Classification Approach Research Articles

The development of RBL-STEM learning tools to improve students' computational thinking skills in solving plant disease classification problems using convolutional neural network segmentation

This research aims to develop Research Based Learning (RBL) and Science, Technology, Engineering and Mathematics (STEM) based learning tools to improve students' computational thinking skills. The tools include a student task design (RTM), a student worksheet (LKM) and a learning outcome test (THB). Using Thiagarajan's 4D development model (define, design, develop, disseminate), the device was tested on students using a Convolutional Neural Network (CNN) approach for citrus plant disease classification using data from quadcopter drones. The validation results showed that the device was valid with an average score of 3.85 (96.42%). The practicality of the device is very high with an implementation score of 3.85 (96.36%) and a positive student response of 90.08%. The effectiveness of the device was demonstrated by 90% of students achieving classical completeness on the post-test and an increase in computational thinking skills from 0% (high) on the pre-test to 92% on the post-test. The paired sample t-test results also confirmed the statistically significant increase. This learning tool proved to be valid, practical and effective, contributing to technology-based learning innovation in modern agriculture.

Latent Variable Interactions with Categorical Indicators: Continuous and Categorical Latent Moderated Structural Equations Approaches

Social science phenomena are often predicted by interactions between variables. When these variables cannot be directly observed, one option is to model them as latent variables that are measured by multiple indicators. When indicators are continuous, latent interactions can be modeled and estimated using the latent moderated structural equations (LMS) approach. A categorical LMS (LMS-cat) approach with full information estimation was more recently developed. While previous research suggests that ordered categorical indicators can sometimes be treated as continuous, comparisons between LMS and LMS-cat are sparse. In this study, we evaluate continuous and categorical LMS for the estimation of latent interactions under ordinal indicators with 2, 3, 5, and 7 categories. Further, we compared the performance of frequentist and Bayesian estimation for both LMS models. Results suggest that categorical approaches are a safer choice, and that frequentist and non-informative Bayesian estimation approaches perform similarly.

Unique Molecular Alteration of Lobular Breast Cancer: Association with Pathological Classification, Tumor Biology and Behavior, and Clinical Management

Invasive lobular carcinoma (ILC), accounting for up to 15% of diagnosed breast cancers, has garnered significant attention due to the loss of the epithelial cell–cell adhesion molecule E-cadherin. This loss contributes to its distinct biological, morphological, and clinical characteristics compared to non-lobular breast cancers. The use of immunohistochemistry (IHC) for E-cadherin and/or the associated cadherin–catenin complex, such as p120-catenin and beta-catenin, in morphologically equivocal cases, has been increasingly adopted in pathology practice. This approach has substantially improved diagnostic accuracy, interobserver reproducibility, and the identification of new morphologic variants of ILC. ILCs exhibit unique tumor biology, which presents considerable challenges in clinical management, especially in preoperative imaging evaluation, surgical management, and neoadjuvant treatment. Recent advances in translational and clinical research have enhanced our understanding of ILC and have spurred the development of new clinical trials specifically targeting these cancers. This review highlights recent progress in various aspects of ILC, including its unique molecular alteration, pathological classification and diagnostic approach, tumor biology and behavior, key clinical management challenges, and ongoing clinical trials, as well as the role of artificial intelligence in diagnosing ILC radiologically and pathologically. The goal of this review is to provide an updated understanding of the tumor biology, clinical manifestations, and molecular landscape of ILC and to help refine current tumor classification and diagnosis, subsequently improving management strategies and overall outcomes for lobular carcinoma patients.

Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning.

Machine learning (ML) has shown great potential in the adaptive immune receptor repertoire (AIRR) field. However, there is a lack of large-scale ground-truth experimental AIRR data suitable for AIRR-ML-based disease diagnostics and therapeutics discovery. Simulated ground-truth AIRR data are required to complement the development and benchmarking of robust and interpretable AIRR-ML methods where experimental data is currently inaccessible or insufficient. The challenge for simulated data to be useful is incorporating key features observed in experimental repertoires. These features, such as antigen or disease-associated immune information, cause AIRR-ML problems to be challenging. Here, we introduce LIgO, a software suite, which simulates AIRR data for the development and benchmarking of AIRR-ML methods. LIgO incorporates different types of immune information both on the receptor and the repertoire level and preserves native-like generation probability distribution. Additionally, LIgO assists users in determining the computational feasibility of their simulations. We show two examples where LIgO supports the development and validation of AIRR-ML methods: (i) how individuals carrying out-of-distribution immune information impacts receptor-level prediction performance and (ii) how immune information co-occurring in the same AIRs impacts the performance of conventional receptor-level encoding and repertoire-level classification approaches. LIgO guides the advancement and assessment of interpretable AIRR-ML methods.

Comparison of Agricultural Crop Type Classifications with Different Machine Learning Algorithms (RF-SVM-ANN-XGBoost) by Generating Ground Truth Data from Farmer Declaration Parcels

In large-scale agricultural crop classification studies (Turkey, Adana, Çukurova Plain, 2500 km²), collecting sufficient and accurate ground truth data is costly, time-consuming, and unsustainable. This study utilized parcels registered in the Farmer Registration System (FRS) as ground truth data. By analyzing time series EVI curves, discrepancies were identified between declared and actual crops. Erroneous parcels were eliminated, and the corrected data were used in the classification process.Using multi-temporal Sentinel-2 images from 2021, this study compared the performance of Random Forests (RF), Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGBoost) algorithms for classifying crops like citrus, cotton, maize, peanut, sunflower, watermelon, wheat, and double-crop combinations (e.g., wheat-cotton, wheat-maize). The classification utilized 121 features (11 images × 10 Sentinel-2 bands + EVI). XGBoost achieved the highest overall accuracy (92.14%), followed by RF (89.15%), SVM (86.14%), and ANN (85.48%).The EVI index proved critical, particularly in separating spectral curves of double crops. While single crops like cotton, maize, and wheat yielded high classification accuracy, double crops with overlapping phenological stages had lower accuracy. The study highlighted that crops at distinct phenological stages performed well across algorithms, whereas crops with similar stages struggled to achieve high accuracy.This method of using corrected farmer-declared parcels (FDP) as ground truth data demonstrated high classification performance across all algorithms, proving its reliability. The findings emphasize that FDP can effectively replace traditional field data collection, reducing costs and improving efficiency. This classification approach supports agricultural production monitoring, yield estimation, water resource analysis, and sustainable policy-making, serving as a robust tool for agricultural evaluation

A Deep Learning Approach for Air Pollution Classification Using InceptionV3 with Transfer Learning

A Deep Learning Approach for Air Pollution Classification Using InceptionV3 with Transfer Learning

Deep Learning-Based Approach for Microscopic Algae Classification with Grad-CAM Interpretability

The natural occurrence of harmful algal blooms (HABs) adversely affects the quality of clean and fresh water. They pose increased risks to human health, aquatic ecosystems, and water bodies. Continuous monitoring and appropriate measures must be taken to combat HABs. Deep learning models that utilize computer vision play a vital role in identifying and classifying harmful algal blooms in aquatic environments and water storage facilities. Inspecting algal blooms using conventional methods, such as algae detection under microscopes, is difficult, expensive, and time-consuming. Deep learning algorithms have shown a notable and remarkable performance in the image classification domain and its applications, including microscopic algae species classification and detection. In this study, we propose a deep learning-based approach for classifying microscopic images of algae using computer vision. This approach employs a convolutional neural network (CNN) model integrated with two additional blocks—squeeze and dense blocks—to determine the presence of algae, followed by adding Grad-CAM to the proposed model to ensure interpretability and transparency. We performed several experiments on our custom dataset of microscopic algae images. Data augmentation techniques were employed to increase the number of images in the dataset, whereas pre-processing techniques were implemented to elevate the overall data quality. Our proposed model was trained on 3200 images consisting of four classes. We also compared our proposed model with the other transfer learning models, i.e., ResNet50 and Vgg16. Our proposed model outperformed the other two deep learning models. The proposed model demonstrated 96.7% accuracy, while Resnet50, EfficientNetB0, and VGG16 showed accuracy of 85.0%, 92.96%, and 93.5%, respectively. The results of this research demonstrate the potential of deep learning-based approaches for algae classification. This deep learning-based algorithm can be deployed in real-time applications to classify and identify algae to ensure the quality of water reservoirs. Computer-assisted solutions are advantageous for tracking freshwater algal blooms. Using deep learning-based models to identify and classify algae species from microscopic images is a novel application in the AI community.

A non-parameter oversampling approach for imbalanced data classification based on hybrid natural neighbors

A non-parameter oversampling approach for imbalanced data classification based on hybrid natural neighbors

Coati optimization algorithm for brain tumor identification based on MRI with utilizing phase-aware composite deep neural network

ABSTRACT Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. Malignant tumours can develop quickly, pass through neighboring tissues, and extend to further brain regions or the central nervous system. In contrast, healthy tumors typically develop slowly and do not invade surrounding tissues. Individuals frequently struggle with sensory abnormalities, motor deficiencies affecting coordination, and cognitive impairments affecting memory and focus. In this research, Utilizing Phase-aware Composite Deep Neural Network Optimized with Coati Optimized Algorithm for Brain Tumor Identification Based on Magnetic resonance imaging (PACDNN-COA-BTI-MRI) is proposed. First, input images are taken from the brain tumour Dataset. To execute this, the input image is pre-processed using Multivariate Fast Iterative Filtering (MFIF) and it reduces the occurrence of over-fitting from the collected dataset; then feature extraction using Self-Supervised Nonlinear Transform (SSNT) to extract essential features like model, shape, and intensity. Then, the proposed PACDNN-COA-BTI-MRI is implemented in Matlab and the performance metrics Recall, Accuracy, F1-Score, Precision Specificity and ROC are analysed. Performance of the PACDNN-COA-BTI-MRI approach attains 16.7%, 20.6% and 30.5% higher accuracy; 19.9%, 22.2% and 30.1% higher recall and 16.7%, 21.9% and 30.8% higher precision when analysed through existing techniques brain tumor identification using MRI-Based Deep Learning Approach for Efficient Classification of Brain Tumor (MRI-DLA-ECBT), MRI-Based Brain Tumor Detection using Convolutional Deep Learning Methods and Chosen Machine Learning Techniques (MRI-BTD-CDMLT) and MRI-Based Brain Tumor Image Detection using CNN-Based Deep Learning Method (MRI-BTID-CNN) methods, respectively.

Pyramid Fine and Coarse Attentions for Land Cover Classification from Compact Polarimetric SAR Imagery

Land cover classification from compact polarimetry (CP) imagery captured by the launched RADARSAT Constellation Mission (RCM) is important but challenging due to class signature ambiguity issues and speckle noise. This paper presents a new land cover classification method to improve the learning of discriminative features based on a novel pyramid fine- and coarse-grained self-attention transformer (PFC transformer). The fine-grained dependency inside a non-overlapping window and coarse-grained dependencies between non-overlapping windows are explicitly modeled and concatenated using a learnable linear function. This process is repeated in a hierarchical manner. Finally, the output of each stage of the proposed method is spatially reduced and concatenated to take advantage of both low- and high-level features. Two high-resolution (3 m) RCM CP SAR scenes are used to evaluate the performance of the proposed method and compare it to other state-of-the-art deep learning methods. The results show that the proposed approach achieves an overall accuracy of 93.63%, which was 4.83% higher than the best comparable method, demonstrating the effectiveness of the proposed approach for land cover classification from RCM CP SAR images.

Rapid Textile Fibers Classification Technology Based on Near-Infrared Spectroscopy and Machine Learning

The accurate identification of textile materials plays a crucial role in production control, quality inspection, and market regulation. In recent years, near-infrared (NIR) spectroscopy has been widely applied in the identification and classification of textile fibers due to its rapid, non-destructive, and efficient characteristics. Therefore, this study utilizes a handheld near-infrared spectrometer and chemometrics to provide a convenient, efficient, non-destructive, and green analytical method for the qualitative analysis of textiles. The article collects spectral data of fiber samples using a handheld NIR spectrometer and analyzes them with chemometric methods. Data preprocessing techniques, feature extraction algorithms, comprehensive sampling methods, and classification algorithms are employed to establish classification models. After ten-fold cross-validation, the optimal SG_RF model is obtained, with a classification accuracy of 93.7% on the test set and an AUC_macro of 0.928, demonstrating excellent performance. The article validates the feasibility of using near-infrared spectroscopy combined with chemometric methods for textile fiber classification, offering a rapid, non-destructive, and efficient approach for textile fiber classification.

Hybrid deep learning approach for brain tumor classification using EfficientNetB0 and novel quantum genetic algorithm

One of the most complex and life-threatening pathologies of the central nervous system is brain tumors. Correct diagnosis of these tumors plays an important role in determining the treatment plans of patients. Traditional classification methods often rely on manual assessments, which can be prone to error. Therefore, multiple classification of brain tumors has gained significant interest in recent years in both the medical and computer science fields. The use of artificial intelligence and machine learning, especially in the automatic classification of brain tumors, is increasing significantly. Deep learning models can achieve high accuracy when trained on datasets in diagnosis and classification. This study examined deep learning-based approaches for automatic multi-class classification of brain tumors, and a new approach combining deep learning and quantum genetic algorithms (QGA) was proposed. The powerful feature extraction ability of the pre-trained EfficientNetB0 was utilized and combined with this quantum genetic algorithms, a new approach was proposed. It is aimed to develop the feature selection method. With this hybrid method, high reliability and accuracy in brain tumor classification was achieved. The proposed model achieved high accuracy of 98.36% and 98.25%, respectively, with different data sets and significantly outperformed traditional methods. As a result, the proposed method offers a robust and scalable solution that will help classify brain tumors in early and accurate diagnosis and contribute to the field of medical imaging with patient outcomes.

Robust fault detection and classification in power transmission lines via ensemble machine learning models

Transmission lines are vital for delivering electricity over long distances, yet they face reliability challenges due to faults that can disrupt power supply and pose safety risks. This research introduces a novel approach for fault detection and classification by analyzing voltage and current patterns across transmission line phases. Leveraging a comprehensive dataset of diverse fault scenarios, various machine learning algorithms—including Random Forest (RF), K-Nearest Neighbors (KNN), and Long Short-Term Memory (LSTM) networks—are evaluated. An ensemble methodology, RF-LSTM Tuned KNN, is proposed to enhance detection accuracy and robustness. Results indicate that RF-LSTM Tuned KNN achieves a remarkable accuracy of 99.96% on a multi-label dataset, outperforming RF (97.50%) and KNN (96.55%). In binary classification, KNN attains the highest accuracy of 99.85%, closely followed by RF at 99.72%. This methodology provides significant advancements in fault detection capabilities, offering valuable insights for improving grid reliability and stability, and ensuring a more resilient power supply.

Enhancing cardiac diagnostics: a deep learning ensemble approach for precise ECG image classification

Cardiovascular diseases are a global health challenge that necessitates improvements in diagnostic accuracy and efficiency. This study examines the potential of deep learning (DL) models for the classification of electrocardiogram (ECG) images to assist in the identification of various cardiac conditions. We initiated a two-tiered experimental framework to investigate the effectiveness of several neural network architectures in this medical application. In the first experiment, eight distinct neural network models were selected based on their top-5 accuracy on the ImageNet validation dataset and were fine-tuned using transfer learning techniques. These models were assessed using a cross-validation scheme, focusing on balanced accuracy, precision, recall, and the F1-score to evaluate their classification capabilities across four cardiac conditions: Myocardial Infarction (MI), abnormal heartbeat, historical MI, and normal ECG patterns. The second experiment extended our inquiry into the power of ensemble learning. By testing all possible combinations of the chosen models, we explored 120 ensemble configurations. The resulting analysis identified the best-performing ensemble set, which did not include the least effective model based on F1 score rankings. The most effective ensemble, composed of Inception, MobileNet, and NASNetLarge, achieved an F1 score of 0.9651 and a balanced accuracy of 0.9640, indicating a superior predictive performance. The ROC curve analysis yielded near-perfect Area Under the Curve (AUC) values for all classes, underscoring the ensemble’s proficiency in distinguishing between the specified cardiac conditions. The outcomes of this research highlight the synergistic benefit of ensembles in DL applications for medical imaging and suggest a promising approach for the early detection and diagnosis of cardiac diseases, potentially improving clinical outcomes and patient care.

In-situ evaluation of hole quality and cutting tool condition in robotic drilling of composite materials using machine learning

Abstract The massive adoption of industrial robots in the manufacturing sector has significantly increased automation of installation and inspection procedures, particularly benefiting the aerospace industry, where large volumes of holes are drilled in each aircraft. However, mechanical drilling remains challenging when dealing with composite materials due to their inherently heterogeneous structure. This work presents a novel approach for in-situ hole quality inspection utilising integrated sensor data of an industrial robotic drill, combined with a machine learning model. Additionally, a novel classification approach for evaluating hole quality is proposed. This study employed a KUKA industrial robot, fitted with a multifunctional end-effector, to drill holes in a composite material used in aerospace applications. An ensemble neural network (ENN) model, which combines an artificial neural network with a genetic algorithm, was used to assess the quality of these drilled holes. The model was specifically developed and tested on the machined holes to relate process input parameters and drilling torques to hole quality. The model predictions were validated with six unseen datasets, of which five were predicted accurately. A full factorial study of the process parameters was conducted using analysis of variance (ANOVA) to investigate the relationship between tool condition and drilling torque. The results of the ANOVA show that tool condition is the largest contributor to drilling torque. The method proposed in this work, which allows real-time monitoring of hole quality, has the potential to improve manufacturing productivity of drilled components while ensuring high-quality end products.

An unsupervised automatic texture classification method for ultrasound images of thyroid nodules

Objective.Ultrasound is the predominant modality in medical practice for evaluating thyroid nodules. Currently, diagnosis is typically based on textural information. This study aims to develop an automated texture classification approach to aid physicians in interpreting ultrasound images of thyroid nodules. However, there is currently a scarcity of pixel-level labeled datasets for the texture classes of thyroid nodules. The labeling of such datasets relies on professional and experienced doctors, requiring a significant amount of manpower. Therefore, the objective of this study is to develop an unsupervised method for classifying nodule textures.Approach.Firstly, we develop a spatial mapping network to transform the one-dimensional pixel value space into a high-dimensional space to extract comprehensive feature information. Subsequently, we outline the principles of feature selection that are suitable for clustering. Then we propose a pixel-level clustering algorithm with a region growth pattern, and a distance evaluation method for texture sets among different nodules is established.Main results.Our algorithm achieves a pixel-level classification accuracy of 0.931 for the cystic and solid region, 0.870 for the hypoechoic region, 0.959 for the isoechoic region, and 0.961 for the hyperechoic region. The efficacy of our algorithm and its concordance with human observation have been demonstrated. Furthermore, we conduct calculations and visualize the distribution of different textures in benign and malignant nodules.Significance.This method can be used for the automatic generation of pixel-level labels of thyroid nodule texture, aiding in the construction of texture datasets, and offering image analysis information for medical professionals.

Multiclass Synthetic Accessibility Prediction.

Evaluating synthetic accessibility of in silico molecules is an integral component of the drug discovery process. While the application of machine learning models to predict whether small molecules are easy or hard to synthesize has gained attention recently, predetermined thresholds and data set imbalances present challenges for these binary classification approaches. In this study, we introduce a novel multiclass fold-ensembled classification approach to predict the minimum number of steps needed to synthesize a small molecule. By ensembling the base models trained on multiple stratified subsampled folds, this approach effectively mitigates the impact of class imbalance through probability aggregation or voting aggregation strategies. Additionally, we propose fuzzy evaluation metrics that account for practical tolerances in predictions, providing a more flexible and realistic assessment of model performance. Through experimentation on two reaction benchmark data sets, we demonstrate the effectiveness of our model in a multiclass synthetic accessibility prediction task and the superiority of our proposed method over six existing models in binary synthetic accessibility prediction tasks.

DGMSCL: A dynamic graph mixed supervised contrastive learning approach for class imbalanced multivariate time series classification.

In the Imbalanced Multivariate Time Series Classification (ImMTSC) task, minority-class instances typically correspond to critical events, such as system faults in power grids or abnormal health occurrences in medical monitoring. Despite being rare and random, these events are highly significant. The dynamic spatial-temporal relationships between minority-class instances and other instances make them more prone to interference from neighboring instances during classification. Increasing the number of minority-class samples during training often results in overfitting to a single pattern of the minority class. Contrastive learning ensures that majority-class instances learn similar features in the representation space. However, it does not effectively aggregate features from neighboring minority-class instances, hindering its ability to properly represent these instances in the ImMTS dataset. Therefor, we propose a dynamic graph-based mixed supervised contrastive learning method (DGMSCL) that effectively fits minority-class features without increasing their number, while also separating them from other instances in the representation space. First, it reconstructs the input sequence into dynamic graphs and employs a hierarchical attention graph neural network (HAGNN) to generate a discriminative embedding representation between instances. Based on this, we introduce a novel mixed contrast loss, which includes weight-augmented inter-graph supervised contrast (WAIGC) and context-based minority class-aware contrast (MCAC). It adjusts the sample weights based on their quantity and intrinsic characteristics, placing greater emphasis on minority-class loss to produce more effective gradient gains during training. Additionally, it separates minority-class instances from adjacent transitional instances in the representation space, enhancing their representational capacity. Extensive experiments across various scenarios and datasets with differing degrees of imbalance demonstrate that DGMSCL consistently outperforms existing baseline models. Specifically, DGMSCL achieves higher overall classification accuracy, as evidenced by significantly improved average F1-score, G-mean, and kappa coefficient across multiple datasets. Moreover, classification results on a real-world power data show that DGMSCL generalizes well to real-world application.

Resource availability for CNS tumor diagnostics in the Asian Oceanian region: A survey by the Asian Oceanian Society of Neuropathology committee for Adapting Diagnostic Approaches for Practical Taxonomy in Resource-Restrained Regions (AOSNP-ADAPTR).

The shift toward a histo-molecular approach in World Health Organization classification of central nervous system tumors (WHO CNS5) emphasizes the critical role of molecular testing, such as next-generation sequencing (NGS) and DNA methylation profiling, for accurate diagnosis. However, implementing these advanced techniques is particularly challenging in resource-constrained countries. To address this, the Asian Oceanian Society of Neuropathology committee for Adapting Diagnostic Approaches for Practical Taxonomy in Resource-Restrained Regions (AOSNP-ADAPTR) was initiated to help pathologists in resource-limited regions to implement WHO CNS5 diagnoses using simpler diagnostic tools, mainly immunohistochemistry. An online survey by this group assessed the in-house/local availability of diagnostic resources and outsourcing capabilities across the Asian Oceanian region, covering 19 countries. Of 318 responding centers, the availability of molecular techniques in-house/locally was limited in lower middle-income countries (LMICs), with 29% having fluorescence in situ hybridization, 10.7% Sanger sequencing, and only 9.4% NGS. DNA methylation was largely unavailable in-house/locally in of LMICs, while in the whole region, its availability stood at a meager 4.4%. Though outsourcing for all diagnostic tests was an easily accessible alternative, outsourcing for NGS and DNA methylation was uncommon because of the financial burden on patients being a significant obstacle. The survey categorized centers into five resource levels (RLs) based on the in-house/local access to diagnostic techniques, which highlighted the variability and disparity in diagnostic capabilities across the region. High-income countries had 80% of centers with advanced RL IV, V resources, in contrast to LMICs, where 70.5% of centers fell into RL I-III. This comprehensive evaluation emphasizes the need for tailored resource level guidelines, with the aim of improving diagnostic accuracy and treatment as well as advocating better healthcare infrastructure in resource-constrained areas. However, the survey's reliance on responses from better-resourced centers may underestimate challenges in lower-resource settings, stressing the need for broader outreach and support.

Multiclass hate speech detection with an aggregated dataset

Abstract Detecting and removing hate speech content in a timely manner remains a challenge for social media platforms. Automated techniques such as deep learning models offer solutions which can keep up with the volume and velocity of user content production. Research in this area has mainly focused on either binary classification or on classifying tweets into generalised categories such as hateful, offensive, or neither. Less attention has been given to multiclass classification of online hate speech into the type of hate or group at which it is directed. By aggregating and re-annotating several relevant hate speech datasets, this study presents a dataset and evaluates several models for classifying tweets into the categories ethnicity, gender, religion, sexuality, and non-hate. We evaluate the dataset by training several models: logistic regression, LSTM, BERT, and GPT-2. For the LSTM model, we assess a range of NLP features using a multi-classification LSTM model, and conclude that the highest performing feature combination consists of word $n$ -grams, character $n$ -grams, and dependency tuples. We show that while more recent larger models can achieve a slightly higher performance, increased model complexity alone is not sufficient to achieve significantly improved models. We also compare this approach with a binary classification approach and evaluate the effect of dataset size on model performance.