Employ Support Vector Machine Research Articles

Alzheimer’s Disease Diagnosis via SVM-Driven Classification of Structural MRI Data

Abstract: In this study we introduce an automated system designed to classify Alzheimers disease (AD) using MRI brain scans. Our technique employs support vector machine (SVM) classification to effectively differentiate between AD patients and older adults without the disease by analyzing brain MRI images. The process includes dividing 2D T1 weighted MR images into regions of interest and extracting gray matter characteristics, from each region. By leveraging SVMs reliability and proven success in classification tasks along with the principle of minimizing risks our research showcases performance in generalizing results. This SVM based approach shows promise in distinguishing AD affected brains from ones presenting a valuable opportunity, for enhancing medical analysis and interpretation of MRI findings.

Labor market forecasting in unprecedented times: A machine learning approach

AbstractThe COVID‐19 pandemic ushered in unprecedented social and economic conditions, alongside unexpected policy responses, challenging the effectiveness of traditional labor market forecasting approaches. This article presents a novel approach that integrates macroeconomic variables, traditional labor market metrics, and Google search data to develop a machine learning‐based indicator for the Colombian labor market. We employ support vector machine for regression and neural networks models to forecast monthly employment and unemployment rates, explicitly focusing on the third wave of COVID‐19 in the first half of 2021. Our study's findings reveal that the proposed models outperform the autoregressive benchmark regarding forecast accuracy, demonstrating a rapid adaptation to labor market shifts.

IDSUML: Intrusion Detection System Using Machine Learning

Abstract: Network and system security is essential to modern digital communication. There will be multiple successful attempts by hackers and other intruders to obtain illegal access to networks and internet services. The development of secure systems is continuously accompanied by new dangers and the corresponding responses. Using intrusion detection systems (IDS) is one choice. An intrusion detection system. Main objective is to safeguard its resources from possible dangers. These acts are categorized as either potential or conventional attacks since it is capable of analysing and forecasting user behaviour. To find network intrusions, we typically employ Support Vector Machines (SVM) and Rough Pure Mathematics (RST). RST is used to preprocess and decrease the amount of data when packets are retrieved from the network. The RST-selected options provided to the SVM model enable it to provide many views and an explanation. A good application of the approach is to reduce the density of knowledge in a certain field. By comparing the data with Principal element analysis, the experiments show how RST and SVM schema may lower the false positive rate and increase accuracy (PCA)

Structural health monitoring of aircraft through prediction of delamination using machine learning.

Structural health monitoring (SHM) is a regular procedure of monitoring and recognizing changes in the material and geometric qualities of aircraft structures, bridges, buildings, and so on. The structural health of an airplane is more important in aerospace manufacturing and design. Inadequate structural health monitoring causes catastrophic breakdowns, and the resulting damage is costly. There is a need for an automated SHM technique that monitors and reports structural health effectively. The dataset utilized in our suggested study achieved a 0.95 R2 score earlier. The suggested work employs support vector machine (SVM) + extra tree + gradient boost + AdaBoost + decision tree approaches in an effort to improve performance in the delamination prediction process in aircraft construction. The stacking ensemble method outperformed all the technique with 0.975 R2 and 0.023 RMSE for old coupon and 0.928 R2 and 0.053 RMSE for new coupon. It shown the increase in R2 and decrease in root mean square error (RMSE).

Spatial representation of multidimensional information in emotional faces revealed by fMRI

Face perception is a complex process that involves highly specialized procedures and mechanisms. Investigating into face perception can help us better understand how the brain processes fine-grained, multidimensional information. This research aimed to delve deeply into how different dimensions of facial information are represented in specific brain regions or through inter-regional connections via an implicit face recognition task. To capture the representation of various facial information in the brain, we employed support vector machine decoding, functional connectivity, and model-based representational similarity analysis on fMRI data, resulting in the identification of three crucial findings. Firstly, despite the implicit nature of the task, emotions were still represented in the brain, contrasting with all other facial information. Secondly, the connection between the medial amygdala and the parahippocampal gyrus was found to be essential for the representation of facial emotion in implicit tasks. Thirdly, in implicit tasks, arousal representation occurred in the parahippocampal gyrus, while valence depended on the connection between the primary visual cortex and the parahippocampal gyrus. In conclusion, these findings dissociate the neural mechanisms of emotional valence and arousal, revealing the precise spatial patterns of multidimensional information processing in faces.

Modelling yielding dilemma zone for drivers at non-signalised crosswalks

In developing countries like China, non-signalised crosswalks represent a considerable risk to pedestrian safety. This study introduces a “yielding dilemma zone” model to tackle the decision-making challenges encountered by drivers at these crosswalks. Field data collected via drones at three non-signalised crosswalks in Xi'an (China) facilitated the analysis of 1687 instances of driver yielding behaviour. The study employed Support Vector Machine (SVM), Artificial Neural Network (ANN), and Binary Logistic Regression (BLR) models to establish the boundaries of the dilemma zone. Additionally, it utilized surrogate safety metrics, such as the deceleration rate required to stop (DRS) and time to crossing (TC), to compare risks between dilemma and non-dilemma zones. Each modelling approach provided insightful findings. The SVM and ANN models identified the speed-distance dilemma zones, while the BLR model determined the multifactorial dilemma zones by considering characteristics such as pedestrian, vehicle, and traffic condition. The findings underscore the significance of the “yielding dilemma zone” for understanding driver-pedestrian interactions at non-signalised crosswalks. The innovative multi-model approach uncovered that pedestrian speed, vehicle type, vehicle approach speed, adjacent vehicle number, driving directions, and pedestrian crossing stage significantly influence this interaction. indicated a higher probability of collisions within dilemma zones compared to non-dilemma zones across all models. The study suggests that the SVM model could be instrumental in creating passive physical warning signs, while the ANN and BLR models could significantly contribute to real-time pedestrian and driver assistance systems. The “yielding dilemma zone” model, with its comprehensive approach, presents a promising avenue for enhancing pedestrian safety, intelligent transportation systems, and traffic management at non-signalised crosswalks.

Experimental investigation and application of soft computing models for predicting flow energy loss in arc-shaped constrictions

Abstract This investigation focuses on flow energy, a crucial parameter in the design of water structures such as channels. The research endeavors to explore the relative energy loss (ΔEAB/EA) in a constricted flow path of varying widths, employing Support Vector Machine (SVM), Artificial Neural Network (ANN), Gene Expression Programming (GEP), Multiple Adaptive Regression Splines (MARS), M5 and Random Forest (RF) models. Experiments span a Froude number range from 2.85 to 8.85. The experimental findings indicate that the ΔEAB/EA exceeds that observed in a classical hydraulic jump with constriction section. Within the SVM model, the linear kernel emerges as the best predictor of ΔEAB/EA, outperforming polynomial, radial basis function (RBF), and sigmoid kernels. In addition, in the ANN model, the MLP network was more accurate compared to the RBF network. The results indicate that the relationship proposed by the MARS model can play a significant role resulting in high accuracy compared to the non-linear regression relationship in predicting the target parameter. Upon comprehensive evaluation, the ANN method emerges as the most promising among the candidates, yielding superior performance compared to the other models. The testing phase results for the ANN-MLP are noteworthy, with R = 0.997, average RE% = 0.63%, RMSE = 0.0069, BIAS = −0.0004, DR = 0.999, SI = 0.0098 and KGE = 0.995.

An Adaptive INS/CNS/SMN Integrated Navigation Algorithm in Sea Area

In this paper, we present an innovative inertial navigation system (INS)/celestial navigation system (CNS)/scene-matching navigation (SMN) adaptive integrated navigation algorithm designed to achieve prolonged and highly precise navigation in sea areas. The algorithm establishes the structure of the INS/CNS/SMN integrated navigation system. To ensure the availability of CNS in the Nanhai Sea (South China Sea) area, a cloud and fog model is meticulously constructed. Three distinct types of sea area landmarks are defined, and an automated classification model for sea area landmarks, employing support vector machines (SVM), is developed. Corresponding matching methods and strategies for these landmarks are also delineated. Concurrently, the observable probability of each landmark is computed to generate a probability cloud, representing the usability of sea area landmarks. The proposed INS/CNS/SMN adaptive integrated navigation algorithm is simulated and validated across varied altitudes and trajectories in the sea area. The results show that CNS and SMN can dynamically assist INS in achieving prolonged and highly precise navigation.

Investigation and Modeling of the Behavior of Temperature Characteristics of 0.3-1.1 GHz Complementary Metal Oxide Semiconductor Class-A Broadband Power Amplifiers.

A power amplifier (PA) stands as a central module within the electronic information system (EIS), and any variation in a PA's specifications has a direct impact on the EIS's performance, especially in the face of temperature fluctuations. In examining the influence of PA specification changes on the EIS, we employed support vector machine (SVM) to model the behavior of the temperature characteristics of 0.3-1.1 GHz complementary metal oxide semiconductor (CMOS) class-A broadband PAs. The results show that the parameters of S11, S12, S21, and S22 can be effectively modeled. SVM outperforms Elman and GRNN in terms of combined modeling time and modeling accuracy. This research can be extended to modeling the behavior of other types of power amplifiers or devices and circuits.

Autism Spectrum Disorder Prediction in Children Using Machine Learning

Life symptoms associated with autism spectrum disorder (ASD) typically manifest during childhood and persist into adolescence and adulthood. ASD, which can be caused by genetic or environmental factors, can be significantly improved through early detection and treatment. Currently, standardized clinical tests are the primary diagnostic method for ASD. However, these tests are time consuming and expensive. Early detection and intervention are pivotal in enhancing the long-term prospects of children diagnosed with ASD. Machine-learning (ML) techniques are being utilized alongside conventional methods to improve the accuracy and efficiency of ASD diagnosis. Therefore, the paper aims to explore the feasibility of employing support vector machines, random forest classifier, naïve Bayes, logistic regression (LR), K-nearest neighbor, and decision tree classification models on our dataset to construct predictive models for predicting and analyzing ASD problems across different age groups: children, adolescents, and adults. The proposed techniques are assessed using publicly available nonclinical ASD datasets of three distinct datasets. The four ASD datasets, namely toddlers, adolescents, children, and adults, were obtained from publicly available repositories, specifically Kaggle and UCI ML. These repositories provide a valuable data source for research and analysis related to ASD. Our main objective is to identify the susceptibility to ASD in children during the early stages, thereby streamlining the diagnosis process. Based on our findings, LR demonstrated the highest accuracy for the selected dataset.

A multi-level decision-making framework for heart-related disease prediction and recommendation

The precise prediction of health-related issues is a significant challenge in healthcare, with heart-related diseases posing a particularly threatening global health problem. Accurate prediction and recommendation for heart-related diseases are crucial for timely and effective treatment solutions. The primary objective of this study is to develop a classification model capable of accurately identifying heart diseases and providing appropriate recommendations for patients. The proposed system utilizes a multilevel-based classification mechanism employing Support Vector Machines. It aims to categorize heart diseases by analyzing patient’s vital parameters. The performance of the proposed model was evaluated by testing it on a dataset containing patient records. The generated recommendations are based on a comprehensive assessment of the severity of clinical features exhibited by patients, including estimating the associated risk of both clinical features and the disease itself. The predictions were evaluated using three metrics: accuracy, specificity, and the receiver operating characteristic curve. The proposed Multilevel Support Vector Machine (MSVM) classification model achieved an accuracy rate of 94.09% in detecting the severity of heart disease. This makes it a valuable tool in the medical field for providing timely diagnosis and treatment recommendations. The proposed model presents a promising approach for accurately predicting heart-related diseases and highlights the potential of soft computing techniques in healthcare. Future research could focus on further enhancing the proposed model’s accuracy and applicability.

Twitter Sentiment Analysis on Chatgpt Tweets

Abstract: This study investigates multilingual sentiment analysis within tweets on ChatGPT, an AI conversational model, employing Support Vector Machines (SVM) and BERT, an advanced language model. It aims to detect and classify emotions, including emoji identification, embedded within diverse messages across multiple languages on Twitter. By leveraging SVM's text classification and BERT's contextual understanding in various languages, the research delves into preprocessing techniques and feature engineering for sentiment analysis, encompassing multilingual and emoji detection. Furthermore, it explores the fusion of traditional SVM methods with BERT's state-of-the-art model for multilingual sentiment analysis, emphasizing emotion and emoji detection in AI-generated content on multilingual social media platforms like Twitter. This research yields insights into the successful detection of multilingual sentiment nuances and emotions, including emoji identification. It offers implications for advancing multilingual sentiment analysis in natural language processing across diverse linguistic contexts.

A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability

BackgroundIntelligent cardiotocography (CTG) classification can assist obstetricians in evaluating fetal health. However, high classification performance is often achieved by complex machine learning (ML)-based models, which causes interpretability concerns. The trade-off between accuracy and interpretability makes it challenging for most existing ML-based CTG classification models to popularize in prenatal clinical applications.MethodsAiming to improve CTG classification performance and prediction interpretability, a hybrid model was proposed using a stacked ensemble strategy with mixed features and Kernel SHapley Additive exPlanations (SHAP) framework. Firstly, the stacked ensemble classifier was established by employing support vector machines (SVM), extreme gradient boosting (XGB), and random forests (RF) as base learners, and backpropagation (BP) as a meta learner whose input was mixed with the CTG features and the probability value of each category output by base learners. Then, the public and private CTG datasets were used to verify the discriminative performance. Furthermore, Kernel SHAP was applied to estimate the contribution values of features and their relationships to the fetal states.ResultsFor intelligent CTG classification using 10-fold cross-validation, the accuracy and average F1 score were 0.9539 and 0.9249 in the public dataset, respectively; and those were 0.9201 and 0.8926 in the private dataset, respectively. For interpretability, the explanation results indicated that accelerations (AC) and the percentage of time with abnormal short-term variability (ASTV) were the key determinants. Specifically, the probability of abnormality increased and that of the normal state decreased as the value of ASTV grew. In addition, the likelihood of the normal status rose with the increase of AC.ConclusionsThe proposed model has high classification performance and reasonable interpretability for intelligent fetal monitoring.

Optimal hybrid artificial intelligence models for predicting the adsorptive removal of dyes and phenols from aqueous solutions using an amine-functionalized graphene oxide/layered triple hydroxide nanocomposite

Water pollution is a serious environmental problem with a significant negative impact on human health and the ecosystem. Adsorption is an attractive process for water decontamination. Developing artificial intelligence models capable of predicting the adsorption performance of newly developed materials could save huge amounts of resources and efforts. In the present study, a novel polyethyleneimine/graphene oxide/layered triple hydroxide (PEI/GO/LTH) nanocomposite was synthesized, characterized, and applied to adoptively remove harmful phenolic (bisphenol A) and azo dye (acid red 1) from wastewater samples. The results revealed that the PEI/GO/LTH nanocomposite is highly effective in removing these two pollutants. The adsorption isotherms and kinetics of these two pollutants are best fitted by the Langmuir isotherm and pseudo-second-order models, respectively. Additionally, the synthesized nanocomposite is easily and highly regenerable with an insignificant loss in performance when repeatedly used. Besides the above investigations, the present study also employs support vector machines (SVM) and Bayesian optimization as tools for predicting the adsorptive removal of acid red 1 (AR1) dye and bisphenol A (BPA) from contaminated water samples by the synthesized PEI/GO/LTH nanocomposite. The models achieved promising results as the correlation coefficients, during the testing phase, reached 97.3 and 96.6 % for the AR1 and BPA data using BO-SVM models, respectively.

Unveiling the past: Utilizing satellite imagery archives to study archaeological landscapes in the northeastern Nile Delta, Egypt

This paper investigates the utilization of satellite imagery archives in the northeastern Nile Delta to examine and explore the evolution of archaeological landscapes. The study incorporates optical satellite imagery from multiple sensors, including Landsat, ASTER, and CORONA, in addition to historical topographic maps, and digital elevation data. The methodology involves employing Support Vector Machine (SVM) algorithm, spatial analysis, and analyzing vegetation indices. The findings reveal significant landscape changes during the period from the prehistoric to the Byzantine era, influenced by natural factors such as sea level changes and land subsidence. Moreover, the landscape changed after the construction of the Aswan High Dam, influenced by human activities such as agriculture and urbanization. The study was successful in detecting the remains of the Tanitic branch, an ancient branch of the Nile River. Additionally, the analysis of vegetation indices showed crop anomalies, which may be indicative of buried structures. However, further investigations, including ground surveys and excavations, are needed for validation. The research contributes to the understanding of the region's historical and cultural significance, highlights the impact of human activities on archaeological sites, and underscores the importance of preserving cultural heritage in the face of environmental changes and urban development.

Analysis of Blood Samples to Identify Leukemia Cells using a Support Vector Machine and K-Nearest Neighbor Algorithms

Abstract: The study proposes an innovative approach using MATLAB to automate the counting of leukemia cells in blood samples, employing Support Vector Machine (SVM) and Nearest Neighbor algorithms. The method involves preprocessing blood sample images to enhance contrast and apply image filters, followed by segmentation techniques for isolating individual cells. SVM and nearest neighbor algorithms are trained using extracted features such as cell size, shape, and texture. Accurate detection and counting of leukemia cells play a crucial role in leukemia diagnosis and management. Leukemia is a group of cancers characterized by abnormal white blood cell proliferation in the bone marrow, leading to symptoms like bleeding, bruising, fatigue, and increased infection risk due to insufficient normal blood cells. Diagnosis typically involves blood tests or bone marrow biopsy. In clinical bioinformatics, SVM algorithms have enabled the development of robust experimental cancer diagnostic models, utilizing gene expression data with a small number of samples and numerous variables. Efficient implementations of SVM algorithms further facilitate practical application. Support Vector Machines excel in mapping data to higher-dimensional spaces through kernel functions, allowing the identification of maximum-margin hyperplanes for separating training instances.

Radiomics-guided prognostic assessment of early-stage hepatocellular carcinoma recurrence post-radical resection.

The prognosis of early-stage hepatocellular carcinoma (HCC) patients after radical resection has received widespread attention, but reliable prediction methods are lacking. Radiomics derived from enhanced computed tomography (CT) imaging offers a potential avenue for practical prognostication in HCC patients. We recruited early-stage HCC patients undergoing radical resection. Statistical analyses were performed to identify clinicopathological and radiomic features linked to recurrence. Clinical, radiomic, and combined models (incorporating clinicopathological and radiomic features) were built using four algorithms. The performance of these models was scrutinized via fivefold cross-validation, with evaluation metrics including the area under the curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) being calculated and compared. Ultimately, an integrated nomogram was devised by combining independent clinicopathological predictors with the Radscore. From January 2016 through December 2020, HCC recurrence was observed in 167 cases (64.5%), with a median time to recurrence of 26.7months following initial resection. Combined models outperformed those solely relying on clinicopathological or radiomic features. Notably, among the combined models, those employing support vector machine (SVM) algorithms exhibited the most promising predictive outcomes (AUC: 0.840 (95% Confidence interval (CI): [0.696, 0.984]), ACC: 0.805, SEN: 0.849, SPE: 0.733). Hepatitis B infection, tumour size > 5cm, and alpha-fetoprotein (AFP) > 400ng/mL were identified as independent recurrence predictors and were subsequently amalgamated with the Radscore to create a visually intuitive nomogram, delivering robust and reliable predictive performance. Machine learning models amalgamating clinicopathological and radiomic features provide a valuable tool for clinicians to predict postoperative HCC recurrence, thereby informing early preventative strategies.

Monitoring montane-subalpine forest ecotone in the Pyrenees through sequential forest inventories and Landsat imagery

Key messageAbies alba Mill.–Pinus uncinata Ramond. ecotone dynamics are examined along both altitudinal and protection level gradients by combining field inventories and Landsat data. An upward expansion of A. alba to the subalpine belt is observed in the last decades as a result of stand maturation after logging cessation.ContextHigh-mountain forests constitute sensitive locations to monitor the impacts of global change on tree-species composition and ecotone dynamics. In this study, we focus on the Spanish Pyrenees where silver fir (Abies alba Mill.) coexists with mountain pine (Pinus uncinata Ramond.) forming montane-subalpine ecotones.AimsThe main goal of this study is to assess the spatiotemporal dynamics of the silver fir–mountain pine ecotone and its underlying driving factors.MethodsWe reconstructed the spatial distribution and dynamics of the species by combining remote sensing imagery and field plot data from 1989 to 2015, employing support vector machine techniques for image classification. Using variance analysis and mixed effects models, we then analyzed the evolution of basal area and replacement index, a measure of relative change in species composition, over time and altitude range. Additionally, we explored their relationship with site factors and protection level (National Park vs. protection buffer zone).ResultsSilver fir has expanded its distribution in both the National Park and the protection buffer zone, whereas mountain pine has remained stable. Both species exhibit increased basal area associated with stand maturation and a higher level of protection. The replacement index indicates a rise in silver fir in the understory on North-facing slopes, attributed to stand densification. These findings are particularly noticeable in the area with the highest level of protection.ConclusionThe cessation of traditional land uses has led to ongoing stand densification, promoting succession and favoring the increased abundance of silver fir at its uppermost locations, where this species outcompetes mountain pine.

Improving Forest Detection Using Machine Learning and Remote Sensing: A Case Study in Southeastern Serbia

Vegetation plays an active role in ecosystem dynamics, and monitoring its patterns and changes is vital for effective environmental resource management. This study explores the possibility of machine learning techniques and remote sensing data to improve the accuracy of forest detection. The research focuses on the southeastern part of the Republic of Serbia as a case study area, using Sentinel-2 multispectral bands. The study employs publicly accessible satellite data and incorporates different vegetation indices to improve classification accuracy. The main objective is to examine the practicability of expanding the input parameters for forest detection using a machine learning approach. The classification process is performed by employing support vector machines (SVM) algorithm and utilising the SVM module in the scikit-learn package. The results demonstrate that including vegetation indices alongside the multispectral bands significantly improves the accuracy of vegetation detection. A comprehensive assessment reveals an overall classification accuracy of up to 99.01% when the selected vegetation indices (MCARI, RENDVI, NDI45, GNDVI, NDII) are combined with the Sentinel-2 bands. This research highlights the potential of machine learning and remote sensing in forest detection and monitoring. The findings underscore the importance of incorporating vegetation indices to enhance classification accuracy using the Python programming language. The study’s outcomes provide valuable insights for environmental resource management and decision-making processes, particularly in regions with diverse forest ecosystems.

On the variants of SVM method for the estimation of soil elastic modulus from TSD model: Numerical parametric study

The accurate estimation of the subgrade resilient modulus MR is a crucial element in designing durable and resilient pavements as it quantifies the soil’s bearing capacity to withstand traffic loads without excessive deformation or failure. Conventional methods and devices that are used to estimate MR, such as the Falling Weight Deflectometer (FWD) have several limitations due to operational and security constraints when measuring at the network level. To overcome these limitations, Traffic Speed Deflectometer (TSD) has been developed, which can continuously assess the pavement bearing capacity without requiring traffic control. However, processing the massive amounts of data generated by TSD requires specialized processing techniques such as Machine Learning (ML). Therefore, the primary objective of this paper is to develop an efficient and effective model that can accurately estimate MR by combining two techniques: (TSD + ML). The study uses numerical simulations of deflection velocity slope SV under TSD as the forward model. The research employs Support Vector Machine (SVM) algorithm for ML to classify and estimate MR modulus values from a synthetic and controlled dataset. SVM model exhibits robust performance in both classification and regression analysis, whereas, 95% of the discrepancies between estimated and tested values did not exceed ±15MPa. Advanced numerical validation, sensitivity analysis and parameter estimation methods were also conducted to optimize the model’s performance. Overall, the approach proposed in this paper has the potential to make a significant impact in the field of subgrade evaluation and can facilitate the development of better road infrastructure.