Machine Learning Techniques Support Vector Research Articles

Hepatitis C prediction using SVM, logistic regression and decision tree

Hepatitis C is an infection of the liver brought on by the HCV virus. In this condition, early diagnosis is challenging because of the delayed onset of symptoms. Predicting well enough can spare patients from permeant liver damage. The primary goal of this work is to use several machine learning methods to forecast this disease based on widely available and reasonably priced blood test data in order to diagnose and treat patients early on. Three machine learning techniques support vector machine (SVM), logistic regression, decision tree, has been applied on one dataset in this work. To find a suitable approach for illness prediction, the confusion matrix, precision, recall, F1 score, accuracy, receiver operating characteristics (ROC), performances of different strategies have been assessed. The SVM model's overall accuracy is 0.92, the highest among the three models.

Study on the use of different machine learning techniques for prediction of concrete properties from their mixture proportions with their deterministic and robust optimisation

The construction industry relies so heavily on concrete that it's crucial to precisely forecast and optimize the strength and workability of concrete mixtures, while reducing costs as much as possible. For this objective, this study tries to predict and optimize the compressive strength and workability (slump) of concrete by using deterministic and robust optimization approaches, so as to determine the optimum concrete mixture proportions, while minimizing cost. Specifically, strength and slump were predicted based on concrete mixture proportions with five different machine learning techniques—support vector machine (SVM), artificial neural network (ANN), fuzzy inference system (FIS), adaptive fuzzy inference system (ANIS), and genetic expression programming (GEP), based on a dataset comprising two hundred concrete mixtures, which has various levels of key ingredients, including cement, water, fine aggregate, coarse aggregate, and size of coarse aggregate, along with their associated measures of strength and workability. These ingredients were used as input parameters, while compressive strength and slump (representing workability) served as output parameters for each mix proportion. Experimental investigations were conducted on fifteen distinct concrete mixes to validate the performance of the five networks, finding that ANFIS can yield the best results both for training and validation. This study provides valuable insights for predicting concrete properties and optimizing concrete mixture proportions, thus helping to maximize strength and workability while minimizing costs.

Deciphering the impact of microenvironmental factors on cuticular hydrocarbon degradation in Lucilia sericata empty Puparia: Bridging ecological and forensic entomological perspectives using machine learning models

Blow flies (Calliphoridae) play essential ecological roles in nutrient recycling by consuming decaying organic matter. They serve as valuable bioindicators in ecosystem management and forensic entomology, with their unique feeding behavior leading to the accumulation of environmental pollutants in their cuticular hydrocarbons (CHCs), making them potential indicators of exposure history. This study focuses on CHC degradation dynamics in empty puparia of Lucilia sericata under different environmental conditions for up to 90 days. The three distinct conditions were considered: outdoor-buried, outdoor-above-ground, and indoor environments. Five predominant CHCs, n-Pentacosane (n-C25), n-Hexacosane (n-C26), n-Heptacosane (n-C27), n-Octacosane (n-C28), and n-Nonacosane (n-C29), were analyzed using Gas Chromatography-Mass Spectrometry (GC–MS). The findings revealed variations in CHC concentrations over time, influenced by environmental factors, with significant differences at different time points. Correlation heatmap analysis indicated negative correlations between weathering time and certain CHCs, suggesting decreasing concentrations over time. Machine learning techniques Support Vector Machine (SVM), Multilayer Perceptron (MLP), and eXtreme Gradient Boosting (XGBoost) models explored the potential of CHCs as age indicators. SVM achieved an R-squared value of 0.991, demonstrating high accuracy in age estimation based on CHC concentrations. MLP also exhibited satisfactory performance in outdoor conditions, while SVM and MLP yielded unsatisfactory results indoors due to the lack of significant CHC variations. After comprehensive model selection and performance evaluations, it was found that the XGBoost model excelled in capturing the patterns in all three datasets. This study bridges the gap between baseline and ecological/forensic use of empty puparia, offering valuable insights into the potential of CHCs in environmental monitoring and investigations. Understanding CHCs' stability and degradation enhances blow flies' utility as bioindicators for pollutants and exposure history, benefiting environmental monitoring and forensic entomology.

State of Aging Classification of Modified-HP Steel Tubes by Eddy Current Test Applying Machine Learning

In the petrochemical industry, steam reforming furnaces play a crucial role in large-scale hydrogen production. These furnaces are equipped with centrifugal cast HP-modified austenitic stainless-steel tubes, which are exposed to temperatures ranging from 600 to 1000ºC for extended periods. As a result, the wall temperature profile of these tubes exhibits a vertical gradient of distinct aging states, labelled as state I to VI in the literature, each characterized by specific microstructures. Given that the reformer tubes are expensive components of the furnace assembly, it is imperative to monitor their service life. Non-destructive testing is a vital tool for evaluating the structural integrity of industrial components. Hence, the objective of this study is to establish a real-time classification system for determining the aging states of HP-modified stainless-steel tubes using a non-destructive magnetic system and the machine learning technique Support Vector Machine (SVM). Two tubes, each measuring 12.6 meters in length, were removed from the same steam reforming furnace after 160,000 hours of service. The inspection was conducted using an eddy current hybrid probe adapted to an inspection vehicle, allowing for real-time data acquisition. The results demonstrated that the developed classification system was capable of accurately identifying the different aging states present along the studied tubes.

Addressing Geological Challenges in Mineral Resource Estimation: A Comparative Study of Deep Learning and Traditional Techniques

Spatial prediction of orebody characteristics can often be challenging given the commonly complex geological structure of mineral deposits. For example, a high nugget effect can strongly impact variogram modelling. Geological complexity can be caused by the presence of structural geological discontinuities combined with numerous lithotypes, which may lead to underperformance of grade estimation with traditional kriging. Deep learning algorithms can be a practical alternative in addressing these issues since, in the neural network, calculation of experimental variograms is not necessary and nonlinearity can be captured globally by learning the underlying interrelationships present in the dataset. Five different methods are used to estimate an unsampled 2D dataset. The methods include the machine learning techniques Support Vector Regression (SVR) and Multi-Layer Perceptron (MLP) neural network; the conventional geostatistical methods Simple Kriging (SK) and Nearest Neighbourhood (NN); and a deep learning technique, Convolutional Neural Network (CNN). A comparison of geologic features such as discontinuities, faults, and domain boundaries present in the results from the different methods shows that the CNN technique leads in terms of capturing the inherent geological characteristics of given data and possesses high potential to outperform other techniques for various datasets. The CNN model learns from training images and captures important features of each training image based on thousands of calculations and analyses and has good ability to define the borders of domains and to construct its discontinuities.

Novel Regression and Least Square Support Vector Machine Learning Technique for Air Pollution Forecasting

Air pollution is the origination of particulate matter, chemicals, or biological substances that brings pain to either humans or other living creatures or instigates discomfort to the natural habitat and the airspace. Hence, air pollution remains one of the paramount environmental issues as far as metropolitan cities are concerned. Several air pollution benchmarks are even said to have a negative influence on human health. Also, improper detection of air pollution benchmarks results in severe complications for humans and living creatures. To address this aspect, a novel technique called, Discretized Regression and Least Square Support Vector (DR-LSSV) based air pollution forecasting is proposed. The results indicate that the proposed DR-LSSV Technique can efficiently enhance air pollution forecasting performance and outperforms the conventional machine learning methods in terms of air pollution forecasting accuracy, air pollution forecasting time, and false positive rate.

Modelling recidivism with binary models and machine learning

The article, reviewing previous study on recidivism prediction model, modeling the bina-ry outcome of recidivism with linear regression model, probit regression model and lo-gistic regression model. The application of machine learning techniques support vector machine (SVM) aids to compare the prediction result of each model and the relationships between recidivism probability and several personal characteristics have been identified. The prediction performances of models are similar with logistic regression model being the most parsimonious one and SVM method is tested to be not significantly superior in terms of prediction compared with traditional statistical models.

Modelling, mapping and monitoring of forest cover changes, using support vector machine, kernel logistic regression and naive bayes tree models with optical remote sensing data

The present study is designed to monitor the spatio-temporal changes in forest cover using Remote Sensing (RS) and Geographic Information system (GIS) techniques from 1990 to 2017. Landsat data from 1990 (Thematic mapper [TM]), 2000 and 2010 (Enhanced Thematic Mapper [ETM+]), and 2013 to 2017 (Operational Land Imager/Thermal Infrared Sensor [OLI/TIRS]) were classified into the classes termed snow, water, barren land, built-up area, forest, and vegetation. The method was built using multitemporal Landsat images and the machine learning techniques Support Vector Machine (SVM), Naive Bayes Tree (NBT) and Kernel Logistic Regression (KLR). According to the results, forest area was decreased from 19,360 km2 (26.0%) to 18,784 km2 (25.2%) from 1990 to 2010, while forest area was increased from 18,640 km2 (25.0%) to 26,765 km2 (35.9%) area from 2013 to 2017 due to “One billion tree Project”. According to our findings, SVM performed better than KLR and NBT on all three accuracy metrics (recall, precision, and accuracy) and the F1 score was >0.89. The study demonstrated that concurrent reforestation in barren land areas improved methods of sustaining the forest and RS and GIS into everyday forestry organization practices in Khyber Pakhtun Khwa (KPK), Pakistan. The study results were beneficial, especially at the decision-making level for the local or provincial government of KPK and for understanding the global scenario for regional planning.

Support vector machine versus convolutional neural network for hyperspectral image classification: A systematic review

AbstractVarious machine learning and deep learning techniques have been proposed for classification purposes in the case of hyperspectral imaging. Among all the machine learning techniques support vector machine (SVM) has been a promising classification algorithm in the case of remote sensing applications, particularly in the field of hyperspectral imaging. In the case of deep learning techniques, the convolutional neural network is gaining much attention from researchers for classification purposes and has given reliable results in this field, thus is being used widely by researchers. So, in this article, a systematic review about the two most used hyperspectral classification techniques that is, SVM and CNN is given. A total of 86 papers from the four well‐known journals belonging to the field of remote sensing have been reviewed where the methodologies used by different authors, datasets used, results acquired, contribution and shortcomings have been put forth. This meta‐analysis generally focuses on the recent SVM and CNN methodologies that are proposed by various authors. A summary of the best results obtained from SVM and CNN in terms of classification accuracies is also provided to help the researchers working in this area achieve better results.

Machine learning based prediction and the influence of complement – Coagulation pathway proteins on clinical outcome: Results from the NEURAPRO trial

BackgroundFunctional outcomes are important measures in the overall clinical course of psychosis and individuals at clinical high-risk (CHR), however, prediction of functional outcome remains difficult based on clinical information alone. In the first part of this study, we evaluated whether a combination of biological and clinical variables could predict future functional outcome in CHR individuals. The complement and coagulation pathways have previously been identified as being of relevance to the pathophysiology of psychosis and have been found to contribute to the prediction of clinical outcome in CHR participants. Hence, in the second part we extended the analysis to evaluate specifically the relationship of complement and coagulation proteins with psychotic symptoms and functional outcome in CHR. Materials and methodsWe carried out plasma proteomics and measured plasma cytokine levels, and erythrocyte membrane fatty acid levels in a sub-sample (n = 158) from the NEURAPRO clinical trial at baseline and 6 months follow up. Functional outcome was measured using Social and Occupational Functional assessment Score (SOFAS) scale. Firstly, we used support vector machine learning techniques to develop predictive models for functional outcome at 12 months. Secondly, we developed linear regression models to understand the association between 6-month follow-up levels of complement and coagulation proteins with 6-month follow-up measures of positive symptoms summary (PSS) scores and functional outcome. Results and conclusionA prediction model based on clinical and biological data including the plasma proteome, erythrocyte fatty acids and cytokines, poorly predicted functional outcome at 12 months follow-up in CHR participants. In linear regression models, four complement and coagulation proteins (coagulation protein X, Complement C1r subcomponent like protein, Complement C4A & Complement C5) indicated a significant association with functional outcome; and two proteins (coagulation factor IX and complement C5) positively associated with the PSS score. Our study does not provide support for the utility of cytokines, proteomic or fatty acid data for prediction of functional outcomes in individuals at high-risk for psychosis. However, the association of complement protein levels with clinical outcome suggests a role for the complement system and the activity of its related pathway in the functional impairment and positive symptom severity of CHR patients.

Network Traffic Analysis Using Machine Learning Techniques in IoT Networks

Internet of things devices are not very intelligent and resource-constrained; thus, they are vulnerable to cyber threats. Cyber threats would become potentially harmful and lead to infecting the machines, disrupting the network topologies, and denying services to their legitimate users. Artificial intelligence-driven methods and advanced machine learning-based network investigation prevent the network from malicious traffics. In this research, a support vector machine learning technique was used to classify normal and abnormal traffic. Network traffic analysis has been done to detect and prevent the network from malicious traffic. Static and dynamic analysis of malware has been done. Mininet emulator was selected for network design, VMware fusion for creating a virtual environment, hosting OS was Ubuntu Linux, network topology was a tree topology. Wireshark was used to open an existing pcap file that contains network traffic. The support vector machine classifier demonstrated the best performance with 99% accuracy.

The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods.

In recent years, machine learning models based on big data have been introduced into marketing in order to transform customer data into meaningful insights and to make strategic decisions by making more accurate predictions. Although there is a large amount of literature on demand forecasting, there is a lack of research about how marketing strategies such as advertising and other promotional activities affect demand. Therefore, an accurate demand-forecasting model can make significant academic and practical contributions for business sustainability. The purpose of this article is to evaluate machine learning methods to provide accuracy in forecasting demand based on advertising expenses. The study focuses on a prediction mechanism based on several Machine Learning techniques—Support Vector Regression (SVR), Random Forest Regression (RFR) and Decision Tree Regressor (DTR) and deep learning techniques—Artificial Neural Network (ANN), Long Short Term Memory (LSTM),—to deal with demand forecasting based on advertising expenses. Deep learning is a powerful technique that can solve marketing problems based on both classification and regression algorithms. Accordingly, a television manufacturer’s real market dataset consisting of advertising expenditures, sales and demand forecasting via chosen machine learning methods was analyzed and compared in terms of the accuracy of demand forecasting. As a result, Long Short Term Memory has been found to be superior to other models in providing highly accurate prediction results for demand forecasting based on advertising expenses.

Septicemic Melioidosis Detection Using Support Vector Machine with Five Immune Cell Types.

Melioidosis, caused by Burkholderia pseudomallei (B. pseudomallei), predominantly occurs in the tropical regions. Of various types of melioidosis, septicemic melioidosis is the most lethal one with a mortality rate of 40%. Early detection of the disease is paramount for the better chances of cure. In this study, we developed a novel approach for septicemic melioidosis detection, using a machine learning technique—support vector machine (SVM). Several SVM models were built, and 19 features characterized by the corresponding immune cell types were generated by Cell type Identification Estimating Relative Subsets Of RNA Transcripts (CIBERSORT). Using these features, we trained a binomial SVM model on the training set and evaluated it on the independent testing set. Our findings indicated that this model performed well with means of sensitivity and specificity up to 0.962 and 0.979, respectively. Meanwhile, the receiver operating characteristic (ROC) curve analysis gave area under curves (AUCs) ranging from 0.952 to 1.000. Furthermore, we found that a concise SVM model, built upon a combination of CD8+ T cells, resting CD4+ memory T cells, monocytes, M2 macrophages, and activated mast cells, worked perfectly on the detection of septicemic melioidosis. Our data showed that its mean of sensitivity was up to 0.976 while that of specificity up to 0.993. In addition, the ROC curve analysis gave AUC close to 1.000. Taken together, this SVM model is a robust classification tool and may serve as a complementary diagnostic technique to septicemic melioidosis.

Building a Medical Chatbot using Support Vector Machine Learning Algorithm

Chatbot is used extensively to check the state of health at any time. It is the same as going to a doctor and having the medication prescribed. This paper discusses about medical chatbot using the machine learning algorithm which predicts the accuracy of the disease. There are many machine learning algorithms that can be used to predict the disease. Support Vector Machine learning technique is primarily used to achieve precise prediction and boost the efficiency of the model. The system uses Natural Language Processing to achieve the style of chatting. Using this approach people can reduce spending time in hospitals and receive low cost or cost-free services.

Do children's expectations about future physical activity predict their physical activity in adulthood?

BackgroundMuch of the population fails to meet recommended physical activity (PA) levels, but there remains considerable individual variation. By understanding drivers of different trajectories, interventions can be better targeted and more effective. One such driver may be a person’s physical activity identity (PAI)—the extent to which a person perceives PA as central to who they are.MethodsUsing survey information and a unique body of essays written at age 11 from the National Child Development Study (N = 10 500), essays mentioning PA were automatically identified using the machine learning technique support vector classification and PA trajectories were estimated using latent class analysis. Analyses tested the extent to which childhood PAI correlated with activity levels from age 23 through 55 and with trajectories across adulthood.Results42.2% of males and 33.5% of females mentioned PA in their essays, describing active and/or passive engagement. Active PAI in childhood was correlated with higher levels of activity for men but not women, and was correlated with consistently active PA trajectories for both genders. Passive PAI was not related to PA for either gender.ConclusionsThis study offers a novel approach for analysing large qualitative datasets to assess identity and behaviours. Findings suggest that at as young as 11 years old, the way a young person conceptualizes activity as part of their identity has a lasting association with behaviour. Still, an active identity may require a supportive sociocultural context to manifest in subsequent behaviour.

Discovering computer networks intrusion using data analytics and machine intelligence

In this era of a digital revolution, the use of the Internet for information storage, access, and dissemination has increased astronomically. Also, the advent of the Internet of Things (IoT) technologies has removed the digital barrier and accentuate the seamless exchange of data and information among many ubiquitous systems. Therefore, the challenge of information theft, privacy, and confidentiality of data and information over the internet has become a major quandary for many users of several online platforms. Network intrusion detection systems are one of the viable approaches to curb the menace of information theft and other data security threats over the internet. In this paper, we show a comparison between two intrusion detection systems–one that uses the association rule data mining approach–Apriori and the other that adapts the use of a machine learning technique–Support Vector Machine (SVM). The performance of the two systems was compared using the Network Security Laboratory Knowledge Discovery and Data Mining (NSL-KDD) dataset and the University of New South Wales–NB 2015 (UNSW-NB15) dataset. Evaluation results show that SVM performs better than Apriori in terms of accuracy, while Apriori gives a better performance in terms of testing speed.

Comparison of the Performance of Partial Least Squares and Support Vector Regressions for Predicting Fatty Acids and Fatty Acid Classes in Marine Oil Dietary Supplements by Using Vibrational Spectroscopic Data

Simple, fast, and accurate analytical techniques for verifying the accuracy of label declarations for marine oil dietary supplements containing eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are required because of the increased consumption of these products. We recently developed broad-based partial least squares regression (PLS-R) models to quantify six fatty acids (FAs) and FA classes by using the spectroscopic data from a portable Fourier transform infrared (FTIR) device and a benchtop Fourier transform near infrared (FT-NIR) spectrometer. We developed an improved quantification method for these FAs and FA classes by incorporating a nonlinear calibration approach based on the machine learning technique support vector machines. For the two spectroscopic methods, high accuracy in prediction was indicated by low root mean square error of prediction and by correlation coefficients (R2) close to 1, indicating excellent model performance. The percent accuracy of the support vector regression (SV-R) model predicted values for EPA and DHA in the reference material was 90 to 110%. In comparison to PLS-R, SV-R accuracy for prediction of FA and FA class concentrations was up to 2.4 times higher for both ATR-FTIR and FT-NIR spectroscopic data. The SV-R models also provided closer agreement with the certified and reference values for the prediction of EPA and DHA in the reference standard. Based on our findings, the SV-R methods had superior accuracy and predictive quality for predicting the FA concentrations in marine oil dietary supplements. The combination of SV-R with ATR-FTIR and/or FT-NIR spectroscopic data can potentially be applied for the rapid screening of marine oil products to verify the accuracy of label declarations.

Insight prediction of receptor binding activity of a set of benzamide derivatives using hybrid QSAR models: GA‐MLR and GA‐SVR

AbstractIn this study, we developed the hybrid QSAR models (HQSAR) for a set of benzamide derivatives by combining genetic algorithms with multivariate regression and support vector machine learning techniques. The genetic algorithm has assisted the selecting process of 2D and 3D molecular descriptors to get a globally optimal HQSARGA‐MLR model with k = 7. The hybrid support vector regression model (HQSARGA‐SVR) received from the selected descriptors of the multivariable regression model (HQSARGA‐MLR) has been operated to predict the pIC50 activity of validation and prediction groups with MARE% of 0.8492 % and 2.8411 %. The hybrid support vector technique has improved the efficiency of the predictability of the multivariate regression model. The predicted activities pIC50 of benzamide derivatives resulting from the HQSARGA‐SVR model are reliable enough and in good agreement with experimental data.

Dynamic readmission prediction using routine postoperative laboratory results after radical cystectomy

ObjectiveTo determine if the addition of electronic health record data enables better risk stratification and readmission prediction after radical cystectomy. Despite efforts to reduce their frequency and severity, complications and readmissions following radical cystectomy remain common. Leveraging readily available, dynamic information such as laboratory results may allow for improved prediction and targeted interventions for patients at risk of readmission. MethodsWe used an institutional electronic medical records database to obtain demographic, clinical, and laboratory data for patients undergoing radical cystectomy. We characterized the trajectory of common postoperative laboratory values during the index hospital stay using support vector machine learning techniques. We compared models with and without laboratory results to assess predictive ability for readmission. ResultsAmong 996 patients who underwent radical cystectomy, 259 patients (26%) experienced a readmission within 30 days. During the first week after surgery, median daily values for white blood cell count, urea nitrogen, bicarbonate, and creatinine differentiated readmitted and nonreadmitted patients. Inclusion of laboratory results greatly increased the ability of models to predict 30-day readmissions after cystectomy. ConclusionsCommon postoperative laboratory values may have discriminatory power to help identify patients at higher risk of readmission after radical cystectomy. Dynamic sources of physiological data such as laboratory values could enable more accurate identification and targeting of patients at greatest readmission risk after cystectomy. This is a proof of concept study that suggests further exploration of these techniques is warranted.

Can alert models for fraud protect the elderly clients of a financial institution?

ABSTRACTUsing account-level transaction data at a major financial institution, we predict the incidence of suspicious activity that can be related to the external financial fraud of its elderly clients. The data consists of over 5 million accounts of clients aged 70 years and older, and over 250 million transactions extending from January 2015 to August 2016. Our main focus is to improve the detection of alerts within a proprietorial transaction monitoring system. Using logistic regression, random forest and support vector machine learning techniques, together with corrections for imbalanced alert samples, we provide a new alert model for the protection of elderly clients at a financial institution, with out-of-sample predictive accuracy. Our findings show the relative influence of client traits and account activity in our select external fraud alert models.