Earticle

In this paper, we established ZIGBEE home network and combined smart-grid and u-Healthcare system. We assisted for amount of electricity management of household by interlocking home devices of wireless sensor, PLC modem, DCU and realized smart grid and u-Healthcare at the same time by verifying body heat, pulse, blood pressure change and proceeded living body signal by using SVM algorithm and variety of ZIGBEE network channel and enabled it to check real-time through IHD which is developed by user interface. In addition, we minimized the rate of energy consumption of each sensor node when living body signal is processed and realized Query Processor which is able to optimize accuracy and speed of query. We were able to check the result that is accuracy of classification 0.848 which is less accounting for average 17.9% of storage more than the real input data by using Mjoin, multiple query process and SVM algorithm.

The present research article focused on the factual findings of the potential usage of the combinational Feed-Forward Back Propagation Neural Network as a judgment making for lung cancer. In this context, Support Vector Machine is integrated with Feed-Forward Back Propagation Neural Network to create a hybrid algorithm that further helps in reducing the computation complexity of the classification. A set of 500 images are utilized in which 75% data is used for the training purpose and the rest 25% is used to achieve the classification. In the view of forgoing, a three-block mechanism is proposed for the classification in which the first block preprocesses the dataset, the second block extracts the features via the SURF technique followed by the optimization using Genetic Algorithm and the terminal block is for the classification via FFBPNN. The hybrid classification algorithm is named as Kernel Attribute Selected Classifier and the overall classification accuracy of the proposed algorithm is 98.08%. Herein, the objective of the study is to enhance the classification accuracy by applying a hybrid classification algorithm.

본 논문에서는 사용자의 EEG(Electroencephalogram)신호를 측정하여 SVM(Support Vector Machine) 알 고리즘을 이용하여 EEG 신호룰 분류하고 신호의 정확도를 측정하였다. 사용자의 EEG 신호를 측정하기 위해 남·여를 구분하여 실험을 진행하였으며, EEG 신호 측정은 단채널 EEG 디바이스를 이용하였다. EEG 디바이스를 이용하여 사용 자의 EEG 신호를 측정한 결과는 R을 이용하여 분석하였다. 또한 SVM의 분류 성능이 최고가 되는 특정 벡터의 조합을 적용시켜 EEG 측정 실험 데이터를 80:20(훈련 데이터: 테스트 데이터) 비율로 예측해 본 결과 인식률 93.2% 의 예측 정확도를 보였다. 본 논문에서는 사용자의 EEG 신호를 약 93.2% 정도로 인식할 수 있었으며, SVM 알고리즘의 간단한 선형 분류만으로 수행이 가능하다는 점은 EEG 신호를 이용하여 생체인증에 다양하게 활용될 수 있음을 제시하였다.

In this paper, we measured the user's EEG signal and classified the EEG signal using the Support Vector Machine algorithm and measured the accuracy of the signal. An experiment was conducted to measure the user's EEG signals by separating men and women, and a single channel EEG device was used for EEG signal measurements. The results of measuring users' EEG signals using EEG devices were analyzed using R. In addition, data in the study was predicted using a 80:20 ratio between training data and test data by applying a combination of specific vectors with the highest classifying performance of the SVM, and thus the predicted accuracy of 93.2% of the recognition rate. This paper suggested that the user's EEG signal could be recognized at about 93.2 percent, and that it can be performed only by simple linear classification of the SVM algorithm, which can be used variously for biometrics using EEG signals.

The performance of video automatic classification algorithm depends largely on the extraction of video features and selection of classification algorithm. From the perspective of video contents and video style type, the paper presents a new feature representation scheme, i.e. MPEG-7 visual description sub-combination model, a new method based on support vector machine (SVM) to solve problems with existing algorithms, by analyzing visual differences between five types of videos. Also we improve the classifier decision scheme and then propose the secondary prediction mechanism based on SVM 1-1 approach, improving the accuracy of SVM multi-classification method. The experimental results indicate that the proposed method manifests differences of different videos about feature selection, enhances the discrimination ability of videos pending for classification and increases the effectiveness of SVM multi-video classification.

A coarse-to-fine traffic sign classification algorithm is proposed. The task for traffic sign classification is to analyze the detected regions and determine the class of the sign in the region. By analyzing existing traffic sign classification algorithms, the major problem affecting the classification accuracy is pointed out. Based on this analysis, a coarse-to-fine classification algorithm is proposed. The algorithm first classifies traffic signs into several super classes, then performs class-specific shape adjustment, and finally gets the fine classification result. Experimental results show that the proposed algorithm outperforms other existing algorithms in classification accuracy, and is robust to many adverse situations.

In the process of ventricular premature beat (PVC) and normal sinus rhythm (NSR) identification base on electrocardiogram (ECG), there exists problems like negative effect from ECG rhythm and low recognition rate. This paper proposes the electrocardiogram PVC classification algorithm based on support vector machine (SVM) and wavelet algorithm. The algorithm uses the wavelet transform to analyze ECG beating model, which is not influenced by the change of ECG waveform. The two feature sets respectively compose of statistical parameters of the wavelet coefficients and the selected wavelet coefficients. PVC and NSR are analyzed by using SVM. The experimental results show that this method improves the recognition rate of ECG.

Short text is a popular text form, which is widely used in real-time network news, short commentary, micro-blog and many other fields. With the development of the application such as QQ, mobile phone text messages and movie websites, the size of data is also becoming larger and larger. Most data is useless for us while other data is significant for us. Therefore, it is necessary for us to extract the useful short text from the big data. However, there are many problems with the short text classification, such as fewer features, irregularity and so on. To solve these problems, we should pretreat the short text set first, and then choose the significant features. This paper use semi-supervised learning method and SVM classifier to improve the traditional methods and it can classify a large number of short texts to mining the useful massage from the short text. The experimental results in this paper also show a good promotion.

In this research paper, we conduct theoretical analysis and numerical analysis on novel image classification algorithm based on multi-feature extraction and modified SVM classifier. Image object classification and detection are two important basic problems in the study of computer vision, image segmentation, object tracking, behavior analysis and so on the basis of other high-level vision tasks. Existing image classification method can make full use of every single feature between the complementary characteristics of the extracted features of a large number of redundant information, which can lead to image classification accuracy is not high. For this, put forward an improved support vector machine (SVM) based on characteristics and integrated method of image classification. This method can extract comprehensive description of image content features, using principal component analysis to extract the characteristics of transformation, remove redundant information. The experimental result proves the effectiveness and feasibility of the proposed algorithm. In the final part, we conclude the paper and set up the prospect for the future research.

According to the merits and shortcomings of the traditional gridsearch algorithm in parameters optimization of support vector machine (SVM), an improved grid search algorithm is proposed. Dichotomous search algorithm is used to reduce target searching range. First, searching range is determined roughly, and a set of parameters are obtained. Then fine search is applied in reduction the range for searching, and searching the optimum parameters.Three kinds of famous tumor gene data set are used in the comparison experiments to validate the classification accuracy of principal component analysis (PCA)-SVM and kernel principal component analysis (KPCA)-SVM. Experiment results and data analysis shows that, comparing with traditional gridsearch algorithm, the proposed method has higher classification accuracy and less search time.

An extremely crucial step in the diagnosis of cancers is to select a small number of informative genes for accurate classification. This issue has become a hot focus in the data mining of gene expression profiles. Especially for data with a large number of cancer types, many conventional classification methods show very poor performance. Here, we proposed a new approach for gene selection and multi-cancer classification based on step-by-step improvement of classification performance (SSiCP). The SSiCP gene selection algorithms were evaluated over the NCI60 and GCM benchmark datasets, with accuracy of 96.6% and 95.5% in 10-fold cross-validation, respectively. Furthermore, the SSiCP outperformed recently published algorithms when applied to another two multi-cancer data sets. Computational evidence indicated that SSiCP can avoid overfitting effectively. Compared with various gene selection algorithms, the implementation of SSiCP is simple and many of the selected genes by SSiCP are shown to be closely related to cancers.

Because the properties of data are becoming more and more complex, the traditional data classification is difficult to realize the data classification according to the complexity characteristic of the data. Support vector machine is a machine learning method with the good generalization ability and prediction accuracy. So an improved ant colony optimization(ACO) algorithm is introduced into the support vector machine(SVM) model in order to propose a new data classification(ERURACO-SVM) method. In the ERURACO-SVM method, the pheromone evaporation rate strategy and pheromone updating rule are introduced into the ACO algorithm to improve the optimization performance of the ACO algorithm, and then the parallelism, global optimization ability, positive feedback mechanism and strong robustness of the improved ACO algorithm is used to find the optimal combination of parameters of the SVM model in order to improve the learning performance and generalization ability of the SVM model and establish the optimal data classification model. Finally, the experimental data from the UCI machine learning database are selected to validate the classification correctness of the ERURACO-SVM method. The experiment results show that the improved ACO(ERURACO) algorithm has better optimization performance for parameters selection of the SVM model and the ERURACO-SVM method has higher classification accuracy and better generalization ability.

본 논문은 '좌', '우' 방향 제어를 위해 취득된 EEG(Electroencephalogram) 신호 기반 분류 알고리즘과 EEG 센서, Labview, DAQ, Matlab, 주행로봇으로 구성된 방향 제어 시스템을 제안한다. 제안된 알고리즘은 DWT(Discrete Wavelet Transform)로 추출된 주파수대역 정보를 특징으로 이용하며, Fishers score를 이용하여 변별력이 높은 주파수 대역의 특징을 선별한다. 또한, SVM (Support Vector Machine)을 이용하여 분류 성능이 최고가 되는 특징벡터의 조합을 제안하고, 잘못된 판정에 의한 오동작을 방지하기 위한 MLD(Maximum Likelihood Decision) 기반의 판정보류 알고리즘도 제안한다. 제안된 알고리즘에 의해 선택된 4개의 특징벡터는 국제 표준 전극 배치법에 따른 P8 채널의 d2(16-32Hz), d5(2-4Hz) 주파수 대역의 전압의 절대 값 평균과 표준편차이다. SVM 분류기로 실험한 결과 98.75%의 정확도와 1.25%의 오류율 성능을 보였다. 또한, 오류 확률 70%를 판정 보류로 규정할 경우, 제안된 알고리즘은 인식률 95.63%의 정확도와 오류율 0%을 보였다.

In this paper, we propose a classification algorithm based on the obtained EEG(Electroencephalogram) signal for the control of 'left' and 'right' turnings of which a driving system composed of EEG sensor, Labview, DAQ, Matlab and driving robot. The proposed algorithm uses features extracted from frequency band information obtained by DWT (Discrete Wavelet Transform) and selects features of high discrimination by using Fisher score. We, also propose the number of feature vectors for the best classification performance by using SVM(Support Vector Machine) classifier and propose a decision pending algorithm based on MLD (Maximum Likelihood Decision) to prevent malfunction due to misclassification. The selected four feature vectors for the proposed algorithm are the mean of absolute value of voltage and the standard deviation of d5(2-4Hz) and d2(16-32Hz) frequency bands of P8 channel according to the international standard electrode placement method. By using the SVM classifier, we obtained 98.75% accuracy and 1.25% error rate. Also, when we specify error probability of 70% for decision pending, we obtained 95.63% accuracy and 0% error rate by using the proposed decision pending algorithm.

This study aimed to develop a precise vegetation cover classification model for small streams using the combination of drone remote sensing and support vector machine (SVM) techniques. The chosen study area was the Idong stream, nestled within Geosan-gun, Chunbuk, South Korea. The initial stage involved image acquisition through a fixed-wing drone named ebee. This drone carried two sensors: the S.O.D.A visible camera for capturing detailed visuals and the Sequoia+ multispectral sensor for gathering rich spectral data. The survey meticulously captured the stream's features on August 18, 2023. Leveraging the multispectral images, a range of vegetation indices were calculated. These included the widely used normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) that factors in soil background, and the normalized difference water index (NDWI) for identifying water bodies. The third stage saw the development of an SVM model based on the calculated vegetation indices. The RBF kernel was chosen as the SVM algorithm, and optimal values for the cost (C) and gamma hyperparameters were determined. The results are as follows: (a) High-Resolution Imaging: The drone-based image acquisition delivered results, providing high-resolution images (1 cm/pixel) of the Idong stream. These detailed visuals effectively captured the stream's morphology, including its width, variations in the streambed, and the intricate vegetation cover patterns adorning the stream banks and bed. (b) Vegetation Insights through Indices: The calculated vegetation indices revealed distinct spatial patterns in vegetation cover and moisture content. NDVI emerged as the strongest indicator of vegetation cover, while SAVI and NDWI provided insights into moisture variations. (c) Accurate Classification with SVM: The SVM model, fueled by the combination of NDVI, SAVI, and NDWI, achieved an outstanding accuracy of 0.903, which was calculated based on the confusion matrix. This performance translated to precise classification of vegetation, soil, and water within the stream area. The study's findings demonstrate the effectiveness of drone remote sensing and SVM techniques in developing accurate vegetation cover classification models for small streams. These models hold immense potential for various applications, including stream monitoring, informed management practices, and effective stream restoration efforts. By incorporating images and additional details about the specific drone and sensors technology, we can gain a deeper understanding of small streams and develop effective strategies for stream protection and management.

This study categorizes several voice characteristics by subjective listening assessment, and investigates correlation between voice characteristics and speech feature parameters. A model was developed to classify voice characteristics into the defined categories using SVM algorithm. To do this, we extracted various speech feature parameters from speech database for men in their 20s, and derived statistically significant parameters correlated with voice characteristics through ANOVA analysis. Then, these derived parameters were applied to the proposed SVM model. The experimental results showed that it is possible to obtain some speech feature parameters significantly correlated with the voice characteristics, and that the proposed model achieves the classification accuracies of 88.5% on average.