Classification Methods In Order Research Articles

A New SCAE-MT Classification Model for Hyperspectral Remote Sensing Images.

Hyperspectral remote sensing images (HRSI) have the characteristics of foreign objects with the same spectrum. As it is difficult to label samples manually, the hyperspectral remote sensing images are understood to be typical "small sample" datasets. Deep neural networks can effectively extract the deep features from the HRSI, but the classification accuracy mainly depends on the training label samples. Therefore, the stacked convolutional autoencoder network and transfer learning strategy are employed in order to design a new stacked convolutional autoencoder network model transfer (SCAE-MT) for the purposes of classifying the HRSI in this paper. In the proposed classification method, the stacked convolutional au-to-encoding network is employed in order to effectively extract the deep features from the HRSI. Then, the transfer learning strategy is applied to design a stacked convolutional autoencoder network model transfer under the small and limited training samples. The SCAE-MT model is used to propose a new HRSI classification method in order to solve the small samples of the HRSI. In this study, in order to prove the effectiveness of the proposed classification method, two HRSI datasets were chosen. In order to verify the effectiveness of the methods, the overall classification accuracy (OA) of the convolutional self-coding network classification method (CAE), the stack convolutional self-coding network classification method (SCAE), and the SCAE-MT method under 5%, 10%, and 15% training sets are calculated. When compared with the CAE and SCAE models in 5%, 10%, and 15% training datasets, the overall accuracy (OA) of the SCAE-MT method was improved by 2.71%, 3.33%, and 3.07% (on average), respectively. The SCAE-MT method is, thus, clearly superior to the other methods and also shows a good classification performance.

Exploring Quality of Life Reported by Norwegian Older Adults Using Classification Tree Approach on Group Profiles

This study aims to explore the variation between- and within subgroups of older adults with regard to low, medium, and high levels of self-reported quality of life (QoL) measured by the WHOQOL-BREF scale. The contribution of interacting personal and contextual life conditions to QoL was examined in a sample of 1,910 (sample frame 6,000) Norwegian men and women aged 62 to 99 years. The data collected by a postal questionnaire were analyzed using the Chi-square Automatic Interaction Detection (CHAID) classification method in order to detect unique profiles of groups who shared common characteristics. The CHAID model revealed 15 relatively homogenous groups, but distinct from one another, whose profiles were defined by unique constellations of several interacting variables significantly related to a given QoL level. Mental functioning was predominantly linked to perception of life meaning along with health status, and/or in some cases living arrangement, loneliness, neighborhood quality, and satisfaction with income, and placed an individual at different likelihood levels of reporting low, medium, or high QoL. Socio-demographics had no statistically significant impact on QoL for any subgroup. Through this individual-oriented approach, a periodically ongoing assessment of subjective quality of life (QoL) may be sufficiently powerful to allow detecting and addressing personal concerns and specific needs that detract from quality of life in advancing age.

Application of digital signal processing and machine learning for Electromyography: A review

This paper reviewed the Application of Digital Signal Processing (DPS) and Machine Learning (ML) for Electromyography (EMG) by previous studies. There is a need of the DSP and ML application into the EMG study to classify the signal in order to minimize the EMG noise of signal and the EMG signal characteristic. The common techniques analysis of signal processing is disccussed and compared to identify the best techniques used in order to process from raw data of EMG signal info EMG signal analysis, then some types of machine learning is discussed to identify which types of machine learning have gave the best performance of EMG signal identification and signal characteristic with the highest percentage of the accuracy and efficiency. Digital signal processing and the technique of signal analysis and machine learning for classification method in order to provide the best method and classification for EMG signal.

ЭВОЛЮЦИЯ ЭЛЕКТРОННОГО ГОЛОСОВАНИЯ В РОССИИ: ПРОБЛЕМЫ КЛАССИФИКАЦИИ И ПЕРИОДИЗАЦИИ

Aim. The study focuses on classifying the types of electronic voting used in Russia in 1996-2020 in the context of the evolution of the corresponding form of political participation. Methodology. In the article, we generalize the experience of the electronic voting in Russia on the basis of the retrospective and comparative analysis. We use classification method in order to divide the electronic voting history into periods. In addition, we use expert survey for better understanding of campaign managers’ opinions on the mentioned practice. Results. In conclusion, the authors subdivide the process of the electronic voting development in Russia into six periods on basis of three criteria, namely, voting in the normal way or electronic voting online, the elections level and the obligatory force of the election returns. Research implications. The article presents the author’s approach to the identification of stages and types of electronic voting in Russia.

Research on Sentiment Classification of Online Travel Review Text

In recent years, the number of review texts on online travel review sites has increased dramatically, which has provided a novel source of data for travel research. Sentiment analysis is a process that can extract tourists’ sentiments regarding travel destinations from online travel review texts. The results of sentiment analysis form an important basis for tourism decision making. Thus far, there has been minimal concern as to how sentiment analysis methods can be effectively applied to improve the effect of sentiment analysis. However, online travel review texts are largely short texts characterized by uneven sentiment distribution, which makes it difficult to obtain accurate sentiment analysis results. Accordingly, in order to improve the sentiment classification accuracy of online travel review texts, this study transformed sentiment analysis into a multi-classification problem based on machine learning methods, and further designed a keyword semantic expansion method based on a knowledge graph. Our proposed method extracts keywords from online travel review texts and obtains the concept list of keywords through Microsoft Knowledge Graph. This list is then added to the review text to facilitate the construction of semantically expanded classification data. Our proposed method increases the number of classification features used for short text by employing the huge corpus of information associated with the knowledge graph. In addition, this article introduces online travel review text preprocessing, keyword extraction, text representation, sampling, establishment classification labeling, and the selection and application of machine learning-based sentiment classification methods in order to build an effective sentiment classification model for online travel review text. Experiments were implemented and evaluated based on the English review texts of four famous attractions in four countries on the TripAdvisor website. Our experimental results demonstrate that the method proposed in this paper can be used to effectively improve the accuracy of the sentiment classification of online travel review texts. Our research attempts to emphasize and improve the methodological relevance and applicability of sentiment analysis for future travel research.

Genetic Algorithm Based Feature Selection With Ensemble Methods For Student Academic Performance Prediction

Student academic performance is an important factor that affect the achievement of an educational institution. Educational Data Mining (EDM) is a data mining process that is applied to explore educational data that can produce information related to student academic performance. The knowledge produced from the data mining process is used by the educational institutions to improve their teaching processes, which aim to improve student academic performance results. In this paper, a method based on Genetic Algorithm (GA) feature selection technique with classification method is proposed in order to predict student academic performance. Almost all previous feature selection techniques apply local search technique throughout the process, so the optimal solution is quite difficult to achieve. Therefore, GA is apply as a technique of features selection with ensemble classification method in order to improve classification accuracy value of student academic performance prediction, as well as it can be used for datasets with high dimensional and imbalanced class. In this paper, the data used for experiments comes from Kaggle repository datasets which consists of three main categories: behaviour, academic, and demographic. The performances evaluation used to evaluate the proposed method is the Area Under the Curve (AUC). Based on the results obtained from the experiments, shows that the proposed method makes an impressive result in the predictions of student academic performance.

The young stellar content of the giant H II regions M 8, G333.6−0.2, and NGC 6357 with VLT/KMOS

Context.The identification and characterisation of populations of young massive stars in (giant) H IIregions provides important constraints on (i) the formation process of massive stars and their early feedback on the environment, and (ii) the initial conditions for population synthesis models predicting the evolution of ensembles of stars.Aims.We identify and characterise the stellar populations of the following young giant H IIregions: M 8, G333.6−0.2, and NGC 6357.Methods.We have acquiredH- andK-band spectra of around 200 stars using theK-band Multi Object Spectrograph on the ESO Very Large Telescope. The targets for M 8 and NGC 6357 were selected from the Massive Young Star-Forming Complex Study in Infrared and X-ray (MYStIX), which combines X-ray observations with near-infrared (NIR) and mid-infrared data. For G333.6−0.2, the sample selection is based on the NIR colours combined with X-ray data. We introduce an automatic spectral classification method in order to obtain temperatures and luminosities for the observed stars. We analysed the stellar populations using their photometric, astrometric, and spectroscopic properties and compared the position of the stars in the Hertzprung-Russell diagram with stellar evolution models to constrain their ages and mass ranges.Results.We confirm the presence of candidate ionising sources in the three regions and report new ones, including the first spectroscopically identified O stars in G333.6−0.2. In M 8 and NGC 6357, two populations are identified: (i) OB main-sequence stars (M > 5M⊙) and (ii) pre-main sequence stars (M ≈ 0.5 − 5 M⊙). The ages of the clusters are ∼1−3 Myr, < 3 Myr, and ∼0.5−3 Myr for M 8, G333.6−0.2, and NGC 6357, respectively. We show that MYStIX selected targets have > 90% probability of being members of the H IIregion, whereas a selection based on NIR colours leads to a membership probability of only ∼70%.

Assessing the freshness of Agadir blue fish using a metal oxide gas sensing array

Abstract Regarding the need of developing a low-cost and portable tool for monitoring and tracking fish quality in real time, we successfully developed a portable gas detection system based only on 5 metal oxide chemical sensors for blue fish freshness monitoring. The variability of the sensing features of the 5 sensors towards the analyzed fish samples (Sardines, Chinchard and Mackerel) was coupled to data classification methods in order to discriminate between the different evolution stages of Agadir blue fish freshness during 10 storage days, in accordance with international standards.

Land use and land cover mapping in wetlands one step closer to the ground: Sentinel-2 versus landsat 8

Environmental studies with Landsat images have revealed many of the problems faced by wetland ecosystem, which are crucial for the conservation of biodiversity and the natural values of our planet. The study of LULC changes in wetlands through remote sensing constantly helps to identify and combat their main environmental threats improving the conservation of these natural habitats. Starting in mid-2015, the Sentinel-2 satellite opens new possibilities in the field of earth observation thanks to its higher spatial, spectral and temporal resolution becoming a powerful source of information for LULC monitoring in wetland areas. However, researchers may ask them selves to what extent Sentinel-2 is an improvement over Landsat 8 for general purposes. This research test if there is a real difference in the quality of the results delivered by both Sentinel-2 and Landsat 8 imagery when basic classification methods are applied.The study uses Sentinel-2 and Landsat 8 imagery to produce LULC maps in a Mediterranean wetland area applying an object based classification method in order to compare the accuracy and reliability in the surface detected by both satellites. The results show that an object based classification using only the Sentinel-2 and Landsat 8 image information, without band indexes or ancillary data, offers very similar results for most LULC classes, being the overall accuracy around 87–88% with slightly better results when using Sentinel-2. Although using Sentinel-2 leads to an increase in file size and processing times, the analysis of certain LULC classes presents an improvement compared to Landsat 8, detecting more linear and small size elements with a better delineation of image features in the classified map. However, these improvements should not underestimate the value of Landsat imagery in the future since both satellites provide high precision information, so they can and should coexist and be used together to increase data availability in order to have the best possible results in remote sensing research.

Text Classification Using the N-Gram Graph Representation Model Over High Frequency Data Streams

A prominent challenge in our information age is the classification over high frequency data streams. In this research, we propose an innovative and high-accurate text stream classification model that is designed in an elastic distributed way and is capable to service text load with fluctuated frequency. In this classification model, text is represented as N-Gram Graphs and the classification process takes place using text preprocessing, graph similarity and feature classification techniques following the supervised machine learning approach. The work involves the analysis of many variations of the proposed model and its parameters, such as various representations of text as N-Gram Graphs, graph comparisons metrics and classification methods in order to conclude to the most accurate setup. To deal with the scalability, the availability and the timely response in case of high frequency text we employ the Beam programming model. Using the Beam programming model the classification process occurs as a sequence of distinct tasks and facilitates the distributed implementation of the most computational demanding tasks of the inference stage. The proposed model and the various parameters that constitute it are evaluated experimentally and the high frequency stream emulated using two public datasets (20NewsGroup and Reuters-21578) that are commonly used in the literature for text classification

Comparison of Parametric and Non-parametric EEG Feature Extraction Methods in Detection of Pediatric Migraine without Aura

Migraine headache without aura is the most common type of migraine especially among pediatric patients. It has always been a great challenge of migraine diagnosis using quantitative electroencephalography measurements through feature classification. It has been proven that different feature extraction and classification methods vary in terms of performance regarding detection and diagnostic accuracy. Previous work on the subject was controversial, hence a comparison of these methods seems necessary. The aim of this research is to compare two parametric and non-parametric feature extraction methods and also two classification methods in order to obtain optimal combinations of diagnostic accuracy. Having recorded background EEG from 24 pediatric migraineurs and 19 control subjects, data was processed by Welch's and Yule-Walker's methods. Features were selected using genetic algorithm, and then given to a support vector machine and the linear discriminant analysis for the classification. Accuracy was calculated for all combinations having the dominant frequency and the correlated absolute power of each EEG wave band (theta, alpha, and beta) and for all wave bands combined. The highest migraine detection accuracy of 93% was obtained utilizing Welch's method for EEG feature extraction alongside support vector machine for a classifier. Besides, Yule-Walker autoregressive method showed better performance than Welch's, when only power bands (and not the dominant frequency) were used as classification input. The superiority of Welch's method over Yule-Walker's and the support vector machine over linear discriminant analysis can be great help for further researches on computer aided EEG-based diagnosis of migraine.

Using unsupervised learning methods for enhancing protein structure insight

Proteins are complex macromolecules which contribute to maintaining cellular environments and thus have fundamental roles in biological processes of living organisms. Understanding the conformational transitions of proteins represents an important stage towards comprehending protein function and would help to identify situations when mutations can occur. In this paper we use clustering as an unsupervised classification method in order to study the relevance of the residues’ relative solvent accessibility (RSA) values to analyze protein internal transitions. With the main goal of studying the evolution of RSA values between conformational transitions, we experimentally show that RSA values are slowly modifying as the protein undergoes conformational changes. The study conducted in this paper is aimed to provide a better apprehension of how proteins’ conformational transitions are evolving in time, with the broader goal of better understanding protein internal dynamics.

Comprehensive investigation on non-parametric classification methods in order to separate urban objects using the integration of very high spatial resolution LiDAR and aerial data

Comprehensive investigation on non-parametric classification methods in order to separate urban objects using the integration of very high spatial resolution LiDAR and aerial data

Scale Invariant static hand-postures detection using Extended Higher-order Local Autocorrelation features

This paper presents scale invariant static hand postures detection methods using extended HLAC features extractedfrom Log-Polar images. Scale changes of a handposture in an image are represented as shift in Log-Polar image. Robustness of the method is achieved through extracting spectral features from theeach row of the Log-Polar image. Linear Discriminant Analysis was used to combine features with simple classification methods in order to realize scale invariant hand postures detection and classification.The method was successful tested by performing experiment using NSU hand posture dataset images which consists 10 classes of postures, 24 samples of images per class, which are captured by the position and size of the hand within the image frame. The results showed that the detection rate using ExtendedHLAC can averaged reach 94.63% higher than using HLAC features on a Intel Core i5-4590 CPU running at 3.3 GHz. General Terms Scale invariant, HLAC features, log polar image, hand posture, linear discriminant analysis, posture detection, posture classification.

A Data Mining Classification Approach for Behavioral Malware Detection

Data mining techniques have numerous applications in malware detection. Classification method is one of the most popular data mining techniques. In this paper we present a data mining classification approach to detect malware behavior. We proposed different classification methods in order to detect malware based on the feature and behavior of each malware. A dynamic analysis method has been presented for identifying the malware features. A suggested program has been presented for converting a malware behavior executive history XML file to a suitable WEKA tool input. To illustrate the performance efficiency as well as training data and test, we apply the proposed approaches to a real case study data set using WEKA tool. The evaluation results demonstrated the availability of the proposed data mining approach. Also our proposed data mining approach is more efficient for detecting malware and behavioral classification of malware can be useful to detect malware in a behavioral antivirus.

Tube Thoracostomy: A Structured Review of Case Reports and a Standardized Format for Reporting Complications.

Although seemingly straightforward, tube thoracostomy (TT) has been associated with complication rates as high as 30 %. A lack of a standardized nomenclature for reporting TT complications makes comparison and evaluation of reports impossible. We aim to develop a classification method in order to standardize the reporting of complications of TT and identify all reported complications of TT and time course in which they occurred to validate the reporting method. A systematic search of MEDLINE, Scopus, EMBASE, and Cochrane Central Register of Controlled Trials and Database of Systematic Reviews from each databases inception through November 5, 2013 was conducted. Original articles written in the English language reporting TT complications were searched. This review adhered to preferred reporting items for systematic reviews and meta-analyses (PRISMA) standards. Duplicate reviewers abstracted case reports for inclusion. Cases were then sorted into one of the five complication categories by two reviewers, and in case of disagreements, settled by a third reviewer. Of 751 papers reporting TT complications, 124 case reports were included for analysis. From these reports, five main categories of TT complications were identified: insertional (n = 65); positional (n = 36); removal (n = 11); infective and immunologic (n = 7); and instructional, educational, or equipment related (n = 5). Placement of TT has occurred in nearly every soft tissue and vascular structure in the thoracic cavity and intra-abdominal organs. Our classification method provides further clarity and systematic standardization for reporting TT complications.

The Improvement of Land Cover Classification by Thermal Remote Sensing

Land cover classification has been widely investigated in remote sensing for agricultural, ecological and hydrological applications. Landsat images with multispectral bands are commonly used to study the numerous classification methods in order to improve the classification accuracy. Thermal remote sensing provides valuable information to investigate the effectiveness of the thermal bands in extracting land cover patterns. k-NN and Random Forest algorithms were applied to both the single Landsat 8 image and the time series Landsat 4/5 images for the Attert catchment in the Grand Duchy of Luxembourg, trained and validated by the ground-truth reference data considering the three level classification scheme from COoRdination of INformation on the Environment (CORINE) using the 10-fold cross validation method. The accuracy assessment showed that compared to the visible and near infrared (VIS/NIR) bands, the time series of thermal images alone can produce comparatively reliable land cover maps with the best overall accuracy of 98.7% to 99.1% for Level 1 classification and 93.9% to 96.3% for the Level 2 classification. In addition, the combination with the thermal band improves the overall accuracy by 5% and 6% for the single Landsat 8 image in Level 2 and Level 3 category and provides the best classified results with all seven bands for the time series of Landsat TM images.

Decision of sensor location and best classification method for entrail and muscle disease detection in healthcare smart clothing based on acceleration measurements

Smart clothes designed for healthcare applications generally enable the monitoring of heart rate, respiration rate, electrocardiograph and temperature. In order to gather information about the user’s health accurately, sensor placement at the right location on a smart garment is essential. In this study, to detect the entrail and muscle disease of a wearer, suitable sensor location on a smart garment was determined using acceleration measurements from different parts of the body. An accelerometer was placed on different locations of the garment to measure the respiration rate, heart rate and muscle tremor in order to determine any respiration difficulty, heart trouble and some mental illnesses. More than 700 measurements were acquired from different carriers with different age groups, different gender groups and variable body postures, such as in a sleeping position, sitting on a chair and running. Additionally, some significant features were extracted from the acquired data by using fast Fourier transform, wavelet and bispectral analysis, and they were categorized with different rule and pattern classification methods in order to detect over- and under-range heart rates and respiration rate. This study represents the pre-cursor of the healthcare smart clothing system design based on signal analysis and data classification methods.

Selective ablation of Copper-Indium-Diselenide solar cells monitored by laser-induced breakdown spectroscopy and classification methods

Laser-induced breakdown spectroscopy (LIBS) and two classification methods, i.e. linear correlation and artificial neural networks (ANN), are used to monitor P1, P2 and P3 scribing steps of Copper-Indium-Diselenide (CIS) solar cells. Narrow channels featuring complete removal of desired layers with minimum damage on the underlying film are expected to enhance efficiency of solar cells. The monitoring technique is intended to determine that enough material has been removed to reach the desired layer based on the analysis of plasma emission acquired during multiple pass laser scribing. When successful selective scribing is achieved, a high degree of similarity between test and reference spectra has to be identified by classification methods in order to stop the scribing procedure and avoid damaging the bottom layer. Performance of linear correlation and artificial neural networks is compared and evaluated for two spectral bandwidths. By using experimentally determined combinations of classifier and analyzed spectral band for each step, classification performance achieves errors of 7, 1 and 4% for steps P1, P2 and P3, respectively. The feasibility of using plasma emission for the supervision of processing steps of solar cell manufacturing is demonstrated. This method has the potential to be implemented as an online monitoring procedure assisting the production of solar cells.

GA-TVRC-Het: genetic algorithm enhanced time varying relational classifier for evolving heterogeneous networks

Evolving heterogeneous networks, which contain different types of nodes and links that change over time, appear in many domains including protein---protein interactions, scientific collaborations, telecommunications. In this paper, we aim to discover temporal information from a heterogenous evolving network in order to improve node classification. We propose a framework, Genetic Algorithm enhanced Time Varying Relational Classifier for evolving Heterogeneous Networks (GA-TVRC-Het), to extract the effects of different relationship types in different time periods in the past. These effects are discovered adaptively by utilizing genetic algorithms. A relational classifier is extended as the classification method in order to be able to work with different types of nodes. The proposed framework is tested on two real world data sets. It is shown that using the optimal time effect improves the classification performance to a large extent. It is observed that the optimal time effect does not necessarily follow a certain functional trend, for example linear or exponential decay in time. Another observation is that the optimal time effect may be different for each type of interaction. Both observations reveal the reason why GA-TVRC-Het outperforms methods that rely on a predefined form of time effect or the same time effect for each link type.