Set Of Statistical Features Research Articles

Credit Card Fraud: Analysis of Feature Extraction Techniques for Ensemble Hidden Markov Model Prediction Approach

In the face of escalating credit card fraud due to the surge in e-commerce activities, effectively distinguishing between legitimate and fraudulent transactions has become increasingly challenging. To address this, various machine learning (ML) techniques have been employed to safeguard cardholders and financial institutions. This article explores the use of the Ensemble Hidden Markov Model (EHMM) combined with two distinct feature extraction methods: principal component analysis (PCA) and a proposed statistical feature set termed MRE, comprising Mean, Relative Amplitude, and Entropy. Both the PCA-EHMM and MRE-EHMM approaches were evaluated using a dataset of European cardholders and demonstrated comparable performance in terms of recall (sensitivity), specificity, precision, and F1-score. Notably, the MRE-EHMM method exhibited significantly reduced computational complexity, making it more suitable for real-time credit card fraud detection. Results also demonstrated that the PCA and MRE approaches perform significantly better when integrated with the EHMM in contrast to the conventional HMM approach. In addition, the proposed MRE-EHMM and PCA-EHMM techniques outperform other classic ML models, including random forest (RF), linear regression (LR), decision trees (DT) and K-nearest neighbour (KNN).

Helical Gearbox Defect Detection with Machine Learning Using Regular Mesh Components and Sidebands.

The current paper presents helical gearbox defect detection models built from raw vibration signals measured using a triaxial accelerometer. Gear faults, such as localized pitting, localized wear on helical pinion tooth flanks, and low lubricant level, are under observation for three rotating velocities of the actuator and three load levels at the speed reducer output. The emphasis is on the strong connection between the gear faults and the fundamental meshing frequency GMF, its harmonics, and the sidebands found in the vibration spectrum as an effect of the amplitude modulation (AM) and phase modulation (PM). Several sets of features representing powers on selected frequency bands or/and associated peak amplitudes from the vibration spectrum, and also, for comparison, time-domain and frequency-domain statistical feature sets, are proposed as predictors in the defect detection task. The best performing detection model, with a testing accuracy of 99.73%, is based on SVM (Support Vector Machine) with a cubic kernel, and the features used are the band powers associated with six GMF harmonics and two sideband pairs for all three accelerometer axes, regardless of the rotation velocities and the load levels.

Hybrid model for rainfall prediction with statistical and technical indicator feature set

As excessive rain may cause numerous disasters, rainfall prediction is very crucial and the prediction should be realistic since it encourages individuals to take preventative steps. This work proposes a new rainfall prediction model by following three major phases: Preprocessing, Extraction of Features and Prediction. The Improved Data Normalization technique is carried out for preprocessing the data. Then, it extracts the technical Indicator features like Average Directional Movement (ADX), Moving Average Convergence Divergence (MACD), RSI, Welles Wilder's Smoothing Average (WWS), statistical & higher order statistical features. According to these features, prediction takes place by a hybrid model that includes the Improved DBN & LSTM methods. By fine-tuning the best weights, the training will be carried out optimally to increase the prediction model's efficacy. For this, the Brownian Motion-based Pelican Optimization Algorithm (BMPOA) is implemented. Finally, the proposed technique has contrasted over existing models concerning different metrics.

Orthogonal projection based statistical feature extraction for continuous process monitoring

Multivariate statistical techniques have been widely applied in industrial processes to detect abnormal behaviors, while their performance could be unsatisfactory due to insufficient extraction of complex data characteristics. A method named Orthogonal transformed statistics Mahalanobis distance (OTSMD) is developed to handle this issue. As a feature-based method, OTSMD simultaneously considers various data characteristics through monitoring statistical features of process variables. Orthogonal transformed components (OTCs) are first calculated to capture variable correlation, and a set of statistical features is determined to extract other crucial characteristics, especially for the process non-stationarity. Statistical features of OTCs, which reveals implied process information, are continuously obtained using a sliding window, and a Mahalanobis distance index is utilized for fault detection. Compared with existing methods, OTSMD extracts data characteristics more comprehensively with a lower dimension, making it more effective in monitoring various faults. The results are illustrated through a numerical example, and two chemical industrial processes.

Fast FPGA-Based Image Feature Extraction for Data Fusion in Autonomous Vehicles.

Computer vision plays a critical role in many applications, particularly in the domain of autonomous vehicles. To achieve high-level image processing tasks such as image classification and object tracking, it is essential to extract low-level features from the image data. However, in order to integrate these compute-intensive tasks into a control loop, they must be completed as quickly as possible. This paper presents a novel FPGA-based system for fast and accurate image feature extraction, specifically designed to meet the constraints of data fusion in autonomous vehicles. The system computes a set of generic statistical image features, including contrast, homogeneity, and entropy, and is implemented on two Xilinx FPGA platforms - an Alveo U200 Data Center Accelerator Card and a Zynq UltraScale+ MPSoC ZCU104 Evaluation Kit. Experimental results show that the proposed system achieves high-speed image feature extraction with low latency, making it well-suited for use in autonomous vehicle systems that require real-time image processing. The presented system can also be easily extended to extract additional features for various image and data fusion applications.

Detection of attention deficit hyperactivity disorder based on EEG signals using Least Square Support Vector Machine (LS-SVM)

ABSTRACT Attention Deficit Hyperactivity Disorder (ADHD) is a mental disorder affecting children in their early stages. Detection of ADHD is considered a challenging task for experts because there is no accurate test to determine whether a child has ADHD or not. Mainly, most of previous studies are used electroencephalography (EEG) signals to detect ADHD. In this study, a robust model for ADHD detection-integrated discrete wavelet transform (DWT), statistical features, and a least square support vector machine (LS-SVM) is proposed to detect ADHD from EEG signals. The EEG signals are decomposed into five bands and then, a set of statistical features are tested to find the optimal feature sets using K-means model. A public dataset is used to evaluate the proposed model. A total of 45 ADHD patients and 45 healthy are involved to evaluate the proposed model. Several metrics are used to evaluate the proposed model including 10-fold cross-validation precision, sensitivity, and specificity metrics. A channel selection is also investigated, and we found that the channels ‘P3, P7, Pz’ are more important and gave better prediction results than other channels. The proposed model obtained an average accuracy of 98.06% with LS-SVM, and 95.46% with the KNN classifier.

Real-time detection of street tree crowns using mobile laser scanning based on pointwise classification

For targeted spraying in urban streets using mobile laser scanning (MLS), it is crucial to continuously detect tree crowns in real time from MLS data collected online. Crown detection based on a pointwise classification method, which predicts whether a point belongs to a crown using a binary classifier composed of a set of local features extracted from the neighbourhood of the point, is suitable for online data processing. However, the computational complexity of pointwise detection methods needs to be reduced to meet the real-time requirements of online targeted spraying. This paper proposes a real-time detection method for street tree crowns using MLS based on pointwise classification. A cubic neighbourhood is adopted from which a set of statistical features are extracted. A coarse-to-fine neighbourhood search method based on an MLS data grid storage structure is proposed to quickly query the neighbours. The classification performance and computational time of single features are evaluated. Then, the features are ranked using the technique for order preference by similarity to the ideal solution (TOPSIS). Finally, a subset of features that are easy to calculate and have high discrimination are selected and fused into a crown detector using a random forest algorithm. During online detection, the point cloud is sampled in terms of the vehicle movement and LiDAR measurement before being detected. An MLS point cloud collected from a 451 m urban street is used in the experiments. When the neighbourhood size is set to 0.3 m, the detection time per scanline is 24.84 ms on the unsampled test set with an F1 score of 0.9927. When the test set is sampled with a total sampling rate of 40, the detection time is reduced to 1% with an F1 score of 0.9906. The experimental results show that the proposed method can provide real-time and accurate detection of street tree crowns for online targeted spraying.

Interpretation of a deep analysis of speech imagery features extracted by a capsule neural network

Speech imagery has been successfully employed in developing Brain–Computer Interfaces because it is a novel mental strategy that generates brain activity more intuitively than evoked potentials or motor imagery. There are many methods to analyze speech imagery signals, but those based on deep neural networks achieve the best results. However, more research is necessary to understand the properties and features that describe imagined phonemes and words. In this paper, we analyze the statistical properties of speech imagery EEG signals from the KaraOne dataset to design a method that classifies imagined phonemes and words. With this analysis, we propose a Capsule Neural Network that categorizes speech imagery patterns into bilabial, nasal, consonant-vocal, and vowels/iy/ and/uw/. The method is called Capsules for Speech Imagery Analysis (CapsK-SI). The input of CapsK-SI is a set of statistical features of EEG speech imagery signals. The architecture of the Capsule Neural Network is composed of a convolution layer, a primary capsule layer, and a class capsule layer. The average accuracy reached is 90.88%±7 for bilabial, 90.15%±8 for nasal, 94.02%±6 for consonant–vowel, 89.70%±8 for word-phoneme, 94.33%± for/iy/ vowel and, 94.21%±3 for/uw/ vowel detection. Finally, with the activity vectors of the CapsK-SI capsules, we generated brain maps to represent brain activity in the production of bilabial, nasal, and consonant-vocal signals.

Performance evaluation of LSTM and Bi-LSTM using non-convolutional features for blockage detection in centrifugal pump

Blockages in the suction or discharge side of the pump adversely affect the pump’s performance by reducing the flow rate and head, increasing vibration, noise, and overheating. Thus, diagnosing pump blockages accurately and quickly is vital. In this study, a test rig is developed to detect blockages in the pump (suction, discharge, and simultaneous) at three levels of severity. Initially, a non-convolutional feature layer is developed to extract sets of statistical features (SF), entropy-based parameters, and Holder’s exponent (HE). Next, two sequential learners, LSTM and Bi-LSTM, are trained with statistical parameters and tuned using grid-search optimization. Model with least validation loss is chosen as the base model. Subsequently, the process is repeated with only non-linear features, combination of SF with HE, and combination of all three sets of features. Thus, eight different models are trained (four each for LSTM and Bi-LSTM). To eradicate the impact of random variations, these models are trained ten times and the average values of performance metrics (precision, recall, and F1score) are noted. It is found that LSTM trained with all sets of features is most accurate at 99.01% with F1 score of 98.95%. Although LSTM is superior, it is marginally ahead of Bi-LSTM, but LSTM consumes almost 40% lesser time for training. Toward the end, the ablation study of LSTM is performed to analyze the effects of the most influencing parameters. Thus, the present study emphasizes an automated and robust method for maintenance engineers to diagnose the blockages in the pump with severities of blockages.

Using the electrodermal activity signal and machine learning for diagnosing sleep

IntroductionThe use of the electrodermal activity (EDA) signal for health diagnostics is becoming increasingly popular. The increase is due to advances in computational methods such as machine learning (ML) and the availability of wearable devices capable of better measuring EDA signals. One field where work on EDA has significantly increased is sleep research, as changes in EDA are related to different aspects of sleep and sleep health such as sleep stages and sleep-disordered breathing; for example, obstructive sleep apnoea (OSA).MethodsIn this work, we used supervised machine learning, particularly the extreme gradient boosting (XGBoost) algorithm, to develop models for detecting sleep stages and OSA. We considered clinical knowledge of EDA during particular sleep stages and OSA occurrences, complementing a standard statistical feature set with EDA-specific variables.ResultsWe obtained an average macro F1-score of 57.5% and 66.6%, depending on whether we considered five or four sleep stages, respectively. When detecting OSA, regardless of the severity, the model reached an accuracy of 83.7% or 78.4%, depending on the measure used to classify the participant's sleep health status.ConclusionThe research work presented here provides further evidence that, in the future, most sleep health diagnostics might well do without complete polysomnography (PSG) studies, as wearables can detect well the EDA signal.

Machine Learning Models for Weight-Bearing Activity Type Recognition Based on Accelerometry in Postmenopausal Women

Hip-worn triaxial accelerometers are widely used to assess physical activity in terms of energy expenditure. Methods for classification in terms of different types of activity of relevance to the skeleton in populations at risk of osteoporosis are not currently available. This publication aims to assess the accuracy of four machine learning models on binary (standing and walking) and tertiary (standing, walking, and jogging) classification tasks in postmenopausal women. Eighty women performed a shuttle test on an indoor track, of which thirty performed the same test on an indoor treadmill. The raw accelerometer data were pre-processed, converted into eighteen different features and then combined into nine unique feature sets. The four machine learning models were evaluated using three different validation methods. Using the leave-one-out validation method, the highest average accuracy for the binary classification model, 99.61%, was produced by a k-NN Manhattan classifier using a basic statistical feature set. For the tertiary classification model, the highest average accuracy, 94.04%, was produced by a k-NN Manhattan classifier using a feature set that included all 18 features. The methods and classifiers within this study can be applied to accelerometer data to more accurately characterize weight-bearing activity which are important to skeletal health.

Condition Monitoring of an All-Terrain Vehicle Gear Train Assembly Using Deep Learning Algorithms with Vibration Signals

Condition monitoring of gear train assembly has been carried out with vibration signals acquired from an all-terrain vehicle (ATV) gearbox. The location of the defect in the gear was identified based on finite element analysis results. The vibration signals were acquired using an accelerometer under good and simulated fault conditions of the gear. The raw vibration signatures acquired from all the possible conditions of the gear train assembly were processed using the descriptive statistics tool. A set of descriptive statistical features were extracted from the raw vibrational signals. This study used a deep learning algorithm based on the tree family, which includes the decision tree, random forest, and random tree algorithms, to classify gear train conditions. Among the tree family algorithms, the random forest algorithm produced maximum classification accuracy of 99%. The decision rules were used to design an online monitoring system to display the gear condition. This study will help to implement online gear health monitoring in ATVs, ensuring the safety of drivers.

Bearing Fault Classification Using Statistical Features And Machine Learning Approach

<p>Bearing degradation is the most common source of faults in machines. In this context, this work presents a monitoring scheme to diagnose bearing faults using machine learning approach. In this approach classification of healthy and faulty conditions of the bearing is carried out using artificial neural network (ANN). A set of statistical features are extracted from the acquired vibration signals. The decision tree technique is used to select significant features out of all statistical extracted features. The selected features were classified using different classifiers. Based on the various classifier results obtained, the ANN classifier achieve the maximum classification accuracy which is recommended for online monitoring and fault diagnosis of the bearing in various machines.</p>

Detection of stage of lung changes in COVID-19 disease based on CT images: a radiomics approach.

The aim of this study is to classify patients suspected from COVID-19 to five stages as normal, early, progressive, peak, and absorption stages using radiomics approach based on lung computed tomography images. Lung CT scans of 683 people were evaluated. A set of statistical texture features was extracted from each CT image. The people were classified using the random forest algorithm as an ensemble method based on the decision trees outputs to five stages of COVID-19 disease. Proposed method attains the highest result with an accuracy of 93.55% (96.25% in normal, 74.39% in early, 100% in progressive, 82.19% in peak, and 96% in absorption stage) compared to the other three common classifiers. Radiomics method can be used for the classification of the stage of COVID-19 disease with good accuracy to help decide the length of time required to hospitalize patients, determine the type of treatment process required for patients in each category, and reduce the cost of care and treatment for hospitalized individuals.

Detection of Uniform Gearbox Wear in Induction Motors Based on the Analysis of Stray Flux Signals Through Statistical Time-Domain Features and Dimensionality Reduction Techniques

Gearboxes are core elements in power transmission systems. Although gearboxes are reliable and high-efficiency components, the occurrence of different faults is frequent since they are often subjected to adverse operating conditions. Classical gearbox condition monitoring approaches are based on the analysis of vibration and current motor signals and rely on the identification of specific fault-related frequency patterns. In this regard, this article proposes a novel diagnosis methodology based on the analysis of stray flux signals for detecting uniform wear in the gear teeth. The proposed methodology is based on the processing of the stray flux signals through feature calculation and extraction stages that lead to a high-performance signal characterization by estimating a set of statistical time domain-based features and then reducing the dimensionally by means of the principal component analysis and linear discriminant analysis techniques. Additionally, an automatic fault diagnosis is achieved through a neural network-based classifier for the detection and identification of uniform wear in a gearbox. The obtained results prove the potential of the proposal for its incorporation in condition maintenance programs in the industry, becoming an excellent alternative to classical approaches.

Genetic Programming-Based Feature Construction for System Setting Recognition and Component-Level Prognostics

Extracting representative feature sets from raw signals is crucial in Prognostics and Health Management (PHM) for components’ behavior understanding. The literature proposes various methods, including signal processing in the time, frequency, and time–frequency domains, feature selection, and unsupervised feature learning. An emerging task in data science is Feature Construction (FC), which has the advantages of both feature selection and feature learning. In particular, the constructed features address a specific objective function without requiring a label during the construction process. Genetic Programming (GP) is a powerful tool to perform FC in the PHM context, as it allows to obtain distinct feature sets depending on the analysis goal, i.e., diagnostics and prognostics. This paper adopts GP to extract system-level features for machinery setting recognition and component-level features for prognostics. Three distinct fitness functions are considered for the GP training, which requires a set of statistical time-domain features as input. The methodology is applied to vibration signals extracted from a test rig during run-to-failure tests under different settings. The performances of constructed features are evaluated through the classification accuracy and the Remaining Useful Life (RUL) prediction error. Results demonstrate that GP-based features classify known and novel machinery operating conditions better than feature selection and learning methods.

A Multi-Layer stacked ensemble classifier model for improved classification accuracy of Maculopathy gradation

Diabetic Maculopathy (DME) is the serious impediments of diabetes, which may cause permanent blindness unless timely detected. Vision impairment because of diabetes is substantially avoidable with well-timed screening and intervention at primary stages. Presence of most primitive and distinctive signs on the retinal surface is micro-aneurysm and haemorrhage, signify as dark spots and hard and soft exudates signifies as bright lesions. Hence, recognition of all these bright lesions is the first step of automated recognition of DME. In this paper, we present a multi class, multi-layer stacked ensemble classifier-based model with four base learners and one meta-learner for improved exudates (EXs) classification accuracy and maculopathy gradation system. The proposed system involves pre-processing, Scale-Space Extrema Detection(SSED) based extraction of clinically significant bright lesions, shape, colour, intensity, and statistical functions-based feature set creation, Minimum Redundancy-Maximum Relevance (mRMR) feature selection, stacking classifier with Bayesian optimization (BO) for hyper-parameter tuning and severity gradation. Information of location of all types of exudates is accounted for to provide the level of severity of DME. At both the image and lesions levels, the proposed system's quantitative assessment is carried out utilising publicly available databases. When compared to other state-of-the-art methodologies, our system's results have achieved competitive performance in three and two class exudates classification and DME gradation.

BotCap: Machine Learning Approach for Botnet Detection Based on Statistical Features

In this paper, we describe a detailed approach to develop a botnet detection system using machine learning (ML)techniques. Detecting botnet member hosts, or identifying botnet traffic has been the main subject of manyresearch efforts. This research aims to overcome two serious limitations of current botnet detection systems:First, the need for Deep Packet Inspection-DPI and the need to collect traffic from several infected hosts. Toachieve that, we have analyzed several botware samples of known botnets. Based on this analysis, we haveidentified a set of statistical features that may help to distinguish between benign and botnet malicious traffic.Then, we have carried several machine learning experiments in order to test the suitability of ML techniques andalso to pick a minimal subset of the identified features that provide best detection. We have implemented ourapproach in a tool called BotCap whose test results showed its proven ability to detect individually infected hostsin a local network.

Bearing Fault Classification Using Statistical Features And Machine Learning Approach

<p>Bearing degradation is the most common source of faults in machines. In this context, this work presents a monitoring scheme to diagnose bearing faults using machine learning approach. In this approach classification of healthy and faulty conditions of the bearing is carried out using artificial neural network (ANN). A set of statistical features are extracted from the acquired vibration signals. The decision tree technique is used to select significant features out of all statistical extracted features. The selected features were classified using different classifiers. Based on the various classifier results obtained, the ANN classifier achieve the maximum classification accuracy which is recommended for online monitoring and fault diagnosis of the bearing in various machines.</p>

Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study

Correctly defining and grouping electrical feeders is of great importance for electrical system operators. In this paper, we compare two different clustering techniques, K-means and hierarchical agglomerative clustering, applied to real data from the east region of Paraguay. The raw data were pre-processed, resulting in four data sets, namely, (i) a weekly feeder demand, (ii) a monthly feeder demand, (iii) a statistical feature set extracted from the original data and (iv) a seasonal and daily consumption feature set obtained considering the characteristics of the Paraguayan load curve. Considering the four data sets, two clustering algorithms, two distance metrics and five linkage criteria a total of 36 models with the Silhouette, Davies–Bouldin and Calinski–Harabasz index scores was assessed. The K-means algorithms with the seasonal feature data sets showed the best performance considering the Silhouette, Calinski–Harabasz and Davies–Bouldin validation index scores with a configuration of six clusters.