Classification Accuracy Of Method Research Articles

Classification and Fast Detection of Transmission Line Faults Using Signal Entropy

A simple, prompt and accurate method of fault identification and classification of faults in a long transmission system is presented here using entropy analysis of post-fault three-phase current signals, measured at the sending end only. Identification of the faulted phase is imperative for restricting unwanted outage of power through the faulted phase, as well as isolation of the same in order to retain the stability of the system. Transmission line fault classification, hence, has become one of the most vital topics of research. The high-frequency transient current oscillations, appearing immediately after the fault, are analyzed in this work. Signal entropy is computed for the modified waveform of each phase using the differentiation method to highlight the edges of oscillation. Entropy measures the randomness of a signal; hence, the sudden disturbance caused in the directly affected faulted line yields much higher entropy compared to the un-faulted lines. It is further observed that the extent of disturbance in the three phases primarily depends on the type of fault caused: ground fault or non-ground fault. An attempt has been made closely to monitor the entropy levels of the three-phase currents to distinguish between each phase in terms of the level of disturbance caused in a specific class of fault using the estimated signal entropy values, thereby aiding in the development of a threshold-based fault classifier entropy signatures. The major findings of the present work are primarily twofold: 100% accuracy of classification and requirement of only (1/20)th of post-fault signal, that is, detection within 1 ms, which is commendably fast compared to several contemporary works. Besides, the variation of fault parameters, such as fault location, fault resistance and power line noise, makes the design more practically suited.

Towards early sepsis detection from measurements at the general ward through deep learning

BackgroundSepsis is one of the leading causes of death in the hospital. Several warning scores have been developed to categorize patients’ degrees of illness, with the purpose of recognizing sepsis onset at an early stage and consequently reducing time before starting treatment. The most accurate classification method, known as the SOFA score, is developed for use in the intensive care unit (ICU). ObjectiveSepsis is not exclusively developing in the ICU and may occur in any hospitalized patient. Therefore, a reliable method for sepsis recognition using routinely gathered information from the general ward is of major importance. MethodsRecently, the use of computational methods has been proposed for early sepsis prediction. Multiple sepsis classifiers have been devised using machine learning methods. We validated a linear classification model and improved upon it using a deep neural network trained on data from the MIMIC-III database. ResultsThe reference model approach yielded an Area Under Precision-Recall Curve (AUPRC) of 0.45 for a 3-h prediction time. Our newly constructed deep neural network outperformed the reference model, reaching an AUPRC of 0.49 for a 3-h prediction time. These results demonstrate to be robust for multiple prediction times, utilizing different datasets and multiple sepsis criteria. ConclusionsOur results are comparable to a high-resolution model, yet using only 8 simple and commonly performed measurements, instead of the complex set of dozens of measurements leveraged by other authors. Moreover, our method proves to adapt to different datasets and sepsis criteria, yielding a high accuracy. Therefore, sepsis onset prediction may also be viable in less monitored environments in the hospital, such as the general ward and the emergency room.

Semi-automatic multi-segmentation classification for land cover change dynamics in North Macedonia from 1988 to 2014

Land cover assessment and monitoring are essential for sustainable management of natural resources and environmental protection. Object-based image analysis (OBIA) for land cover classification has become an area of interest due to its superiority over the pixel-based classification method. The main objective of this paper is developing a method for land cover classification on the national and sub-national level in the Republic of North Macedonia for mapping and monitoring the land cover changes in the study area from 1988 to 2014. For that purpose, in this study, we combine OBIA with rule set semi-automated multi-segmentation classification for large-scale areas over medium-resolution satellite imagery. Thus, Landsat image collections over North Macedonia have been combined with topographic and settlement layers for land cover classification. Based on the knowledge of certain land cover features, rule-based classification has been developed using two different segmentation parameters. The results show that the overall agreement of the new semi-automatic classification method developed for North Macedonia is 83%. The most significant change in the land cover can be noticed in the forest class, with a total increase of 8% on national and 15% in the South-East region. These results confirm that this new semi-automatic, cost-effective, and accurate land cover classification method can be easily employed and adjusted for different study areas and can be used in numerous remote sensing applications.

Machine learning and deep learning algorithms used to diagnosis of Alzheimer’s: Review

A notable contribution to medical diagnosis is made by artificial intelligence algorithms. The objective of this contribution is to help researchers and clinicians with the required machine learning algorithm to classify Alzheimer's. In this article, we demonstrate the previous work in the medical research area of Alzheimer’s disease, compared the efficiency and error of various algorithms. Therefore, the purpose of this study is to include all the relevant knowledge about the machine learning models used in the identification of Alzheimer's. This paper represents the results of different algorithms which are used for the diagnosis of this. The production of this work provides a list of the best machine learning algorithms with precision for disease diagnosis. In recent years, several high-dimensional, accurate, and effective classification methods have been proposed for the automatic discrimination of the subject between Alzheimer’s disease (AD) or its prodromal phase (i.e., mild cognitive impairment (MCI)) and healthy control (HC) persons based on T1-weighted structural magnetic resonance imaging (sMRI). These methods emphasis only on using the individual feature from sMRI images for the classification of AD, MCI, and HC subjects and their achieved classification accuracy is low. However, latest multimodal studies have shown that combining multiple features from different sMRI analysis techniques can improve the classification accuracy for these types of subjects.

Crop classification of modern agricultural park based on time-series Sentinel-2 images

Quickly and accurately grasping the spatial distribution of crops, estimating the area and scope of different crops were of great significance for the country to formulate macroscopic agricultural policies and guide farmers in agricultural production. To explore an efficient and accurate crop classification method, this paper took the agricultural area of Jalaid Banner of Hinggan League in Inner Mongolia Autonomous Region of China as the study area and extracted main crop classification based on the Sentinel-2 satellite remote sensing image data from May to October 2019. By analyzing the time-series curves of the four characteristic indexes of NDVI, RVI, EVI and Ref (NIR) in the study area, a total of four classification methods including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Maximum Likelihood (ML) were used to classify various crops in the study area. The RF results were compared with the classification results of DT, SVM and ML, and the spatial distribution of major crops such as rice, corn, stevia, dry rice and soybean were successfully extracted and identified. The results showed that RF had the highest overall classification accuracy of 95.8% with a Kappa coefficient of 0.944, DT, SVM and ML had classification accuracy of 92.2%, 91.6% and 86.5%, respectively. The above results indicate that the multitemporal Sentinel-2 remote sensing images can be extracted by spectral index time-varying features, and the crop classification using the random forest algorithm can obtain high accuracy results, which provides effective technical support for the fine guidance of large-scale agricultural production in the park.

Detection of Phishing Websites Using Ensemble Machine Learning Approach

In this paper, we propose the use of Ensemble Machine Learning Methods such as Random Forest Algorithm and Extreme Gradient Boosting (XGBOOST) Algorithm for efficient and accurate phishing website detection based on its Uniform Resource Locator. Phishing is one of the most widely executed cybercrimes in the modern digital sphere where an attacker imitates an existing - and often trusted - person or entity in an attempt to capture a victim’s login credentials, account information, and other sensitive data. Phishing websites are visually and semantically similar to real ones. The rise in online trading activities has resulted in a rise in the number of phishing scams. Cybersecurity jobs are the most difficult to fill, and the development of an automated system for phishing website detection is the need of the hour. Machine Learning is one of the most feasible methods to approach this situation, as it is capable of handling the dynamic nature of phishing techniques, in addition to providing an accurate method of classification.

Arrhythmia Classification Using Biased Dropout and Morphology-Rhythm Feature With Incremental Broad Learning

In recent times, people have become increasingly health-conscious. To obtain timely and accurate information on the status of the heart, one of the most important organs of the human body, there is a growing demand among individuals and doctors for accurate and real-time automatic classification of arrhythmias. Consequently, this paper proposes a fast and accurate classification method for arrhythmias. In the proposed method, we build an incremental broad learning (IBL) classification model based on the biased dropout technique for arrhythmia-type recognition. In particular, we extract the morphological-rhythm features of the denoised signal as the input data of the IBL model in the electrocardiogram signal preprocessing. The IBL model enhances the classification effect of the node optimization model by using improved features. To the best of our knowledge, this study is the first application of the IBL model to the study of arrhythmia classification. The results of experiments conducted on the MIT-BIH database indicate that the proposed method is effective and achieves superior classification results. The average classification accuracy for six types of arrhythmias was 99%, and the training time required was only 2.7 s. In addition, based on the evaluation index recommended by the ANSI/AAMI EC57:2012 standard, our method is superior to existing methods on all indexes, except for the positive predictive rate of ventricular ectopic beats. Therefore, the proposed classification method outperforms state-of-the-art methods in terms of real-time performance and accuracy and provides a new approach for further improvements in arrhythmia classification.

Analysis of the Influence of Vegetation Index Choice on the Classification of Satellite Images for Monitoring Forest Pathology

Rational use of natural resources and control over their recovery, as well as over destruction due to natural and technogenic causes, is currently one of the most urgent problems of the humanity. Forests are no exception. Multispectral images from Earth’s satellites are most often used for monitoring changes in forest planting. This is due to the fact that merging images taken in certain spectra makes it possible to recognize vegetation containing chlorophyll quite well. It also allows to detect changes in the level of chlorophyll, which shows the differences between healthy and damaged plants. Large areas of planted forests create the need to process huge amounts of data, which is difficult to do manually. One of the most important stages of image processing is the classification of objects in these images. This paper deals with various classification methods used to solve the problem of classifying images of remote sensing of the Earth. As a result, it was decided to evaluate the accuracy of classification methods on various vegetation indices. In the course of the study, the evaluation algorithm was determined, as well as one of the options for analyzing the results obtained. Conclusions were made about the work of classification methods on different vegetation indices.

AML, ALL, and CML classification and diagnosis based on bone marrow cell morphology combined with convolutional neural network

Leukemia diagnosis based on bone marrow cell morphology primarily relies on the manual microscopy of bone marrow smears. However, this method is greatly affected by subjective factors and tends to lead to misdiagnosis. This study proposes using bone marrow cell microscopy images and employs convolutional neural network (CNN) combined with transfer learning to establish an objective, rapid, and accurate method for classification and diagnosis of LKA (AML, ALL, and CML). We collected cell microscopy images of 104 bone marrow smears (including 18 healthy subjects, 53 AML patients, 23 ALL patients, and 18 CML patients). The perfect reflection algorithm and a self-adaptive filter algorithm were first used for preprocessing of bone marrow cell images collected from experiments. Subsequently, 3 CNN frameworks (Inception-V3, ResNet50, and DenseNet121) were used to construct classification models for the raw dataset and preprocessed dataset. Transfer learning was used to improve the prediction accuracy of the model. Results showed that the DenseNet121 model based on the preprocessed dataset provided the best classification results, with a prediction accuracy of 74.8%. The prediction accuracy of the DenseNet121 model that was obtained by transfer learning optimization was 95.3%, which was increased by 20.5%. In this model, the prediction accuracies of the normal groups, AML, ALL, and CML were 90%, 99%, 97%, and 95%, respectively. The results showed that the leukemic cell morphology classification and diagnosis based on CNN combined with transfer learning is feasible. Compared with conventional manual microscopy, this method is more rapid, accurate, and objective.

GFNB: Gini index-based Fuzzy Naive Bayes and blast cell segmentation for leukemia detection using multi-cell blood smear images.

The blood cell counting and classification ensures the evaluation and diagnosis of a number of diseases. The analysis of white blood cells (WBCs) permits us to detect the acute lymphoblastic leukemia (ALL), a type of blood cancer that causes fatality when untreated. At present, the morphological analysis of blood cells is performed manually by skilled operators, which holds numerous drawbacks. The manual techniques for leukemia detection are time-consuming and show less accurate results. Hence, there is a need for an automatic method for detecting leukemia. In order to overcome the demerits associated with the manual methods of counting and classifying, an automatic method of blast cell counting and leukemia classification is progressed. This paper proposes a leukemia detection method, using the Gini index-based Fuzzy Naive Bayes (GFNB) classifier that is the integration of Gini index and Fuzzy Naive Bayes classifier. Initially, the input multi-cell blood smear image is subjected to pre-processing, and the blast cell is segmented using the adaptive thresholding. Then, the blast cells are counted using the proposed classifier so as to decide the presence of leukemia for the effective diagnosis. Experimental analysis using the ALL-IDB1 database confirms that the proposed method operates better than the existing methods in terms of accuracy, specificity, and sensitivity that are found to be 0.9591, 0.9599, and 1, respectively. The experimental results reveal that the proposed method is reliable and accurate. Also, the proposed system can help the physicians to improve and speed up their process.Graphical abstract Leukemia is caused by the excess production of the immature leucocytes in the bone marrow that expose the human body to lose the tendency to fight against the diseases. Leukemia classification is highly needed as in the later stage, failure of the diagnosis steps may lead to the death of the person. Moreover, some countries do not have any study against the diagnosis steps of leukemia and it highly exists among the low-income people. In order to analyze the type of leukemia and to provide an effective diagnosis strategy, the paper presents a fast and highly accurate classification method. The main aim of the paper is to propose a method to perform the leukemia classification through the segmentation and classification of the WBC cells using the multi-cell blood smear images. The major steps involved in the leukemia classification are pre-processing, segmentation, feature extraction, and classification. The input blood smear image is enhanced in the pre-processing step and the pre-processed image is subjected to segmentation using the LUV color transformation and Adaptive Thresholding strategy. The features are extracted from the individual segments and they are presented to the classifier for the classification. The features extracted are shape, texture, and count of the blast cells, for which the grid-based shape extraction, local gradient pattern (LGP)-based texture features, and pixel threshold-based counting of the blast cells are employed. The proposed classifier is developed using the Gini index and Fuzzy Naive Bayes classifier.

A novel antibody population optimization based artificial immune system for rotating equipment anomaly detection

Rotating machines are one of the most common equipments in modern industry, effective fault detection and diagnosis methods are vital to equipment health monitoring. In industrial production, the known information of fault types is insufficient generally, especially for constructing complex equipment and components. In previous studies of equipment fault detection, accurate fault classification and diagnosis methods have been presented, while seldom takes the condition of paucity of fault data into account. Therefore, this paper presents a novel antibody population optimization based artificial immune system (APO-AIS) for rotating equipment anomaly detection. The proposed approach can detect abnormal events while monitoring the operating condition. Meanwhile, an antigen-based antibody selecting method, a density-based antibody screening method and an optimized judgment rule based on individual difference are presented for improving the iteration evolution. The presented methods and optimized judgment rule enhance the robustness and reduces training burden for the proposed approach, which leads to accurate anomaly detection in strong background noise and in practical industrial environment. The effectiveness and robustness of the proposed method has been proven experimentally by bearing fault diagnosing and centrifugal pump condition monitoring in this paper.

A Pipeline Leak Classification and Location Estimation System With Convolutional Neural Networks

An accurate pipeline leak classification and location estimation method can help to control and reduce the damage to the environment when spills happen. Some of the current research on this topic rely on the direct analysis of target frequencies from the monitoring of sensors. However, this assumes that the frequencies may be known before hand and such analysis can be very cumbersome. In this article, we propose convolutional neural network-based classification and location estimation methods, which use raw data instead of prefiltered (or preconditioned) information. The design approach is fully described and the network structure is discussed. Finally, analysis of experimental results validate the proposed network demonstrating that the classification and location estimation can be done with good accuracy.

Identification of Tyre and Plastic Waste from Combined Copernicus Sentinel-1 and -2 Data

As a result of tightened waste regulation across Europe, reports of waste crime have been on the rise. Significant stockpiles of tyres and plastic materials have been identified as a threat to both human and environmental health, leading to water and livestock contamination, providing substantial fuel for fires, and cultivating a variety of disease vectors. Traditional methods of identifying illegal stockpiles usually involve laborious field surveys, which are unsuitable for national scale management. Remotely-sensed investigations to tackle waste have been less explored due to the spectrally variable and complex nature of tyres and plastics, as well as their similarity to other land covers such as water and shadow. Therefore, the overall objective of this study was to develop an accurate classification method for both tyre and plastic waste to provide a viable platform for repeatable, cost-effective, and large-scale monitoring. An augmented land cover classification is presented that combines Copernicus Sentinel-2 optical imagery with thematic indices and Copernicus Sentinel-1 microwave data, and two random forests land cover classification algorithms were trained for the detection of tyres and plastics across Scotland. Testing of the method identified 211 confirmed tyre and plastic stockpiles, with overall classification accuracies calculated above 90%.

Adaptive Channel Weighted CNN With Multisensor Fusion for Condition Monitoring of Helicopter Transmission System

Deep learning-based multi-sensor fusion approach has been widely used for machine condition monitoring. Complementary information from different physical sensors or the same sensors on multiple locations of the monitored target can effectively improve the accuracy of condition monitoring. While, how to distinguish importance of every single sensor for condition monitoring is rarely researched. To address this gap, an adaptive channel weighted convolutional neural network (ACW-CNN) is proposed in this paper to investigate importance of different sensors in fusion approach. The designed ACW layer in a deep neural network can calibrate sensors weight according to sensors importance. The recalibrated channel weights can be used as the guidance for sensor position optimization. Furthermore, a new loss function, that is, Focal Loss, is introduced into ACW-CNN to deal with class imbalance generated in real helicopter transmission system (HTS) monitoring. To validate the effectiveness of the proposed ACW-CNN for condition monitoring, two case studies including condition monitoring to gearbox transmission system and helicopter transmission system are carried out. The results of comparative experiments show the proposed method, that is ACW-CNN with Focal Loss, is superior to other methods in classification accuracy, G-mean, and F-measure for condition monitoring of HTS.

An Efficient and Accurate Method for Detection and Classification of Power Quality Disturbances

Aiming to the problem of large computation amount and high redundancy of traditional S‐transform methods available in the detection and classification of transient power quality disturbances, an efficient and accurate method for the detection and classification is proposed. First, the maximum power spectrum dynamics method is used to extract the main frequency points. Then, a called modified incomplete S‐transform proposed in the paper is performed on the main frequency points so as to obtain a modulus time‐frequency matrix involving the characteristic information of disturbances. The modified incomplete S‐transform is implemented by introducing a bi‐Gaussian window function with two parameters to replace the Gaussian window function of the traditional S‐transform. And then, the characteristic quantities for detecting power quality disturbance and identifying the classification of the disturbances are extracted based on the matrix. Finally, the disturbance parameters such as the amplitude, start time, and stop time of the disturbances are calculated based on the relevant theories and rules, and also the classification of the disturbances is identified by extracting some important characteristic quantities. A great number of simulation experiments are conducted. The results obtained show that by the method, the start time, stop time, and amplitude changes of power quality disturbance signals can be fast and accurately detected, and the classification of the disturbances can be fast and accurately identified, and that the method has the characteristics of less operation time and strong anti‐interference ability. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Impaired Balance Assessment in Older People with Muscle Weakness Caused by Stroke

Stroke causes a severe impact in daily life movements. It affects a static balance in the human who has an impaired balance of their movement control. Assessing the evaluation criteria can be superseded via the movement, especially the Berg Balance scale, which is the gold standard in the assessment, even bringing a motion detector. To evaluate the balance assessment in stroke patient body, the physiotherapist uses the statistical methods to analyze the data. This research proposes the classification algorithm for evaluating the balance in the body of stroke patients with muscle weakness condition. It improves the algorithm’s accuracy in data prediction for measuring the validity of regaining balance while standing. The dataset has three main factors, including personal information, a diagnosis from a physiotherapist, and the performance standing on the Nintendo Wii Fit Balance Board. By evaluating with various scenario of different amount of attributes, the dataset with three attributes has the highest accuracy rate. Classifying information is used as a guideline in assessing the information on how to regain a patient’s balance control during standing. The most accurate classification method is the Artificial Neural Network with accurate 93% of the times for prediction and classification.

Improved recognition of bacterial species using novel fractional-order orthogonal descriptors

Detection and distinguishing between different species of bacteria using experimental microbiology is an expensive, time-consuming, and risky process. Automatic computer-based methods for accurate detection and classification of bacteria species significantly reduce the cost, time, and avoiding scientists the risk of infection. This paper presents a novel computer-based approach for highly accurate recognition of bacterial species. The proposed method consists of two main stages. First, a novel set of fractional-order orthogonal moments proposed to extract the fine features from the color images of bacteria. Second, a new method for feature selection, SSATLBO, is proposed. In this method, the teaching-based learning optimization (TLBO) as local operators is used to improve the exploitation ability of the Salp Swarm Algorithm (SSA) to avoid the local point. The proposed detection and classification method tested by using the DIBaS dataset (Digital Image of Bacterial Species), which includes 660 images with 33 various genera and classes of bacteria. The proposed method achieved a bacterial species recognition rate, 98.68%. The obtained results ensure the superiority of the proposed method over the traditional SSA and TLBO methods and the other Metaheuristic methods.

A New Fast and Accurate Fault Location and Classification Method on MTDC Microgrids Using Current Injection Technique, Traveling-Waves, Online Wavelet, and Mathematical Morphology

A New Fast and Accurate Fault Location and Classification Method on MTDC Microgrids Using Current Injection Technique, Traveling-Waves, Online Wavelet, and Mathematical Morphology

Accurate classification of ECG arrhythmia using MOWPT enhanced fast compression deep learning networks

Accurate classification of electrocardiogram (ECG) signals is of significant importance for automatic diagnosis of heart diseases. In order to enable intelligent classification of arrhythmias with high accuracy, an accurate classification method based intelligent ECG classifier using the fast compression residual convolutional neural networks (FCResNet) is proposed. In the proposed method, the maximal overlap wavelet packet transform (MOWPT), which provides a comprehensive time-scale paving pattern and possesses the time-invariance property, was utilized for decomposing the original ECG signals into sub-signal samples of different scales. Subsequently, the samples of the five arrhythmia types were utilized as input to the FCResNet such that the ECG arrhythmia types were identified and classified. In the proposed FCResNet model, a fast down-sampling module and several residual block structural units were incorporated. The proposed deep learning classifier can substantially alleviate the problems of low computational efficiency, difficult convergence and model degradation. Parameter optimizations of the FCResNet were investigated via single-factor experiments. The datasets from MIT-BIH arrhythmia database were employed to test the performance of the proposed deep learning classifier. An averaged accuracy of 98.79% was achieved when the number of the wide-stride convolution in fast down-sampling module was set as 2, the batch size parameter was set as 20 and wavelet subspaces of low frequency bands in MOWPT were selected as input of the classifier. These analysis results were compared with those generated by some comparison methods to validate the superiorities and enhancements of the proposed method.

Remote Sensing of Boreal Wetlands 2: Methods for Evaluating Boreal Wetland Ecosystem State and Drivers of Change

The following review is the second part of a two part series on the use of remotely sensed data for quantifying wetland extent and inferring or measuring condition for monitoring drivers of change on wetland environments. In the first part, we introduce policy makers and non-users of remotely sensed data with an effective feasibility guide on how data can be used. In the current review, we explore the more technical aspects of remotely sensed data processing and analysis using case studies within the literature. Here we describe: (a) current technologies used for wetland assessment and monitoring; (b) the latest algorithmic developments for wetland assessment; (c) new technologies; and (d) a framework for wetland sampling in support of remotely sensed data collection. Results illustrate that high or fine spatial resolution pixels (≤10 m) are critical for identifying wetland boundaries and extent, and wetland class, form and type, but are not required for all wetland sizes. Average accuracies can be up to 11% better (on average) than medium resolution (11–30 m) data pixels when compared with field validation. Wetland size is also a critical factor such that large wetlands may be almost as accurately classified using medium-resolution data (average = 76% accuracy, stdev = 21%). Decision-tree and machine learning algorithms provide the most accurate wetland classification methods currently available, however, these also require sampling of all permutations of variability. Hydroperiod accuracy, which is dependent on instantaneous water extent for single time period datasets does not vary greatly with pixel resolution when compared with field data (average = 87%, 86%) for high and medium resolution pixels, respectively. The results of this review provide users with a guideline for optimal use of remotely sensed data and suggested field methods for boreal and global wetland studies.