Support Vector Machine Classification Research Articles

A novel bounded loss framework for support vector machines

This paper introduces a novel bounded loss framework for SVM and SVR. Specifically, using the Pinball loss as an illustration, we devise a novel bounded exponential quantile loss (Leq-loss) for both support vector machine classification and regression tasks. For Leq-loss, it not only enhances the robustness of SVM and SVR against outliers but also improves the robustness of SVM to resampling from a different perspective. Furthermore, EQSVM and EQSVR were constructed based on Leq-loss, and the influence functions and breakdown point lower bounds of their estimators are derived. It is proved that the influence functions are bounded, and the breakdown point lower bounds can reach the highest asymptotic breakdown point of 1/2. Additionally, we demonstrated the robustness of EQSVM to resampling and derived its generalization error bound based on Rademacher complexity. Due to the Leq-loss being non-convex, we can use the concave–convex procedure (CCCP) technique to transform the problem into a series of convex optimization problems and use the ClipDCD algorithm to solve these convex optimization problems. Numerous experiments have been conducted to confirm the effectiveness of the proposed EQSVM and EQSVR.

Diagnostic value of regional homogeneity and fractional amplitude of low-frequency fluctuations in the classification of schizophrenia and bipolar disorders.

Schizophrenia (SCZ) and bipolar disorders (BD) show significant neurobiological and clinical overlap. In this study, we wanted to identify indexes of intrinsic brain activity that could differentiate these disorders. We compared the diagnostic value of the fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo) estimated from resting-state functional magnetic resonance imaging in a support vector machine classification of 59 healthy controls (HC), 40 individuals with SCZ, and 43 individuals with BD type I. The best performance, measured by balanced accuracy (BAC) for binary classification relative to HC was achieved by a stacking model (87.4% and 90.6% for SCZ and BD, respectively), with ReHo performing better than fALFF, both in SCZ (86.2% vs. 79.4%) and BD (89.9% vs. 76.9%). BD were better differentiated from HC by fronto-temporal ReHo and striato-temporo-thalamic fALFF. SCZ were better classified from HC using fronto-temporal-cerebellar ReHo and insulo-tempo-parietal-cerebellar fALFF. In conclusion, we provided evidence of widespread aberrancies of spontaneous activity and local connectivity in SCZ and BD, demonstrating that ReHo features exhibited superior discriminatory power compared to fALFF and achieved higher classification accuracies. Our results support the complementarity of these measures in the classification of SCZ and BD and suggest the potential for multivariate integration to improve diagnostic precision.

Discrimination of Fe-Ni-Laterites from Bauxites Using a Novel Support Vector Machines-Based Methodology on Sentinel-2 Data

Currently, the global shift towards green energy is at the forefront of efforts introducing a new era, thus rendering exploration for critical raw materials essential. To this purpose, the utilization of advanced machine learning methods in remote sensing has emerged as a rapid and cost-effective approach. This study proposes a new methodology, utilizing Sentinel-2 satellite data, to distinguish ferronickel (Fe-Ni-) laterite from bauxite across pre-mining, mining, and post-mining occurrences worldwide. Both ores contain mineral raw materials such as nickel, iron, cobalt, and alumina and their discrimination is generally macroscopically challenging, especially when their locations are often in geographical proximity. The proposed method is based on Support Vector Machines (SVM) classification using spectral signatures of known Fe-Ni-laterite and bauxite-bearing pixels in Greece, Cuba, and Jamaica. The highest classification accuracies are obtained by combining 𝑏12 with 𝑏6 or 𝑏7 spectral bands. Comparisons with specific ore mineralogies show that 𝑏6 and 𝑏7 are strongly linked to the ferric phase, while 𝑏12 is mainly associated with the argillic mineralogies, the latter probably being the key discriminating factor between the two ores. From laboratory chemical analyses, we also establish that 𝑏12 and 𝑏6 or 𝑏7 are strongly associated with Al2O3 and Fe2O3 content correspondingly. The proposed method is accurate, it has reduced prospection costs, and it can facilitate the initial screening of broad areas by automatically characterizing whether an ore is bauxite or Fe-Ni-laterite. This underscores the methodology’s significance in ore differentiation and exploration within the context of green energy endeavors.

Altered Intrinsic Brain Activity in Ischemic Stroke Patients Assessed Using the Percent Amplitude of a Fluctuation Method.

Ischemic stroke is a vascular disease that may cause cognitive and behavioral abnormalities. This study aims to assess abnormal brain function in ischemic stroke patients using the percent amplitude of fluctuation (PerAF) method and further explore the feasibility of PerAF as an imaging biomarker for investigating ischemic stroke pathophysiology mechanisms. Sixteen ischemic stroke patients and 22 healthy controls (HCs) underwent resting state functional magnetic resonance imaging (rs-fMRI) scanning, and the resulting data were analyzed using PerAF. Then a correlation analysis was conducted between PerAF values and Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores. Finally, the abnormal PerAF values were extracted and defined as features for support vector machine (SVM) analysis. Compared with HCs, ischemic stroke patients showed decreased PerAF in the bilateral cuneus, left middle frontal gyrus, precuneus and right inferior temporal gyrus, and increased PerAF in the bilateral orbital part of middle frontal gyrus and right orbital part of superior frontal gyrus. Correlation analyses revealed that PerAF values in the left orbital part of middle frontal gyrus was negatively correlated with the MoCA scores. The SVM classification of the PerAF values achieved an area under the curve (AUC) of 0.98 and an accuracy of 94.74%. Abnormal brain function has been found among ischemic stroke patients, which may be correlated with visual impairment, attention deficits, and dysregulation of negative emotions following a stroke. Our findings may support the potential of PerAF as a sensitive biomarker for investigating the underlying mechanisms of ischemic stroke.

A proposed two-level classification approach for forensic detection of diesel adulteration using NMR spectroscopy and machine learning.

Adulteration of diesel fuel poses a major concern in most developing countries including Ghana despite the many regulatory schemes adopted. The solvent tracer analysis approach currently used in Ghana has over the years suffered several limitations which affect the overall implementation of the scheme. There is therefore a need for alternative or supplementary tools to help detect adulteration of automotive fuel. Herein we describe a two-level classification method that combines NMR spectroscopy and machine learning algorithms to detect adulteration in diesel fuel. The training sets used in training the machine learning algorithms contained 20-40% w/w adulterant, a level typically found in Ghana. At the first level, a classification model is built to classify diesel samples as neat or adulterated. Adulterated samples are passed on to the second stage where a second classification model identifies the type of adulterant (kerosene, naphtha, or premix) present. Samples were analyzed by 1H NMR spectroscopy and the data obtained were used to build and validate support vector machine (SVM) classification models at both levels. At level 1, the SVM model classified all 200 samples with only 2.5% classification errors after validation. The level 2 classification model developed had no classification errors for kerosene and premix in diesel. However, 2.5% classification error was recorded for samples adulterated with naphtha. Despite the great performance of the proposed schemes, it showed significantly erratic predictions with adulterant levels below 20% w/w as the training sets for both models contained adulterants above 20% w/w. The proposed method, nevertheless, proved to be a potential tool that could serve as an alternative to the marking system in Ghana for the fast detection of adulterants in diesel.

Associations between Radiomics and Genomics in Non-Small Cell Lung Cancer Utilizing Computed Tomography and Next-Generation Sequencing: An Exploratory Study

Background: Radiomics, an evolving paradigm in medical imaging, involves the quantitative analysis of tumor features and demonstrates promise in predicting treatment responses and outcomes. This study aims to investigate the predictive capacity of radiomics for genetic alterations in non-small cell lung cancer (NSCLC). Methods: This exploratory, observational study integrated radiomic perspectives using computed tomography (CT) and genomic perspectives through next-generation sequencing (NGS) applied to liquid biopsies. Associations between radiomic features and genetic mutations were established using the Area Under the Receiver Operating Characteristic curve (AUC-ROC). Machine learning techniques, including Support Vector Machine (SVM) classification, aim to predict genetic mutations based on radiomic features. The prognostic impact of selected gene variants was assessed using Kaplan–Meier curves and Log-rank tests. Results: Sixty-six patients underwent screening, with fifty-seven being comprehensively characterized radiomically and genomically. Predominantly males (68.4%), adenocarcinoma was the prevalent histological type (73.7%). Disease staging is distributed across I/II (38.6%), III (31.6%), and IV (29.8%). Significant correlations were identified with mutations of ROS1 p.Thr145Pro (shape_Sphericity), ROS1 p.Arg167Gln (glszm_ZoneEntropy, firstorder_TotalEnergy), ROS1 p.Asp2213Asn (glszm_GrayLevelVariance, firstorder_RootMeanSquared), and ALK p.Asp1529Glu (glcm_Imc1). Patients with the ROS1 p.Thr145Pro variant demonstrated markedly shorter median survival compared to the wild-type group (9.7 months vs. not reached, p = 0.0143; HR: 5.35; 95% CI: 1.39–20.48). Conclusions: The exploration of the intersection between radiomics and cancer genetics in NSCLC is not only feasible but also holds the potential to improve genetic predictions and enhance prognostic accuracy.

Identification of Bloodstains by Species Using Extreme Learning Machine and Hyperspectral Imaging Technology.

How to identify bloodstains and obtain some potential evidence is of great significance for solving criminal cases. First, the spectral data of different species of bloodstain samples (human blood and animal blood) were acquired by using a hyperspectral imager. Then, an extreme learning machine (ELM) algorithm was used to build the training models of different species of bloodstain samples. Meanwhile, two traditional support vector machine and random forest classification algorithms were also compared with the ELM algorithm. The prediction results showed that the precision, sensitivity, specificity, and F1 score of the ELM algorithm were the highest. This indicates that hyperspectral technology, together with an ELM algorithm, could identify bloodstain species rapidly, non-destructively, and accurately. It has provided a new technical reference for bloodstain detection and identification.

Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms

In this study, the satellite data of ASTER and Landsat 8 OLI were used for the discrimination of lithological units covering the Khyber range. Of the 24 tested band combinations, the most suitable include 632 and 468 of ASTER and 754 and 147 of OLI in the RGB sequence. The data were also tested with two conventional machine learning algorithms (MLAs), namely maximum likelihood classification (MLC) and support vector machine (SVM), for lithological mapping. Principal component analysis (PCA), minimum noise fraction (MNF), band ratios, and color composites in combination with available lithological maps and field data were utilized for training sample collection for the MLC and SVM models to classify the lithological units. The accuracy assessment of SVM and MLC was performed using a confusion matrix, which revealed a higher accuracy of 74.8419% and 72.1217% for ASTER and an accuracy of 58.4833% and 60.0257% for OLI, respectively. The results indicate that ASTER imagery is more suitable for lithological discrimination in the study area due to its high spectral resolution in the VNIR to SWIR range. The experiment revealed that the SVM classification offered the highest overall accuracy of nearly 75% and the kappa coefficient value of 0.7 on ASTER data. This demonstrates the effectiveness of SVM classification in exploring lithological mapping in dry to semi-arid regions.

WORLD GREENHOUSE GAS EMISSION CLASSIFICATION USING SUPPORT VECTOR MACHINE (SVM) METHOD

The phenomenon of Heatwaves has struck several countries across the globe due to climate change. This climate change has led to an increase in greenhouse gas emissions surpassing the limits set by the IPCC Fourth Assessment Report GWPs. This study utilizes the Support Vector Machine (SVM) classification method to identify and categorize greenhouse gas emission data from 1990 to 2020 using four kernels function such as linear, polynomial, radial basis function (RBF), and sigmoid. The SVM method demonstrates excellent performance in constructing classification models with a polynomial kernel function. This is evidenced by high values of training accuracy, testing accuracy, and F1-score, accompanied by short training and testing analysis times. Successively, these values are 97.39%, 97.69%, 96.82%, 0.59 seconds, and 0.22 seconds.

Unveiling Neurophysiological Markers of Consciousness Levels through EEG Exploration

The concept of consciousness levels typically refers to various aspects and tiers related to an individual’s cognition, perception, thinking, and awareness. Although neurophysiological markers have not yet been definitively identified to distinguish between these nuanced levels, this paper introduces a robust marker, the Approximate Entropy (ApEn), which quantifies the complexity of EEG signals to differentiate states of altered consciousness. Utilizing ApEn, we analyze EEG data from the frontal lobe—a region closely associated with consciousness—in states indicative of severely altered conditions, specifically anesthesia, coma, and brain death. To enhance the precision of consciousness levelassessment, we employ a Support Vector Machine (SVM) model, which classifies the states based on EEG complexity measures. This approach not only provides valuable insights into the neural correlations associated with changes in these critical states but also underscores the potential of combining quantitative EEG analysis with machine learning techniques to advance our understanding of consciousness. The findings demonstrate that EEG complexity, when analyzed using ApEn coupled with SVM classification, offers a novel and effective method for assessing and distinguishing between degrees of consciousness. This approach promises significant implications for clinical diagnostics and patient monitoring.

Identifying human elephant conflict hotspots through satellite remote sensing and GIS to support conflict mitigation

Human-elephant conflict (HEC) is a significant issue in Sri Lanka and many parts of the world where elephants and humans coexist. To address HEC, this study integrates remote sensing and GIS analysis, focusing on monitoring changes in greenery. The study prepared the latest land cover and land use (LCLU) maps with Sentinel-2 satellite data, correlating them with reported HEC incidents reported in 2021 and 2022 to identify HEC hotspots in two forest-dominated regions of Southeast Sri Lanka. High-resolution sentinel-2 satellite imagery were used to detect areas of human activities and elephant habitats in proximity to each other. Random Forest (RF) and Support Vector Machine (SVM) classification methods were used for LCLU classification. The overall accuracy of the classification was 97.31 and 94.62, and kappa was 0.95 and 0.90 for RF and SVM, respectively. Multi-temporal normalised difference vegetation index (NDVI) analysis provided insights into vegetation health and coverage, offering a clear picture of greenery changes. Monthly changes in vegetation cover readings were quantified using NDVI values derived from MODIS data, identifying suitable regions for elephants to forage frequently. Furthermore, Kernel density estimation identified high-density areas for reported incidents of human and elephant deaths. This process involved assigning weight to conflict incidents within a 5 km radius, considering the proximity to the forest, and evaluating greenery changes using NDVI values, revealing varying levels of HEC risk, ranging from very high to low. The LCLU map, created using the RF classifier, indicates that all potential HEC hotspots for very high and high HEC risks are closely aligned with forest boundaries. The findings support HEC mitigation strategies through community awareness, HEC hotspots mapping and restoration practices to ensure a sustainable human-elephant coexistence. This method will help policymakers in wildlife conservation to identify high risk HEC zones to support HEC mitigation. In conclusion, this study highlights the potential of integrating remote sensing and GIS techniques in demarcating HEC hotspots in Sri Lanka to support conflict mitigation efforts.

Analysis of gasoline quality by ATR-FTIR spectroscopy with multivariate techniques

In this paper, chemometric methods were used for exploratory analysis, categorization, and quantification of gasoline fuel using Fourier Transform Infrared Spectroscopy (FTIR) data. During exploratory analysis, Principal Component Analysis (PCA) was employed, and to categorise the gasoline samples, Support Vector Machine Classification (SVMC), Linear Discrimination Analysis (LDA), and Partial Least Squares Discriminant Analysis (PLS-DA) were used. The concentration of Benzene, Methyl Tert-butyl Ether (MTBE), and Public Distribution System (PDS) Kerosene were determined using the Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and Support Vector Machine Regression (SVMR). All of the chemometric models had 100% accuracy and high R-square and RMSEC significance values. The SVM classification techniques were identified as a suitable choice for both classifying oxygenated and adulterated samples among all approaches. Both PLSR and PCR can also be suitable choices for quantification when dealing with high dimensional data. As a result, the research reported in this study has the potential to become a significant tool for improving gasoline sample discrimination and as a resource for quality control in the petroleum industry.

Endovascular Detection of Catheter-Thrombus Contact by Vacuum Excitation.

The objective of this work is to introduce and demonstrate the effectiveness of a novel sensing modality for contact detection between an off-the-shelf aspiration catheter and a thrombus. A custom robotic actuator with a pressure sensor was used to generate an oscillatory vacuum excitation and sense the pressure inside the extracorporeal portion of the catheter. Vacuum pressure profiles and robotic motion data were used to train a support vector machine (SVM) classification model to detect contact between the aspiration catheter tip and a mock thrombus. Validation consisted of benchtop accuracy verification, as well as user study comparison to the current standard of angiographic presentation. Benchtop accuracy of the sensing modality was shown to be 99.67%. The user study demonstrated statistically significant improvement in identifying catheter-thrombus contact compared to the current standard. The odds ratio of successful detection of clot contact was 2.86 (p = 0.03) when using the proposed sensory method compared to without it. The results of this work indicate that the proposed sensing modality can offer intraoperative feedback to interventionalists that can improve their ability to detect contact between the distal tip of a catheter and a thrombus. By offering a relatively low-cost technology that affords off-the-shelf aspiration catheters as clot-detecting sensors, interventionalists can improve the first-pass effect of the mechanical thrombectomy procedure while reducing procedural times and mental burden.

Alterations in dynamic regional homogeneity within default mode network in patients with thyroid-associated ophthalmopathy.

Thyroid-associated ophthalmopathy (TAO) is a significant autoimmune eye disease known for causing exophthalmos and substantial optic nerve damage. Prior investigations have solely focused on static functional MRI (fMRI) scans of the brain in TAO patients, neglecting the assessment of temporal variations in local brain activity. This study aimed to characterize alterations in dynamic regional homogeneity (dReHo) in TAO patients and differentiate between TAO patients and healthy controls using support vector machine (SVM) classification. Thirty-two patients with TAO and 32 healthy controls underwent resting-state fMRI scans. We calculated dReHo using sliding-window methods to evaluate changes in regional brain activity and compared these findings between the two groups. Subsequently, we employed SVM, a machine learning algorithm, to investigate the potential use of dReHo maps as diagnostic markers for TAO. Compared to healthy controls, individuals with active TAO demonstrated significantly higher dReHo values in the right angular gyrus, left precuneus, right inferior parietal as well as the left superior parietal gyrus. The SVM model demonstrated an accuracy ranging from 65.62 to 68.75% in distinguishing between TAO patients and healthy controls based on dReHo variability in these identified brain regions, with an area under the curve of 0.70 to 0.76. TAO patients showed increased dReHo in default mode network-related brain regions. The accuracy of classifying TAO patients and healthy controls based on dReHo was notably high. These results offer new insights for investigating the pathogenesis and clinical diagnostic classification of individuals with TAO.

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.

IGATE analysis improves the interpretability of single-cell immune landscape of influenza infection.

Influenza poses a persistent health burden worldwide. To design equitable vaccines effective across all demographics, it is essential to better understand how host factors such as genetic background and aging affect the single-cell immune landscape of influenza infection. Cytometry by time-of-flight (CyTOF) represents a promising technique in this pursuit, but interpreting its large, high-dimensional data remains difficult. We have developed a new analytical approach, in silico gating annotating training elucidating (iGATE), based on probabilistic support vector machine classification. By rapidly and accurately "gating" tens of millions of cells in silico into user-defined types, iGATE enabled us to track 25 canonical immune cell types in mouse lung over the course of influenza infection. Applying iGATE to study effects of host genetic background, we show that the lower survival of C57BL/6 mice compared with BALB/c was associated with a more rapid accumulation of inflammatory cell types and decreased IL-10 expression. Furthermore, we demonstrate that the most prominent effect of aging is a defective T cell response, reducing survival of aged mice. Finally, iGATE reveals that the 25 canonical immune cell types exhibited differential influenza infection susceptibility and replication permissiveness in vivo, but neither property varied with host genotype or aging. The software is available at https://github.com/UmichWenLab/iGATE.

“Intelligent tutoring effects on induced emotions and cognitive load of learning-disabled learners”

Purpose This study addresses the learning requirements of learners with learning difficulties by monitoring their learning experience in an Intelligent Tutoring System. Intelligent Tutoring Systems were developed to enrich the teaching-learning process. Materials and Methods In the present work, the interface is designed and developed utilizing the potential of Artificial Intelligence to meet their individual needs. Designing an online learning platform for a learners with learning difficulties requires consideration of their learning problems and preferences. The interface was developed focusing on all the requirements of the LD learners. The objective of the present study is to monitor the learning experience in the form of induced emotions and cognitive load of the learners to determine the impact of learning. Results 83 learners were observed during various stage of learning. The results obtained through the Support Vector machine (SVM) classification technique showed the positive attitude towards intelligent tutoring. The analysis revealed that a total of 0.23% of learners were positively induced. Their learning experience was positive and effective. The cognition load on learners was minimum with single-mode instruction and least disturbed. Conclusions The system was improved based on preference feedback on design features. This helps in improving content design and creating device independent and responsive visual design. The fatigue effect analysis on cognitive load implied that multiple modes of instruction increased drowsiness. Single mode of instruction have a positive impact on the learning process and it reduces the cognitive load of the learners. Implications for Rehabilitation The user interface designed and developed for learners with Dyslexia, Dysgraphia and Dyscalculia has learning disabled-friendly features. These can be used to create a device-independent and responsive design. Learning experience is monitored along with the impact on cognitive load of the learners. The research helps in understanding the stimulation and response of learners with learning disability for different learning conditions. Most existing learning systems are limited to non-learning-disabled learners. The ITS developed during research presents a Universal learning design helpful for all learners with and without learning disability.

Integrating OpenPose and SVM for Quantitative Postural Analysis in Young Adults: A Temporal-Spatial Approach.

Noninvasive tracking devices are widely used to monitor real-time posture. Yet significant potential exists to enhance postural control quantification through walking videos. This study advances computational science by integrating OpenPose with a Support Vector Machine (SVM) to perform highly accurate and robust postural analysis, marking a substantial improvement over traditional methods which often rely on invasive sensors. Utilizing OpenPose-based deep learning, we generated Dynamic Joint Nodes Plots (DJNP) and iso-block postural identity images for 35 young adults in controlled walking experiments. Through Temporal and Spatial Regression (TSR) models, key features were extracted for SVM classification, enabling the distinction between various walking behaviors. This approach resulted in an overall accuracy of 0.990 and a Kappa index of 0.985. Cutting points for the ratio of top angles (TAR) and the ratio of bottom angles (BAR) effectively differentiated between left and right skews with AUC values of 0.772 and 0.775, respectively. These results demonstrate the efficacy of integrating OpenPose with SVM, providing more precise, real-time analysis without invasive sensors. Future work will focus on expanding this method to a broader demographic, including individuals with gait abnormalities, to validate its effectiveness across diverse clinical conditions. Furthermore, we plan to explore the integration of alternative machine learning models, such as deep neural networks, enhancing the system's robustness and adaptability for complex dynamic environments. This research opens new avenues for clinical applications, particularly in rehabilitation and sports science, promising to revolutionize noninvasive postural analysis.

Rapid non-destructive monitoring and quality assessment of the fumigation process of Shanxi aged vinegar based on Vis-NIR hyperspectral imaging combined with multiple chemometric algorithms

The quality of the grains during the fumigation process can significantly affect the flavour and nutritional value of Shanxi aged vinegar (SAV). Hyperspectral imaging (HSI) was used to monitor the extent of fumigated grains, and it was combined with chemometrics to quantitatively predict three key physicochemical constituents: moisture content (MC), total acid (TA) and amino acid nitrogen (AAN). The noise reduction effects of five spectral preprocessing methods were compared, followed by the screening of optimal wavelengths using competitive adaptive reweighted sampling. Support vector machine classification was employed to establish a model for discriminating fumigated grains, and the best recognition accuracy reached 100%. Furthermore, the results of partial least squares regression slightly outperformed support vector machine regression, with correlation coefficient for prediction (Rp) of 0.9697, 0.9716, and 0.9098 for MC, TA, and AAN, respectively. The study demonstrates that HSI can be employed for rapid non-destructive monitoring and quality assessment of the fumigation process in SAV.

Assessing urban growth through morphological spatial pattern analysis in cloud computing platform

PurposeThis study aims to quantify and analyse the dynamics of land use and land cover (LULC) changes over three decades in the rapidly urbanizing city of Abha, Saudi Arabia, and to assess urban growth using Morphological Spatial Pattern Analysis (MSPA).Design/methodology/approachUsing the Support Vector Machine (SVM) classification in Google Earth Engine, changes in land use in Abha between 1990 and 2020 are accurately assessed. This method leverages cloud computing to enhance the efficiency and accuracy of big data analysis. Additionally, MSPA was employed in Google Colab to analyse urban growth patterns.FindingsThe study demonstrates significant expansion of urban areas in Abha, growing from 62.46 km² in 1990 to 271.45 km² in 2020, while aquatic habitats decreased from 1.36 km² to 0.52 km². MSPA revealed a notable increase in urban core areas from 41.66 km² in 2001 to 194.97 km² in 2021, showcasing the nuanced dynamics of urban sprawl and densification.Originality/valueThe novelty of this study lies in its integrated approach, combining LULC and MSPA analyses within a cloud computing framework to capture the dynamics of city and environment. The insights from this study are poised to influence policy and planning decisions, particularly in fostering sustainable urban environments that accommodate growth while preserving natural habitats. This approach is crucial for devising strategies that can adapt to and mitigate the environmental impacts of urban expansion.