Multidimensional Feature Space Research Articles

Enhancing EEG-Based MI-BCIs with Class-Specific and Subject-Specific Features Detected by Neural Manifold Analysis.

This paper presents an innovative approach leveraging Neuronal Manifold Analysis of EEG data to identify specific time intervals for feature extraction, effectively capturing both class-specific and subject-specific characteristics. Different pipelines were constructed and employed to extract distinctive features within these intervals, specifically for motor imagery (MI) tasks. The methodology was validated using the Graz Competition IV datasets 2A (four-class) and 2B (two-class) motor imagery classification, demonstrating an improvement in classification accuracy that surpasses state-of-the-art algorithms designed for MI tasks. A multi-dimensional feature space, constructed using NMA, was built to detect intervals that capture these critical characteristics, which led to significantly enhanced classification accuracy, especially for individuals with initially poor classification performance. These findings highlight the robustness of this method and its potential to improve classification performance in EEG-based MI-BCI systems.

Interpretable machine learning methods to predict the mechanical properties of ABX3 perovskites

This paper proposes the utility of interpretable ensemble learning models for predicting the mechanical properties (bulk, shear and Young moduli) of ABX3 perovskite compounds with the A, B, and X referring to the 3 elements that make the cubic 3-dimensional framework of the perovskite compounds. These models consist of 3 ensemble learning techniques namely CatBoost, Random Forest, and XGBoost. To expand the feature space, robust first-principles density functional theory calculations were used to generate some of the input features, namely elastic constants, density, volume per atom, and ground state energy per atom. The order of the input feature ranking that influences the machine learning (ML) model decisions was then determined. For this, we performed correlation analysis on the multi-dimensional input feature space, suppressed features with high collinearity, and selected features with limited correlation. We trained the three ensemble learning techniques on the desired vectorial input feature representation to predict the mechanical properties. Furthermore, we employed the Shapley Additive Explanations (SHAP) algorithm for analysing the intrinsic decision-making rationality of the ensemble learning models. We measured the performance in the context of the error metrics and coefficient of determination, R2. The results show that XGBoost outperforms other approaches when predicting the shear modulus or Young modulus of the perovskite compounds yielding the least error metrics and the highest R2 value (0.97) in the testing phase. However, both CatBoost and Random Forest outperformed XGBoost when attempting to predict the bulk modulus in the testing phase. The deficiency of the XGBoost in predicting the bulk modulus can be ascribed to an overfitting problem which can occur when the ML model gives accurate predictions for training data but not for test data. Furthermore, the SHAP algorithm provides an insight into the order of feature importance (from highest to lowest). Additionally, we conducted a post-analysis using a holistic ranking to analyse the relative importance of the SHAP feature impact comprehension for the examined ensemble learning techniques. Our findings indicate that the elastic constants are the most important input features influencing the predictive decision of the ensemble learning models.

A 10-m scale chemical industrial parks map along the Yangtze River in 2021 based on machine learning

Strengthening industrial pollution control in the Yangtze River is a fundamental national policy of China. There is a lack of detailed distribution of chemical industrial parks (CIPs). This Study utilized random forest (RF) and active learning to generate the distribution map of CIPs along the Yangtze River at 10-m resolution. Based on Sentinel-2 imagery, spectral and texture features are extracted. Combined with the Points of Interest (POI), a multidimensional feature space is constructed. By employing partitioned training, classification of CIPs map is achieved on Google Earth Engine (GEE). Technical validation along the entire Yangtze River demonstrates a model accuracy of 80%. Compared to traditional manual survey methods, this approach saves significant time and economic costs while also being timelier. As the first publicly available CIPs map within a 5-km range along the Yangtze River, this research will provide a scientific basis for the fine governance of chemical industries in the region. Additionally, it offers a model guide for the accurate identification of the chemical industry.

Verification of the person’s dynamic signature on a limited number of samples

Objectives. The goal of the research is to develop a new person-dependent method for verification of a signature of one person made on a tablet with a stylus in the presence of a limited number of signature samples of this person.Methods. The paper shows how to construct an individual pattern of the dynamic signatures of any person, which is described by points in a multidimensional feature space and is intended for subsequent verification of the authenticity of the signatures of a given person. It is constructed using 5<N<20 samples of genuine human signatures. The pattern forms a convex object in a multidimensional feature space. It describes the peculiar properties of a signature performed by a specific person.Results . The dynamics of signature execution is represented by three discrete parametric functions: coordinates of the stylus X, Y and its pressure on the tablet P, recorded at fixed time intervals. In the process of research, a number of secondary functions-features were selected and calculated from them. Since these data sets have different lengths, the dynamic time warping algorithm is used to compare them. The results of this transformation are distances between the dynamic features of two signatures, which serve as coordinates of a point in the feature space that describes the similarity of these signatures. The set of such points describes similarity of all pairs of genuine human signatures presented for verification in a multidimensional feature space. The convex hull of the cloud of these points is used as a pattern of a particular person's signature. The genuine signatures of any person are always different from each other; significant differences between them can distort the verification result.Conclusion. Experimental studies performed on genuine and fake signatures of 498 people from the largest available database of dynamic signatures, DeepSignDB, showed a verification accuracy of about 98 % when analyzing 24,900 signatures. Half of them are genuine, half are fake

Reliable object tracking by multimodal hybrid feature extraction and transformer-based fusion

Visual object tracking, which is primarily based on visible light image sequences, encounters numerous challenges in complicated scenarios, such as low light conditions, high dynamic ranges, and background clutter. To address these challenges, incorporating the advantages of multiple visual modalities is a promising solution for achieving reliable object tracking. However, the existing approaches usually integrate multimodal inputs through adaptive local feature interactions, which cannot leverage the full potential of visual cues, thus resulting in insufficient feature modeling. In this study, we propose a novel multimodal hybrid tracker (MMHT) that utilizes frame-event-based data for reliable single object tracking. The MMHT model employs a hybrid backbone consisting of an artificial neural network (ANN) and a spiking neural network (SNN) to extract dominant features from different visual modalities and then uses a unified encoder to align the features across different domains. Moreover, we propose an enhanced transformer-based module to fuse multimodal features using attention mechanisms. With these methods, the MMHT model can effectively construct a multiscale and multidimensional visual feature space and achieve discriminative feature modeling. Extensive experiments demonstrate that the MMHT model exhibits competitive performance in comparison with that of other state-of-the-art methods. Overall, our results highlight the effectiveness of the MMHT model in terms of addressing the challenges faced in visual object tracking tasks.

The speech neuroprosthesis.

Loss of speech after paralysis is devastating, but circumventing motor-pathway injury by directly decoding speech from intact cortical activity has the potential to restore naturalcommunication and self-expression. Recent discoveries have defined how key features of speech production are facilitated by the coordinated activity of vocal-tract articulatory and motor-planning cortical representations. In this Review, we highlight such progress and how it has led to successful speech decoding, first in individuals implanted with intracranial electrodes for clinicalepilepsy monitoringand subsequently in individuals with paralysis as part ofearlyfeasibility clinical trials to restore speech. We discuss high-spatiotemporal-resolution neural interfaces and the adaptation of state-of-the-art speech computational algorithms that have driven rapidandsubstantial progress indecoding neural activity into text, audible speech, and facial movements. Although restoring natural speech is a long-term goal, speech neuroprostheses already have performance levels that surpass communication rates offered by current assistive-communication technology. Given this accelerated rate of progress in the field, we propose key evaluation metrics for speed and accuracy, among others, to help standardize across studies. We finish by highlighting several directions to more fully explore the multidimensional feature space of speech and language, which will continue to accelerate progress towards a clinically viable speech neuroprosthesis.

ПЕРЕОБУЧЕНИЕ В МАШИННОМ ОБУЧЕНИИ: ПРОБЛЕМЫ И РЕШЕНИЯ

Overfitting is one of the most important factors affecting the performance of machine lear¬ning algorithms. When solving machine learning problems, it is important to be able to effectively solve the problem of overfitting. The research objective. The purpose of this article is to study the problem of overfitting in machine learning tasks. The article discusses effective learning methods aimed at preventing overfitting. Material and methods. The focus of the article is on various non-standard issues related to overfitting that are important from a practical point of view. Various causes of overfitting, its consequences and methods of combating overfitting are considered. The dependence of overfitting and generalizing abi¬lity on the quality of features and properties of the training set is studied. Particular attention is paid to the features of training and the formation of a training sample in multidimensional feature spaces. The question of the correct formation of the training set and the correct addition of data to the training set from the point of view of overfitting prevention, as well as the impact of incorrect distribution of the target variable on overfitting, is considered. It is explained why the methods of adding incorrect data to the training set, such as MixUp and CutMix, can improve the quality of training. The problem of the algorithm's confidence in its predictions is considered, as well as the problem of algorithm overconfidence in incorrect predictions, which is also typical for ChatGPT. The problem of assessing the quality of the algorithm is considered. It is shown why normalization can help avoid overfitting. Results. An algorithm for training decision trees Random Samples Mix-Up is proposed to combat overfitting, which improves the quality of training decision trees. A comparative analysis of the quality of models before and after the application of this method of combating overfitting is carried out. Experiments on real data confirm effectiveness of this method. Conclusion. The results of the study can be useful in developing new machine learning algorithms and improving the efficiency of existing ones. The results of the study can be useful for developers of machine learning algorithms and specialists in the field of artificial intelligence.

Abstract 1003: Blood Pressure Metrics In Predicting Successful Thoracic Endovascular Aortic Repair

Blood pressure (BP) is a well-known risk factor for propagation of type B aortic dissections; however, there is limited data on its impact following surgical repair. While metrics such as BP variability have been associated with higher risk of cardiovascular events, such metrics has not been explored post TEVAR where management has largely focused on reducing systolic BP. By pairing unique BP metrics with preoperative aortic geometry, which has been shown to predict post-TEVAR outcomes, we can better understand what BP metrics are most useful. BP data collected in the first 24 hours after TEVAR for a cohort of 31 patients was used to calculate 24 unique BP metrics, including measures of centrality, variability, and stationarity. Preoperative CTA imaging data were used to create surface models for each aorta, which were then used to define aortic geometry as a unique point in the validated shape-size feature space. Logistic regression models with the BP and aortic geometry metrics were created to predict successful TEVAR outcomes using standard feature selection procedures and were validated using cross-validation (CV). Training and cross-validation accuracy were then compared between the models to determine the most generalizable model. A simple model using only preoperative aortic geometry in the two-dimensional shape-size feature space yielded a training accuracy of 77.4% and CV accuracy of 74.8%. An overfit model with all 24 BP metrics calculated from all available BP data (18.1 ± 6.0 BPs, range 5 - 29) had a 100% training accuracy but CV accuracy of 77.6%. Using the coefficients from this overfit model, we selected the most important features to train the next model. This resulted in a model using aortic geometry in addition to two systolic BP stationarity metrics and diastolic BP standard deviation. This model yielded a 96.8% training accuracy and 93.8% CV accuracy. Adding BP metrics creates a novel multi-dimensional aortic geometric-physiologic feature space and improves our ability to predict post-TEVAR outcomes. The BP variables that best contributed to this model were all measures of variability and stationarity, indicating that trends in BP must be evaluated to better identify patients at risk of poor outcomes.

Multidimensional Alternating Kernel Method for cortical layer segmentation in 3D reconstructed histology

The neocortex of the brain can be divided into six layers each with a distinct cell composition and connectivity pattern. Recently, sensory deprivation, including congenital deafness, has been shown to alter cortical structure (e.g. the cortical thickness) of the feline auditory cortex with variable and inconsistent results. Thus, understanding these complex changes will require further study of the constituent cortical layers in three-dimensional space. Further progress crucially depends on the use of objective computational techniques that can reliably characterize spatial properties of the complex cortical structure. Here a method for cortical laminar segmentation is derived and applied to the three-dimensional cortical areas reconstructed from a series of histological sections from four feline brains. In this approach, the Alternating Kernel Method was extended to fit a multi-variate Gaussian mixture model to a feature space consisting of both staining intensity and a biologically plausible equivolumetric depth map.This research method•Extends the Alternating Kernel Method to multi-dimensional feature spaces.•Uses it to segment the cortical layers in reconstructed histology volume. Segmentation features include staining intensity and a biologically plausible equivolumetric depth map.•Validates results in auditory cortical areas of feline brains, two with normal hearing and two with congenital deafness.

Исследование и разработка методов автоматического поиска признаков болезни Паркинсона и эссенциального тремора на базе AUC-диаграмм

Methods and optimization algorithms for automatic search for AUC-diagram-based features of Parkinson's disease and essential tremor were studied and developed. AUC diagrams are a new method for statistical analysis of biomedical signals, based on visualizing the parameters of wave train electrical activity in the brain and muscles. The effectiveness of this method has been demonstrated in solving problems of early and differential diagnosis of Parkinson's disease and essential tremor. The disadvantage of this method is the need to construct and analyze a large number of graphic diagrams. In this regard, automation of the analysis of AUC diagrams is an urgent task. The mathematical problem of finding features based on the analysis of AUC diagrams is reduced to an optimization problem in a multidimensional feature space. A distinctive feature of the space constructed using AUC diagrams is the presence of relatively large compact areas containing local maxima and minima. This property of the feature space facilitates the search for solutions to the optimization problem, but at the same time requires the selection of optimization algorithms and fitness functions that increase the likelihood of detecting global extrema. In this work, methods for automatically searching for global extrema in the multidimensional space of features of wave train electrical activity are investigated and developed.

Many morphs: Parsing gesture signals from the noise

Parsing signals from noise is a general problem for signallers and recipients, and for researchers studying communicative systems. Substantial efforts have been invested in comparing how other species encode information and meaning, and how signalling is structured. However, research depends on identifying and discriminating signals that represent meaningful units of analysis. Early approaches to defining signal repertoires applied top-down approaches, classifying cases into predefined signal types. Recently, more labour-intensive methods have taken a bottom-up approach describing detailed features of each signal and clustering cases based on patterns of similarity in multi-dimensional feature-space that were previously undetectable. Nevertheless, it remains essential to assess whether the resulting repertoires are composed of relevant units from the perspective of the species using them, and redefining repertoires when additional data become available. In this paper we provide a framework that takes data from the largest set of wild chimpanzee (Pan troglodytes) gestures currently available, splitting gesture types at a fine scale based on modifying features of gesture expression using latent class analysis (a model-based cluster detection algorithm for categorical variables), and then determining whether this splitting process reduces uncertainty about the goal or community of the gesture. Our method allows different features of interest to be incorporated into the splitting process, providing substantial future flexibility across, for example, species, populations, and levels of signal granularity. Doing so, we provide a powerful tool allowing researchers interested in gestural communication to establish repertoires of relevant units for subsequent analyses within and between systems of communication.

A 10-m national-scale map of ground-mounted photovoltaic power stations in China of 2020

We provide a remote sensing derived dataset for large-scale ground-mounted photovoltaic (PV) power stations in China of 2020, which has high spatial resolution of 10 meters. The dataset is based on the Google Earth Engine (GEE) cloud computing platform via random forest classifier and active learning strategy. Specifically, ground samples are carefully collected across China via both field survey and visual interpretation. Afterwards, spectral and texture features are calculated from publicly available Sentinel-2 imagery. Meanwhile, topographic features consisting of slope and aspect that are sensitive to PV locations are also included, aiming to construct a multi-dimensional and discriminative feature space. Finally, the trained random forest model is adopted to predict PV power stations of China parallelly on GEE. Technical validation has been carefully performed across China which achieved a satisfactory accuracy over 89%. Above all, as the first publicly released 10-m national-scale distribution dataset of China’s ground-mounted PV power stations, it can provide data references for relevant researchers in fields such as energy, land, remote sensing and environmental sciences.

ПРОГНОЗНЫЕ ПАРАМЕТРЫ РЕЗУЛЬТАТИВНОСТИ УПРАВЛЕНИЯ ЦИФРОВОЙ ТРАНСФОРМАЦИЕЙ ЭКОНОМИЧЕСКИХ БИЗНЕС-СИСТЕМ

The article presents a mathematical apparatus for predicting the effectiveness of managing the digital transformation of economic business systems. The methodological basis of the study is an adapted version of the KNN (k-Nearest Neighbors) method or the random forest method when working with a large amount of data. The proposed approach allows us to determine the type of digitalization of the business ecosystem (strong, medium-strong, etc.). Particular attention is paid to the applicability of the KNN method for predicting digital transformation management parameters. The stages of implementation of the method are presented, starting from collecting initial data and ending with assessing the quality of the model using the RMSE (Root Mean Square Error) or R-square metrics. The possibilities of using the algorithm are described using specific examples, including determining the optimal number of nearest neighbors (k) and choosing a distance metric. The analysis is performed on the basis of a multidimensional feature space in which the distance between objects is measured using the Euclidean metric. The algorithm for dividing data into training and test samples, as well as the computational aspects of applying the method, including program code in Python using the scikit-learn, NumPy and SciPy libraries, are discussed in detail. The study has practical significance for specialists in the field of digital transformation management, providing tools for qualitative and quantitative analysis of management parameters and making informed decisions.

Small groups in multidimensional feature space: Two examples of supervised two-group classification from biomedicine.

Some biomedical datasets contain a small number of samples which have large numbers of features. This can make analysis challenging and prone to errors such as overfitting and misinterpretation. To improve the accuracy and reliability of analysis in such cases, we present a tutorial that demonstrates a mathematical approach for a supervised two-group classification problem using two medical datasets. A tutorial provides insights on effectively addressing uncertainties and handling missing values without the need for removing or inputting additional data. We describe a method that considers the size and shape of feature distributions, as well as the pairwise relations between measured features as separate derived features and prognostic factors. Additionally, we explain how to perform similarity calculations that account for the variation in feature values within groups and inaccuracies in individual value measurements. By following these steps, a more accurate and reliable analysis can be achieved when working with biomedical datasets that have a small sample size and multiple features.

Stimulus intensity modulates perceived tactile distance.

Several features of tactile stimuli modulate the perceived distance between touches. In particular, distances are perceived as farther apart when the time interval between them is longer, than when it is shorter. Such effects have been interpreted as a form of 'psychological relativity', analogous to Einstein's conception of a four-dimensional space-time. We investigated whether similar effects occur for stimulus features other than time, specifically stimulus intensity. We hypothesised that perceived distance would be increased when the two stimuli differed in intensity, since they would then be farther apart in a multi-dimensional feature space. Participants made verbal estimates of the perceived distance between two touches on their left hand. Intensity was manipulated such that both stimuli could be intense, both could be light, or one could be intense and the other light. We found no evidence for change in perceived tactile distance when stimuli intensity mis-matched. In contrast, there were clear effects of average stimulus intensity on perceived distance. Intense stimuli were judged as farther apart than light stimuli, and mixed stimuli were intermediate. These results are consistent with theories of general magnitude representation, which argue that multiple dimensions of magnitude are dependent on a shared underlying representation of domain-general magnitude.

Monitoring of deforestation events in the tropics using multidimensional features of Sentinel 1 radar data

Many countries and regions are currently developing new forest strategies to better address the challenges facing forest ecosystems. Timely and accurate monitoring of deforestation events is necessary to guide tropical forest management activities. Synthetic aperture radar (SAR) is less susceptible to weather conditions and plays an important role in high-frequency monitoring in cloudy regions. Currently, most SAR image-based deforestation identification uses manually supervised methods, which rely on high quality and sufficient samples. In this study, we aim to explore radar features that are sensitive to deforestation, focusing on developing a method (named 3DC) to automatically extract deforestation events using radar multidimensional features. First, we analyzed the effectiveness of radar backscatter intensity (BI), vegetation index (VI), and polarization feature (PF) in distinguishing deforestation areas from the background environment. Second, we selected the best-performing radar features to construct a multidimensional feature space model and used an unsupervised K-mean clustering method to identify deforestation areas. Finally, qualitative and quantitative methods were used to validate the performance of the proposed method. The results in Paraguay, Brazil, and Mexico showed that (1) the overall accuracy (OA) and F1 score (F1) of 3DC were 88.1–98.3% and 90.2–98.5%, respectively. (2) 3DC achieved similar accuracy to supervised methods without the need for samples. (3) 3DC matched well with Global Forest Change (GFC) maps and provided more detailed spatial information. Furthermore, we applied the 3DC to deforestation mapping in Paraguay and found that deforestation events occurred mainly in the second half of the year. To conclude, 3DC is a simple and efficient method for monitoring tropical deforestation events, which is expected to serve the restoration of forests after deforestation. This study is also valuable for the development and implementation of forest management policies in the tropics.

A Multi-Layer Feature Fusion Model Based on Convolution and Attention Mechanisms for Text Classification

Text classification is one of the fundamental tasks in natural language processing and is widely applied in various domains. CNN effectively utilizes local features, while the Attention mechanism performs well in capturing content-based global interactions. In this paper, we propose a multi-layer feature fusion text classification model called CAC, based on the Combination of CNN and Attention. The model adopts the idea of first extracting local features and then calculating global attention, while drawing inspiration from the interaction process between membranes in membrane computing to improve the performance of text classification. Specifically, the CAC model utilizes the local feature extraction capability of CNN to transform the original semantics into a multi-dimensional feature space. Then, global attention is computed in each respective feature space to capture global contextual information within the text. Finally, the locally extracted features and globally extracted features are fused for classification. Experimental results on various public datasets demonstrate that the CAC model, which combines CNN and Attention, outperforms models that solely rely on the Attention mechanism. In terms of accuracy and performance, the CAC model also exhibits significant improvements over other models based on CNN, RNN, and Attention.

Ecodatacube.eu: analysis-ready open environmental data cube for Europe.

The article describes the production steps and accuracy assessment of an analysis-ready, open-access European data cube consisting of 2000-2020+ Landsat data, 2017-2021+ Sentinel-2 data and a 30 m resolution digital terrain model (DTM). The main purpose of the data cube is to make annual continental-scale spatiotemporal machine learning tasks accessible to a wider user base by providing a spatially and temporally consistent multidimensional feature space. This has required systematic spatiotemporal harmonization, efficient compression, and imputation of missing values. Sentinel-2 and Landsat reflectance values were aggregated into four quarterly averages approximating the four seasons common in Europe (winter, spring, summer and autumn), as well as the 25th and 75th percentile, in order to retain intra-seasonal variance. Remaining missing data in the Landsat time-series was imputed with a temporal moving window median (TMWM) approach. An accuracy assessment shows TMWM performs relatively better in Southern Europe and lower in mountainous regions such as the Scandinavian Mountains, the Alps, and the Pyrenees. We quantify the usability of the different component data sets for spatiotemporal machine learning tasks with a series of land cover classification experiments, which show that models utilizing the full feature space (30 m DTM, 30 m Landsat, 30 m and 10 m Sentinel-2) yield the highest land cover classification accuracy, with different data sets improving the results for different land cover classes. The data sets presented in the article are part of the EcoDataCube platform, which also hosts open vegetation, soil, and land use/land cover (LULC) maps created. All data sets are available under CC-BY license as Cloud-Optimized GeoTIFFs (ca. 12 TB in size) through SpatioTemporal Asset Catalog (STAC) and the EcoDataCube data portal.

Toward interpretability of machine learning methods for the classification of patients with major depressive disorder based on functional network measures

We address the interpretability of the machine learning algorithm in the context of the relevant problem of discriminating between patients with major depressive disorder (MDD) and healthy controls using functional networks derived from resting-state functional magnetic resonance imaging data. We applied linear discriminant analysis (LDA) to the data from 35 MDD patients and 50 healthy controls to discriminate between the two groups utilizing functional networks’ global measures as the features. We proposed the combined approach for feature selection based on statistical methods and the wrapper-type algorithm. This approach revealed that the groups are indistinguishable in the univariate feature space but become distinguishable in a three-dimensional feature space formed by the identified most important features: mean node strength, clustering coefficient, and the number of edges. LDA achieves the highest accuracy when considering the network with all connections or only the strongest ones. Our approach allowed us to analyze the separability of classes in the multidimensional feature space, which is critical for interpreting the results of machine learning models. We demonstrated that the parametric planes of the control and MDD groups rotate in the feature space with increasing the thresholding parameter and that their intersection increases with approaching the threshold of 0.45, for which classification accuracy is minimal. Overall, the combined approach for feature selection provides an effective and interpretable scenario for discriminating between MDD patients and healthy controls using measures of functional connectivity networks. This approach can be applied to other machine learning tasks to achieve high accuracy while ensuring the interpretability of the results.

Segmentation of optic disc in retinal images for glaucoma diagnosis by saliency level set with enhanced active contour model

<p>Glaucoma is an ophthalmic disease which is among the chief causes of visual impairment across the globe. The clarity of the optic disc (OD) is crucial for recognizing glaucoma. Since existing methods are unable to successfully integrate multi-view information derived from shape and appearance to precisely explain OD for segmentation, this paper proposes a saliency-based level set with an enhanced active contour method (SL-EACM), a modified locally statistical active contour model, and entropy-based optical disc localization. The significant contributions are that i) the SL-EACM is introduced to address the often noticed problem of intensity inhomogeneity brought on by defects in imaging equipment or fluctuations in lighting; ii) to prevent the integrity of the OD structures from being compromised by pathological alterations and artery blockage, local image probability data is included from a multi-dimensional feature space around the region of interest in the model; and iii) the model incorporates prior shape information into the technique, for enhancing the accuracy in identifying the OD structures from surrounding regions. Public databases such as CHASE_DB, DRIONS-DB, and Drishti-GS are used to evaluate the proposed model. The findings from numerous trials demonstrate that the proposed model outperforms state-of-the-art approaches in terms of qualitative and quantitative outcomes.</p>