original features or Manifold features. However, the shared classifier may be ineffective due to the high granularity at the category level of the features. Inspired by these, we propose the unsupervised domain adaptation v关键词: Classifier adaptation;Center-based distances;Discriminative feature learning;Laplacian Regularization;Unsupervised domain adaptation
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摘要: Hop Count Matrix (HCM) contains rich connectivity information, which is very important for various Internet of Things (IoT) applications, especially for obtaining the locations of sensor nodes. However, some items of HCMs may be missing due to attacks by malicious nodes or unexpected termination of flooding operations. To solve this problem, two methods, called HCMR-AM and HCMR-DT, are proposed to recover the missing items. In HCMR-AM, the collected partial hop counts are employed to construct A关键词: Internet of Things (IoT);Adjacency Matrix (AM);Decision Tree classifier (DT);Hop Count Matrix Recovery (HCMR);Node localization
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Costache Romulus;Arabameri Alireza;Costache Iulia;Crăciun Anca;Md Towfiqul Islam Abu Reza;Abba S.I.;Sahana Mehebub;Pham Binh Thai;
Journal of Environmental ManagementVolume 316, Issue , 2022, PP 115316-115316
摘要: It is difficult to predict and model with an accurate model the floods, that are one of the most destructive risks across the earth's surface. The main objective of this research is to show the prediction power of three ensemble algorithms with respect to flood susceptibility estimation. These algorithms are: Iterative Classifier Optimizer – Alternating Decision Tree – Frequency Ratio (ICO-ADT-FR), Iterative Classifier Optimizer – Deep Learning Neural Network – Frequency Ratio (ICO-DLNN-FR) and 关键词: Flood susceptibility;Romania;Iterative classifier optimizer;Neural networks;Decision tree
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He Yu-Lin;Ou Gui-Liang;Fournier-Viger Philippe;Huang Joshua Zhexue;Suganthan Ponnuthurai Nagaratnam;
Expert Systems With ApplicationsVolume 199, Issue , 2022, PP
摘要: How to design an efficient method to handle mixed-attribute data classification (MADC) problems has become a hot topic in data mining and machine learning. Current MADC methods mostly transform mixed-attribute data into discrete-attribute data or continuous-attribute data before classification algorithms are trained. The discretization of continuous-attribute data usually results in information loss, while the binarization of discrete-attribute data generally yield more discrete-attributes. To a关键词: Mixed-attribute data classification;Attribute independence;Random vector functional link network;Naive Bayes classifier;One-hot encoding;Attribute discretization
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摘要: Human face detection along with its localization is a difficult task when the face is presented in the cluttered scene in an unconstrained scenario that might be with arbitrary pose variations, occlusions, random backgrounds and infrared (IR) environment. This paper proposes a novel face detection method which can address some of these issues and challenges quite successfully during face detection in unconstrained as well as infrared environments. It makes use of Fast Successive Mean Quantizatio关键词: Face detection;FastSMQT;Split up SNOW classifier;Pose;Occlusion;Blur;Labeled faces;Crowd faces
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摘要: In spite of the increasing prevalence of asthma worldwide, it often remains underdiagnosed and untreated, causing permanent damage to lungs. In this scenario, early diagnosis and continuous monitoring is extremely necessary to control this by maintaining proper pulmonary function. Despite their wide applications in diagnosing asthma, conventional methods often suffer from bulky setup, high cost, huge dependency on patient effort, and intrusion in natural breathing. This leads to a greater demand关键词: Asthma;Complete ensemble empirical mode decomposition with adaptive noise;ECG derived respiration;Principal component analysis;Supervised classifier
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摘要: Pipelines are one of the most common systems for storing and transporting petroleum products, both liquid and gaseous. Despite the durable structures, leakages can occur for many reasons, causing environmental disasters, energy waste, and, in some cases, human losses. The object of the ESTHISIS project is the development of a low-cost and low-energy wireless sensor system for the immediate detection of leaks in metallic piping systems for the transport of liquid and gaseous petroleum products in关键词: Leakage detection;Oil pipeline;Deep learning;CNN classifier;LSTM autoencoders
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摘要: The main intent of this work is to implement an intelligent method for the diabetes detection and blood glucose level prediction. Initially, the data’s are taken from the significant benchmark datasets known as the PIMA and the UCI dataset. It is subjected to the deep feature extraction using hybrid meta-heuristic-based Convolutional Neural Network (CNN) with two max pooling and two convolutional layers. A new novel algorithm is developed named MF-CSA that can handle the multi-objective optimiza关键词: Diabetes detection;Glucose level prediction;Hybrid ;meta;-heuristic-based CNN;Modified fuzzy classifier;Enhanced recurrent neural network;Moth flame-based crow search algorithm
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摘要: Improving workers’ safety and health is one of the most critical issues in the construction industry. Research attempts have been made to better identify construction hazards on a jobsite by analyzing workers’ physical responses (e.g., stride and balance) or physiological responses (e.g., brain waves and heart rate) collected from the wearable devices. Among them, electroencephalogram (EEG) holds unique potential since it reveals abnormal patterns immediately when a hazard is perceived and recog关键词: Construction safety;Hazard classification;Electroencephalogram (EEG);Wearable EEG;Virtual reality (VR);EEG classifier
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