Convolutional Neural Network Support Vector Machine Research Articles

Rapid identification and early warning of axial compressor stall based on multiscale CNN-SVM-FC model

Early prewarning of compressor stall and surge is crucial to avoid aircraft engine instability, yet it is challenging due to the complex and unstable flow field characterized by multiple modes and multiscale features. To enhance the multi-scale feature representation capability of Convolutional Neural Network-Support Vector Machine (CNN-SVM) algorithm, a novel classifier modelling method combined multiscale windows with CNN-SVM is introduced for stall prewarning in this paper, named Multiscale CNN-SVM-FC. Multiscale detection windows are utilized to adaptively identify various pressure features during the stall process. Additionally, to reduce the false alarm rate, a fuzzy control algorithm is integrated with the temporal accumulation of prediction results from the multi-branch network for joint analysis. A series of test data from a five-stage axial compressor at different operating speeds is used to verify this method. The results indicate that the proposed Multiscale CNN-SVM-FC method enhances the accuracy of classification and reduces the false alarm rate compared to the standard CNN-SVM model, achieving over 99% accuracy in identifying unstable states under various speeds. Compared to three traditional stall prewarning methods, the Multiscale CNN-SVM-FC model provides an average warning signal 164 milliseconds ahead of stall, and reduces the uncertainty associated with threshold selection, which typically relies on engineering experience.

Predicting hourly heating load in residential buildings using a hybrid SSA–CNN–SVM approach

This study proposes a hybrid prediction model using sparrow search algorithm (SSA) to optimize the convolutional neural network (CNN) and support vector machine (SVM), in order to perform accurate prediction of secondary supply temperature (Ts2). The historical operation data of Weifang residential building thermal station was adopted and reasonable data preprocessing was performed to suppress the interference of abnormal data. The input variables of the prediction model were screened using the correlation analysis method, taking the influence of the hysteresis effect into consideration. The SSA–CNN–SVM model was then developed for prediction. The performance of the model was evaluated by the root mean square error, mean absolute error, mean absolute percentage error (MAPE), and absolute value of relative error of each time step. The results obtained demonstrated that the SSA–CNN–SVM model has high prediction accuracy. The MAPE values of the two heat exchange stations were between 2.28 % and 2.4 %. The indoor temperature significantly affected the prediction accuracy of Ts2. After the introduction of the indoor temperature, the MAPE of the predicted values of the hybrid model was reduced by 0.35 %. The maximum reduction in MAPE of the SSA–CNN–SVM model was 1.5 % compared with other prediction models.

Rapid Identification of the Species of Bloodstain Based on Near Infrared Spectroscopy and Convolutional Neural Network-Support Vector Machine Algorithm

As one of the most important types of evidence at the scene of the crime, the rapid identification of the human bloodstain is of great significance to solve the criminal case. In this paper, the spectral data of different species of bloodstain samples including human, chicken and pig were acquired by using a hand-held near-infrared spectrometer. Then, the training models were established via convolutional neural network-support vector machine algorithm. Meanwhile, the traditional support vector machine, genetic algorithm-back propagation and random forest classification algorithms were also compared. The results showed that the prediction accuracy of convolutional neural network-support vector machine algorithm was the highest and the overall performance of the model was the best. The rapid detection method based on a handheld near-infrared spectrometer and convolutional neural network-support vector machine algorithm could identify the species of bloodstain efficiently, non-destructively, quickly and accurately and it provided a new technical reference for bloodstains detection and identification.

A hybrid CNN-SVM model for enhanced autism diagnosis.

Autism is a representative disorder of pervasive developmental disorder. It exerts influence upon an individual's behavior and performance, potentially co-occurring with other mental illnesses. Consequently, an effective diagnostic approach proves to be invaluable in both therapeutic interventions and the timely provision of medical support. Currently, most scholars' research primarily relies on neuroimaging techniques for auxiliary diagnosis and does not take into account the distinctive features of autism's social impediments. In order to address this deficiency, this paper introduces a novel convolutional neural network-support vector machine model that integrates resting state functional magnetic resonance imaging data with the social responsiveness scale metrics for the diagnostic assessment of autism. We selected 821 subjects containing the social responsiveness scale measure from the publicly available Autism Brain Imaging Data Exchange dataset, including 379 subjects with autism spectrum disorder and 442 typical controls. After preprocessing of fMRI data, we compute the static and dynamic functional connectivity for each subject. Subsequently, convolutional neural networks and attention mechanisms are utilized to extracts their respective features. The extracted features, combined with the social responsiveness scale features, are then employed as novel inputs for the support vector machine to categorize autistic patients and typical controls. The proposed model identifies salient features within the static and dynamic functional connectivity, offering a possible biological foundation for clinical diagnosis. By incorporating the behavioral assessments, the model achieves a remarkable classification accuracy of 94.30%, providing a more reliable support for auxiliary diagnosis.

Classification of circulating tumor cell clusters by morphological characteristics using convolutional neural network-support vector machine

Metastasis is the leading cause of cancer-associated deaths, and the circulating tumor cell (CTC) cluster plays a significant role as a precursor to metastasis. Thus, there is a great demand for high-throughput identification of rare CTC clusters for prognostic diagnosis. Immunofluorescence staining is considered the gold standard for identifying CTCs. However, as CTC clusters are extremely heterogeneous cells, multiple staining markers are required for accurate discrimination. Additionally, the staining procedure is tedious and the analysis of large amounts of stained images is labor-intensive and error-prone. Recently, machine learning-based identification has been introduced to achieve accurate discrimination, but they still rely on immunofluorescence staining for dataset preparation. In this study, we developed a hybrid algorithm, a convolutional neural network support vector machine (CNN-SVM), for the accurate classification of CTC clusters without immunofluorescence staining. In dataset preparation, the Wright–Giemsa staining was used to highlight the morphological features of the cells. Four morphological characteristics that display the unique traits of cells were drawn with each eigenvector, as a result of learning, the algorithm classified CTC clusters of various configurations with a sensitivity and specificity of > 90%. Therefore, our algorithm is expected to be a powerful tool for cancer diagnosis and prognosis.

Research on fault diagnosis of electric submersible pump based on improved convolutional neural network with Bayesian optimization

A conventional intelligent fault diagnosis approach for electric submersible pumps (ESPs) is heavily dependent on manual expertise for feature extraction. Meanwhile, a conventional convolutional neural network (CNN) exhibits an excess of parameters and requires a substantial volume of training samples. In this paper, a fault diagnosis algorithm of ESPs based on a Bayesian optimization-one-dimensional convolutional neural network-support vector machine (BO-1DCNN-SVM) is proposed by combining a 1DCNN with a SVM and using the algorithm BO to tune the improved 1DCNN. First, the method uses the self-extracting feature capability of the 1DCNN to solve the problem that traditional diagnosis methods over-rely on manual experience extraction. Meanwhile, the last layer of the convolutional neural network Softmax layer is replaced by the SVM to effectively process a few sample data. The accuracy and generalization ability of the fault classification of the electric submersible pump are improved. Then, the Bayesian optimization algorithm is used to find the optimal combination of hyperparameters for the improved 1DCNN-SVM model to further improve the prediction performance of the fault diagnosis model. Finally, the experimental results achieved a classification accuracy of 96.64%, which is 5% higher than existing CNN approaches with data samples of similar scale in this paper. The proposed method also proved to be highly accurate and robust in fault diagnosis.

Covid CNN–SVM: Automated Categorization of Novel Coronavirus Disease from X-Ray Images

In the computer-aided diagnostic (CAD) system, automated Coronavirus infection disclosure plays a crucial role in early identifying positive patients to prevent the disease from spreading further. The advent of algorithms for deep learning and machine learning has tackled classification tasks with promising results, especially in classifying images. However, the small size of the databases for medical images is a limitation associated with train deep neural networks. We use a combination of convolutional neural network (CNN) features and a support vector machine (SVM) for X-ray image classification to overcome this problem. This research work used CNN methods to extract features from 1,338 Chest X-ray frontal view image data. An SVM is used with CNN features to classify images in two classes: COVID-19 and Normal cases for enhanced performance. We conducted and evaluated our experiments on several public databases, which have been used in the recently published articles. The performance of the proposed method revealed accuracy, AUC, sensitivity, specificity of 0.995, 0.999, and 0.995 for classification, respectively. The high performance of the detection system achieved in this research reveals the effectiveness of deep features and the machine learning classifier approach for detecting COVID-19 cases using X-ray images. This would be extremely helpful in accelerating disease diagnosis with the available resources.

A SENTIMENT ANALYSIS MODEL FOR ELECTROENCEPHALOGRAM SIGNALS OF STUDENTS IN UNIVERSITIES USING A CONVOLUTIONAL NEURAL NETWORK AND SUPPORT VECTOR MACHINE MODELS

Sentiment analysis in teaching evaluation has significant implications. By analyzing students’ sentiments toward instructors, educational institutions can gain valuable insights into teaching effectiveness. These data can guide curriculum development, instructional improvements, and faculty training initiatives. Positive sentiment indicates effective teaching methods, engagement, and student satisfaction; negative sentiment flags areas that need attention. Sentiment analysis can help identify patterns, trends, and outliers, aiding in targeted interventions and personalized support. It also enables comparisons across different courses, instructors, and departments. However, it is crucial to ensure the accuracy and fairness of sentiment analysis algorithms, considering potential biases and the contextual nature of the feedback. This study proposes a sentiment classification model CNN–SVM that combines a convolutional neural network (CNN) and a support vector machine (SVM). Taking students majoring in art in comprehensive colleges and universities as the research object, by collecting the electroencephalogram (EEG) signals of students during teaching evaluation. CNN–SVM is used as the emotional analysis model to obtain the emotional analysis of teaching evaluation results. EEG is a typical physiological signal, and data based on this signal can more truly reflect student emotions. The adaptive CNN feature extraction function and the super generalization classification performance of SVM can reduce the individual differences and data noise between data, thereby improving sentiment classification performance. The experimental results demonstrate that using technology to analyze sentiment can assist educational institutions in more properly comprehending the feedback and opinions of students on instruction. With regard to sentiment analysis, the CNN–SVM method that is derived to produce the fusion algorithm has solid performance.

Optimization of machine learning classification models for tumor cells based on cell elements heterogeneity with laser-induced breakdown spectroscopy.

The rapid and accurate diagnosis of cancer is an important topic in clinical medicine. In the present work, an innovative method based on laser-induced breakdown spectroscopy (LIBS) combined with machine learning was developed to distinguish and classify different tumor cell lines. The LIBS spectra of cells were first acquired. Then the spectral pre-processing was performed as well as detailed optimization to improve the classification accuracy. After that, the convolutional neural network (CNN), support vector machine (SVM), and K-nearest neighbors were further compared for the optimized classification ability of tumor cells. Both the CNN algorithm and SVM algorithm have achieved impressive discrimination performances for tumor cells distinguishing, with an accuracy of 97.72%. The results show that the heterogeneity of elements in tumor cells plays an important role in distinguishing the cells. It also means that the LIBS technique can be used as a fast classification method for classifying tumor cells.

A Machine Learning-Based Approach to Predict Prognosis and Length of Hospital Stay in Adults and Children With Traumatic Brain Injury: Retrospective Cohort Study

The treatment and care of adults and children with traumatic brain injury (TBI) constitute an intractable global health problem. Predicting the prognosis and length of hospital stay of patients with TBI may improve therapeutic effects and significantly reduce societal health care burden. Applying novel machine learning methods to the field of TBI may be valuable for determining the prognosis and cost-effectiveness of clinical treatment. We aimed to combine multiple machine learning approaches to build hybrid models for predicting the prognosis and length of hospital stay for adults and children with TBI. We collected relevant clinical information from patients treated at the Neurosurgery Center of the Second Affiliated Hospital of Anhui Medical University between May 2017 and May 2022, of which 80% was used for training the model and 20% for testing via screening and data splitting. We trained and tested the machine learning models using 5 cross-validations to avoid overfitting. In the machine learning models, 11 types of independent variables were used as input variables and Glasgow Outcome Scale score, used to evaluate patients' prognosis, and patient length of stay were used as output variables. Once the models were trained, we obtained and compared the errors of each machine learning model from 5 rounds of cross-validation to select the best predictive model. The model was then externally tested using clinical data of patients treated at the First Affiliated Hospital of Anhui Medical University from June 2021 to February 2022. The final convolutional neural network-support vector machine (CNN-SVM) model predicted Glasgow Outcome Scale score with an accuracy of 93% and 93.69% in the test and external validation sets, respectively, and an area under the curve of 94.68% and 94.32% in the test and external validation sets, respectively. The mean absolute percentage error of the final built convolutional neural network-support vector regression (CNN-SVR) model predicting inpatient time in the test set and external validation set was 10.72% and 10.44%, respectively. The coefficient of determination (R2) was 0.93 and 0.92 in the test set and external validation set, respectively. Compared with back-propagation neural network, CNN, and SVM models built separately, our hybrid model was identified to be optimal and had high confidence. This study demonstrates the clinical utility of 2 hybrid models built by combining multiple machine learning approaches to accurately predict the prognosis and length of stay in hospital for adults and children with TBI. Application of these models may reduce the burden on physicians when assessing TBI and assist clinicians in the medical decision-making process.

A CNN–SVM MODEL USING IMU FOR LOCOMOTION MODE RECOGNITION IN LOWER EXTREMITY EXOSKELETON

Human activity intention is indispensable for wearable powered lower extremity exoskeleton such that ensuring the compliant control of the robot. Lots of researches have been done on gait phase detection, which served as a sub-module of locomotion mode recognition to support the follow-on task. Therefore, it is self-evident that locomotion mode recognition is of great importance. Many model-based recognition methods are usually applied in manual extraction of cumbersome features, such as the traditional neural network (NN), support vector machine (SVM), etc. In contrast, the feature mapping layer coming with the convolutional neural network (CNN) can effectively solve the above time-consuming problem. Given that the training of NN is prone to overfitting, SVM with optimal characteristics is considered. A hybrid CNN–SVM model is proposed to identify human locomotion modes by collecting multi-channel inertial measurement unit (IMU) signals and is integrated with the error correction function of the finite state machine (FSM). Therefore, the CNN–SVM model has great influence on the generalization performance and recognition accuracy. The recognition rates of five single locomotion modes and eight mixed locomotion modes reach 97.91% and 98.93%, respectively. The system meets the demand of real-time performance, and the recognition time exceeds 370[Formula: see text]ms on heel strike.

Research on the lying pattern of grouped pigs using unsupervised clustering and deep learning

Changes in lying pattern and lying center position can reflect information about the production efficiency, health, and welfare of pigs. This study investigates the lying pattern and lying center position of grouped pigs by using unsupervised clustering, deep learning, and image processing technology under commercial farming conditions. Images of the lying pattern of grouped pigs under commercial conditions were collected over 110 days, and then ellipse fitting was used to calculate the center of the target pigs. Unsupervised clustering was used to calculate the cluster center of pigs (lying pattern central location). Variance and standard deviation of the distance from the cluster center to each pig were calculated, Finally, the lying patterns of pigs were classified as “Crowded,” “Close,” “Normal,” “Dispersed,” and “Far.” These patterns were based on the standard deviation. In this research, a Convolutional Neural Network-Support Vector Machine (CNN-SVM) classification model was used to classify five lying patterns of grouped pigs; classification accuracy was up to 97.1%. We studied the change in the lying pattern center of grouped pigs over time, and found that the lying pattern center of grouped pigs showed positional variations over time. We collected experimental site data during the period from July 15, 2021 to October 31, 2021 and analyzed the lying patterns of the grouped pigs. Our results showed that pigs mainly had three lying patterns: “Crowded,” “Close,” and “Normal.” Their proportions were 35%, 33.75%, and 18%, respectively. In addition, the study also found that there were significant changes among the five lying patterns in a day. Standard deviation of grouped pigs showed positive correlation with pigpen temperature, and lying pattern variation of grouped pigs was affected by pigpen temperature. Based on these conclusions, suitable environmental conditions could be created for grouped pigs. In the future, more experiments and research will be extended to other types of livestock, as this technology is helpful for large-scale automatic welfare breeding of livestock.

Classification of macular abnormalities using a lightweight CNN-SVM framework

Macular abnormalities are the main reason for central vision loss, especially in elderly people. Due to global population aging, a heavy burden will be placed on the health care system. Therefore, it is urgent and necessary to develop an automatic and intelligent tool to identify macular abnormalities. Optical coherence tomography is a non-invasive rapid imaging technique to diagnose macular abnormalities. We propose a lightweight convolutional neural network–support vector machine (CNN-SVM) framework consisting of a novel lightweight CNN backbone and an SVM classifier for the accurate detection of macular abnormalities. The CNN-SVM framework achieves excellent performance based on various metrics (precision, recall, F1-score, and accuracy) with an accuracy of 99.8% and demonstrates strong interpretability using heatmap visualization, outperforming several state-of-the-art models (Joint-Attention Network, OpticNet, MobileNet-V3, DenseNet-169, ResNet-50, lesion-aware CNN, Atten-ResNet, least-squares generative adversarial network and others). The proposed CNN-SVM framework is a feasible and reliable tool for the classification of macular abnormalities and shows potential for diagnostic ophthalmology in clinical practice.

Automatic scoring of postures in grouped pigs using depth image and CNN-SVM

Animal posture is a manifestation of animal behavior, and an animal’s behavior provides information about their health, welfare, and living environment. In recent years, machine vision and machine learning technologies have been widely used to detect individual or group behavior of pigs. The purpose of this study is to use machine vision and deep learning technologies to recognize and score multiple postures (standing, sitting, sternal recumbency, ventral recumbency and lateral recumbency) of pigs under commercial conditions based on depth images. In this study, the Azure Kinect DK depth camera with a top view was used to obtain the depth image of pigs, and the target pig image was obtained by GrabCut image segmentation and watershed segmentation of target object calibration. Then, based on the characteristics of the image, the convex hull, boundary, and the depth distance of the shoulder and the hip were obtained. The ratio of the convex hull perimeter to the boundary and the ratio of the convex hull area to the boundary, as well as the depth distance of the shoulder and the hip, and the depth distance ratio of the shoulder to the hip were obtained as the input of the Convolutional Neural Network-Support Vector Machine (CNN-SVM) classification model, and the model was trained and tested. In various classifier detection experiments, the performance of our pig posture classifier for standing posture and lateral recumbency posture was better, with the area under the receiver operating characteristic (AUC) values being 0.9969 and 0.9967, respectively. However, the performance of sitting posture, sternal recumbency posture and ventral recumbency posture classifier was slightly worse but still had good performance: AUC values were 0.9790, 0.9355 and 0.9795, respectively. The model in this article was used to detect the average postures of pigs in one day (taking the average for eight consecutive days), and it was found that the proportion of lying postures was higher than other postures (lying postures were 72%, standing postures were 20%, and sitting postures were 8%). The proportion of standing postures in the daytime was higher than that in the evening, and lying posture was the opposite. The proportion of the three lying postures also changes over time. This study compared the difference of posture recognition accuracy between the model in this paper (CNN-SVM), SVM and CNN; using the same training data and experimental data, the accuracy of posture recognition of the three models was 94.6368%, 92.2175% and 90.5396%, respectively. Therefore, the recognition accuracy of the model in this paper was improved greatly compared with CNN and SVM.

Multi-Method Diagnosis of Blood Microscopic Sample for Early Detection of Acute Lymphoblastic Leukemia Based on Deep Learning and Hybrid Techniques.

Leukemia is one of the most dangerous types of malignancies affecting the bone marrow or blood in all age groups, both in children and adults. The most dangerous and deadly type of leukemia is acute lymphoblastic leukemia (ALL). It is diagnosed by hematologists and experts in blood and bone marrow samples using a high-quality microscope with a magnifying lens. Manual diagnosis, however, is considered slow and is limited by the differing opinions of experts and other factors. Thus, this work aimed to develop diagnostic systems for two Acute Lymphoblastic Leukemia Image Databases (ALL_IDB1 and ALL_IDB2) for the early detection of leukemia. All images were optimized before being introduced to the systems by two overlapping filters: the average and Laplacian filters. This study consists of three proposed systems as follows: the first consists of the artificial neural network (ANN), feed forward neural network (FFNN), and support vector machine (SVM), all of which are based on hybrid features extracted using Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM) and Fuzzy Color Histogram (FCH) methods. Both ANN and FFNN reached an accuracy of 100%, while SVM reached an accuracy of 98.11%. The second proposed system consists of the convolutional neural network (CNN) models: AlexNet, GoogleNet, and ResNet-18, based on the transfer learning method, in which deep feature maps were extracted and classified with high accuracy. All the models obtained promising results for the early detection of leukemia in both datasets, with an accuracy of 100% for the AlexNet, GoogleNet, and ResNet-18 models. The third proposed system consists of hybrid CNN–SVM technologies, consisting of two blocks: CNN models for extracting feature maps and the SVM algorithm for classifying feature maps. All the hybrid systems achieved promising results, with AlexNet + SVM achieving 100% accuracy, Goog-LeNet + SVM achieving 98.1% accuracy, and ResNet-18 + SVM achieving 100% accuracy.

The Path of Film and Television Animation Creation Using Virtual Reality Technology under the Artificial Intelligence

The purpose is to provide a more reliable human-computer interaction (HCI) guarantee for animation works under virtual reality (VR) technology. Inspired by artificial intelligence (AI) technology and based on the convolutional neural network—support vector machine (CNN-SVM), the differences between animation works under VR technology and traditional animation works are analyzed through a comprehensive analysis of VR technology. The CNN-SVM gesture recognition algorithm using the error correction strategy is designed based on HCI recognition. To have better recognition performance, the advantages of depth image and color image are combined, and the collected information is preprocessed including the relations between the times of image training iterations and the accuracy of different methods in the direction of the test set. After experiments, the maximum accuracy of the preprocessed image can reach 0.86 showing the necessity of image preprocessing. The recognition accuracy of the optimized CNN-SVM is compared with other algorithm models. Experiments show that the accuracy of the optimized CNN-SVM has an upward trend compared with the previous CNN-SVM, and the accuracy reaches 0.97. It proves that the designed algorithm can provide good technical support for VR animation, so that VR animation works can interact well with the audience. It is of great significance for the development of VR animation and the improvement of people’s artistic life quality.

A CNN-SVM hybrid model for the classification of thyroid nodules in medical ultrasound images

The thyroid nodule is one of the endocrine issues which is caused by the formation of irregular cells in the thyroid region. The recent success of machine and deep learning techniques in image recognition task leads to solve challenges in diagnostic imaging. An effective Convolutional Neural Network-Support Vector Machine (CNN-SVM) hybrid model is proposed using hinge loss function to achieve better results and stable convergence. The efficiency of the proposed model has been evaluated on public and collected data sets having 1180 and 2616 thyroid USG images after data augmentation. The proposed model has achieved an accuracy of 94.57%, specificity of 91.89%, sensitivity of 96.70% and f-measure of 95.64% on dataset-1 and an accuracy of 96%, specificity of 93.93%, sensitivity of 97.80% and f-measure of 98.33% on dataset-2. It has shown an improvement of 3% to 5% on dataset-1 and 4% to 6% on dataset-2 in comparison with state-of-the-art models.

A Hybrid Convolutional Neural Network-Support Vector Machine for X-ray Computed Tomography Images on CancerA Hybrid Convolutional Neural Network-Support Vector Machine for X-ray Computed Tomography Images on Cancer

BACKGROUND: Cancer is a major health problem not only in Indonesia but also throughout the world. Cancer is the growth and spread of abnormal cells that have distinctive characteristics, that if can no longer be controlled will usually cause death. The number of deaths due to cancer is generally caused by late diagnosis and inappropriate treatment. To reduce mortality from cancer, it is necessary to strive for early detection and monitoring of cancer in patients undergoing therapy. Convolutional neural networks (CNNs) as one of machine learning methods are designed to produce or process data from two dimensions that have a network tier and many applications carried out in the image. Moreover, support vector machines (SVMs) as a hypothetical space in the form of linear functions feature have high dimensions and trained algorithm based on optimization theory. AIM: In connection with the above, this paper discusses the role of the machine learning technique named a hybrid CNN-SVM. METHODS: The proposed method is used in the detection and monitoring of cancers by determining the classification of cancers in X-ray computed tomography (CT) patients’ images. Several types of cancer that used for determination in detection and monitoring of cancers diagnosis are also discussed in this paper, such as lung, liver, and breast cancer. RESULTS: From the discussion, the results show that the combining model of hybrid CNN-SVM has the best performance with 99.17% accuracy value. CONCLUSION: Therefore, it can be concluded that machine learning plays a very important role in the detection and management of cancer treatment through the determination of classification of cancers in X-ray CT patients’ images. As the proposed method can detect cancer cells with an effective mechanism of action so can has the potential to inhibit in the future studies with more extensive data materials and various diseases.

Cervical cancer classification using convolutional neural network-support vector machine

Cervical cancer is the second most common cancer in women worldwide, and occurs when there are presences of abnormal cells in the cervix, which continue to grow uncontrollably. In the early stages, cervical cancer indications are not perceptible; however, it is easily detected with different forms of machine learning methods, such as the convolutional neural network (CNN). This is a popular method with a wide range of applications and known for its high accuracy value. Moreover, there is a support vector machine (SVM) with several kernel functions that is commonly used in the classification of diseases, and also known for its high accuracy value. Therefore, the combination of CNN–SVM with several linear kernels functions as classifier for the categorization of cervical cancer.

WGNCS: A robust hybrid cross-version defect model via multi-objective optimization and deep enhanced feature representation

For a constantly-evolving software project with multiple releasing versions, Cross-Version Defect Prediction (CVDP) can identify the potential defect in the latter one by mining historical defect information from the prior releasing software versions. Unfortunately, the imbalanced class distribution and the complex intrinsic structure in software projects make it challenging to obtain suitable defect features and construct a predominant CVDP model. To address these challenges, we propose a robust hybrid CVDP model named WGNCS based on WGAN-GP (Wasserstein GAN with Gradient Penalty), multi-objective NSGA-III (Non-dominated Sorting Genetic Algorithm - III) algorithm and hybrid CNN-SVM (Convolutional Neural Network – Support Vector Machine) in this study, which has three main merits: (1) employ a powerful deep learning generative model – WGAN-GP to conduct data augmentation tasks, thereby achieving defect class balance and generating more training instances. (2) utilize the multi-objective NSGA-III algorithm to select the fewest representative training feature subset for the minimum error. (3) construct a single powerful defect predictor CNN-SVM by cascading a high-level deep semantic feature extractor (CNN) and a classifier (SVM) with the fixed non-linear Gaussian kernel function. We verify the CVDP performance of WGNCS on 32 cross-version pairs derived from 45 software project versions. The experimental results demonstrate that the WGNCS model can exhibit encouraging performance.