Machine Learning Classification Algorithms Research Articles

P-428 Identifying key factors for achieving biochemical pregnancy in first autologous IVF cycles: an artificial intelligence-assisted approach

Abstract Study question What key factors determine a positive biochemical pregnancy outcome in first autologous IVF cycles, as identified by machine learning (ML) algorithms? Summary answer Maternal age, AMH, BMI, number of day 3 embryos and total embryos transferred emerged as top predictors for biochemical pregnancy across several ML methods. What is known already ML methods are powerful tools that discern patterns from multiple features to predict outcomes. Beyond their clear predictive capabilities, ML algorithms also offer valuable clinical insights by uncovering underlying data trends. These insights come from understanding the algorithms’ decision-making processes, revealing the rationale behind their predictions. While maternal age is known to influence IVF success, other pre-treatment and in-cycle factors also play a significant role. Understanding these factors can enhance patient management and guide clinical advice and interventions. Study design, size, duration This multicenter retrospective study included data from 9,945 cycles performed between January 2010 and July 2021. Only first autologous cycles using fresh oocytes and donor or patient sperm (fresh or frozen) were included. Preimplantation genetic testing (PGT) cycles were excluded. Both pre-treatment and in-cycle variables were considered, with treatment outcome expressed as cumulative biochemical pregnancy. Cases and variables deemed to have poor data quality were excluded. The final dataset comprised 80 variables and 7350 cycles. Participants/materials, setting, methods Five ML classifier algorithms were applied to predict cumulative biochemical pregnancy using pre-treatment and in-cycle variables. The models tested included logistic regression, support vector machine, random forests, gradient boosting and multilayer perceptron. Shapley values analysis was performed to explain the decision-making structures of all the algorithms tested, quantifying the impact of each included feature on outcome prediction. Main results and the role of chance Within our cohort, mean maternal age was 37.3±4.7 years, mean serum AMH was 2.4±2.1 ng/ml, mean BMI was 23.7±4.0 and mean antral follicle count (AFC) was 11.6± 7.6. Notably, 40.1% of cycles used donor sperm, while 33.8% of male patients were normozoospermic. The majority of cycles (90.8%) used antagonist stimulation protocols, yielding an average of 7.8±5.5 oocytes per patient. Fertilization rate was 70.6%, with a mean of 2.7±1.8 usable embryos obtained per cycle. Fresh embryo transfers accounted for 73% of cases, with an average of 1.5±0.9 transfers performed per cycle. The embryo utilization rate was 64.6% and 48.1% of cycles resulted in a biochemical pregnancy. Among the evaluated algorithms, the gradient boosting model demonstrated superior performance, with an accuracy of 70.6±1.4% and AUC of 77.3±1.7%. Shapley values across all five algorithms consistently revealed the negative correlation between maternal age and biochemical pregnancy rates. Among pre-treatment variables, AMH levels had a positive influence on outcomes, followed by BMI, AFC, and parity status. Regarding in-cycle variables, the number of embryos available at day 3 and the total number of embryos transferred, positively affected the chances of pregnancy. The number of mature oocytes was also an important in-cycle predictive feature. Limitations, reasons for caution his retrospective study spans an extended timeframe, suggesting potential variations in clinical and laboratory practices over the years. Therefore, caution is advised in generalizing the results to other patient populations or to contemporary data gathered using newer and enhanced clinical protocols. Wider implications of the findings The consistent identification of maternal age, ovarian reserve markers, and embryo quantity as primary predictors of pregnancy by the five AI models corroborates existing clinical understanding. This consistency emphasizes the strength and dependability of AI to reinforce and complement clinical knowledge with data-driven evidence. Trial registration number not applicable

Explainable artificial intelligence and machine learning algorithms for classification of thyroid disease

A common endocrine issue affecting millions globally is thyroid illness. For this ailment to be effectively treated and managed, an early and accurate diagnosis is essential. Machine learning algorithms have attracted a lot of attention recently in the healthcare industry and have the potential to improve thyroid disease diagnosis and categorization. The implementation of machine learning methods for the classification of thyroid disease is presented in this study. To create predictive models, the study makes use of a dataset that includes a variety of thyroid-related factors, including age, gender, and hormone levels. To evaluate the effectiveness of several machine learning techniques in classifying thyroid diseases, including random forest, support vector machines, XG-Boost, and ensemble classifier, they are implemented and compared. To ensure robust model performance, the methodology includes data preparation, feature selection, and model training, as well as strategies for hyperparameter adjustment and cross-validation. To assess the algorithms’ efficiency in differentiating between several thyroid illness classifications, such as hyperthyroidism, hypothyroidism, and the study measures the algorithms’ accuracy, precision, recall, F1-score, voting, and area under the ROC curve.

Machine Learning-Based Feature Extraction and Classification of EMG Signals for Intuitive Prosthetic Control

Signals play a fundamental role in science, technology, and communication by conveying information through varying patterns, amplitudes, and frequencies. This paper introduces innovative methodologies for processing electromyographic (EMG) signals to develop artificial intelligence systems capable of decoding muscle activity for controlling arm movements. The study investigates advanced signal processing techniques and machine learning classification algorithms using the GRABMyo dataset, aiming to enhance prosthetic control systems and rehabilitation technologies. A comprehensive analysis was conducted on signal processing techniques, including signal filtering and discrete wavelet transform (DWT), alongside a composite feature set comprising Mean Absolute Value (MAV), Waveform Length (WL), Zero Crossing (ZC), Slope Sign Changes (SSC), Root Mean Square (RMS), Enhanced Waveform Length (EWL), and Enhanced Mean Absolute Value (EMAV). These features, refined through Linear Discriminant Analysis (LDA) for dimensionality reduction, were classified using Support Vector Machine (SVM) algorithms. Signal filtering and DWT improved signal quality, facilitating better feature extraction, while the diverse feature set enhanced classification accuracy. LDA further improved accuracy by isolating the most informative features, and the SVM achieved optimal performance in decoding complex EMG patterns. Machine learning models, including K-Nearest Neighbor (KNN), Naïve Bayes (NB), and the SVM, were evaluated, with the SVM outperforming the others. The significance of these results lies in their potential applications in prosthetic control systems and rehabilitation technologies. By accurately decoding muscle activity, the developed systems can facilitate more intuitive and responsive robotic arm movements, contributing to the advancement of innovative solutions for individuals requiring prosthetic devices or undergoing rehabilitation, hence improving the quality of life for users. This research marks a significant step forward in the integration of advanced signal processing and machine learning in the field of EMG analysis.

Comparison of deep learning architectures for predicting amyloid positivity in Alzheimer's disease, mild cognitive impairment, and healthy aging, from T1-weighted brain structural MRI.

Abnormal β-amyloid (Aβ) accumulation in the brain is an early indicator of Alzheimer's disease (AD) and is typically assessed through invasive procedures such as PET (positron emission tomography) or CSF (cerebrospinal fluid) assays. As new anti-Alzheimer's treatments can now successfully target amyloid pathology, there is a growing interest in predicting Aβ positivity (Aβ+) from less invasive, more widely available types of brain scans, such as T1-weighted (T1w) MRI. Here we compare multiple approaches to infer Aβ + from standard anatomical MRI: (1) classical machine learning algorithms, including logistic regression, XGBoost, and shallow artificial neural networks, (2) deep learning models based on 2D and 3D convolutional neural networks (CNNs), (3) a hybrid ANN-CNN, combining the strengths of shallow and deep neural networks, (4) transfer learning models based on CNNs, and (5) 3D Vision Transformers. All models were trained on paired MRI/PET data from 1,847 elderly participants (mean age: 75.1 yrs. ± 7.6SD; 863 females/984 males; 661 healthy controls, 889 with mild cognitive impairment (MCI), and 297 with Dementia), scanned as part of the Alzheimer's Disease Neuroimaging Initiative. We evaluated each model's balanced accuracy and F1 scores. While further tests on more diverse data are warranted, deep learning models trained on standard MRI showed promise for estimating Aβ + status, at least in people with MCI. This may offer a potential screening option before resorting to more invasive procedures.

A novel method for identifying rice seed purity using hybrid machine learning algorithms

In the grain industry, identifying seed purity is a crucial task because it is an important factor in evaluating seed quality. For rice seeds, this attribute enables the minimization of unexpected influences of other varieties on rice yield, nutrient composition, and price. However, in practice, they are often mixed with seeds from other varieties. This study proposes a novel method for automatically identifying the purity of a specific rice variety using hybrid machine learning algorithms. The core concept involves leveraging deep learning architectures to extract pertinent features from raw data, followed by the application of machine learning algorithms for classification. Several experiments are conducted to evaluate the performance of the proposed model through practical implementation. The results demonstrate that the novel method substantially outperformed the existing methods, demonstrating the potential for effective rice seed purity identification systems.

COVIDHealth: A novel labeled dataset and machine learning-based web application for classifying COVID-19 discourses on Twitter

The COVID-19 pandemic has sparked widespread health-related discussions on social media platforms like Twitter (now named ‘X’). However, the lack of labeled Twitter data poses significant challenges for theme-based classification and tweet aggregation. To address this gap, we developed a machine learning-based web application that automatically classifies COVID-19 discourses into five categories: health risks, prevention, symptoms, transmission, and treatment. We collected and labeled 6,667 COVID-19-related tweets using the Twitter API, and applied various feature extraction methods to extract relevant features. We then compared the performance of seven classical machine learning algorithms (Decision Tree, Random Forest, Stochastic Gradient Descent, Adaboost, K-Nearest Neighbor, Logistic Regression, and Linear SVC) and four deep learning techniques (LSTM, CNN, RNN, and BERT) for classification. Our results show that the CNN achieved the highest precision (90.41%), recall (90.4%), F1 score (90.4%), and accuracy (90.4%). The Linear SVC algorithm exhibited the highest precision (85.71%), recall (86.94%), and F1 score (86.13%) among classical machine learning approaches. Our study advances the field of health-related data analysis and classification, and offers a publicly accessible web-based tool for public health researchers and practitioners. This tool has the potential to support addressing public health challenges and enhancing awareness during pandemics. The dataset and application are accessible at https://github.com/Bishal16/COVID19-Health-Related-Data-Classification-Website.

A Machine Learning-Based Web Tool for the Severity Prediction of COVID-19.

Predictive tools provide a unique opportunity to explain the observed differences in outcome between patients of the COVID-19 pandemic. The aim of this study was to associate individual demographic and clinical characteristics with disease severity in COVID-19 patients and to highlight the importance of machine learning (ML) in disease prognosis. The study enrolled 344 unvaccinated patients with confirmed SARS-CoV-2 infection. Data collected by integrating questionnaires and medical records were imported into various classification machine learning algorithms, and the algorithm and the hyperparameters with the greatest predictive ability were selected for use in a disease outcome prediction web tool. Of 111 independent features, age, sex, hypertension, obesity, and cancer comorbidity were found to be associated with severe COVID-19. Our prognostic tool can contribute to a successful therapeutic approach via personalized treatment. Although at the present time vaccination is not considered mandatory, this algorithm could encourage vulnerable groups to be vaccinated.

Generalisation capabilities of machine-learning algorithms for the detection of the subthalamic nucleus in micro-electrode recordings.

Micro-electrode recordings (MERs) are a key intra-operative modality used during deep brain stimulation (DBS) electrode implantation, which allow for a trained neurophysiologist to infer the anatomy in which the electrode is placed. As DBS targets are small, such inference is necessary to confirm that the electrode is correctly positioned. Recently, machine learning techniques have been used to augment the neurophysiologist's capability. The goal of this paper is to investigate the generalisability of these methods with respect to different clinical centres and training paradigms. Five deep learning algorithms for binary classification of MER signals have been implemented. Three databases from two different clinical centres have also been collected with differing size, acquisition hardware, and annotation protocol. Each algorithm has initially been trained on the largest database, then either directly tested or fine-tuned on the smaller databases in order to estimate their generalisability. As a reference, they have also been trained from scratch on the smaller databases as well in order to estimate the effect of the differing database sizes and annotation systems. Each network shows significantly reduced performance (on the order of a 6.5% to 16.0% reduction in balanced accuracy) when applied out-of-distribution. This reduction can be ameliorated through fine-tuning the network on the new database through transfer learning. Although, even for these small databases, it appears that retraining from scratch may still offer equivalent performance as fine-tuning with transfer learning. However, this is at the expense of significantly longer training times. Generalisability is an important criterion for the success of machine learning algorithms in clinic. We have demonstrated that a variety of recent machine learning algorithms for MER classification are negatively affected by domain shift, but that this can be quickly ameliorated through simple transfer learning procedures that can be readily performed for new centres.

Optimasi Algoritma Machine Learning Menggunakan Teknik Bagging Pada Klasifikasi Diagnosis Kanker Payudara

Classification algorithms have a very important role in Machine Learning, but not all algorithms have the same performance in every case. Algorithm performance can be affected by the type of data used, differences in problem characteristics, and the parameters used. Additionally, ensemble learning techniques such as Bagging can affect algorithm performance. Therefore, the problem arises of how to choose the most suitable algorithm for a particular classification task and how to optimize the performance of the algorithm. This research aims to carry out a comparative analysis and optimization of classification algorithms in Machine Learning. Classification algorithms that will be evaluated include Support Vector Machine (SVM), Neural Network, Logistic Regression, Decision Tree, and K-Nearest Neighbors (K-NN). Evaluation of the performance of these algorithms will be carried out using the confusion matrix, Receiver Operating Characteristic (ROC) Curve, and Area Under Curva (AUC). The result of this research is a comparative analysis of the optimization of classification algorithms using the bagging technique. After carrying out the evaluation process using the confusion matrix and ROC curve, it was found that the algorithm optimization using the bagging technique only had an effect on the Decision Tree (DT) and K-Nearest Neighbors (KNN) algorithms. . The accuracy of the DT algorithm increased by 0.6% while the accuracy of KNN increased by 1.3%. The AUC value for the DT algorithm increased by 1.4% and the KNN algorithm increased by 0.3%.

Integrating PointNet-Based Model and Machine Learning Algorithms for Classification of Rupture Status of IAs.

The rupture of intracranial aneurysms (IAs) would result in subarachnoid hemorrhage with high mortality and disability. Predicting the risk of IAs rupture remains a challenge. This paper proposed an effective method for classifying IAs rupture status by integrating a PointNet-based model and machine learning algorithms. First, medical image segmentation and reconstruction algorithms were applied to 3D Digital Subtraction Angiography (DSA) imaging data to construct three-dimensional IAs geometric models. Geometrical parameters of IAs were then acquired using Geomagic, followed by the computation of hemodynamic clouds and hemodynamic parameters using Computational Fluid Dynamics (CFD). A PointNet-based model was developed to extract different dimensional hemodynamic cloud features. Finally, five types of machine learning algorithms were applied on geometrical parameters, hemodynamic parameters, and hemodynamic cloud features to classify and recognize IAs rupture status. The classification performance of different dimensional hemodynamic cloud features was also compared. The 16-, 32-, 64-, and 1024-dimensional hemodynamic cloud features were extracted with the PointNet-based model, respectively, and the four types of cloud features in combination with the geometrical parameters and hemodynamic parameters were respectively applied to classify the rupture status of IAs. The best classification outcomes were achieved in the case of 16-dimensional hemodynamic cloud features, the accuracy of XGBoost, CatBoost, SVM, LightGBM, and LR algorithms was 0.887, 0.857, 0.854, 0.857, and 0.908, respectively, and the AUCs were 0.917, 0.934, 0.946, 0.920, and 0.944. In contrast, when only utilizing geometrical parameters and hemodynamic parameters, the accuracies were 0.836, 0.816, 0.826, 0.832, and 0.885, respectively, with AUC values of 0.908, 0.922, 0.930, 0.884, and 0.921. In this paper, classification models for IAs rupture status were constructed by integrating a PointNet-based model and machine learning algorithms. Experiments demonstrated that hemodynamic cloud features had a certain contribution weight to the classification of IAs rupture status. When 16-dimensional hemodynamic cloud features were added to the morphological and hemodynamic features, the models achieved the highest classification accuracies and AUCs. Our models and algorithms would provide valuable insights for the clinical diagnosis and treatment of IAs.

Network Intrusion Detection Based on Machine Learning Classification Algorithms: A Review

The worldwide internet continues to spread, presenting numerous escalating hazards with significant potential. Existing static detection systems necessitate frequent updates to signature-based databases and solely detect known malicious threats. Efforts are currently being made to develop network intrusion detection systems that can utilize machine learning techniques to accurately detect and classify hazardous content. This would result in a decrease in the overall workload required. Network Intrusion Detection Systems are created with a diverse range of machine learning algorithms. The objective of the review is to provide a comprehensive overview of the existing machine learning-based intrusion detection systems, with the aim of assisting those involved in the development of network intrusion detection systems.

Multi-file dynamic compression method based on classification algorithm in DNA storage.

The exponential growth in data volume has necessitated the adoption of alternative storage solutions, and DNA storage stands out as the most promising solution. However, the exorbitant costs associated with synthesis and sequencing impeded its development. Pre-compressing the data is recognized as one of the most effective approaches for reducing storage costs. However, different compression methods yield varying compression ratios for the same file, and compressing a large number of files with a single method may not achieve the maximum compression ratio. This study proposes a multi-file dynamic compression method based on machine learning classification algorithms that selects the appropriate compression method for each file to minimize the amount of data stored into DNA as much as possible. Firstly, four different compression methods are applied to the collected files. Subsequently, the optimal compression method is selected as a label, as well as the file type and size are used as features, which are put into seven machine learning classification algorithms for training. The results demonstrate that k-nearest neighbor outperforms other machine learning algorithms on the validation set and test set most of the time, achieving an accuracy rate of over 85% and showing less volatility. Additionally, the compression rate of 30.85% can be achieved according to k-nearest neighbor model, more than 4.5% compared to the traditional single compression method, resulting in significant cost savings for DNA storage in the range of $0.48 to 3 billion/TB. In comparison to the traditional compression method, the multi-file dynamic compression method demonstrates a more significant compression effect when compressing multiple files. Therefore, it can considerably decrease the cost of DNA storage and facilitate the widespread implementation of DNA storage technology.

Machine learning model comparison for freezing of gait prediction in advanced Parkinson's disease.

Freezing of gait (FOG) is a paroxysmal motor phenomenon that increases in prevalence as Parkinson's disease (PD) progresses. It is associated with a reduced quality of life and an increased risk of falls in this population. Precision-based detection and classification of freezers are critical to developing tailored treatments rooted in kinematic assessments. This study analyzed instrumented stand-and-walk (SAW) trials from advanced PD patients with STN-DBS. Each patient performed two SAW trials in their OFF Medication-OFF DBS state. For each trial, gait summary statistics from wearable sensors were analyzed by machine learning classification algorithms. These algorithms include k-nearest neighbors, logistic regression, naïve Bayes, random forest, and support vector machines (SVM). Each of these models were selected for their high interpretability. Each algorithm was tasked with classifying patients whose SAW trials MDS-UPDRS FOG subscore was non-zero as assessed by a trained movement disorder specialist. These algorithms' performance was evaluated using stratified five-fold cross-validation. A total of 21 PD subjects were evaluated (average age 64.24 years, 16 males, mean disease duration of 14 years). Fourteen subjects had freezing of gait in the OFF MED/OFF DBS. All machine learning models achieved statistically similar predictive performance (p < 0.05) with high accuracy. Analysis of random forests' feature estimation revealed the top-ten spatiotemporal predictive features utilized in the model: foot strike angle, coronal range of motion [trunk and lumbar], stride length, gait speed, lateral step variability, and toe-off angle. These results indicate that machine learning effectively classifies advanced PD patients as freezers or nonfreezers based on SAW trials in their non-medicated/non-stimulated condition. The machine learning models, specifically random forests, not only rely on but utilize salient spatial and temporal gait features for FOG classification.

Performance Comparative Study of Machine Learning Classification Algorithms for Food Insecurity Experience by Households in West Java

Performance Comparative Study of Machine Learning Classification Algorithms for Food Insecurity Experience by Households in West Java

Second-order optimization strategies for neural network quantum states.

The Variational Monte Carlo (VMC) method has recently seen important advances through the use of neural network quantum states. While more and more sophisticated ansatze have been designed to tackle a wide variety of quantum many-body problems, modest progress has been made on the associated optimization algorithms. In this work, we revisit the Kronecker-Factored Approximate Curvature (KFAC), an optimizer that has been used extensively in a variety of simulations. We suggest improvements in the scaling and the direction of this optimizer and find that they substantially increase its performance at a negligible additional cost. We also reformulate the VMC approach in a game theory framework, to propose a novel optimizer based on decision geometry. We find that on a practical test case for continuous systems, this new optimizer consistently outperforms any of the KFAC improvements in terms of stability, accuracy and speed of convergence. Beyond VMC, the versatility of this approach suggests that decision geometry could provide a solid foundation for accelerating a broad class of machine learning algorithms. This article is part of the theme issue 'The liminal position of Nuclear Physics: from hadrons to neutron stars'.

Mobile App for Enhanced Anterior Cruciate Ligament (ACL) Assessment in Conscious Subjects: "Pivot-Shift Meter".

Anterior cruciate ligament (ACL) instability poses a considerable challenge in traumatology and orthopedic medicine, demanding precise diagnostics for optimal treatment. The pivot-shift test, a pivotal assessment tool, relies on subjective interpretation, emphasizing the need for supplementary imaging. This study addresses this limitation by introducing a machine learning classification algorithm integrated into a mobile application, leveraging smartphones' built-in inertial sensors for dynamic rotational stability assessment during knee examinations. Orthopedic specialists conducted knee evaluations on a cohort of 52 subjects, yielding valuable insights. Quantitative analyses, employing the Intraclass Correlation Coefficient (ICC), demonstrated robust agreement in both intraobserver and interobserver assessments. Specifically, ICC values of 0.94 reflected strong concordance in the timing between maneuvers, while signal amplitude exhibited consistency, with the ICC ranging from 0.71 to 0.66. The introduced machine learning algorithms proved effective, accurately classifying 90% of cases exhibiting joint hypermobility. These quantifiable results underscore the algorithm's reliability in assessing knee stability. This study emphasizes the practicality and effectiveness of implementing machine learning algorithms within a mobile application, showcasing its potential as a valuable tool for categorizing signals captured by smartphone inertial sensors during the pivot-shift test.

Federated Learning Approach for Remote Sensing Scene Classification

In classical machine learning algorithms, used in many analysis tasks, the data are centralized for training. That is, both the model and the data are housed within one device. Federated learning (FL), on the other hand, is a machine learning technique that breaks away from this traditional paradigm by allowing multiple devices to collaboratively train a model without each sharing their own data. In a typical FL setting, each device has a local dataset and trains a local model on that dataset. The local models are next aggregated at a central server to produce a global model. The global model is then distributed back to the devices, which update their local models accordingly. This process is repeated until the global model converges. In this article, a FL approach is applied for remote sensing scene classification for the first time. The adopted approach uses three different RS datasets while employing two types of CNN models and two types of Vision Transformer models, namely: EfficientNet-B1, EfficientNet-B3, ViT-Tiny, and ViT-Base. We compare the performance of FL in each model in terms of overall accuracy and undertake additional experiments to assess their robustness when faced with scenarios of dropped clients. Our classification results on test data show that the two considered Transformer models outperform the two models from the CNN family. Furthermore, employing FL with ViT-Base yields the highest accuracy levels even when the number of dropped clients is significant, indicating its high robustness. These promising results point to the notion that FL can be successfully used with ViT models in the classification of RS scenes, whereas CNN models may suffer from overfitting problems.

Automated diagnosis of schizophrenia based on spatial–temporal residual graph convolutional network

BackgroundSchizophrenia (SZ), a psychiatric disorder for which there is no precise diagnosis, has had a serious impact on the quality of human life and social activities for many years. Therefore, an advanced approach for accurate treatment is required.New methodIn this study, we provide a classification approach for SZ patients based on a spatial–temporal residual graph convolutional neural network (STRGCN). The model primarily collects spatial frequency features and temporal frequency features by spatial graph convolution and single-channel temporal convolution, respectively, and blends them both for the classification learning, in contrast to traditional approaches that only evaluate temporal frequency information in EEG and disregard spatial frequency features across brain regions.ResultsWe conducted extensive experiments on the publicly available dataset Zenodo and our own collected dataset. The classification accuracy of the two datasets on our proposed method reached 96.32% and 85.44%, respectively. In the experiment, the dataset using delta has the best classification performance in the sub-bands.Comparison with existing methodsOther methods mainly rely on deep learning models dominated by convolutional neural networks and long and short time memory networks, lacking exploration of the functional connections between channels. In contrast, the present method can treat the EEG signal as a graph and integrate and analyze the temporal frequency and spatial frequency features in the EEG signal.ConclusionWe provide an approach to not only performs better than other classic machine learning and deep learning algorithms on the dataset we used in diagnosing schizophrenia, but also understand the effects of schizophrenia on brain network features.

Arabic text classification based on analogical proportions

AbstractText classification is the process of labelling a given set of text documents with predefined classes or categories. Existing Arabic text classifiers are either applying classic Machine Learning algorithms such as k‐NN and SVM or using modern deep learning techniques. The former are assessed using small text collections and their accuracy is still subject to improvement while the latter are efficient in classifying big data collections and show limited effectiveness in classifying small corpora with a large number of categories. This paper proposes a new approach to Arabic text classification to treat small and large data collections while improving the classification rates of existing classifiers. We first demonstrate the ability of analogical proportions (AP) (statements of the form ‘x is to as is to ’), which have recently been shown to be effective in classifying ‘structured’ data, to classify ‘unstructured’ text documents requiring preprocessing. We design an analogical model to express the relationship between text documents and their real categories. Next, based on this principle, we develop two new analogical Arabic text classifiers. These rely on the idea that the category of a new document can be predicted from the categories of three others, in the training set, in case the four documents build together a ‘valid’ analogical proportion on all or on a large number of components extracted from each of them. The two proposed classifiers (denoted AATC1 and AATC2) differ mainly in terms of the keywords extracted for classification. To evaluate the proposed classifiers, we perform an extensive experimental study using five benchmark Arabic text collections with small or large sizes, namely ANT (Arabic News Texts) v2.1 and v1.1, BBC‐Arabic, CNN‐Arabic and AlKhaleej‐2004. We also compare analogical classifiers with both classical ML‐based and Deep Learning‐based classifiers. Results show that AATC2 has the best average accuracy (78.78%) over all other classifiers and the best average precision (0.77) ranked first followed by AATC1 (0.73), NB (0.73) and SVM (0.72) for the ANT corpus v2.1. Besides, AATC1 shows the best average precisions (0.88) and (0.92), respectively for the BBC‐Arabic corpus and AlKhaleej‐2004, and the best average accuracy (85.64%) for CNN‐Arabic over all other classifiers. Results demonstrate the utility of analogical proportions for text classification. In particular, the proposed analogical classifiers are shown to significantly outperform a number of existing Arabic classifiers, and in many cases, compare favourably to the robust SVM classifier.

Credit Card Fraud Detection Based on Machine Learning Classification Algorithm

Credit risk analysis is a critical process in the financial industry, as it helps lenders assess the likelihood of borrowers defaulting on their loans. With the advent of machine learning algorithms, there has been a growing interest in leveraging these techniques for more accurate and efficient credit risk prediction. Traditional credit risk models often rely on manual processes and limited data sources, resulting in potential biases and inaccuracies. Additionally, the rapid growth of credit card usage and the increasing complexity of financial transactions have made it challenging to accurately assess credit risk using conventional methods. This review paper aims to provide a comprehensive overview of machine learning algorithms used for credit risk prediction in the context of credit card lending. It explores classification techniques and their applications in credit risk analysis. The paper also discusses the challenges and limitations associated with these algorithms, including data quality, overfitting, and interpretability.