Support Vector Machine Ensemble Research Articles

Brain Tumor Segmentation from Multimodal MRI Data based on GLCM and SVM Classifier

The segmentation of MRI brain tumors utilizes computer technology to segment and label tumors and normal tissues automatically on multimodal brain images, which plays an important role in disease diagnosis, treatment planning, and surgical navigation. We propose a solution using gray-level co-occurrence matrix (GLCM) texture and an ensemble Support Vector Machine (SVM) structure.This manuscript per the authors focus on the effects of GLCM texture on brain tumor segmentation. The result is different from the application of the GLCM texture in other types of image processing.The experimental material was a dataset called BraTs2015. The segmented five different labels are normal brain, necrosis, edema, non-enhancing tumor, and enhancing tumor. The proposed model was verified with the Dice coefficient. The result demonstrated that this method has a better capacity and higher segmentation accuracy with a low computation cost.

Tool wear monitoring by ensemble learning and sensor fusion using power, sound, vibration, and AE signals

Sensor-fusion and different machine-learning methods were used for tool condition monitoring (TCM) when sawing wood in harsh conditions using power, sound, vibration, and acoustic emission (AE) signals. Tool classification was performed using two ensemble learning (XGBoost and random forest) methods and SVM. It was discussed that the optimal combination of sensors for monitoring is a trade-off between the accuracy of classifiers and the tolerance for sensor redundancy. AE was shown to be the critical sensor, which combined with power signals and XGBoost resulted in ∼92% classification accuracy. Ensemble learning outperformed the SVM and showed superior performance for TCM using multi-sensory-features.

Spectral spatial joint feature based convolution neural network for hyperspectral image classification

AbstractHyperspectral sensor generates huge datasets which conveys abundance of information. However, it poses many challenges in the analysis and interpretation of these data. Deep networks like VGG16, VGG19 are difficult to directly apply for hyperspectral image (HSI) classification because of its higher number of layers which in turn requires high level of system resources. This article suggests a novel framework with lesser number of layers for hyperspectral image classification (HSIC) that takes into account spectral‐spatial context sensitivity of HSI, which focuses on enhancing the discriminating capability of HSIC. The model uses available spectral feature as well as spatial contexts of HSI and consecutively learn the distinctive features. A small training set has been used to optimize the network parameters while the overfitting problem is alleviated using the validation set. Regularization has been performed using batch normalization (BN) layer after each convolution layer. The cost of the model is measured in terms of training and testing time duration under the same platform, which has further been compared with some ensemble learning methods, SVM and other three recent state‐of‐the‐art methods. Experimental results establish that the proposed model performs very well with the three benchmark datasets: Indian Pines, Salinas and University of Pavia, which mostly contain land cover of agriculture, forest, soil, rural, and urban area.

Calibration-Free Cuffless Blood Pressure Estimation Based on a Population With a Diverse Range of Age and Blood Pressure.

This study presents a new blood pressure (BP) estimation algorithm utilizing machine learning (ML). A cuffless device that can measure BP without calibration would be precious for portability, continuous measurement, and comfortability, but unfortunately, it does not currently exist. Conventional BP measurement with a cuff is standard, but this method has various problems like inaccurate BP measurement, poor portability, and painful cuff pressure. To overcome these disadvantages, many researchers have developed cuffless BP estimation devices. However, these devices are not clinically applicable because they require advanced preparation before use, such as calibration, do not follow international standards (81060-1:2007), or have been designed using insufficient data sets. The present study was conducted to combat these issues. We recruited 127 participants and obtained 878 raw datasets. According to international standards, our diverse data set included participants from different age groups with a wide variety of blood pressures. We utilized ML to formulate a BP estimation method that did not require calibration. The present study also conformed to the method required by international standards while calculating the level of error in BP estimation. Two essential methods were applied in this study: (a) grouping the participants into five subsets based on the relationship between the pulse transit time and systolic BP by a support vector machine ensemble with bagging (b) applying the information from the wavelet transformation of the pulse wave and the electrocardiogram to the linear regression BP estimation model for each group. For systolic BP, the standard deviation of error for the proposed BP estimation results with cross-validation was 7.74 mmHg, which was an improvement from 17.05 mmHg, as estimated by the conventional pulse-transit-time-based methods. For diastolic BP, the standard deviation of error was 6.42 mmHg for the proposed BP estimation, which was an improvement from 14.05mmHg. The purpose of the present study was to demonstrate and evaluate the performance of the newly developed BP estimation ML method that meets the international standard for non-invasive sphygmomanometers in a population with a diverse range of age and BP.

Combined ensemble multi-class SVM and fuzzy NSGA-II for trend forecasting and trading in Forex markets

Foreign exchange (Forex) market is the biggest currency exchange market in the world. Existing trading systems in Forex markets based on technical analysis use crisp technical indicators to provide Buy/Sell signals to the trader, only when the indicator value crosses a given threshold level. This strict and noise-sensitive condition can be replaced through uncertainty handling of indicators using fuzzy numbers to generate Buy/Sell signals with fuzzy memberships functions. To achieve this purpose, this paper presents a combined technique based on ensemble multi-class support vector machine (EmcSVM) and fuzzy NSGA-II for efficient trend classification and trading in Forex markets. At first, EmcSVM is used to forecast and classify the future market trend into uptrend, sideway, and downtrend. Then, NSGA-II is applied to optimize the hyperparameters of the proposed fuzzy trading system comprising multiple AND-OR Buy/Sell technical rules for uptrend/downtrend markets. The hyperparameters include indicator selection within each rule, importance weights of the different rules, and final decision thresholds for Buy/Sell models, while the objective is to maximize average return on investment (ROI) and minimize average draw-down of all transactions. The proposed method has been successfully developed and tested on real data from the Forex market for EUR/USD currency pair in a 6-year timeframe from 2014 to 2019. Obtained results show that the proposed method outperforms the existing crisp trading systems, with 80.8% precision, 72.4% recall, 94.1% annual ROI, and 0.58% draw down.

Arrhythmic Heartbeat Classification Using Ensemble of Random Forest and Support Vector Machine Algorithm

In this article, an automated heartbeat classification has been proposed to prevent the growing threats of cardiovascular diseases around the world. The MIT-BIH arrhythmia database has been used for the training and testing of the proposed approach. The database contains 90 095 normal (N), 2781 supraventricular (SVEB), 7008 ventricular (VEB), 802 fusions (F), and 15 unclassified (Q) beats each of 30 min duration. Total 61 features have been extracted using the time-series feature extraction library (TSFEL). Feature selection or reduction methods applied are feature scaling, removal of highly correlated and low variance features, and random forest (RF) recursive feature elimination. The methodological novelty of this study is mainly the incorporation of TSFEL during feature extractions, synthetic minority oversampling technique to create a balanced dataset, and an ensemble of RF and support vector machine (SVM) using weighted majority algorithm for heartbeat classification to improve the results. Grid search has been performed to optimize the hyperparameters of RF and SVM classifiers. A final evaluation has been carried out considering a “subject-specific” scheme. The sensitivity that our approach has achieved for the arrhythmic heartbeat classes is as follows: N: 99.50%, SVEB (S): 74.20%, VEB (V): 94.22%, F: 73.21%, and Q: 0%. The corresponding positive predictive values are N: 98.67%, SVEB (S): 90.09%, VEB (V): 95.95%, F: 88.35%, and Q: 0%. In comparison with machine learning and deep learning based state-of-the-art approaches, significant improvement in efficiency has been found. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>Impact Statement—</i>Automated classifications of heartbeats from ECG signals are very much required to speed up the diagnosis of cardiovascular diseases (CVDs). Our study demonstrates 98.21% accuracy compared to existing state-of-the-art approaches. This study impacts the intensive health care system by offering an easy, quick, and cost-effective diagnosis of CVDs. Besides, remote patients can also receive the treatments timely. Early intervention and prevention of CVDs can prevent almost 30% of the total death every year. The proposed approach has a societal and economic impact too, as the health, welfare, and productivity of the citizens as well as of the country will be improved significantly. Approximately 2% of gross domestic product (GDP) will be improved across low- and middle-income countries as the proposed approaches will reduce the direct (treatment cost) and indirect costs (loss in productivities at workplaces) associated with CVDs.

IPro2L-PSTKNC: A Two-Layer Predictor for Discovering Various Types of Promoters by Position Specific of Nucleotide Composition

Promoters are DNA regulatory elements located proximal to the transcription start site, which are in charge of the initiation of specific gene transcription. In Escherichia coli, promoters can be recognized by σ factors that have multiple families based on distinct function and structure, such as σ24, σ28, σ32, σ38, σ54 and σ70. At present, biological methods are mainly used to identify these promoters. However, because it is time-consuming and material-consuming to do biological experiments, computational biology algorithm has emerged as a more effective way to predict the classification. In this study, we develop a novel two-layer seamless predictor called iPro2L-PSTKNC to identify the promoters of the E. coli genome, which based on the feature extraction model we newly proposed that is named as the position specific tendencies of k-mer nucleotide composition (PSTKNC). On the first layer, it is a binary classification predicting whether a sequence is promoter or not. And the second layer is a multiple classification identifying which type the identified promoter belongs to. The ensemble classification SVM performsbest comparing with other algorithms, which gets a promising accuracy and the Matthews correlation coefficient (MCC) at [Formula: see text] and [Formula: see text]. Our data and code are available at https://github.com/lyuyinuo/iPro2L-PSTKNC.

Prediction of response to repetitive transcranial magnetic stimulation in phantom sounds based on individual brain anatomy

Non-invasive brain stimulation can reduce the severity of tinnitus phantom sounds beyond the time of stimulation by inducing regional neuroplastic changes. However, there are no good clinical predictors for treatment outcome. We used machine learning to investigate whether brain anatomy can predict therapeutic outcome. Sixty-one chronic tinnitus patients received repetitive transcranial magnetic stimulation of left dorsolateral prefrontal and temporal cortex. Before repetitive transcranial magnetic stimulation, a structural magnetic resonance image was obtained from all patients. To predict individual treatment response in new subjects, we employed a support vector machine ensemble for individual out-of-sample prediction. In the cross-validation, the support vector machine ensemble based on stratified sub-sampling and feature selection yielded an area under the curve of 0.87 for prediction of therapy success in new, previously unseen subjects. This corresponded to a balanced accuracy of 83.5%, sensitivity of 77.2% and specificity of 87.2%. Investigating the most selected features showed the involvement of the auditory cortex but also revealed a network of non-auditory brain areas. These findings suggest that idiosyncratic brain patterns accurately predict individual responses to repetitive transcranial magnetic stimulation treatment for tinnitus. Our findings may hence pave the way for future investigations into the precision treatment of tinnitus, involving automatic identification of the appropriate treatment method for the individual patient.

Utilizing support vector machines for predictive analytics in chronic kidney diseases

Chronic kidney disease (CKD), due to rising patients, the high probability of deterioration towards end-stage renal disease and inaccurate estimates of morbidity and mortality, constitutes a heavy burden on the sanitary infrastructure. The aim of this research is to build a model for machine-learning, which uses comorbidity and data on drugs and predicts population prevalence. Predictive health care prediction using machine learning is a daunting activity to help clinicians assess the precise therapies for life-saving. In this paper, the study applies machine learning method in combination with ensemble learning for estimation of chronic kidney disease with clinical evidence. They are based on chronic kidney disease datasets and the efficiency of these models is compared to choose the best classifier for chronic kidney disease prediction. The comparative analysis is estimated in terms of various metrics like classification accuracy, f-measure, percentage error, etc. The results of simulation shows that the proposed ensemble machine learning classifier namely Ensemble Support Vector Machine predicts well the chronic kidney disease from the datasets than other existing ensemble methods.

A multiparameter model for prediction of pathological nodal status in clinically early stage oral cancer using machine learning.

e18025 Background: The current approach to neck treatment in clinical T1-2 oral cancers is to offer elective nodal dissection to all patients, despite the fact that the majority of patients are pathologically node negative. This is due to the poor predictive ability of clinico-radiological assessment and subsequently poorer survival in those in whom neck dissection is omitted based on this. A robust prediction model for pathological nodal status may allow individualized decisions for neck dissection. Our aim was to develop a multiparameter prediction model to identify pathological node-negative status using machine learning. Methods: We identified 497 patients with cT1-2 oral cancer from a single institutional database from 2011-2018 who underwent primary resection and neck dissection. We compared the sensitivity, positive predictive value and accuracy of prediction of pathologically negative neck from clinico-radiological staging alone vs. a model created from multiple parameters including clinical features (clinico-radiological nodal status, ages, sex, subsite of primary lesion) and pathological features of the resected primary tumor (maximum dimension, depth of invasion, lymphovascular invasion, perineural invasion, grade and margins of resection). The multiparameter model was built from a training dataset of the first 400 patients using an ensemble of logistic regression, random forests and support vector machines. A cohort of 97 patients was used for independent validation. Results: In this cohort 232 (47%) were clinico-radiologically node negative, while 307(62%) were pathologically node negative. The sensitivity, positive predictive value and accuracy of the clinico-radiologically assigned nodal status was 56%, 74% and 61%, while that of the multiparameter machine learning model was 87%, 89% and 89% respectively. The area under curve (AUC) of the clinico-radiological prediction was 0.62 whereas that of the multiparameter predictive model was 0.91. In the validation dataset, 58/62 pathologically node negative patients were predicted correctly by the model. The accuracy of the model on the external validation dataset was 82%. Conclusions: The performance of the multiparameter predictive model was considerably superior to clinico-radiological neck staging for prediction of pathological node negative neck. This could be validated on an independent dataset. This could be considered for prospective clinical evaluation of individualized neck dissection.

Detection of deep myometrial invasion in endometrial cancer MR imaging based on multi-feature fusion and probabilistic support vector machine ensemble

The depth of myometrial invasion affects the treatment and prognosis of patients with endometrial cancer (EC), conventionally evaluated using MR imaging (MRI). However, only a few computer-aided diagnosis methods have been reported for identifying deep myometrial invasion (DMI) using MRI. Moreover, these existing methods exhibit relatively unsatisfactory sensitivity and specificity. This study proposes a novel computerized method to facilitate the accurate detection of DMI on MRI. This method requires only the corpus uteri region provided by humans or computers instead of the tumor region. We also propose a geometric feature called LS to describe the irregularity of the tissue structure inside the corpus uteri triggered by EC, which has not been leveraged for the DMI prediction model in other studies. Texture features are extracted and then automatically selected by recursive feature elimination. Utilizing a feature fusion strategy of strong and weak features devised in this study, multiple probabilistic support vector machines incorporate LS and texture features, which are then merged to form the ensemble model EPSVM. The model performance is evaluated via leave-one-out cross-validation. We make the following comparisons, EPSVM versus the commonly used classifiers such as random forest, logistic regression, and naive Bayes; EPSVM versus the models using LS or texture features alone. The results show that EPSVM attains an accuracy, sensitivity, specificity, and F1 score of 93.7%, 94.7%, 93.3%, and 87.8%, all of which are higher than those of the commonly used classifiers and the models using LS or texture features alone. Compared with the methods in existing studies, EPSVM exhibits high performance in terms of both sensitivity and specificity. Moreover, LS can achieve an accuracy, sensitivity, and specificity of 89.9%, 89.5%, and 90.0%. Thus, the devised geometric feature LS is significant for DMI detection. The fusion of LS and texture features in the proposed EPSVM can provide more reliable prediction. The computer-aided classification based on the proposed method can assist radiologists in accurately identifying DMI on MRI.

EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer

BackgroundToday, there are a lot of markers on the prognosis and diagnosis of complex diseases such as primary breast cancer. However, our understanding of the drivers that influence cancer aggression is limited.MethodsIn this work, we study somatic mutation data consists of 450 metastatic breast tumor samples from cBio Cancer Genomics Portal. We use four software tools to extract features from this data. Then, an ensemble classifier (EC) learning algorithm called EARN (Ensemble of Artificial Neural Network, Random Forest, and non-linear Support Vector Machine) is proposed to evaluate plausible driver genes for metastatic breast cancer (MBCA). The decision-making strategy for the proposed ensemble machine is based on the aggregation of the predicted scores obtained from individual learning classifiers to be prioritized homo sapiens genes annotated as protein-coding from NCBI.ResultsThis study is an attempt to focus on the findings in several aspects of MBCA prognosis and diagnosis. First, drivers and passengers predicted by SVM, ANN, RF, and EARN are introduced. Second, biological inferences of predictions are discussed based on gene set enrichment analysis. Third, statistical validation and comparison of all learning methods are performed by some evaluation metrics. Finally, the pathway enrichment analysis (PEA) using ReactomeFIVIz tool (FDR < 0.03) for the top 100 genes predicted by EARN leads us to propose a new gene set panel for MBCA. It includes HDAC3, ABAT, GRIN1, PLCB1, and KPNA2 as well as NCOR1, TBL1XR1, SIRT4, KRAS, CACNA1E, PRKCG, GPS2, SIN3A, ACTB, KDM6B, and PRMT1. Furthermore, we compare results for MBCA to other outputs regarding 983 primary tumor samples of breast invasive carcinoma (BRCA) obtained from the Cancer Genome Atlas (TCGA). The comparison between outputs shows that ROC-AUC reaches 99.24% using EARN for MBCA and 99.79% for BRCA. This statistical result is better than three individual classifiers in each case.ConclusionsThis research using an integrative approach assists precision oncologists to design compact targeted panels that eliminate the need for whole-genome/exome sequencing. The schematic representation of the proposed model is presented as the Graphic abstract.Graphic abstract

Ensemble Classification and IoT-Based Pattern Recognition for Crop Disease Monitoring System

Internet of Things (IoT) in the agriculture field provides crops-oriented data sharing and automatic farming solutions under single network coverage. The components of IoT collect the observable data from different plants at different points. The data gathered through IoT components, such as sensors and cameras, can be used to be manipulated for a better farming-oriented decision-making process. This work proposes a system that observes the crops' growth and leaf diseases continuously for advising farmers in need. To provide analytical statistics on plant growth and disease patterns, the proposed framework uses machine learning (ML) techniques, such as support vector machine (SVM) and convolutional neural network (CNN). This framework produces efficient crop condition notifications to terminal IoT components which are assisting in irrigation, nutrition planning, and environmental compliance related to the farming lands. In this regard, this work proposes ensemble classification and pattern recognition for crop monitoring system (ECPRC) to identify plant diseases at the early stages. The proposed ECPRC uses ensemble nonlinear SVM (ENSVM) for detecting leaf and crop diseases. In addition, this work performs comparative analysis between various ML techniques, such as SVM, CNN, naïve Bayes, and K-nearest neighbors. In this experimental section, the results show that the proposed ECPRC system works optimally compared to the other systems.

Transfer learning-based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data.

The novel discovered disease coronavirus popularly known as COVID-19 is caused due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and declared a pandemic by the World Health Organization (WHO). An early-stage detection of COVID-19 is crucial for the containment of the pandemic it has caused. In this study, a transfer learning–based COVID-19 screening technique is proposed. The motivation of this study is to design an automated system that can assist medical staff especially in areas where trained staff are outnumbered. The study investigates the potential of transfer learning–based models for automatically diagnosing diseases like COVID-19 to assist the medical force, especially in times of an outbreak. In the proposed work, a deep learning model, i.e., truncated VGG16 (Visual Geometry Group from Oxford) is implemented to screen COVID-19 CT scans. The VGG16 architecture is fine-tuned and used to extract features from CT scan images. Further principal component analysis (PCA) is used for feature selection. For the final classification, four different classifiers, namely deep convolutional neural network (DCNN), extreme learning machine (ELM), online sequential ELM, and bagging ensemble with support vector machine (SVM) are compared. The best performing classifier bagging ensemble with SVM within 385 ms achieved an accuracy of 95.7%, the precision of 95.8%, area under curve (AUC) of 0.958, and an F1 score of 95.3% on 208 test images. The results obtained on diverse datasets prove the superiority and robustness of the proposed work. A pre-processing technique has also been proposed for radiological data. The study further compares pre-trained CNN architectures and classification models against the proposed technique.

A band selection approach based on wavelet support vector machine ensemble model and membrane whale optimization algorithm for hyperspectral image

Hyperspectral Image (HSI) has become one of the important remote sensing sources for object interpretation by its abundant band information. Among them, band selection is considered as the main theme in HSI classification to reduce the data dimension, and it is a combinatorial optimization problem and difficult to be completely solved by previous techniques. Whale Optimization Algorithm (WOA) is a newly proposed swarm intelligence algorithm that imitates the predatory strategy of humpback whales, and membrane computing is able to decompose the band information into a series of elementary membranes that decreases the coding length. In addition, Support Vector Machine (SVM) combined with wavelet kernel is adapted to HSI datasets with high dimension and small samples, ensemble learning is an effective tool that synthesizes multiple sub-classifiers to solve the same problem and obtains accurate category label for each sample. In the paper, a band selection approach based on wavelet SVM (WSVM) ensemble model and membrane WOA (MWOA) is proposed, experimental results indicate that the proposed HSI classification technique is superior to other corresponding and newly proposed methods, achieves the optimal band subset with a fast convergence speed, and the overall classification accuracy has reached 93% for HSIs.

Differential Diagnosis of Malignant Thyroid Calcification Nodule Based on Computed Tomography Image Texture

Purpose: Calcification nodules in thyroid can be found in thyroid disease. Current clinical computed tomography systems can be used to detect calcification nodules. Our aim is to identify the nature of thyroid calcification nodule based on plain CT images. Method: Sixty-three patients (36 benign and 27 malignant nodules) found thyroid calcification nodules were retrospectively analyzed, together with computed tomography images and pathology finding. The regions of interest (ROI) of 6464 pixels containing calcification nodules were manually delineated by radiologists in CT plain images. We extracted thirty-one texture features from each ROI. And nineteen texture features were picked up after feature optimization by logistic regression analysis. All the texture features were normalized to [0, 1]. Four classification algorithms, including ensemble learning, support vector machine, K-nearest neighbor, decision tree, were used as classification algorithms to identity the benign and malignant nodule. Accuracy, PPV, NPV, SEN, and AUC were calculated to evaluate the performance of different classifiers. Results: Nineteen texture features were selected after feature optimization by logistic regression analysis (P &lt;0.05). Both Ensemble Learning and Support Vector Machine achieved the highest accuracy of 97.1%. The PPV, NPV, SEN, and SPC are 96.9%, 97.4%, 98.4%, and 95.0%, respectively. The AUC was 1. Conclusion: Texture features extracted from calcification nodules could be used as biomarkers to identify benign or malignant thyroid calcification.

Towards a self-sufficient face verification system

The absence of a previous collaborative manual enrolment represents a significant handicap towards designing a face verification system for face re-identification purposes. In this scenario, the system must learn the target identity incrementally, using data from the video stream during the operational authentication phase. So, manual labelling cannot be assumed apart from the first few frames. On the other hand, even the most advanced methods trained on large-scale and unconstrained datasets suffer performance degradation when no adaptation to specific contexts is performed. This work proposes an adaptive face verification system, for the continuous re-identification of target identity, within the framework of incremental unsupervised learning. Our Dynamic Ensemble of SVM is capable of incorporating non-labelled information to improve the performance of any model, even when its initial performance is modest. The proposal uses the self-training approach and is compared against other classification techniques within this same approach. Results show promising behaviour in terms of both knowledge acquisition and impostor robustness.

Depressed Patients Intelligent Recognition in Smart Home Environment

Depression is one of the most harmful diseases in society today, and the etiology and pathological mechanism of depression is one of the most complicated mental illnesses. As the population of people with depression grows, the patient's long duration of illness and the harmfulness of the results make the disease the biggest challenge in the diagnosis of mental illness. How to improve the recognition rate of depression and make diagnosis and treatment as early as possible is the most effective way. According to the clinical medical manifestations of patients with depression, it is found that there is a very obvious difference between the patients with depression and the normal group in terms of speech characteristics, such as lower tones and slower speech speed. Therefore, this paper proposes a method for intelligent recognition of depression based on speech signals in combination with the contemporary smart home environment. A novel ensemble support vector machine (ESVM) algorithm is proposed in this article, which is applied to several classic depression speech data sets. The organic combination of depression recognition and smart home environment can adapt to the development of future technology.

Depressed Patients Intelligent Recognition in Smart Home Environment

Depression is one of the most harmful diseases in society today, and the etiology and pathological mechanism of depression is one of the most complicated mental illnesses. As the population of people with depression grows, the patient's long duration of illness and the harmfulness of the results make the disease the biggest challenge in the diagnosis of mental illness. How to improve the recognition rate of depression and make diagnosis and treatment as early as possible is the most effective way. According to the clinical medical manifestations of patients with depression, it is found that there is a very obvious difference between the patients with depression and the normal group in terms of speech characteristics, such as lower tones and slower speech speed. Therefore, this paper proposes a method for intelligent recognition of depression based on speech signals in combination with the contemporary smart home environment. A novel ensemble support vector machine (ESVM) algorithm is proposed in this article, which is applied to several classic depression speech data sets. The organic combination of depression recognition and smart home environment can adapt to the development of future technology.

Breast Cancer Diagnosis Using an Ensemble Transfer Support Vector Machine

As the number of breast cancer patients increases and the age of onset is younger, early detection and prevention have become the key to prevention and treatment of breast cancer. At present, many classification or clustering algorithms are used to diagnose breast cancer data. However, these algorithms directly lose the minimum source domain information, resulting in a significant improvement in the recognition rate. Based on this, this paper proposes an ensemble transfer support vector machine (ET-SVM) algorithm based on classic support vector machine (SVM). The algorithm can effectively use the knowledge in the source domain to guide the learning of the target task. The result of a single SVM is usually the local optimal solution. And its performance is unstable and its generalization performance is poor. Therefore, this article introduces an ensemble strategy based on AdaBoost algorithm. Experiments on the Wisconsin breast cancer data set proved that the proposed ET-SVM algorithm can achieve better classification results and good generalization performance.