Sequential Minimal Optimization Research Articles

Forecasting of COVID-19 Cases in India: A Predictive Study

An outbreak of new coronavirus (COVID-19) originated by SARS-CoV has reached 212 countries throughout the world. India is the second-highest populated country, so it is critical to forecasting the confirmed cases and deaths due to pandemic. To fulfil the purpose, three machine learning models Linear Regression, Multilayer Perceptron, and Sequential Minimal Optimization Regression are used. The predictive data of three geographic regions (India, Maharashtra, and Tamil Nadu) are compared with the data considered to be adequate in practice. The analysis concluded that Sequential Minimal Optimization Regression can be adopted for possible pandemic predictions such as COVID-19.

Parameter evolution of the classifiers for disease diagnosis with offline data-driven hybrid systems

Automatic disease diagnosis is, in essence, a classification problem where the classifier has to be trained based on patients’ datasets and not entirely on doctors’ expert knowledge. In this paper, we present the design of such data-driven disease classifiers and fine-tuning classifier performance by a multi-objective evolutionary algorithm. We have used sequential minimal optimization (SMO) classifier as the base classifier and three evolutionary algorithms namely Cat Swarm Optimization (CSO), Invasive Weed Optimization (IWO) and Eagle Search based Invasive Weed Optimization (ESIWO) to diagnose disease from datasets available. In that sense, our approach is an offline data-driven approach with 18 benchmark medical datasets, and the obtained results demonstrate the superiority of the proposed diagnoses in terms of multiple objectives such as classification Prediction accuracy, Sensitivity, and Specificity. Relevant statistical tests have been carried out to substantiate the cogence of the obtained results.

A multilevel hybrid anomaly detection scheme for industrial wireless sensor networks

SummaryReal‐time sensing plays an important role in ensuring the reliability of industrial wireless sensor networks (IWSNs). Sensor nodes in IWSNs have inherent limitations that give rise to different anomalies in the network. These anomalies can lead to disastrous and harmful situations or even serious system failures. This article presents a formulation to the design of an anomaly detection scheme for detecting the anomalous node along with the type of anomaly. The proposed scheme is divided into two major parts. First, spatiotemporal correlation within a cluster is obtained for the normal and anomalous behavior of sensor nodes. Second, the multilevel hybrid classifier is used by combining the sequential minimal optimization support vector machine (SMO‐SVM) as a binary classifier with optimally pruned extreme learning machine (OP‐ELM) as a multiclass classifier for detection of an anomalous node and type of anomalies, respectively. Mahalanobis distance‐based lightweight K‐Medoid clustering is used to build a new set of training datasets that represents the original training dataset, by significantly reducing the training time of a multilevel hybrid classifier. Results are analyzed using standard WSN datasets. The proposed model shows high accuracy, i.e., 94.79% and detection rate, i.e., 94.6% with a reduced false positive rate as compared to existing hybrid methods.

Student Placement Prediction System using Machine Learning

This paper presents an overview of the machine learning techniques that can be used to predict a student’s placement performance. The ability to predicting the performance of a student is a very essential task of all educational institutions. Since this is the task of predicting student’s placement of undergraduate students. This paper can be used to predict the probability of an undergraduate student getting placed by applying different machine learning algorithms. In this system, multilayer perceptron (MLP), logistic model tree (LMT), sequential minimal optimization (SMO), simple logistic, and logistic classifiers are applied to predict student performance. These classifiers independently predict the results and then compare the accuracy of the algorithms, which is based on the data set. After performing analysis on different matrices (time taken to build classifier, correctly classified instances, root mean squared error, incorrectly classified instances, precision, recall, F-measure, ROC area) by different machine learning algorithms, we are able to find which algorithm is performing better than other on the student data set, so that we are able to make a guideline for future improvement of student placement performance in education.

SVM &Ga-clustering Based Feature Selection Approach for Breast Cancer Detection

Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data processing tools, we tackled this disease analysis. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. Several clinical breast cancer studies were conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier, easier, or more comprehensive than others. This research is focused on genetic programming and machine learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise the testing algorithm. We used genetic programming methods to choose classification machines' best features and parameter values. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F. Tree processes, i.e. 97.71%.

Classification of malignant tumors in breast ultrasound using a pretrained deep residual network model and support vector machine

In this study, a transfer learning method was utilized to recognize and classify benign and malignant breast tumors, using two-dimensional breast ultrasound (US) images, to decrease the effort expended by physicians and improve the quality of clinical diagnosis. The pretrained deep residual network model was utilized for image feature extraction from the convolutional layer of the trained network; whereas, the linear support vector machine (SVM), with a sequential minimal optimization solver, was used to classify the extracted feature. We used an image dataset with 2099 unlabeled two-dimensional breast US images, collected from 543 patients (benign: 302, malignant: 241). The classification performance yielded a sensitivity of 94.34 % and a specificity of 93.22 % for malignant images (Area under curve = 0.938). The positive and negative predictive values were 92.6 and 94.8, respectively. A comparison between the diagnosis made by the physician and the automated classification by a trained classifier, showed that the latter had significantly better outcomes. This indicates the potential applicability of the proposed approach that incorporates both the pretrained deep learning network and a well-trained classifier, to improve the quality and efficacy of clinical diagnosis.

Prognostic value of texture analysis from cardiac magnetic resonance imaging in patients with Takotsubo syndrome: a machine learning based proof-of-principle approach

Cardiac magnetic resonance (CMR) imaging has become an important technique for non-invasive diagnosis of takotsubo syndrome (TTS). The long-term prognostic value of CMR imaging in TTS has not been fully elucidated yet. This study sought to evaluate the prognostic value of texture analysis (TA) based on CMR images in patients with TTS using machine learning. In this multicenter study (InterTAK Registry), we investigated CMR imaging data of 58 patients (56 women, mean age 68 ± 12 years) with TTS. CMR imaging was performed in the acute to subacute phase (median time after symptom onset 4 days) of TTS. TA of the left ventricle was performed using free-hand regions-of-interest in short axis late gadolinium-enhanced and on T2-weighted (T2w) images. A total of 608 TA features adding the parameters age, gender, and body mass index were included. Dimension reduction was performed removing TA features with poor intra-class correlation coefficients (ICC ≤ 0.6) and those being redundant (correlation matrix with Pearson correlation coefficient r > 0.8). Five common machine-learning classifiers (artificial neural network Multilayer Perceptron, decision tree J48, NaïveBayes, RandomForest, and Sequential Minimal Optimization) with tenfold cross-validation were applied to assess 5-year outcome including major adverse cardiac and cerebrovascular events (MACCE). Dimension reduction yielded 10 TA features carrying prognostic information, which were all based on T2w images. The NaïveBayes machine learning classifier showed overall best performance with a sensitivity of 82.9% (confidence interval (CI) 80–86.2), specificity of 83.7% (CI 75.7–92), and an area-under-the receiver operating characteristics curve of 0.88 (CI 0.83–0.92). This proof-of-principle study is the first to identify unique T2w-derived TA features that predict long-term outcome in patients with TTS. These features might serve as imaging prognostic biomarkers in TTS patients.

Machine learning based quantitative texture analysis of CT images for diagnosis of renal lesions

Renal cancer is among the prominent causes of an upsurge in death rates around the globe that can be minimized by premature diagnosis. Computed tomography (CT) is the first-line imaging technique employed for analysis of renal lesions in clinical practice. Moreover, it is difficult for the radiologist to analyze a large number of medical images which may lead to misclassification of renal lesions that eventually results in cancer evolution or needless chemotherapy. Thus, there is a requirement to propose machine learning based techniques for classification of renal tissue based on their texture, and these techniques can be considered as a promising second opinion for the clinicians to minimize false-negative results. The present study aims to estimate the diagnosis performance of machine-learning based quantifiable texture examination of CT images to distinguish renal lesions. Texture analysis is performed to quantify the renal parenchyma tissues by employing different texture models, and the paper suggests a feature vector for the categorization of benign and malignant renal lesions based on their texture analysis. Further, dimensionality reduction is performed to recognize the discriminative features using distinct statistical, similarity and information knowledge-based techniques. The effectiveness of the resultant ranked feature vector is evaluated by using a Support vector machine (SVM), naive Bayes, Sequential minimal optimization (SMO), random forest, and K nearest neighbour (KNN) classifiers. The classification accuracy equivalent to 84% is observed by KNN with 5-fold cross-validation. Additionally, the practical implications, generalizability issues along with certain open challenges are highlighted that may open avenues for further research.

Student Performance Prediction Model based on Supervised Machine Learning Algorithms

Higher education institutions aim to forecast student success which is an important research subject. Forecasting student success can enable teachers to prevent students from dropping out before final examinations, identify those who need additional help and boost institution ranking and prestige. Machine learning techniques in educational data mining aim to develop a model for discovering meaningful hidden patterns and exploring useful information from educational settings. The key traditional characteristics of students (demographic, academic background and behavioural features) are the main essential factors that can represent the training dataset for supervised machine learning algorithms. In this study, we compared the performances of several supervised machine learning algorithms, such as Decision Tree, Naïve Bayes, Logistic Regression, Support Vector Machine, K-Nearest Neighbour, Sequential Minimal Optimisation and Neural Network. We trained a model by using datasets provided by courses in the bachelor study programmes of the College of Computer Science and Information Technology, University of Basra, for academic years 2017–2018 and 2018–2019 to predict student performance on final examinations. Results indicated that logistic regression classifier is the most accurate in predicting the exact final grades of students (68.7% for passed and 88.8% for failed).

A kernel-free double well potential support vector machine with applications

As a well-known machine learning technique, support vector machine (SVM) with a kernel function achieves much success in nonlinear binary classification tasks. Recently, some quadratic surface SVM models are proposed and studied by utilizing quadratic surfaces for nonlinear binary separations. In this paper, a kernel-free soft quartic surface SVM model is proposed by utilizing the double well potential function for highly nonlinear binary classification. Mathematical analysis on the theoretical properties of the proposed model, including the existence, uniqueness and support vector representation of optimal solutions, is shown. The sequential minimal optimization algorithm is adopted to implement the proposed model for computational efficiency. Numerical results on some artificial and public benchmark data sets demonstrate its effectiveness over well-known SVM models with or without kernel functions. The proposed model is extended to successfully handle some real-life corporate and personal credit data sets for applications.

Prediction of Coding Intricacy in a Software Engineering Team through Machine Learning to Ensure Cooperative Learning and Sustainable Education

Coding deliverables are vital part of the software project. Teams are formed to develop a software project in a term. The performance of the team for each milestone results in the success or failure of the project. Coding intricacy is a major issue faced by students as coding is believed to be a complex field demanding skill and practice. Future education demands a smart environment for understanding students. Prediction of the coding intricacy level in teams can assist in cultivating a cooperative educational environment for sustainable education. This study proposed a boosting-based approach of a random forest (RF) algorithm of machine learning (ML) for predicting the coding intricacy level among software engineering teams. The performance of the proposed approach is compared with viable ML algorithms to evaluate its excellence. Results revealed promising results for the prediction of coding intricacy by boosting the RF algorithm as compared to bagging, J48, sequential minimal optimization (SMO), multilayer perceptron (MLP), and Naïve Bayes (NB). Logistic regression-based boosting (LogitBoost) and adaptive boosting (AdaBoost) are outperforming with 85.14% accuracy of prediction. The concerns leading towards high coding intricacy level can be resolved by discussing with peers and instructors. The proposed approach can ensure a responsible attitude among software engineering teams and drive towards fulfilling the goals of education for sustainable development by optimizing the learning environment.

Sequential minimal optimization for quantum-classical hybrid algorithms

We propose a sequential minimal optimization method for quantum-classical hybrid algorithms, which converges faster, is robust against statistical error, and is hyperparameter-free. Specifically, the optimization problem of the parameterized quantum circuits is divided into solvable subproblems by considering only a subset of the parameters. In fact, if we choose a single parameter, the cost function becomes a simple sine curve with period $2\pi$, and hence we can exactly minimize with respect to the chosen parameter. Furthermore, even in general cases, the cost function is given by a simple sum of trigonometric functions with certain periods and hence can be minimized by using a classical computer. By repeatedly performing this procedure, we can optimize the parameterized quantum circuits so that the cost function becomes as small as possible. We perform numerical simulations and compare the proposed method with existing gradient-free and gradient-based optimization algorithms. We find that the proposed method substantially outperforms the existing optimization algorithms and converges to a solution almost independent of the initial choice of the parameters. This accelerates almost all quantum-classical hybrid algorithms readily and would be a key tool for harnessing near-term quantum devices.

Automatic segmentation and classification of lung tumour using advance sequential minimal optimisation techniques

A chronic disorder caused by abnormal growth of the lung cells in the pulmonary tumour. This study suggests a modern automated approach to improve efficiency and decrease the difficulty of lung tumour diagnosis. The proposed algorithm for lung tumour sensing consists of four phases: pre-processing, segmentation, extraction, and characteristics classification. The first stage is the image acquisition here input lung image is read and then resized. The second stage is the image pre-processing here Perona–Malik diffusion with unsharp masking filter is proposed for enhancement purposes. The third stage is the segmentation here the improved histogram–based fast 2D Otsu's thresholding is proposed for lung tumour segmentation purposes. Finally, linear discriminant analysis classifier, support vector machine (SVM) classifier, SVM–sequence minimal optimisation classifier, Naive Bayes classifier, SVM–advance sequence minimal optimisation (SVM–ASMO) classification [proposed] included in the various classifier groups adopted in this report. Overall performance accuracy of 0.962 is obtained using the proposed SVM–ASMO method that helps to diagnose the cancer cells using the feature extraction process, which is done automatically. The specificity, precision, recall, and F1 score of the proposed method is found to be a value of 0.984, 0.974, 0.98, and 0.984,respectively.

Machine learning techniques for mortality prediction in critical traumatic patients: anatomic and physiologic variables from the RETRAUCI study

BackgroundInterest in models for calculating the risk of death in traumatic patients admitted to ICUs remains high. These models use variables derived from the deviation of physiological parameters and/or the severity of anatomical lesions with respect to the affected body areas. Our objective is to create different predictive models of the mortality of critically traumatic patients using machine learning techniques.MethodsWe used 9625 records from the RETRAUCI database (National Trauma Registry of 52 Spanish ICUs in the period of 2015–2019). Hospital mortality was 12.6%. Data on demographic variables, affected anatomical areas and physiological repercussions were used. The Weka Platform was used, along with a ten-fold cross-validation for the construction of nine supervised algorithms: logistic regression binary (LR), neural network (NN), sequential minimal optimization (SMO), classification rules (JRip), classification trees (CT), Bayesian networks (BN), adaptive boosting (ADABOOST), bootstrap aggregating (BAGGING) and random forest (RFOREST). The performance of the models was evaluated by accuracy, specificity, precision, recall, F-measure, and AUC.ResultsIn all algorithms, the most important factors are those associated with traumatic brain injury (TBI) and organic failures. The LR finds thorax and limb injuries as independent protective factors of mortality. The CT generates 24 decision rules and uses those related to TBI as the first variables (range 2.0–81.6%). The JRip detects the eight rules with the highest risk of mortality (65.0–94.1%). The NN model uses a hidden layer of ten nodes, which requires 200 weights for its interpretation. The BN find the relationships between the different factors that identify different patient profiles. Models with the ensemble methodology (ADABOOST, BAGGING and RandomForest) do not have greater performance. All models obtain high values ​​in accuracy, specificity, and AUC, but obtain lower values ​​in recall. The greatest precision is achieved by the SMO model, and the BN obtains the best recall, F-measure, and AUC.ConclusionMachine learning techniques are useful for creating mortality classification models in critically traumatic patients. With clinical interpretation, the algorithms establish different patient profiles according to the relationship between the variables used, determine groups of patients with different evolutions, and alert clinicians to the presence of rules that indicate the greatest severity.

Period Study Accuracy Prediction using Sequential Minimal Optimization Algorithm

The study period is quite influential in the assessment of a university. The imbalance in the ratio of students to lecturers causes the quality of teaching and learning to decline, this is because one lecturer has to manage many students. Acquisition of accreditation scores and society's assumptions about higher education are also strongly influenced by the number of student graduations on time. Therefore, the prediction of the accuracy of the study period is needed as consideration for related parties to solve the problem of student learning delay. Sources of data in this study were taken from a database stored at the University of Surakarta, namely the Temporary Achievement Index with data of 209 instances and 5 attributes. The proposed method in this study is the Sequential Minimal Optimization algorithm. The validation method uses k-fold Cross-Validation with a value of K = 10. This method is compared with other methods such as naive Bayes, KNN, and Decision Tree. The results of this study, the proposed method can predict the accuracy of the study period quite well with the acquisition of accuracy of 88.52%. However, several other methods such as NaiveBayes obtained better accuracy of 90.91%, KNN of 91.86%, and Decision Tree of 96.65%. From the results of the comparison of these methods, the Decision Tree obtained the highest accuracy value. In future studies, researchers aim to enrich features in the prediction process. These features are related to student activities, such as student backgrounds, social activities, additional activities on campus and off-campus, and other aspects.

An elderly health monitoring system based on biological and behavioral indicators in internet of things

Advancement of sensor technologies has conducted to the rapid evolution of platforms, tools and approaches such as Internet of Things (IoT) for developing behavioral and physiological monitoring systems. Nowadays, According to growing number of elderlies living alone without their relatives scattered over the wide geographical areas, it is significantly essential to track their health function status continuously. In this paper, an IoT-based health monitoring system is proposed to check vital signs and detect biological and behavioral changes via smart elderly care technologies. It provides a health monitoring system for the involved medical teams to continuously monitor and assess a disabled or elderly’s behavioral activity as well as the biological parameters, applying sensor technology through the IoT devices. In this approach, vital data is collected via IoT monitoring objects and then, data analysis is carried out through different machine learning methods such as Decision Tree (J48), Sequential Minimal Optimization (SMO), Multi-Layer Perceptron (MLP) and Naive Bayes (NB) classifiers for detecting the level of probable risks of elderly’s physiological and behavioral changes. The experimental results confirm that the SMO, MLP and NB classifiers meet approximately close performance considering the accuracy, precision, recall, and f-score factors. However, the J48 method shows the highest performance for health function status predicting in our scenario with 99%, of accuracy and precision, 100% of recall and 97% of f-score. Moreover, the J48 performs with the lowest execution time in comparison to the other applied classifiers.

Cowbree: A novel dataset for fine-grained visual categorization

Fine-grained visual categorization (FGVC) dealt with objects belonging to one class with intra-class differences into subclasses. FGVC is challenging due to the fact that it is very difficult to collect enough training samples. This study presents a novel image dataset named Cowbreefor FGVC. Cowbree dataset contains 4000 images belongs to eight different cow breeds. Images are properly categorized under different breed names (labels) based on different texture and color features with the help of experts. While evidence shows that the existing dataset are of low quality, targeting few breeds with less number of images. To validate the dataset, three state of the art classifiers sequential minimal optimization (SMO), Multiclass classifier and J48 were used. Their results in term of accuracy are 68.81%, 55.81% and 57.45% respectively. Where results shows that SMO out performed with 68.81% accuracy, 68.4% precision and 68.8% recall.

Evaluating the cytotoxicity of a large pool of metal oxide nanoparticles to Escherichia coli: Mechanistic understanding through In Vitro and In Silico studies

The toxic effect of eight metal oxide nanoparticles (MONPs) on Escherichia coli was experimentally evaluated following standard bioassay protocols. The obtained cytotoxicity ranking of these studied MONPs is Er2O3, Gd2O3, CeO2, Co2O3, Mn2O3, Co3O4, Fe3O4/WO3 (in descending order). The computed EC50 values from experimental data suggested that Er2O3 and Gd2O3 were the most acutely toxic MONPs to E. coli. To identify the mechanism of toxicity of these 8 MONPs along with 17 other MONPs from our previous study, we employed seven classifications and machine learning (ML) algorithms including linear discriminant analysis (LDA), naïve bayes (NB), multinomial logistic regression (MLogitR), sequential minimal optimization (SMO), AdaBoost, J48, and random forest (RF). We also employed 1st and 2nd generation periodic table descriptors developed by us (without any sophisticated computing facilities) along with experimentally analyzed Zeta-potential, to model the cytotoxicity of these MONPs. Based on qualitative validation metrics, the LDA model appeared to be the best among the 7 tested models. The core environment of metal defined by the ratio of the number of core electrons to the number of valence electrons and the electronegativity count of oxygen showed a positive impact on toxicity. The identified properties were important for understanding the mechanisms of nanotoxicity and for predicting the potential environmental risk associated with MONPs exposure. The developed models can be utilized for environmental risk assessment of any untested MONP to E. coli, thereby providing a scientific basis for the design and preparation of safe nanomaterials.

A Robust Least Squares Support Vector Machine Based on L∞-norm

A family of norm-based least squares support vector machines (LSSVMs) is the effective statistical learning tool and has attracted considerable attentions. However, there are two critical problems: (1) LSSVM is ill-conditioned or singular when the sample size is much less than feature number and thus causes over-fitting for small sample size (SSS) problem, while other norm-based LSSVMs own slower training speed and cannot deal with large scale data. (2) Norm-based LSSVMs pay less attention to the edge points that are important for learning the final classifier. To overcome the above drawbacks, by replacing $$ L_{2}\text{-}norm $$ with $$ L_{\infty }\text{-}norm $$ in empirical loss, we construct a robust classifier, named $$ L_{\infty }\text{-}LSSVM $$ in short. After adjustment, $$ L_{\infty }\text{-}LSSVM $$ can detect edge points effectively and enhances the capability of the robustness and the generalization. Also, inspired by the idea of sequential minimal optimization (SMO), we design a novel SMO-typed iterative algorithm. The new algorithm not only guarantees the convergence of optimum solution in theory, but also owns the lower computational time and storage space when data size is large. Finally, extensive numerical experiments validate the above opinions again on four groups of artificial data, Non-i.i.d data: fault detection of railway turnout, four SSS datasets, two types of massive datasets and six benchmark datasets.

A Smart Architecture for Diabetic Patient Monitoring Using Machine Learning Algorithms.

Continuous monitoring of diabetic patients improves their quality of life. The use of multiple technologies such as the Internet of Things (IoT), embedded systems, communication technologies, artificial intelligence, and smart devices can reduce the economic costs of the healthcare system. Different communication technologies have made it possible to provide personalized and remote health services. In order to respond to the needs of future intelligent e-health applications, we are called to develop intelligent healthcare systems and expand the number of applications connected to the network. Therefore, the 5G network should support intelligent healthcare applications, to meet some important requirements such as high bandwidth and high energy efficiency. This article presents an intelligent architecture for monitoring diabetic patients by using machine learning algorithms. The architecture elements included smart devices, sensors, and smartphones to collect measurements from the body. The intelligent system collected the data received from the patient, and performed data classification using machine learning in order to make a diagnosis. The proposed prediction system was evaluated by several machine learning algorithms, and the simulation results demonstrated that the sequential minimal optimization (SMO) algorithm gives superior classification accuracy, sensitivity, and precision compared to other algorithms.