Support Vector Machine Technique 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.

Determining the potato samples processed manner with an electronic nose system supported by a machine-learning approach

Through several studies, electronic nose (E-nose) devices have been demonstrated to serve as useful measurement equipment for the fast and reliable analysis of complex odour profiles in a wide range of fields, including the area of ensuring food safety. In the same perspective, i.e. checking food safety comes this work, where potatoes are assessed using an E-nose principally made of five metal oxide gas sensors supported with a machine learning algorithm, said evaluation is based on their origin fields: naturally treated, treated with chemical NPK fertilizers, or treated with chicken manure. The technique of support vector machines has been exploited for the data obtained from the multi-sensor system in order to distinguish the potato types mentioned by following two methods: a direct method where all the data have been exploited with a rate of success of 91.7%, and a composed method where the classification was first between naturally treated samples and those treated differently with a success rate of 96.7%, then a classification between those treated differently had a 92.5% success rate. A microbiological analysis was also carried out and its results were compared with those obtained by the E-nose. As per the two methods’ results, the distinction of the potato types by the nature of the cultivated fields is possible with the recording of the multisensory system superiority due to response time, low cost, simplicity, and portability.

Prediction of the flow resistance in non-prismatic compound channels

ABSTRACT Achieving an accurate estimation of the flow resistance in open channel flows is crucial for resolving several critical engineering difficulties. In instances when there is excessive flow on both banks of a river, it results in the breach of the primary channel, leading to the discharge of water into the adjacent floodplain. The alteration of floodplain geometry occurs as a consequence of agricultural and developmental practises, leading to the emergence of compound channels that exhibit converging, diverging, or skewed characteristics throughout the course of the flow. The efficacy of conventional equations in accurately forecasting flow resistance is limited due to their heavy reliance on empirical approaches. As a result of this phenomenon, there persists a significant need for methodologies that possess both novelty and precision. The objective of this work is to use the support vector machine (SVM) technique for the estimation of the Manning's roughness coefficient in a compound channel with converging floodplains. Statistical indicators are used to validate the constructed models in the experimental investigation, enabling the assessment of their performance and efficacy. The findings indicate a significant correlation between the Manning's roughness coefficient predicted by SVM and both experimental data and prior research outcomes.

Groundwater potential zone mapping of middle Andaman using multi-criteria decision-making and support vector machine

Groundwater is a vital resource crucial to human survival worldwide and plays a significant role in addressing climate change. The multi-criteria decision method and support vector machine technique were used to demarcate the groundwater potential zones (GWPZ) of Middle Andaman. A total of 11 factors including geology, geomorphology, LULC map, lineament density, distance from Active Fault, groundwater level, distance from the sea, slope, drainage density, distance from major earthquakes, and distance from epicenter hotspots were used to calculate GWPZ. Spatially distributed maps/thematic layers of all of the above factors were generated using remotely sensed data as well as ground data. Each factor was assigned weights based on preceding global and regional research and the influence of each layer on groundwater. Finally, all thematic layers were used for multi-criteria decision analysis to create a map depicting the GWPZ of Middle Andaman. A support vector machine (SVM) tool was used to demarcate the potential zone based on the signature obtained from overlay analysis. The resulting map was divided into four categories: very high, high, low, and very low groundwater potential zones. SVM classification result showed that most of Middle Andaman's areas come under the low to very low GWPZ (63.4%), followed by high GWPZ (27%), and 10.6% of very high GWPZ. SVM classification resulted in overall accuracy, kappa coefficient, and the area in the ROC curve of 91.66%, 0.87, and 0.64 respectively. The analysis revealed a correlation between each factor and the Multi-Criteria Decision Method (MCDM) and Support Vector Machine (SVM), indicating that the majority of geological and geomorphological features are linked to lower groundwater potential. Further, the result was compared with groundwater fluctuation data of the middle Andaman. Finally, this study provides an expedient approach to delineating the GWPZ for practical management and use of the area's groundwater resources to maintain the island's sustainability.

Machine Learning for Prediction of Tuberculosis Detection: Case Study of Trained African Giant Pouched Rats.

Technological advancement has led to the growth and rapid increase of tuberculosis (TB) medical data generated from different health care areas, including diagnosis. Prioritizing better adoption and acceptance of innovative diagnostic technology to reduce the spread of TB significantly benefits developing countries. Trained TB-detection rats are used in Tanzania and Ethiopia for operational research to complement other TB diagnostic tools. This technology has increased new TB case detection owing to its speed, cost-effectiveness, and sensitivity. During the TB detection process, rats produce vast amounts of data, providing an opportunity to identify interesting patterns that influence TB detection performance. This study aimed to develop models that predict if the rat will hit (indicate the presence of TB within) the sample or not using machine learning (ML) techniques. The goal was to improve the diagnostic accuracy and performance of TB detection involving rats. APOPO (Anti-Persoonsmijnen Ontmijnende Product Ontwikkeling) Center in Morogoro provided data for this study from 2012 to 2019, and 366,441 observations were used to build predictive models using ML techniques, including decision tree, random forest, naïve Bayes, support vector machine, and k-nearest neighbor, by incorporating a variety of variables, such as the diagnostic results from partner health clinics using methods endorsed by the World Health Organization (WHO). The support vector machine technique yielded the highest accuracy of 83.39% for prediction compared to other ML techniques used. Furthermore, this study found that the inclusion of variables related to whether the sample contained TB or not increased the performance accuracy of the predictive model. The inclusion of variables related to the diagnostic results of TB samples may improve the detection performance of the trained rats. The study results may be of importance to TB-detection rat trainers and TB decision-makers as the results may prompt them to take action to maintain the usefulness of the technology and increase the TB detection performance of trained rats.

Innovative approach towards early prediction of ovarian cancer: Machine learning- enabled XAI techniques

Globally, ovarian cancer affects women disproportionately, causing significant morbidity and mortality rates. The early diagnosis of ovarian cancer is necessary for enhancing patient health and survival rates. This research article explores the utilization of Machine Learning (ML) techniques alongside eXplainable Artificial Intelligence (XAI) methodologies to aid in the early detection of ovarian cancer. ML techniques have recently gained popularity in developing predictive models to detect early-stage ovarian cancer. These predictions are made using XAI in a transparent and understandable way for healthcare professionals and patients. The primary aim of this study is to evaluate the effectiveness of various ovarian cancer prediction methodologies. This includes assessing K Nearest Neighbors, Support Vector Machines, Decision trees, and ensemble learning techniques such as Max Voting, Boosting, Bagging, and Stacking. A dataset of 349 patients with known ovarian cancer status was collected from Kaggle. The dataset included a comprehensive range of clinical features such as age, family history, tumor markers, and imaging characteristics. Preprocessing techniques were applied to enhance input data, including feature scaling and dimensionality reduction. A Minimum Redundancy Maximum Relevance (MRMR) algorithm was used to select the features in the model. Our experimental results demonstrate that in Support Vector Machines, we found 85 % base model accuracy and 89 % accuracy after stacking several ensemble learning techniques. With the help of XAI, complex ML algorithms can be given more profound insights into their decision-making, improving their applicability. This paper aims to introduce the best practices for integrating ML and artificial intelligence in biomarker evaluation. Building and evaluating Shapley values-based classifiers and visualizing results were the focus of our investigation. The study contributes to the field of oncology and women's health by offering a promising approach to the early diagnosis of ovarian cancer.

Optimization heart disease prediction using independent component analysis and support vector machine

Prediction models play a crucial role in early detection and intervention for cardiac diseases. However, their effectiveness is often hindered by limitations inherent in current methodologies. This paper proposes a novel approach to address these challenges by integrating Independent Component Analysis (ICA) with the Support Vector Machine (SVM) technique. Utilizing a comprehensive Cleveland dataset, our model achieves notable performance metrics, including an accuracy of 90.16%, an Area Under the Curve (AUC) of 96.66%, precision of 90.02%, recall of 90.00%, F1-score of 90.00%, and a minimal log loss of 3.54. Our methodology not only surpasses previous methodologies through extensive comparative analysis but also addresses common constraints identified in existing literature. These limitations encompass insufficient feature representation, overfitting, and a lack of proactive intervention strategies. By amalgamating ICA with SVM, our model enhances feature extraction, mitigates overfitting, and facilitates proactive diagnosis and intervention in individuals suspected of having heart disease. This study underscores the importance of mitigating current literature limitations and underscores the potential of integrating contemporary machine-learning techniques to advance prediction models for heart disease.

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.

A Hybrid Intrusion Detection System to Mitigate Biomedical Malicious Nodes

This paper proposes an intrusion detection system to prevent malicious node attacks that may result in failure links in wireless body area networks. The system utilizes a combination of Optimized Convolutional Neural Networks and Support Vector Machine techniques to classify nodes as malicious or not, and links as failure or not. In case of detection, the system employs a trust-based routing strategy to isolate malicious nodes or failure links and ensure a secure path. Furthermore, sensitive data is encrypted using a modified RSA encryption algorithm. The experimental results demonstrate the improved network performance in terms of data rate, delay, packet delivery ratio, energy consumption, and network security, by providing effective protection against malicious node attacks and failure links. The proposed system achieves the highest classification rate and sensitivity, surpassing similar methods in all evaluation metrics.

CNN‐based defect detection in manufacturing

AbstractThis research introduces an advanced algorithm based on convolutional neural networks for the detection and categorization of surface defects in manufacturing processes. At its core, the algorithm employs a deep learning model that integrates residual networks and attention mechanisms to effectively extract features. Additionally, we have developed a novel feature selection method, named NR, which synergistically combines neighborhood component analysis and ReliefF techniques. This approach enables the selection of more representative deep features for subsequent analysis. For the classification task, we utilize the support vector machine technique, which demonstrates versatility in handling both binary and multi‐class classification scenarios. The reliability and superiority of our algorithm are further validated through a comparative analysis using a dataset specifically tailored for this context. The results indicate that our approach outperforms existing algorithms in accurately identifying manufacturing defects.

Spatial analysis and machine learning prediction of forest fire susceptibility: a comprehensive approach for effective management and mitigation

Forest fires (FF) in tropical seasonal forests impact ecosystem. Addressing FF in tropical ecosystems has become a priority to mitigate impacts on biodiversity loss and climate change. The escalating frequency and intensity of FF globally have become a mounting concern. Understanding their tendencies, patterns, and vulnerabilities is imperative for conserving ecosystems and facilitating the development of effective prevention and management strategies. This study investigates the trends, patterns, and spatiotemporal distribution of FF for the period of 2001–2022, and delineates the forest fire susceptibility zones in Odisha State, India. The study utilized: (a) MODIS imagery to examine active fire point data; (b) Kernel density tools; (c) FF risk prediction using two machine learning algorithms, namely Support Vector Machine (SVM) and Random Forest (RF); (d) Receiver Operating Characteristic and Area Under the Curve, along with various evaluation metrics; and (e) a total of 19 factors, including three topographical, seven climatic, four biophysical, and five anthropogenic, to create a map indicating areas vulnerable to FF. The validation results revealed that the RF model achieved a precision exceeding 94 % on the validation datasets, while the SVM model reached 89 %. The estimated forest fire susceptibility zones using RF and SVM techniques indicated that 20.14 % and 16.72 % of the area, respectively, fall under the “Very High Forest Fire” susceptibility class. Trend analysis reveals a general upward trend in forest fire occurrences (R2 = 0.59), with a notable increase after 2015, peaking in 2021. Notably, Angul district was identified as the most affected area, documenting the highest number of forest fire incidents over the past 22 years. Additionally, forest fire mitigation plans have been developed by drawing insights from forest fire management strategies implemented in various countries worldwide. Overall, this analysis provides valuable insights for policymakers and forest management authorities to develop effective strategies for forest fire prevention and mitigation.

Functional support vector machine.

Linear and generalized linear scalar-on-function modeling have been commonly used to understand the relationship between a scalar response variable (e.g. continuous, binary outcomes) and functional predictors. Such techniques are sensitive to model misspecification when the relationship between the response variable and the functional predictors is complex. On the other hand, support vector machines (SVMs) are among the most robust prediction models but do not take account of the high correlations between repeated measurements and cannot be used for irregular data. In this work, we propose a novel method to integrate functional principal component analysis with SVM techniques for classification and regression to account for the continuous nature of functional data and the nonlinear relationship between the scalar response variable and the functional predictors. We demonstrate the performance of our method through extensive simulation experiments and two real data applications: the classification of alcoholics using electroencephalography signals and the prediction of glucobrassicin concentration using near-infrared reflectance spectroscopy. Our methods especially have more advantages when the measurement errors in functional predictors are relatively large.

Wavelet Transform and SVM Based Heart Disease Monitoring for Flexible Wearable Devices

INTRODUCTION: Heart disease has been a major health challenge globally, therefore the development of reliable and real-time heart disease monitoring methods is crucial for the prevention and management of heart health. The aim of this study is to explore a flexible wearable device approach based on wavelet transform and support vector machine (SVM) to improve the accuracy and portability of heart disease monitoring. OBJECTIVES: The main objective of this study is to develop a wearable device that combines wavelet transform and SVM techniques to achieve accurate monitoring of physiological signals of heart diseases. METHODS: An integrated method for heart disease monitoring was constructed using flexible sensor technology combined with a wavelet transform and support vector machine. The Marr wavelet transform was applied to the ECG signals, and the feature vectors were constructed by feature parameter extraction. Then, the radial basis kernel SVM was utilized to identify the three ECG signals. The performance of the algorithm was optimized by adjusting the SVM parameters to improve the accurate monitoring of heart diseases. RESULTS: The experimental results show that the proposed wavelet transform and SVM-based approach for flexible wearable devices achieves satisfactory results in heart disease monitoring. In particular, the algorithm successfully extracted feature vectors and accurately classified different ECG signals by skillfully combining the wavelet transform and SVM techniques for the processing of premature beat signals. CONCLUSION: The potential application value of the wavelet transform and SVM-based flexible wearable device approach in heart disease monitoring is emphasized. By efficiently processing ECG signals, the method provides an innovative and comfortable solution for real-time monitoring of cardiac diseases.

Predicting psycho-somatic disorders in online activity using multi-layer perceptron

<p>Internet services such as social media, blogs, and websites make it possible for people to acquire knowledge instantly. Due to these websites, it is now considerably easier to communicate information. As a result, individuals increasingly devote a higher amount of time to social networking programmes. This study provides estimates about the potential future ramifications of how individuals will utilise social networks. This work presents an accurate and applicable model for forecasting undesirable consequences. The model is of sufficient quality to be useful. This has been the case throughout. Using the model that has been proposed, significant properties are identified from datasets. After recovering the properties, they are categorised using the complicated computational method of multi-layer perceptron-based (MLP) artificial neural networks (ANN). 70% of this data was utilised during the training phase of the machine learning algorithm, while the remaining 30% was utilised during the validation phase of model construction. The proposed model's results were compared to those of more standard machine learning techniques. The approach utilises social networks to predict the issue. The simulation results indicate that the suggested model generates more precise predictions than the support vector machine, logistic regression, and random forest decision tree classifier techniques combined.</p>

Comparing the performance of machine learning methods in estimating the shear wave transit time in one of the reservoirs in southwest of Iran.

Shear wave transit time is a crucial parameter in petroleum engineering and geomechanical modeling with significant implications for reservoir performance and rock behavior prediction. Without accurate shear wave velocity information, geomechanical models are unable to fully characterize reservoir rock behavior, impacting operations such as hydraulic fracturing, production planning, and well stimulation. While traditional direct measurement methods are accurate but resource-intensive, indirect methods utilizing seismic and petrophysical data, as well as artificial intelligence algorithms, offer viable alternatives for shear wave velocity estimation. Machine learning algorithms have been proposed to predict shear wave velocity. However, until now, a comprehensive comparison has not been made on the common methods of machine learning that had an acceptable performance in previous researches. This research focuses on the prediction of shear wave transit time using prevalent machine learning techniques, along with a comparative analysis of these methods. To predict this parameter, various input features have been employed: compressional wave transit time, density, porosity, depth, Caliper log, and Gamma-ray log. Among the employed methods, the random forest approach demonstrated the most favorable performance, yielding R-squared and RMSE values of 0.9495 and 9.4567, respectively. Furthermore, the artificial neural network, LSBoost, Bayesian, multivariate regression, and support vector machine techniques achieved R-squared values of 0.878, 0.8583, 0.8471, 0.847 and 0.7975, RMSE values of 22.4068, 27.8158, 28.0138, 28.0240 and 37.5822, respectively. Estimation analysis confirmed the statistical reliability of the Random Forest model. The formulated strategies offer a promising framework applicable to shear wave velocity estimation in carbonate reservoirs.

Identification of Bearing Clearance in Sugar Centrifuge Using Dimension Theory and Support Vector Machine on Vibration Measurement

Abstract Bearing fatigue life is significantly influenced by bearing clearance. Vibration monitoring of bearing clearance deviations can efficiently reveal bearing wear and give sufficient lead time for maintenance. This study investigates the dynamics of roller bearings utilizing the dimension theory with the support vector machine (SVM) technique for diagnosing the bearing clearance faults of revolving machines. The generation of local defects in rotating machines is closely related to the clearance behavior of the rotor-bearing system. A dynamic model of bearing with dimension theory by matrix method with SVM is developed for characteristics of bearing clearance considering the influence of local defects on the inner and outer bearings races. The characteristics of bearing internal radial clearance considering the impact of the defect on the bearing are analyzed. An experimental study has been performed to capture the vibration signature of radial clearance for different speeds and radial loads of the rotor. The rotor-bearing system equations are numerically integrated, and the results are validated with experimental findings. The collective effects among the four parameters (radial load, speed, defect size, radial clearance) are investigated in detail for the rotor-bearing system. The noisy signal is subsequently eliminated using the modulation signal bispectrum (MSB), and the peaks of the MSB results are represented by the bearing clearance indicator. The efficiency and reliability of the stated approach are evaluated using a specialized bearing test and a run-to-failure sugar centrifuge test. The results suggest that the proposed approach can detect a change in bearing clearance up to 40 µm.

Fractal based feature extraction technique for classifying EEG signal for color visualization tasks

Exploring and finding Significant features for colour visualization tasks using the EEG signals is crucial in developing a robust Brain-machine Interface (BMI). The visually evoked potential carries multiple pieces of information, and finding its best feature is a tedious task. The main objective of this research is to concentrate on various linear and non-linear features which classifies the visually evoked potential when visualizing various colours for a certain period with reduced computational time and with higher accuracy. The feature extraction techniques utilized for extracting the features of EEG signals while visualizing various colours are Power Spectral Intensity (PSI), Spectral Entropy (SE), Detrended Fluctuation analysis (DFA), Higuchi Fractal Dimension (HFD), Petrossian Fractal Dimension (PFD), Multifractal Detrended Fluctuation Analysis (MFDFA). The extracted features were classified using the Multiclass classifier using one vs rest technique Support Vector Machine algorithm. The result shows that the MFDFA method with multiclass classifier combination has achieved 97.4 percent of classification accuracy when compared with other features.

Analysis of Fault Detection in Analog Circuits Using WSF-SKC Optimized SVM Technique

Many industrial applications and control systems depend heavily on analogue electrical circuitry. The conventional method of diagnosing such circuit faults can be time-consuming and erroneous, which might have a severe impact on the industrial output. The fault detection and analysing of analogue circuits with intelligent effective model is proposed in this work. The suggested technique primarily consists of two main stages one is extraction of features and the other is classification of faults. The analysis is performed on the response of frequency in analogue circuits. For extracting features particle swarm optimization (PSO) is utilized. The PSO is used to evaluate the fitness function of Wilks A-Statistic Filters sallen-key circuit (WSF-SKC). With fault characteristics retrieved using the particle swarm approach that are carefully selected, the fault classes may be separated more quickly. To categorise different failures in a benchmark circuit, a Support Vector Machine (SVM) classifier is built. Utilising firefly optimisation, the classifier is improved. Different fault codes were tested in experiments for defect detection and identification. The findings of the experiment indicate that this proposed technique can significantly increase the accuracy of fault diagnosis. The accuracy obtained for WS-LPF is 99.95%, WS-HPF is 99.97 and WS-BPF is 99.90% respectively.

Accuracy Assessment of Land Use/Land Cover Indices for Al-Rusafa in Baghdad Governorate by Remote Sensing Technology and GIS

The management and planning of natural and artificial resources depend on accurately monitoring land cover changes. Land cover change mapping and monitoring used to require expensive field surveys. Remote sensing is cheaper and more practical for mapping land use and cover changes. The Tigris River divides the Iraqi capital, Baghdad, into two parts: Karkh and Rusafa. Al-Rusafa was selected as a study area for current research, which has had rapid population and urban growth in recent decades. The current research applies the support vector machine technique to supervised LU/LC maps’ classification into barren regions, water bodies, vegetation cover and built-up regions. Spectral indicators were calculated: Enhanced Vegetation Index, Modified Normalized Difference Water Index, Normalized Built-Up Area Index, Dry Bareness Index in addition to calculating the accuracy assessment and Kappa coefficient. Using the Landsat 9 satellite image, ArcGIS 10.8 and Envi5.3 software were used to analyze and evaluate the results and field points observed by GPS devices. The results showed that the SVM classification algorithm accurately revealed the categories of LU/LC, where the classification accuracy reached 95%, and that the arid lands covered most of the study area 848.864 km2 and water bodies 76.747 km2, the vegetation and the built-up regions 466.459 km2 and 439.077 km2, respectively. The spectral indices showed slightly different areas of barren lands (DBSI 752.589 km2, 93% accuracy), vegetation (EVI 423.651 km2, 96% accuracy), and water bodies (MNDWI 73.187 km2, 98% accuracy) and built-up areas (NBAI 501,731 km2, 90%accuracy). The Support Vector Machine method outperforms other classification methods, and the spectral indicators employed in this work are useful and dependable for extracting each LU/LC category. In conclusion, Landsat 9 satellite data can reliably and swiftly detect ground cover.

Power demand estimation techniques applied to microgrid

ABSTRACT The increasing global demand for power necessitates a focus on renewable energy sources to reduce environmental impact and diversify energy generation. Estimating power demand in real-time electrical systems is crucial in this context. However, the literature contains various estimation techniques without clear guidance on the most suitable option based on performance and field-specific use. To address this gap, a study compares these techniques and identifies the most effective method for power demand estimation through a training process that assesses the accuracy of predictions against actual values. This comparison is conducted using the JUPYTER platform and historical data from the ENTSO-E power systems database. Results indicate that Linear Regression and Support Vector Machine techniques are commonly used for model evaluation, but the Adaptive Neural Network consistently performs as the most robust estimation method. The choice of technique depends on researcher preference, but it's essential to note that inadequate data preparation can lead to suboptimal results. In summary, as the world's power demand rises, the study identifies the Adaptive Neural Network as a promising tool for accurate power demand estimation, emphasizing the significance of proper data preparation in this process.