Classification Method Research Articles

Graph Classification Method Based on Fuzzy Entropy Causality in a Network Formal Context

Graph Classification Method Based on Fuzzy Entropy Causality in a Network Formal Context

Comparative analysis of vestibular endolymphatic hydrops grading methods and hearing loss in Ménière's disease: a retrospective MRI study using 3D-real inversion recovery sequence.

To compare the correlation between different grading methods of vestibular endolymphatic hydrops (EH) and the severity of hearing loss in Ménière's disease (MD), and evaluate the diagnostic value of these methods in diagnosing MD. This retrospective study included 30 patients diagnosed with MD from June 2021 to August 2023. All patients underwent inner ear MR gadolinium-enhanced imaging using three-dimensional (3D)-real inversion recovery sequences and pure-tone audiometry. The EH levels were independently evaluated according to the classification methods outlined by Nakashima et al. (Acta Otolaryngol Suppl 5-8, 2009. https://doi.org/10.1080/00016480902729827 ) (M1), Fang et al. (J Laryngol Otol 126:454-459, 2012. https://doi.org/10.1017/S0022215112000060 ) (M2), Barath et al. (Am J Neuroradiol 35:1387-1392, 2014. https://doi.org/10.3174/ajnr.A3856 ), (M3), Liu et al. (Front Surg 9:874971, 2022. https://doi.org/10.3389/fsurg.2022.874971 ), (M4), and Bernaerts et al. (Neuroradiology 61:421-429, 2019. https://doi.org/10.1007/s00234-019-02155-7 ) (M5), with a subsequent comparison of interobserver agreement. After achieving a consensus, an analysis was performed to explore the correlations between vestibular EH grading using different methods, the average hearing thresholds at low-mid, high-, and full frequencies and clinical stages. The diagnostic capabilities of these methods for MD were then compared. The interobserver consistency of M2-M5 was superior to that of M1. The EH grading based on M4 showed a significant correlation with the average hearing thresholds at low-mid, high-, and full frequencies and clinical stages. M1, M2, M3, and M5 correlated with some parameters. A receiver operating characteristic curve analysis indicated that M5 significantly outperformed M1, M2, M3, and M4 in terms of diagnostic efficiency for MD. M4 showed the strongest correlation with the degree of hearing loss in patients with MD, whereas M5 showed the highest diagnostic performance.

A Machine Learning-Driven Surface-Enhanced Raman Scattering Analysis Platform for the Label-Free Detection and Identification of Gastric Lesions.

Gastric lesions pose significant clinical challenges due to their varying degrees of malignancy and difficulty in early diagnosis. Early and accurate detection of these lesions is crucial for effective treatment and improved patient outcomes. This paper proposed a label-free and highly sensitive classification method for serum of patients with different degrees of gastric lesions by combining surface-enhanced Raman scattering (SERS) and machine learning analysis. Specifically, we prepared Au lotus-shaped (AuLS) nanoarrays substrates using seed-mediated and liquid-liquid interface self-assembly method for measuring SERS spectra of serum, and then the collected spectra were processed by principal component analysis (PCA) - multi-local means based nearest neighbor (MLMNN) model to achieve differentiation. By employing this pattern analysis, AuLS nanoarray substrates can achieve fast, sensitive, and label-free serum spectral detection. The classification accuracy can reach 97.5%, the sensitivity is higher than 96.7%, and the specificity is higher than 95.0%. Moreover, by analyzing the PCs loading plots, the most critical spectral features distinguishing different degrees of gastric lesions were successfully captured. This discovery lays the foundation for combining SERS with machine learning for real-time diagnosis and recognition of gastric lesions.

HybridCBAMNet: Enhancing time series binary classification with convolutional recurrent networks and attention mechanisms

The rapid advancement of Internet of Things technology and the increasing availability of big data have resulted in an exponential growth of time series data, highlighting a pressing need for effective classification methods. This study introduces HybridCBAMNet, a novel convolutional recurrent neural network model enhanced with recurrent networks and attention mechanisms for binary time series classification. The architecture integrates Conv1D-based feature extraction modules to extract relevant features, alongside attention enhancement modules and convolutional block attention modules. Additionally, bidirectional recurrent units are utilized to capture temporal dependencies and contextual information in both forward and backward directions. The model achieves top F1-scores in seven out of thirty-six binary classification tasks, significantly surpassing the performance of fourteen existing state-of-the-art models from the UCR archive. These results demonstrate that HybridCBAMNet not only enhances classification accuracy but also improves the model generalization capabilities, contributing valuable insights to the field of time series analysis.

Timely ICU Outcome Prediction Utilizing Stochastic Signal Analysis and Machine Learning Techniques with Readily Available Vital Sign Data.

The ICU is a specialized hospital department that offers critical care to patients at high risk. The massive burden of ICU-requiring care requires accurate and timely ICU outcome predictions for alleviating the economic and healthcare burdens imposed by critical care needs. Existing research faces challenges such as feature extraction difficulties, low accuracy, and resource-intensive features. Some studies have explored deep learning models that utilize raw clinical inputs. However, these models are considered non-interpretable black boxes, which prevents their wide application. The objective of the study is to develop a new method using stochastic signal analysis and machine learning techniques to effectively extract features with strong predictive power from ICU patients' real-time time series of vital signs for accurate and timely ICU outcome prediction. The results show the proposed method extracted meaningful features and outperforms baseline methods, including APACHE IV (AUC = 0.750), deep learning-based models (AUC = 0.732, 0.712, 0.698, 0.722), and statistical feature classification methods (AUC = 0.765) by a large margin (AUC = 0.869). The proposed method has clinical, management, and administrative implications since it enables healthcare professionals to identify deviations from prognostications timely and accurately and, therefore, to conduct proper interventions.

The color features and k-nearest neighbor algorithm for classifying betel leaf image

Piper betle L. (betel) is a species that belongs to the genus Piper and is a type of medicinal plant that is quite well known to the general population. The varieties of the leaf color may distinguish are red, green, and black betel. However, consumers still need assistance determining the differences between the many types of betel leaf. Therefore, using image processing techniques, this research contributes to building a classification method for distinguishing betel leaves based on color attributes. This approach anoints for the region of interest detection, feature extraction, and classification. In addition, three different classifiers, naïve Bayes, support vector machine, and k-nearest neighbors (k-NN), were used during the classification process. The evaluation for this study used a percentage split to divide a total of 180 images between the training and testing phases. The method’s performance provided the highest accuracy value possible, 100%, by utilizing the color characteristics with the k-NN classifier.

Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests

Efficient diagnosis of apple diseases and pests is crucial to the healthy development of the apple industry. However, the existing single-source image-based classification methods have limitations due to the constraints of single-source input image information, resulting in low classification accuracy and poor stability. Therefore, a classification method for apple disease and pest areas based on multi-source image fusion is proposed in this paper. Firstly, RGB images and multispectral images are obtained using drones to construct an apple diseases and pests canopy multi-source image dataset. Secondly, a vegetation index selection method based on saliency attention is proposed, which uses a multi-label ReliefF feature selection algorithm to obtain the importance scores of vegetation indices, enabling the automatic selection of vegetation indices. Finally, an apple disease and pest area multi-label classification model named AMMFNet is constructed, which effectively combines the advantages of RGB and multispectral multi-source images, performs data-level fusion of multi-source image data, and combines channel attention mechanisms to exploit the complementary aspects between multi-source data. The experimental results demonstrated that the proposed AMMFNet achieves a significant subset accuracy of 92.92%, a sample accuracy of 85.43%, and an F1 value of 86.21% on the apple disease and pest multi-source image dataset, representing improvements of 8.93% and 10.9% compared to prediction methods using only RGB or multispectral images. The experimental results also proved that the proposed method can provide technical support for the coarse-grained positioning of diseases and pests in apple orchards and has good application potential in the apple planting industry.

Strategic planning: attracting investments in agro-industrial enterprises

Since agriculture is an important sector of the Russian economy, the problem of lack of capital in this industry is still relevant. The article shows that the effective functioning of agricultural enterprises in modern conditions of a dynamic and rapidly changing business environment in the long term depends on the development of new and more advanced approaches to investment planning. The problem of the study is related to the absence or imperfection of strategic management of investment policy at many enterprises in the agricultural sector, which leads to their difficult financial situation, slows down their development and reduces competitiveness. The main purpose of the presented research is to form proposals for improving the strategic planning model by developing and implementing investment strategies of enterprises of the agro-industrial complex (hereinafter referred to as AIC). In the course of the study, a review of domestic and foreign literature has been conducted reflecting the issues of the investment planning. Within the framework of the modern approach to the problem, the following research methods are used in the article: literature analysis, classification, comparative analysis, description, observation, and statistical method. Special attention in the methodology is paid to the digital transformation of the investment activity of the AIC in Russia and the world. The author substantiates that the modern strategy of attracting investments in the AIC enterprises should radically differ from previous approaches and be implemented with consideration to the development of the information technology market. It is necessary that this approach facilitates the transition of the Russian AIC to the formats of “Agriculture 4.0” and “Agriculture 5.0”.

Enhanced Rubber Yield Prediction in High-Density Plantation Areas Using a GIS and Machine Learning-Based Forest Classification and Regression Model

Rubber is a perennial plant grown for natural rubber production, which is used in various global products. Ensuring the sustainability of rubber cultivation is crucial for smallholder farmers and economic development. Accurately predicting rubber yields is necessary to maintain price stability. Remote sensing technology is a valuable tool for collecting spatial data on a large scale. However, for smaller plots of land owned by smallholder farmers, it is necessary to process productivity estimates from high-resolution satellite data that are accurate and reliable. This study examines the impact of spatial factors on rubber yield and evaluates the technical suitability of using grouping analysis with the forest classification and regression (FCR) method. We developed a high-density variable using spatial data from rubber plots in close proximity to each other. Our approach incorporates eight environmental variables (proximity to streamlines, proximity to main river, soil drainage, slope, aspect, NDWI, NDVI, and precipitation) using an FCR model and GIS. We obtained a dataset of 1951 rubber yield locations, which we split into a training set (60%) for model development and a validation set (40%) for assessment using area under the curve (AUC) analysis. The results of the alternative FCR models indicate that Model 1 performs the best. It achieved the lowest root mean square error (RMSE) value of 19.15 kg/ha, the highest R-squared (R2) value (FCR) of 0.787, and also the highest R2 (OLS) value of 0.642. The AUC scores for Model 1, Model 2, and Model 3 were 0.792, 0.764, and 0.732, respectively. Overall, Model 4 exhibited the highest performance according to the AUC scores, while Model 3 performed the poorest with the lowest AUC score. Based on these findings, it can be concluded that Model 1 is the most effective in predicting FCR compared to the other alternative models.

Performance Analysis of NLP-Based Machine Learning Algorithms in Cyberbullying Detection

In today's pervasive online landscape, the escalating threat of cyberbullying demands advanced detection and mitigation tools. This study utilizes Natural Language Processing (NLP) techniques to confront this imperative challenge, particularly in the dynamic realm of social media, focusing on tweets. A comprehensive NLP-based classification methods is deployed to uncover instances of cyberbullying. Nine prominent machine learning algorithms are meticulously evaluated: Logistic Regression, Decision Tree, Random Forest, Naive Bayes, K-Nearest Neighbor, Support Vector Machine, XGBoost, AdaBoost, and Gradient Boosting. Through the analysis, encompassing accuracy, precision, recall, and F1 score metrics, the study offers insights into the strengths and limitations of each approach. The findings carry profound implications for online user safeguarding and cyberbullying prevalence reduction. Notably, Random Forest and XGBoost classifiers emerge as pioneers with accuracy rates of 93.34% and 93.32%, respectively. This comparative research underscores the pivotal role of expert algorithmic choices in addressing the urgency of cyberbullying and has the potential to be a valuable resource for academics and practitioners engaged in combatting this pressing societal issue.

Assessment of Landscape Change and its Relationships with Surface Temperature and Geo-spatial Indices during 1992 - 2022 at Haldia Industrial Region, West Bengal, India

Unplanned local development and alterations in Land Use and Land Cover (LULC) are an early alert for towns and cities around the world. The population pressure has resulted in the conversion of natural land into impermeable areas, which has altered the surface energy budget and created microclimatic differences locally. The main objective of the present study is to estimate LULC changes and its relation to increases Land Surface Temperature (LST) in the Haldia Industrial Region (HIR) of West Bengal using multi-temporal Landsat 5 Thematic Mapper (TM) of 1992 and Landsat 8 Operational Land Imager (OLI) of 2022 with 30-m spatial resolution images were obtained from the USGS website. The supervised classification method was applied by using Maximum Likelihood classification (MLC) to classify the satellite images into various LULC classes such as settlement, waterbody, vegetation, agriculture land, and fallow land, respectively. The result suggested an overall increase of settlement area from 13.49 km2 in 1992 to 44.54 km2 in 2022. On the other hand, the agriculture land decreased from 214.26 km2 to 152.34 km2 due to fast urbanization, decline green lands, and expansion of barren lands. The outcome result from LST was exhibited that the LST value has increased 3.77°C during 1992 to 2022 in this study area. The Normalized Difference Vegetation Index (NDVI) was showed a strong negative relationship with LST with a determination coefficient (R2) values found as 0.491 (1992) and 0.356 (2022). The positive determination coefficient between LST with Normalized Difference Water Index (NDWI) was found as 0.0002 (1992), and 0.0417 (2022). R2 values were estimated as 0.174 (1992), and 0.0347 (2022) between LST with Normalized Difference Built-up Index (NDBI) that indicated a positive determination coefficient. The findings enhanced understanding of the relationship between urban LST and LULC in developing an inclusive climate resilience policy and making the HIR more sustainable to the effects of climate change.

Comparative Study of Machine Learning Based Diabetes Predictive System

Diabetes is one of the most lethal diseases in the world. It is also a precursor to various other disorders such as coronary failure, blindness, and kidney diseases. Patients often need to visit diagnostic centers to get their reports after consultation, which requires a significant investment of time and money. However, with the growth of machine learning methods, we now have the ability to address this issue. Advanced systems utilizing information processing can forecast whether a patient has diabetes or not. Furthermore, early prediction of the disease can provide patients with critical interventions before it fully develops. Data mining techniques can extract hidden information from large datasets of diabetes-related information. The aim of this research is to develop a system that can predict the diabetic risk level of a patient with higher accuracy. The model development is based on classification methods such as K-Nearest Neighbors, Decision Tree, and Support Vector Machine (SVM) algorithms. For K-Nearest Neighbors, the models achieve an accuracy of 71%, 78% for SVM, and 70% for the Decision Tree algorithm. The outcomes demonstrate a significant accuracy of these methods.

Migrating birds optimization-based feature selection for text classification

Text classification tasks, particularly those involving a large number of features, pose significant challenges in effective feature selection. This research introduces a novel methodology, MBO-NB, which integrates Migrating Birds Optimization (MBO) approach with naïve Bayes as an internal classifier to address these challenges. The motivation behind this study stems from the recognized limitations of existing techniques in efficiently handling extensive feature sets. Traditional approaches often fail to adequately streamline the feature selection process, resulting in suboptimal classification accuracy and increased computational overhead. In response to this need, our primary objective is to propose a scalable and effective solution that enhances both computational efficiency and classification accuracy in text classification systems. To achieve this objective, we preprocess raw data using the Information Gain algorithm, strategically reducing the feature count from an average of 62,221 to 2,089. Through extensive experiments, we demonstrate the superior effectiveness of MBO-NB in feature reduction compared to other existing techniques, resulting in significantly improved classification accuracy. Furthermore, the successful integration of naïve Bayes within MBO offers a comprehensive and well-rounded solution to the feature selection problem. In individual comparisons with Particle Swarm Optimization (PSO), MBO-NB consistently outperforms by an average of 6.9% across four setups. This research provides valuable insights into enhancing feature selection methods, thereby contributing to the advancement of text classification techniques. By offering a scalable and effective solution, MBO-NB addresses the pressing need for improved feature selection methods in text classification, thereby facilitating the development of more robust and efficient classification systems.

Drone and ground-truth data collection, image annotation and machine learning: A protocol for coastal habitat mapping and classification

Aerial drone imaging is an efficient tool for mapping and monitoring of coastal habitats at high spatial and temporal resolution. Specifically, drone imaging allows for time- and cost-efficient mapping covering larger areas than traditional mapping and monitoring techniques, while also providing more detailed information than those from airplanes and satellites, enabling for example to differentiate various types of coastal vegetation. Here, we present a systematic method for shallow water habitat classification based on drone imagery. The method includes:•Collection of drone images and creation of orthomosaics.•Gathering ground-truth data in the field to guide the image annotation and to validate the final map product.•Annotation of drone images into – potentially hierarchical – habitat classes and training of machine learning algorithms for habitat classification.As a case study, we present a field campaign that employed these methods to map a coastal site dominated by seagrass, seaweed and kelp, in addition to sediments and rock. Such detailed but efficient mapping and classification can aid to understand and sustainably manage ecologically and valuable marine ecosystems.

Evaluating missing data handling methods for developing building energy benchmarking models

This study explored methods for handling missing data in the development of machine learning-based energy benchmarking models, assessing their training time, performance, and variance. Unlike the common assumption of missing completely at random, this study adopted a missing at random (MAR) perspective, which is more appropriate for building data. We compared the inherent missing data handling method of extreme gradient boosting (XGBoost) with the Median, k-nearest neighbors (KNN), and classification and regression trees (CART) methods, alongside Shapley additive explanation (SHAP) method for model interpretability. The findings indicate that, despite its computational demands, the CART method most accurately mirrors the original data distribution, thereby enhancing model performance and stability. The KNN method is effective, while the XGBoost method is viable under computational time constraints. This work highlights the importance of reliable test data for performing accurate evaluations of imputation methods. These results offer guidelines for the selection of imputation methods in model development, contributing to the improved accuracy of energy benchmarking models. The MAR-based approach for missing data analysis holds promise for future research on building energy data, providing crucial insights for accurate energy benchmark model performance assessments.

36: deep learning method for classification of congenital and secondary biliary dilatation

36: deep learning method for classification of congenital and secondary biliary dilatation

Detecting wheel slip from railway operational data through a combined wavelet, long short-term memory and neural network classification method

— Detecting wheel slip is important in railway operations, to prevent damage to wheels and tracks, reduce maintenance costs, improve safety and enhance passenger comfort. Slip activity is characterised by reduced adhesion between the wheel and the rail and limits effective braking or acceleration, causing also operational risks. It is influenced by environmental conditions, vehicle load, track and axle quality, contaminants, inclines, rail oxidation, and braking forces. This paper introduces an innovative method for wheel slip detection in operational trains, utilizing wavelet analysis combined with Long-Short Term Memory (LSTM) modelling. This method analyzes operational data to effectively identify wheel slip, showing promising results when compared to traditional classification-based machine learning methods such as decision trees, forests, logistic regression, naïve Bayes, and support vector machines. This novel approach addresses the complexities of wheel slip detection and is capable of identifying the conditions leading to slip several seconds prior to the commencement of the slip event, offering a practical solution for real-world railway systems.

Whole genome–based comparative analysis of the genus Streptomyces reveals many misclassifications

Streptomyces species are experts in the production of bioactive secondary metabolites; however, their taxonomy has fallen victim of the tremendous interest shown by the scientific community, evident in the discovery of numerous synonymous in public repositories. Based on genomic data from NCBI Datasets and nomenclature from the LPSN database, we compiled a dataset of 600 Streptomyces species along with their annotations and metadata. To pinpoint the most suitable taxonomic classification method, we conducted a comprehensive assessment comparing multiple methodologies, including analysis of 16S rRNA, individual housekeeping genes, multilocus sequence analysis (MLSA), and Fast Average Nucleotide Identity (FastANI) on a subset of 409 species with complete data. Due to insufficient resolution of 16S rRNA and inconsistency observed in individual housekeeping genes, we performed a more in-depth analysis, comparing only FastANI and MLSA, which expanded our dataset to include 502 species. With FastANI validated as the preferred method, we conducted pairwise analysis on the entire dataset identifying 59 non-unique species among the 600, and subsequently refined the dataset to 541 unique species. Additionally, we collected data on 724 uncharacterized Streptomyces strains to investigate the uniqueness potential of the unannotated fraction of the Streptomyces genus. Utilizing FastANI, 289 strains could be successfully classified into one of the 541 Streptomyces species.Key points• Evaluation of taxonomic classification methods for Streptomyces species.• Whole genome analysis, specifically FastANI, has been chosen as preferred method.• Various reclassifications are proposed within the Streptomyces genus.Graphical abstract

Machine vision-based detection of forbidden elements in the high-speed automatic scrap sorting line

Highly efficient industrial sorting lines require fast and reliable classification methods. Various types of sensors are used to measure the features of an object to determine which output class it belongs to. One technique involves the use of an RGB camera and a machine learning classifier. The paper is focused on protecting the sorting process against prohibited and dangerous items potentially present in the sorted material that pose a threat to the sorting process or the subsequent metallurgical process. To achieve this, a convolutional neural network classifier was applied under real-life conditions to detect forbidden elements in copper-based metal scrap. A laboratory stand simulating the working conditions in a high-speed scrap sorting line was prepared. Using this custom stand, training and test sets for machine learning were gathered and labeled. An image preprocessing algorithm was designed to increase the robustness of the resulting forbidden element detector system. The performance of multiple neural network architectures and data set augmentations was analyzed. The highest accuracy of 98.03% and F1-score of 97.16% were achieved with a DenseNet-based classifier. The results of this paper show the feasibility of using the presented solution on a high-speed industrial line.

Comparison of Performance of Some Classification Methods to Evaluate the Quality of Vegetables from its Morphology

One important aspect of Data Science is its ability to classify subjects into non-overlapping groups based on one or several input variables. Several methods and algorithms are available in the literature for classifying subjects based on the values of multiple observed variables. Such classification tools are Naive Bayesian Classifiers, Logistic Regression, Discriminant Analysis, k-nearest neighbourhood etc. This paper attempts to recognise if the morphological variables, identified either through literature review or from expert opinion, can be utilised to understand the quality of vegetables. Consequently, the current researchers obtained primary data about the morphology of the vegetables through experimentation. The outcome variable is the quality of the vegetables classified as eatable or not-eatable because of worm attack. Several classification methods are then compared for the classification exercise by building the model based on the training sample and testing the performance of the models in the holdout sample. Methods of classification performance statistics like sensitivity, specificity, precision etc. are used for their comparison. The study finds that Naive Bayes and Logistic Regression models perform better for this classification exercise. For example, only eggplant (brinjal) is considered for the study.