Support Vector Machine Clustering Research Articles

Pattern recognition of urban traffic accidents: Application of cluster analysis and support vector machine in accident pattern recognition

Urban traffic accidents impose a significant threat to public safety because of its frequent occurrence and potential for severe injuries and fatalities. Hence, an effective analysis of accident patterns is crucial for designing accident prevention strategies. Recent advancement in data analytics have provided opportunities to improve the pattern of urban traffic accidents. However, the existing works face several challenges in adapting the complex dynamics, and heterogeneity of the accident data. To overcome these challenges, we proposed an innovative solution by combining the K-means clustering and Support Vector Machine to precisely predict the traffic accident patterns. By leveraging the efficiencies of clustering technique and machine learning, this work intends to identify the intricate patterns within the traffic database. Initially, a traffic accident database was collected and fed into the system. The collected database was pre-processed to improve and standardize the raw dataset. Further, cluster analysis is employed to identify distinct patterns within the dataset and group similar accidents into clusters. This clustering enables the system to recognize common accident scenarios and identify recent accident trends. Subsequently, a Support Vector Machine is deployed to classify accidents into distinct categories through intensive training with identified clusters. The combination enables the system to understand the complex relationships among diverse accident variables, making it an effective framework for real-time pattern recognition. The proposed strategy is implemented in Python and validated using the publicly available traffic accident database. The experimental results manifest that the proposed method achieved 99.65% accuracy, 99.53% precision, 99.62% recall, and 99.57% f-measure. Finally, the comparison with the existing techniques shows that the developed strategy offers improved accuracy, precision, recall, and f-measure compared to existing ones. shows that the developed strategy offers improved accuracy, precision, recall, and f-measure compared to existing ones.

A New Approach in Using Random Support Vector Machine Cluster in Analyzing Prostate Cancer Gene Expression Data

A New Approach in Using Random Support Vector Machine Cluster in Analyzing Prostate Cancer Gene Expression Data

A steps-ahead tool wear prediction method based on support vector regression and particle filtering

This paper develops a steps-ahead tool wear prediction method based on particle filtering and support vector regression. A degradation phase classification method is presented based on clustering algorithm and support vector machine. The support vector regression models are established to achieve the mapping between degradation features and the flank tool wear values. In online prediction, the measured signals are input into the SVM model to judge the current phase of the tool, and the particle filtering algorithm is used for online steps-ahead prediction of the features. The prediction of tool wear can be obtained by inputting the feature prediction results into the SVR models. The experimental tool wear dataset is introduced as an application example to test the proposed method. The results demonstrate the effectiveness of the proposed method, and the comparison with other tool wear prediction shows the advantage of the proposed method in prediction accuracy.

Multi-exposure Laser Speckle Contrast Imaging (MECI)-Based Prediction of Blood Flow Using Random Forest (RF) With K-Means (KM).

Blood flow prediction is very important for medical diagnosis, drug development, tissue engineering, and continuous monitoring. One commonly used method for studying blood flow is called multi-exposure laser speckle contrast imaging (MECI). It provides valuable insights into how blood flows through tissues and helps in diagnosing circulatory diseases. In our study, we used MECI to measure blood flow in real-time by taking multiple measurements with different exposure times and contrasts. To predict different blood flow rates ranging from 0.1 to 1 mm/s, we employed machine learning (ML) techniques like clustering and random forest (RF) or support vector machine (SVM) algorithms. The study showed that RF with K-means performance is found to be the most accurate technique for flow classification, with an accuracy of 98.5%, a precision of 92%, a specificity of 98.9%, and a classification error of 1.5%. Our study demonstrates that employing clustering and RF algorithms in MECI provides a robust and effective approach to predicting blood flow. This technique holds great potential for a wide range of applications in the medical and healthcare fields.

COVID-19 Cluster Identification and SVM Classifier Model Construction Using Global Healthcare and Socio-economic Features.

Coronaviruses of the human variety have been the culprit of global epidemics of varying levels of lethality, including COVID-19, which has impacted more than 200 countries and resulted in 5.7 million fatalities as of May 2022. Effective clinical management necessitates the allocation of sufficient resources and the employment of appropriately skilled personnel. The elderly population and individuals with diabetes are at increased risk of more severe manifestations of COVID-19. Countries with a higher gross domestic product (GDP) typically exhibit superior health outcomes and reduced mortality rates. Here, we suggest a predictive model for the density of medical doctors and nursing personnel for 134 countries using a support vector machine (SVM). The model was trained in 107 countries and tested in 27, with promising results shown by the kappa statistics and ROC analysis. The SVM model used for predictions showed promising results with a high level of agreement between actual and predicted cluster values.

Quantification of the ilio-femoral arteries tortuosity and data cluster modelling for preoperative examination

Detecting spatial tortuosity and atherosclerotic changes of the ilio-femoral arteries are crucial for planning endovascular access. The aim of this study was to find a reliable quantification procedure of arterial lumen and tortuosity to qualify patients for a suitable endovascular procedure. We conducted computed tomographic angiography in 76 patients. All ilio-femoral segments of the arterial tree were visualized using Osirix Dicom Viewer software to help qualify the patients to one of two groups: with possible or non-recommended vascular access. The same tomograms were then analyzed with image processing algorithms to perform ilio-femoral artery segmentation and quantification. We chose a set of arterial tortuosity and lumen measuring methods, such as the modified Gustafson-Kessel clustering algorithm and Support Vector Machine classifier, to automatically classify arterial-tree regions. The two 2D feature spaces were selected with the modified Gustafson-Kessel clusterization to create a combined model to assign around 2/3 cases to the access groups with high specificity (more than 88%) whereas the remaining patients were selected for re-evaluation. We concluded that the novel modification of the Gustafson-Kessel clustering algorithm is more suitable to the highly inseparable data than commonly used approaches. To identify ilio-femoral access limitations, we recommend more complex decision model. This study confirmed high usability of our chosen methodology in the quantitative examination of arteries for endovascular access planning.

Construction of Innovative Working Mode of College Counselors Based on Data Mining Technology

Data mining technology can analyze and mine university management data, provide more data support for university management, and play an important role in optimizing teaching quality, but these technologies are rarely applied to the work of university counselors. Based on this, this paper studies the construction and research of college counselors’ innovative work mode based on data mining technology. Based on the simple analysis of the impact of big data on the traditional working mode of counselors, this paper introduces the application of data mining commonly used in colleges and universities and puts forward the existing shortcomings. The innovative working mode of college counselors is designed. In the analysis of students’ daily behavior, the advantages of cluster analysis and support vector machine are used to analyze students’ consumption behavior. The Apriori algorithm is applied to the student achievement early warning management to improve the Apriori algorithm. Simulation results show that the proposed algorithm can shorten the running time, reduce the number of frequent item sets, and improve the classification accuracy.

Control chart pattern recognition using spectral clustering technique and support vector machine under gamma distribution

Most studies on control chart patterns (CCPs) recognition assume that the measured values of process characteristic follow a normal distribution. However, by collecting large-size on-line datasets, it could be found that this assumption may not be tenable. In the present study, a method to combine spectral clustering technique with support vector machine (SVM) for recognizing CCPs under a gamma distribution, which is typically employed to represent various probability distributions, is proposed. Spectral clustering is a useful tool for segmenting the observation window to obtain the correct feature set. Multiple SVM classifiers are applied to recognize CCPs. Recognition accuracy and average run length (ARL) are indicators that are used to validate the CCP recognition ability of our proposed method. Based on the comparative studies, our proposed method in the recognition efficiency of most CCP types generally outperforms the Shewhart X¯ chart and EWMA chart with run rules. It is also noted that a more skewed underlying distribution always leads to a higher false alarm rate for our proposed method. A real-world case study of the tire manufacturing process is given to illustrate how our proposed method can be practically applied to identify and recognize the CCPs.

Brain Tumor Segmentation and Classification in MRI using Clustering and Kernel-Based SVM

Brain tumor and other nervous systems cancer are one of the leading causes of death for many patients. Magnetic resonance imaging (MRI) is the most important medical imaging modality for diagnosing brain tumors and other disorders in the brain. Manual evaluation of several MRI images by radiologists or experts for diagnosing brain tumors especially at early stages is a challenging task. Hence, this paper proposes an automated framework for the segmentation and classification of brain tumors using K-means clustering and kernel-based support vector machine (K-SVM). The major steps of the proposed framework consist of preprocessing, segmentation, feature extraction with selection, and classification. In the preprocessing step, the regions of interest (ROI) are extracted using skull stripping and a median filter. In the next step, the tumor is segmented using an optimized K-means algorithm. Further, discrete wavelet transform (DWT)-based texture features are used for feature extraction, and significant features are selected by principal component analysis (PCA). Finally, the kernel-based support vector machine (K-SVM) is used for the classification of brain tumor types into benign and malignant, with a dataset using 160 MRI images, consisting of 20 normal and 140 abnormal. Experimental findings demonstrated the efficacy of the proposed framework with 98.75% accuracy, 95.43% precision, and 97.65% recall. The simulation findings emphasize the importance of the proposed system as compared to state-of-the-art techniques in terms of coherence parameters and performance.

Identification of Pathogenetic Brain Regions via Neuroimaging Data for Diagnosis of Autism Spectrum Disorders

Autism spectrum disorder (ASD) is a kind of neurodevelopmental disorder that often occurs in children and has a hidden onset. Patients usually have lagged development of communication ability and social behavior and thus suffer an unhealthy physical and mental state. Evidence has indicated that diseases related to ASD have commonalities in brain imaging characteristics. This study aims to study the pathogenesis of ASD based on brain imaging data to locate the ASD-related brain regions. Specifically, we collected the functional magnetic resonance image data of 479 patients with ASD and 478 normal subjects matched in age and gender and used a machine-learning framework named random support vector machine cluster to extract distinctive brain regions from the preprocessed data. According to the experimental results, compared with other existing approaches, the method used in this study can more accurately distinguish patients from normal individuals based on brain imaging data. At the same time, this study found that the development of ASD was highly correlated with certain brain regions, e.g., lingual gyrus, superior frontal gyrus, medial gyrus, insular lobe, and olfactory cortex. This study explores the effectiveness of a novel machine-learning approach in the study of ASD brain imaging and provides a reference brain area for the medical research and clinical treatment of ASD.

Multi View Video Summarization Using RNN and SURF Based High Level Moving Object Feature Frames

Multi-View Video summarization is a process to ease the storage consumption that facilitates organized storage, and perform other mainline videos analytical task. This in-turn helps quick search or browse and retrieve the video data with minimum time and without losing crucial data. In static video summarization, there is less challenge in time and sequence issues to rearrange the video-synopsis. The low-level features are easy to compute and retrieve. But for high-level features like event detection, emotion detection, object recognition, face detection, gesture detection, and others requires the comprehension of the video content. This research is to propose an approach to over- come the difficulties in handling the high-level features. The distinguishable contents from the videos are identified by object detection and feature-based area strategy. The major aspect of the proposed solution is to retrieve the attributes of a motion source from a video frame. By dividing the details of the object that are available in the video frame wavelet decomposition are achieved. The motion frequency scoring method records the time of motions in the video. The frequency motion feature of video usage is a challenge given the continuous change of objects shape. Therefore, the object position and corner points are spotted using Speeded Up Robust Features (SURF) feature points. Support vector machine clustering extracts keyframes. The memory-based re- current neural network (RNN) recognizes the object in the video frame and remembers a long sequence. RNN is an artificial neural network where nodes form a temporal relationship. The attention layer in the proposed RNN network extracts the details about the objects in motion. The motion objects identified using the three video clippings is finally summarized using video summarization algorithm. To perform the simulation, MATLAB R 2014b software was used.

Histopathology Breast Cancer Detection and Classification using Optimized Superpixel Clustering Algorithm and Support Vector Machine

Histopathology Breast Cancer Detection and Classification using Optimized Superpixel Clustering Algorithm and Support Vector Machine

A Neighborhood and Machine Learning-Enabled Information Fusion Approach for the WSNs and Internet of Medical Things.

Data redundancy or fusion is one of the common issues associated with the resource-constrained networks such as Wireless Sensor Networks (WSNs) and Internet of Things (IoTs). To resolve this issue, numerous data aggregation or fusion schemes have been presented in the literature. Generally, it is used to decrease the size of the collected data and, thus, improve the performance of the underlined IoTs in terms of congestion control, data accuracy, and lifetime. However, these approaches do not consider neighborhood information of the devices (cluster head in this case) in the data refinement phase. In this paper, a smart and intelligent neighborhood-enabled data aggregation scheme is presented where every device (cluster head) is bounded to refine the collected data before sending it to the concerned server module. For this purpose, the proposed data aggregation scheme is divided into two phases: (i) identification of neighboring nodes, which is based on the MAC address and location, and (ii) data aggregation using k-mean clustering algorithm and Support Vector Machine (SVM). Furthermore, every CH is smart enough to compare data sets of neighboring nodes only; that is, data of nonneighbor is not compared at all. These algorithms were implemented in Network Simulator 2 (NS-2) and were evaluated in terms of various performance metrics, such as the ratio of data redundancy, lifetime, and energy efficiency. Simulation results have verified that the proposed scheme performance is better than the existing approaches.

Autoregressive moving average based anycast with support vector machine clustering in mobile ad‐hoc networks

AbstractNodes in mobile ad‐hoc networks (MANETs) are battery‐powered, therefore, need energy‐efficient techniques to cut down the cost of energy consumption. Energy consumption is directly related to route discovery, which could be reduced through anycasting. Anycast is an operation in MANETs, where the source sends messages to one particular destination node of anycast region. In general, the message transmitted is divided into certain packets, and those are transmitted continuously one after the other through selected routes. If the link from anycast source to destination breaks in between or an anycast destination goes out of the anycast region, in that case, a new anycast destination will have to be selected on a new route to the destination. This will incur huge message costs and energy. In this work, we select desirable anycast destinations based on their eligibility by using autoregressive moving average and support vector machine. Eligibility is modeled as a function of residual energy, location, the relative velocity concerning neighbors, and the number of packets successfully forwarded earlier by those neighbors' locations. Reducing the number of anycast receivers reduces the message cost of protocols significantly. We have compared our protocol's performance with the same of anycast‐AODV, anycast‐DSR, anycast‐FAIR, and MQAR (mobility and QoS‐aware anycast routing protocol) results show significant improvement. Collective improvement amounts to 89% more energy preservation and 39% delay on average.

Multilevel Clustering-Evolutionary Random Support Vector Machine Cluster Algorithm-Based Blood Oxygenation Level-Dependent Functional Magnetic Resonance Imaging Images in Analysis of Therapeutic Effects on Cerebral Ischemic Stroke

The study aimed to explore the relationship between cerebral ischemic stroke (CIS) and the patient’s limb movement through the blood oxygenation level-dependent functional magnetic resonance imaging (BOLD-fMRI) based on multilevel clustering-evolutionary random support vector machine cluster (MCRSVMC). Specifically, 20 CIS patients were defined as the experimental group; another 20 healthy volunteers were defined as the control group. All subjects performed finger movement and verb association task. The performance of support vector machine (SVM) and MCRSVMC algorithm was compared and applied to functional magnetic resonance imaging (fMRI) of blood oxygen level in all subjects. The results showed that the average accuracy of MCRSVMC algorithm was significantly higher than that of support vector machine (86.75%, 65.84%; P < 0.05 ). The sensitivity of MCRSVMC algorithm was significantly higher than that of support vector machine (92.52%, 75.41%; P < 0.05 ). In addition, the specificity of MCRSVMC algorithm was significantly higher than that of support vector machine (86.39%, 68.24%; P < 0.05 ). When CIS patients performed finger exercise, the sensory motor areas on both sides were significantly activated, and the activated sensory motor areas on both sides were significantly bigger than the ipsilateral area. The activation rate of the left-sensory motor area (L-SM1) was 87.5%, the activation rate of the right-sensory motor area (R-SM1) was 25%, the activation rate of the left-side auxiliary motor area (L-SMA) was 62.5%, and the activation rate of the right-side auxiliary motor area (R-SMA) was 37.5%. In conclusion, the MCRSVMC algorithm proposed in this study is highly efficient and stable. BOLD-fMRI diagnosis of motor function in CIS patients is mainly related to compensation around the lesion, which occurs on the healthy side after recovery.

Multilevel Clustering-Evolutionary Random Support Vector Machine Cluster Algorithm-Based Functional Magnetic Resonance Imaging in Diagnosing Cerebral Ischemic Stroke

This study was to explore the value of the blood oxygenation level dependent-functional magnetic resonance imaging (BOLD-fMRI) image classification based on the multilevel clustering-evolutionary random support vector machine cluster (MCRSVMC) algorithm in the diagnosis and treatment of patients with cognitive impairment after cerebral ischemic stroke (CIS). The MCRSVMC algorithm was optimized using a clustering algorithm, and it was compared with other algorithms in terms of accuracy (ACC), sensitivity (SEN), and specificity (SPE) of classifying the brain area images. 36 patients with cognitive impairment after CIS and nondementia patients were divided into a control group (drug treatment) and an intervention group (drug + acupuncture) according to different treatment methods, with 18 cases in each group. The changes in regional homogeneity (ReHo) of BOLD-fMRI images and the differences in scores of the Montreal Cognitive Assessment Scale (MoCA), scores of Loewenstein Occupational Therapy Cognitive Assessment (LOTCA), and scores of Functional Independence Measure (FIM) between the two groups of patients were compared before and after treatment. The results revealed that the average classification ACC, SEN, and SPE of the MCRSVMC algorithm were 84.25 ± 4.13%, 91.07 ± 3.51%, and 89 ± 3.96%, respectively, which were all obviously better than those of other algorithms ( P < 0.01 ). When the number of support vector machine (SVM) classifiers and the number of important features were 410 and 260, respectively, the classification ACC of MCRSVMC algorithm was 0.9429 and 0.9092, respectively. After treatment, the MoCA score, LOTCA score, and FIM score of the patients in the intervention group were higher than those of the control group ( P < 0.05 ). The ReHo values of the right inferior temporal gyrus and right inferior frontal gyrus of patients in the intervention group were much higher than those of the control group ( P < 0.05 ). It indicated that the classification ACC, SEN, and SPE of the magnetic resonance imaging (MRI) based on the MCRSVMC algorithm in this study were greatly improved, and the acupuncture method was more effective in the treatment of patients with cognitive dysfunction after CIS.

A Deep Learning-Based Approach for Detection of Dementia from Brain Mri

In this paper, a user-friendly system has been developed which will provide the result of medical analysis of digital images like magnetization resonance of image scan of the brain for detection and classification of dementia. The small structural differences in the brain can slowly and gradually become a major disease like dementia. The progression of dementia can be slowed when identified early. Hence, this paper aims at developing a robust system for classification and identifying dementia at the earliest. The method used in this paper for initial disclosure and diagnosis of dementia is deep learning since it can give important results in a shorter period of time. Deep Learning methods such as K-means clustering, Pattern Recognition, and Multi-class Support Vector Machine (SVM) have been used to classify different stages of dementia. The goal of this study is to provide a user interface for deep learning-based dementia classification using brain magnetic resonance imaging data. The results show that the created method has an accuracy of 96% and may be utilized to detect people who have dementia or are in the early stages of dementia.

QUBO formulations for training machine learning models

Training machine learning models on classical computers is usually a time and compute intensive process. With Moore’s law nearing its inevitable end and an ever-increasing demand for large-scale data analysis using machine learning, we must leverage non-conventional computing paradigms like quantum computing to train machine learning models efficiently. Adiabatic quantum computers can approximately solve NP-hard problems, such as the quadratic unconstrained binary optimization (QUBO), faster than classical computers. Since many machine learning problems are also NP-hard, we believe adiabatic quantum computers might be instrumental in training machine learning models efficiently in the post Moore’s law era. In order to solve problems on adiabatic quantum computers, they must be formulated as QUBO problems, which is very challenging. In this paper, we formulate the training problems of three machine learning models—linear regression, support vector machine (SVM) and balanced k-means clustering—as QUBO problems, making them conducive to be trained on adiabatic quantum computers. We also analyze the computational complexities of our formulations and compare them to corresponding state-of-the-art classical approaches. We show that the time and space complexities of our formulations are better (in case of SVM and balanced k-means clustering) or equivalent (in case of linear regression) to their classical counterparts.

Atrial fibrillation detection based on multi-feature extraction and convolutional neural network for processing ECG signals

Background and objectiveThe incidence of atrial fibrillation is increasing annually. We develop an automatic detection system, which is of great significance for the early detection and treatment of atrial fibrillation. This can lead to the reduction of the incidence of critical illnesses and mortality. MethodsWe propose an atrial fibrillation detection algorithm based on multi-feature extraction and convolutional neural network of atrial activity via electrocardiograph signals, and compare its detection based on cluster analysis, one-versus-one rule and support vector machine, using accuracy, specificity, sensitivity and true positive rate as evaluation criteria. ResultsThe atrial fibrillation detection algorithm proposed in this paper has an accuracy rate of 98.92%, a specificity of 97.04%, a sensitivity of 97.19%, and a true positive rate of 96.47%. The average accuracy of the algorithms we compared is 80.26%, and the accuracy of our algorithm is 23.25% higher than this average pertaining to the other algorithms. ConclusionWe implemented an atrial fibrillation detection algorithm that meets the requirements of high accuracy, robustness and generalization ability. It has important clinical and social significance for early detection of atrial fibrillation, improvement of patient treatment plans and improvement of medical diagnosis.

A Rapid Ultrasound Vascular Disease Screening Method using PauTa Criterion

Pulsed Wave Doppler (PW) is a traditional ultrasound technique used for the diagnosis of vascular diseases. The conventional diagnostic method is mainly based on hemodynamic parameters obtained from the PW spectrum. However, it relies on clinical observation and medical experience through lots of patient data investigation and analysis. The collected patient data are varied by different ultrasound equipment, detection regions and operation techniques, resulting in different image styles, which decreases the application and generality of the conventional method. And this method also has a strong dependence on patients’ data, especially on negative samples. Thus this paper proposes a rapid disease screening method, named as PauTa Criterion, which is based on statistical distribution characteristics for screening out anomalous targets. The proposed rapid screening method is based on multiple hemodynamic parameters to detect the outliers that are different from healthy samples. Compared with the conventional methods, the proposed method does not rely on a fixed or single ultrasound system and has low sensitivity to system noise. The experimental results show that the proposed method reaches a high accuracy of 93.14%, which is at least 20% higher than existing clustering methods, K-Means and Support Vector Machine (SVM). Accordingly, high accuracy and fast convergence makes the prospect of the proposed method to be used for rapid disease screening possible.