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Journal articles on the topic 'Evaluation of extreme classifiers'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Balasubramanian, Kishore, and N. P. Ananthamoorthy. "Analysis of hybrid statistical textural and intensity features to discriminate retinal abnormalities through classifiers." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 233, no. 5 (2019): 506–14. http://dx.doi.org/10.1177/0954411919835856.

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Retinal image analysis relies on the effectiveness of computational techniques to discriminate various abnormalities in the eye like diabetic retinopathy, macular degeneration and glaucoma. The onset of the disease is often unnoticed in case of glaucoma, the effect of which is felt only at a later stage. Diagnosis of such degenerative diseases warrants early diagnosis and treatment. In this work, performance of statistical and textural features in retinal vessel segmentation is evaluated through classifiers like extreme learning machine, support vector machine and Random Forest. The fundus ima

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Michau, Gabriel, Yang Hu, Thomas Palmé, and Olga Fink. "Feature learning for fault detection in high-dimensional condition monitoring signals." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 234, no. 1 (2019): 104–15. http://dx.doi.org/10.1177/1748006x19868335.

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Complex industrial systems are continuously monitored by a large number of heterogeneous sensors. The diversity of their operating conditions and the possible fault types make it impossible to collect enough data for learning all the possible fault patterns. This article proposes an integrated automatic unsupervised feature learning and one-class classification for fault detection that uses data on healthy conditions only for its training. The approach is based on stacked extreme learning machines (namely hierarchical extreme learning machines) and comprises an autoencoder, performing unsuperv

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Afolabi, Hassan A., and Abdurazzag A. Aburas. "Statistical performance assessment of supervised machine learning algorithms for intrusion detection system." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 266–77. https://doi.org/10.11591/ijai.v13.i1.pp266-277.

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Several studies have shown that an ensemble classifier's effectiveness is directly correlated with the diversity of its members. However, the algorithms used to build the base learners are one of the issues encountered when using a stacking ensemble. Given the number of options, choosing the best ones might be challenging. In this study, we selected some of the most extensively applied supervised machine learning algorithms and performed a performance evaluation in terms of well-known metrics and validation methods using two internet of things (IoT) intrusion detection datasets, namely network

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Afolabi, Hassan A., and Aburas A. Abdurazzag. "Statistical performance assessment of supervised machine learning algorithms for intrusion detection system." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 266. http://dx.doi.org/10.11591/ijai.v13.i1.pp266-277.

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<span lang="EN-US">Several studies have shown that an ensemble classifier's effectiveness is directly correlated with the diversity of its members. However, the algorithms used to build the base learners are one of the issues encountered when using a stacking ensemble. Given the number of options, choosing the best ones might be challenging. In this study, we selected some of the most extensively applied supervised machine learning algorithms and performed a performance evaluation in terms of well-known metrics and validation methods using two internet of things (IoT) intrusion detection

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Raza, Ali, Furqan Rustam, Hafeez Ur Rehman Siddiqui, et al. "Predicting Genetic Disorder and Types of Disorder Using Chain Classifier Approach." Genes 14, no. 1 (2022): 71. http://dx.doi.org/10.3390/genes14010071.

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Genetic disorders are the result of mutation in the deoxyribonucleic acid (DNA) sequence which can be developed or inherited from parents. Such mutations may lead to fatal diseases such as Alzheimer’s, cancer, Hemochromatosis, etc. Recently, the use of artificial intelligence-based methods has shown superb success in the prediction and prognosis of different diseases. The potential of such methods can be utilized to predict genetic disorders at an early stage using the genome data for timely treatment. This study focuses on the multi-label multi-class problem and makes two major contributions

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Thiamchoo, Nantarika, and Pornchai Phukpattaranont. "Evaluation of feature projection techniques in object grasp classification using electromyogram signals from different limb positions." PeerJ Computer Science 8 (May 6, 2022): e949. http://dx.doi.org/10.7717/peerj-cs.949.

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A myoelectric prosthesis is manipulated using electromyogram (EMG) signals from the existing muscles for performing the activities of daily living. A feature vector that is formed by concatenating data from many EMG channels may result in a high dimensional space, which may cause prolonged computation time, redundancy, and irrelevant information. We evaluated feature projection techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and spectral regression extreme learning machine (SRELM), applied to object

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Nateghi, Masoud, Mahdi Rahbar Alam, Hossein Amiri, Samaneh Nasiri, and Reza Sameni. "Model-Based Electroencephalogram Instantaneous Frequency Tracking: Application in Automated Sleep–Wake Stage Classification." Sensors 24, no. 24 (2024): 7881. https://doi.org/10.3390/s24247881.

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Understanding sleep stages is crucial for diagnosing sleep disorders, developing treatments, and studying sleep’s impact on overall health. With the growing availability of affordable brain monitoring devices, the volume of collected brain data has increased significantly. However, analyzing these data, particularly when using the gold standard multi-lead electroencephalogram (EEG), remains resource-intensive and time-consuming. To address this challenge, automated brain monitoring has emerged as a crucial solution for cost-effective and efficient EEG data analysis. A critical component of sle

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Tian, Zhang, Chen, Geng, and Wang. "Selective Ensemble Based on Extreme Learning Machine for Sensor-Based Human Activity Recognition." Sensors 19, no. 16 (2019): 3468. http://dx.doi.org/10.3390/s19163468.

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Sensor-based human activity recognition (HAR) has attracted interest both in academic and applied fields, and can be utilized in health-related areas, fitness, sports training, etc. With a view to improving the performance of sensor-based HAR and optimizing the generalizability and diversity of the base classifier of the ensemble system, a novel HAR approach (pairwise diversity measure and glowworm swarm optimization-based selective ensemble learning, DMGSOSEN) that utilizes ensemble learning with differentiated extreme learning machines (ELMs) is proposed in this paper. Firstly, the bootstrap

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Peng, Sizhong, Congjun Feng, Zhen Qiu, et al. "Prediction of Lithofacies in Heterogeneous Shale Reservoirs Based on a Robust Stacking Machine Learning Model." Minerals 15, no. 3 (2025): 240. https://doi.org/10.3390/min15030240.

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The lithofacies of a reservoir contain key information such as rock lithology, sedimentary structures, and mineral composition. Accurate prediction of shale reservoir lithofacies is crucial for identifying sweet spots for oil and gas development. However, obtaining shale lithofacies through core sampling during drilling is challenging, and the accuracy of traditional logging curve intersection methods is insufficient. To efficiently and accurately predict shale lithofacies, this study proposes a hybrid model called Stacking, which combines four classifiers: Random Forest, HistGradient Boosting

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Tariq, Muhammad Arham, Allah Bux Sargano, Muhammad Aksam Iftikhar, and Zulfiqar Habib. "Comparing Different Oversampling Methods in Predicting Multi-Class Educational Datasets Using Machine Learning Techniques." Cybernetics and Information Technologies 23, no. 4 (2023): 199–212. http://dx.doi.org/10.2478/cait-2023-0044.

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Abstract Predicting students’ academic performance is a critical research area, yet imbalanced educational datasets, characterized by unequal academic-level representation, present challenges for classifiers. While prior research has addressed the imbalance in binary-class datasets, this study focuses on multi-class datasets. A comparison of ten resampling methods (SMOTE, Adasyn, Distance SMOTE, BorderLineSMOTE, KmeansSMOTE, SVMSMOTE, LN SMOTE, MWSMOTE, Safe Level SMOTE, and SMOTETomek) is conducted alongside nine classification models: K-Nearest Neighbors (KNN), Linear Discriminant Analysis (

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Tomita, Katsuyuki, Akira Yamasaki, Ryohei Katou, et al. "Construction of a Diagnostic Algorithm for Diagnosis of Adult Asthma Using Machine Learning with Random Forest and XGBoost." Diagnostics 13, no. 19 (2023): 3069. http://dx.doi.org/10.3390/diagnostics13193069.

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An evidence-based diagnostic algorithm for adult asthma is necessary for effective treatment and management. We present a diagnostic algorithm that utilizes a random forest (RF) and an optimized eXtreme Gradient Boosting (XGBoost) classifier to diagnose adult asthma as an auxiliary tool. Data were gathered from the medical records of 566 adult outpatients who visited Kindai University Hospital with complaints of nonspecific respiratory symptoms. Specialists made a thorough diagnosis of asthma based on symptoms, physical indicators, and objective testing, including airway hyperresponsiveness. W

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FAUST, OLIVER, U. RAJENDRA ACHARYA, LIM CHOO MIN, and BERNHARD H. C. SPUTH. "AUTOMATIC IDENTIFICATION OF EPILEPTIC AND BACKGROUND EEG SIGNALS USING FREQUENCY DOMAIN PARAMETERS." International Journal of Neural Systems 20, no. 02 (2010): 159–76. http://dx.doi.org/10.1142/s0129065710002334.

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The analysis of electroencephalograms continues to be a problem due to our limited understanding of the signal origin. This limited understanding leads to ill-defined models, which in turn make it hard to design effective evaluation methods. Despite these shortcomings, electroencephalogram analysis is a valuable tool in the evaluation of neurological disorders and the evaluation of overall cerebral activity. We compared different model based power spectral density estimation methods and different classification methods. Specifically, we used the autoregressive moving average as well as from Yu

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Al-Gethami, Khalid M., Mousa T. Al-Akhras, and Mohammed Alawairdhi. "Empirical Evaluation of Noise Influence on Supervised Machine Learning Algorithms Using Intrusion Detection Datasets." Security and Communication Networks 2021 (January 15, 2021): 1–28. http://dx.doi.org/10.1155/2021/8836057.

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Optimizing the detection of intrusions is becoming more crucial due to the continuously rising rates and ferocity of cyber threats and attacks. One of the popular methods to optimize the accuracy of intrusion detection systems (IDSs) is by employing machine learning (ML) techniques. However, there are many factors that affect the accuracy of the ML-based IDSs. One of these factors is noise, which can be in the form of mislabelled instances, outliers, or extreme values. Determining the extent effect of noise helps to design and build more robust ML-based IDSs. This paper empirically examines th

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Okwonu, Friday Zinzendoff, Nor Aishah Ahad, Nicholas Oluwole Ogini, Innocent Ejiro Okoloko, and Wan Zakiyatussariroh Wan Husin. "COMPARATIVE PERFORMANCE EVALUATION OF EFFICIENCY FOR HIGH DIMENSIONAL CLASSIFICATION METHODS." Journal of Information and Communication Technology 21, No.3 (2022): 437–64. http://dx.doi.org/10.32890/jict2022.21.3.6.

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This paper aimed to determine the efficiency of classifiers for high-dimensional classification methods. It also investigated whether an extreme minimum misclassification rate translates into robust efficiency. To ensure an acceptable procedure, a benchmark evaluation threshold (BETH) was proposed as a metric to analyze the comparative performance for high-dimensional classification methods. A simplified performance metric was derived to show the efficiency of different classification methods. To achieve the objectives, the existing probability of correct classification (PCC) or classification

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Sakri, Sapiah, and Shakila Basheer. "Fusion Model for Classification Performance Optimization in a Highly Imbalance Breast Cancer Dataset." Electronics 12, no. 5 (2023): 1168. http://dx.doi.org/10.3390/electronics12051168.

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Accurate diagnosis of breast cancer using automated algorithms continues to be a challenge in the literature. Although researchers have conducted a great deal of work to address this issue, no definitive answer has yet been discovered. This challenge is aggravated further by the fact that most available datasets have imbalanced class issues, meaning that the number of cases in one class vastly outnumbers those of the others. The goal of this study was to (i) develop a reliable machine-learning-based prediction model for breast cancer based on the combination of the resampling technique and the

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Walavalkar, Praniket, Ansh Dasrapuria, Meghna Sarda, and Lynette Dmello. "A Token-based Approach to Detect Fraud in Ethereum Transactions." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 34–42. http://dx.doi.org/10.22214/ijraset.2024.59690.

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Abstract: As a consequence of mass unemployment being the byproduct of COVID-19, people around the world discovered investment in cryptocurrency as a means to tackle their declining financial condition. Subsequently, the prominence of Ethereum as a platform for crypto transactions also gave rise to fraudulent transactions. The need to detect these frauds exists even today. This study proposes a token-based approach to detect fraud in Ethereum transactions incorporating the ERC20 standard, by employing machine learning techniques. After cleaning and preprocessing of the dataset, the transaction

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Ami, Shamril Kamaruddin, Fikri Hadrawi Mohd, Bee Wah Yap, and Aliman Sharifah. "An evaluation of nature-inspired optimization algorithms and machine learning classifiers for electricity fraud prediction." An evaluation of nature-inspired optimization algorithms and machine learning classifiers for electricity fraud prediction 32, no. 1 (2023): 468–77. https://doi.org/10.11591/ijeecs.v32.i1.pp468-477.

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This study evaluated the nature-inspired optimization algorithms to improve classification involving imbalanced class problems. The particle swarm optimization (PSO) and grey wolf optimizer (GWO) were used to adaptively balance the distribution and then four supervised machine learning classifiers artificial neural network (ANN), support vector machine (SVM), extreme gradient-boosted tree (XGBoost), and random forest (RF) were applied to maximize the classification performance for electricity fraud prediction. The imbalance data was balanced using random undersampling (RUS) and two nature-insp

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Lashari, Saima Anwar, Muhammad Muntazir Khan, Abdullah Khan, Sana Salahuddin, and Muhammad Noman Ata. "Comparative Evaluation of Machine Learning Models for Mobile Phone Price Prediction: Assessing Accuracy, Robustness, and Generalization Performance." Journal of Informatics and Web Engineering 3, no. 3 (2024): 147–63. http://dx.doi.org/10.33093/jiwe.2024.3.3.9.

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These days, mobile phones are the most commonly purchased goods. Thousands of new models with improved features, designs, and specifications are released yearly. An autonomous mobile price prediction system is required to assist customers in determining whether or not they can afford these devices. Many machine learning models exhibit varying performance degrees based on their architecture and learning properties. Ten widely used classifiers were assessed in this study: Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Extreme Gradient Boosti

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Kamaruddin, Ami Shamril, Mohd Fikri Hadrawi, Yap Bee Wah, and Sharifah Aliman. "An evaluation of nature-inspired optimization algorithms and machine learning classifiers for electricity fraud prediction." Indonesian Journal of Electrical Engineering and Computer Science 32, no. 1 (2023): 468. http://dx.doi.org/10.11591/ijeecs.v32.i1.pp468-477.

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<span>This study evaluated the nature-inspired optimization algorithms to improve classification involving imbalanced class problems. The particle swarm optimization (PSO) and grey wolf optimizer (GWO) were used to adaptively balance the distribution and then four supervised machine learning classifiers artificial neural network (ANN), support vector machine (SVM), extreme gradient-boosted tree (XGBoost), and random forest (RF) were applied to maximize the classification performance for electricity fraud prediction. The imbalance data was balanced using random undersampling (RUS) and two

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20

Wirot, Yotsawat, Wattuya Pakaket, and Srivihok Anongnart. "Improved credit scoring model using XGBoost with Bayesian hyper-parameter optimization." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5477–87. https://doi.org/10.11591/ijece.v11i6.pp5477-5487.

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Several credit-scoring models have been developed using ensemble classifiers in order to improve the accuracy of assessment. However, among the ensemble models, little consideration has been focused on the hyperparameters tuning of base learners, although these are crucial to constructing ensemble models. This study proposes an improved credit scoring model based on the extreme gradient boosting (XGB) classifier using Bayesian hyper-parameters optimization (XGB-BO). The model comprises two steps. Firstly, data pre-processing is utilized to handle missing values and scale the data. Secondly, Ba

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Kuntiyellannagari, Bhagyalaxmi, Bhoopalan Dwarakanath, and Panuganti VijayaPal Reddy. "Hybrid model for brain tumor detection using convolution neural networks." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 1775. http://dx.doi.org/10.11591/ijeecs.v33.i3.pp1775-1781.

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<div>The development of abnormal cells in the brain, some of which may turn out to be cancerous, is known as a brain tumor. Magnetic resonance imaging (MRI) is the most common technique for detecting brain tumors. Information about the abnormal tissue growth in the brain is visible from the MRI scans. In most research papers, machine learning (ML) and deep learning (DL) algorithms are applied to detect brain tumors. The radiologist can make speedy decisions because of this prediction. The proposed work creates a hybrid convolution neural networks (CNN) model and logistic regression (LR).

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Kuntiyellannagari, Bhagyalaxmi, Bhoopalan Dwarakanath, and Panuganti VijayaPal Reddy. "Hybrid model for brain tumor detection using convolution neural networks." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 1775–81. https://doi.org/10.11591/ijeecs.v33.i3.pp1775-1781.

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The development of abnormal cells in the brain, some of which may turn out to be cancerous, is known as a brain tumor. Magnetic resonance imaging (MRI) is the most common technique for detecting brain tumors. Information about the abnormal tissue growth in the brain is visible from the MRI scans. In most research papers, machine learning (ML) and deep learning (DL) algorithms are applied to detect brain tumors. The radiologist can make speedy decisions because of this prediction. The proposed work creates a hybrid convolution neural networks (CNN) model and logistic regression (LR). The visual

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23

Bibi, Ruqia, Zahid Mehmood, Asmaa Munshi, Rehan Mehmood Yousaf, and Syed Sohail Ahmed. "Deep features optimization based on a transfer learning, genetic algorithm, and extreme learning machine for robust content-based image retrieval." PLOS ONE 17, no. 10 (2022): e0274764. http://dx.doi.org/10.1371/journal.pone.0274764.

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The recent era has witnessed exponential growth in the production of multimedia data which initiates exploration and expansion of certain domains that will have an overwhelming impact on human society in near future. One of the domains explored in this article is content-based image retrieval (CBIR), in which images are mostly encoded using hand-crafted approaches that employ different descriptors and their fusions. Although utilization of these approaches has yielded outstanding results, their performance in terms of a semantic gap, computational cost, and appropriate fusion based on problem

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Vasantha, Sandhya Venu Chellapilla V. K. N. S. N. Moorthy Preeti S. Patil Navnath D. Kale Chetan Vikram Andhare Mukesh Kumar Tripathi. "Analyzing electroencephalogram signals with machine learning to comprehend online learning media." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1876–85. https://doi.org/10.11591/ijeecs.v35.i3.pp1876-1885.

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In E-learning, evaluating students' comprehension of lecture video content is significant. The surge in online platform usage due to the pandemic has been remarkable, but the pressing issue is that learning outcomes still need to match the growth. Addressing this, a scientific system that gauges the comprehensibility of lecture videos becomes crucial for the effective design of future courses. This research paper is based on a cognitive approach utilizing EEG signals to determine student's level of comprehension. The study involves the design, evaluation, and comparison of multiple machines le

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Guo, Weian, Yan Zhang, Ming Chen, Lei Wang, and Qidi Wu. "Fuzzy performance evaluation of Evolutionary Algorithms based on extreme learning classifier." Neurocomputing 175 (January 2016): 371–82. http://dx.doi.org/10.1016/j.neucom.2015.10.069.

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Yotsawat, Wirot, Pakaket Wattuya, and Anongnart Srivihok. "Improved credit scoring model using XGBoost with Bayesian hyper-parameter optimization." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5477. http://dx.doi.org/10.11591/ijece.v11i6.pp5477-5487.

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<span>Several credit-scoring models have been developed using ensemble classifiers in order to improve the accuracy of assessment. However, among the ensemble models, little consideration has been focused on the hyper-parameters tuning of base learners, although these are crucial to constructing ensemble models. This study proposes an improved credit scoring model based on the extreme gradient boosting (XGB) classifier using Bayesian hyper-parameters optimization (XGB-BO). The model comprises two steps. Firstly, data pre-processing is utilized to handle missing values and scale the data.

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Leng, Qian, Honggang Qi, Jun Miao, Wentao Zhu, and Guiping Su. "One-Class Classification with Extreme Learning Machine." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/412957.

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One-class classification problem has been investigated thoroughly for past decades. Among one of the most effective neural network approaches for one-class classification, autoencoder has been successfully applied for many applications. However, this classifier relies on traditional learning algorithms such as backpropagation to train the network, which is quite time-consuming. To tackle the slow learning speed in autoencoder neural network, we propose a simple and efficient one-class classifier based on extreme learning machine (ELM). The essence of ELM is that the hidden layer need not be tu

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Deng, Weiquan, Bo Ye, Jun Bao, Guoyong Huang, and Jiande Wu. "Classification and Quantitative Evaluation of Eddy Current Based on Kernel-PCA and ELM for Defects in Metal Component." Metals 9, no. 2 (2019): 155. http://dx.doi.org/10.3390/met9020155.

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Eddy current testing technology is widely used in the defect detection of metal components and the integrity evaluation of critical components. However, at present, the evaluation and analysis of defect signals are still mostly based on artificial evaluation. Therefore, the evaluation of defects is often subjectively affected by human factors, which may lead to a lack in objectivity, accuracy, and reliability. In this paper, the feature extraction of non-linear signals is carried out. First, using the kernel-based principal component analysis (KPCA) algorithm. Secondly, based on the feature ve

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Jafarzadeh, Hamid, Masoud Mahdianpari, Eric Gill, Fariba Mohammadimanesh, and Saeid Homayouni. "Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation." Remote Sensing 13, no. 21 (2021): 4405. http://dx.doi.org/10.3390/rs13214405.

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In recent years, several powerful machine learning (ML) algorithms have been developed for image classification, especially those based on ensemble learning (EL). In particular, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) methods have attracted researchers’ attention in data science due to their superior results compared to other commonly used ML algorithms. Despite their popularity within the computer science community, they have not yet been well examined in detail in the field of Earth Observation (EO) for satellite image classification. As such, this

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Ding, Hu, Jiaming Na, Shangjing Jiang, et al. "Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping—A Case Study of the Loess Plateau, China." Remote Sensing 13, no. 5 (2021): 1021. http://dx.doi.org/10.3390/rs13051021.

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Artificial terraces are of great importance for agricultural production and soil and water conservation. Automatic high-accuracy mapping of artificial terraces is the basis of monitoring and related studies. Previous research achieved artificial terrace mapping based on high-resolution digital elevation models (DEMs) or imagery. As a result of the importance of the contextual information for terrace mapping, object-based image analysis (OBIA) combined with machine learning (ML) technologies are widely used. However, the selection of an appropriate classifier is of great importance for the terr

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Tawil, Arar Al, Lara Al-Shboul, Laiali Almazaydeh, and Mohammad Alshinwan. "Fortifying network security: machine learning-powered intrusion detection systems and classifier performance analysis." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 5 (2024): 5894. http://dx.doi.org/10.11591/ijece.v14i5.pp5894-5905.

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Intrusion detection systems (IDS) protect networks from threats; they actively monitor network activity to identify and prevent malicious actions. This study investigates the application of machine learning methods to strengthen IDS, explicitly emphasizing the comprehensive CICIDS 2017 dataset. The dataset was refined by implementing stringent preprocessing methods such as feature normalization, class imbalance management, feature reduction, and feature selection to ensure its quality and lay the foundation for developing robust models. The performance evaluation of three classifiers-support v

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R P, Prawin. "Performance Evaluation and Comparative Analysis of Several Machine Learning Classification Techniques Using a Data-driven Approach in Predicting Renal Failure." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 3522–30. http://dx.doi.org/10.22214/ijraset.2023.54343.

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Abstract: Renal failure is characterized by progressive kidney function loss over time. It is a serious medical condition that affects millions of people worldwide. It is caused by the inability of the kidneys to properly filter waste and excess fluids from the blood. Renal failure can be a consequence of chronic kidney disease. Chronic kidney disease is a long-term condition that causes the kidneys to gradually lose function over time. If chronic kidney disease is not adequately managed, the kidney’s function may continue to decline, leading to renal failure. It is essential to monitor and ma

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K., Bhagyalaxmi, and B. Dwarakanath. "Hybrid model for detection of brain tumor using convolution neural networks." Computer Science and Information Technologies 5, no. 1 (2024): 78–84. http://dx.doi.org/10.11591/csit.v5i1.pp78-84.

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The development of aberrant brain cells, some of which may turn cancerous, is known as a brain tumor. Magnetic resonance imaging (MRI) scans are the most common technique for finding brain tumors. Information about the aberrant tissue growth in the brain is discernible from the MRI scans. In numerous research papers, machine learning, and deep learning algorithms are used to detect brain tumors. It takes extremely little time to forecast a brain tumor when these algorithms are applied to MRI pictures, and better accuracy makes it easier to treat patients. The radiologist can make speedy decisi

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K., Bhagyalaxmi, and B. Dwarakanath. "Hybrid model for detection of brain tumor using convolution neural networks." Computer Science and Information Technologies 5, no. 1 (2024): 78–84. http://dx.doi.org/10.11591/csit.v5i1.p78-84.

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The development of aberrant brain cells, some of which may turn cancerous, is known as a brain tumor. Magnetic resonance imaging (MRI) scans are the most common technique for finding brain tumors. Information about the aberrant tissue growth in the brain is discernible from the MRI scans. In numerous research papers, machine learning, and deep learning algorithms are used to detect brain tumors. It takes extremely little time to forecast a brain tumor when these algorithms are applied to MRI pictures, and better accuracy makes it easier to treat patients. The radiologist can make speedy decisi

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35

K., Bhagyalaxmi, and B. Dwarakanath. "Hybrid model for detection of brain tumor using convolution neural networks." Computer Science and Information Technologies 5, no. 1 (2024): 84–90. http://dx.doi.org/10.11591/csit.v5i1.p84-90.

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The development of aberrant brain cells, some of which may turn cancerous, is known as a brain tumor. Magnetic resonance imaging (MRI) scans are the most common technique for finding brain tumors. Information about the aberrant tissue growth in the brain is discernible from the MRI scans. In numerous research papers, machine learning, and deep learning algorithms are used to detect brain tumors. It takes extremely little time to forecast a brain tumor when these algorithms are applied to MRI pictures, and better accuracy makes it easier to treat patients. The radiologist can make speedy decisi

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36

K., Bhagyalaxmi, and B. Dwarakanath. "Hybrid model for detection of brain tumor using convolution neural networks." Computer Science and Information Technologies 5, no. 1 (2024): 84–90. http://dx.doi.org/10.11591/csit.v5i1.pp84-90.

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The development of aberrant brain cells, some of which may turn cancerous, is known as a brain tumor. Magnetic resonance imaging (MRI) scans are the most common technique for finding brain tumors. Information about the aberrant tissue growth in the brain is discernible from the MRI scans. In numerous research papers, machine learning, and deep learning algorithms are used to detect brain tumors. It takes extremely little time to forecast a brain tumor when these algorithms are applied to MRI pictures, and better accuracy makes it easier to treat patients. The radiologist can make speedy decisi

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K., Bhagyalaxmi, and B. Dwarakanath. "Hybrid model for detection of brain tumor using convolution neural networks." Computer Science and Information Technologies 5, no. 1 (2024): 78–84. https://doi.org/10.11591/csit.v5i1.pp78-84.

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The development of aberrant brain cells, some of which may turn cancerous, is known as a brain tumor. Magnetic resonance imaging (MRI) scans are the most common technique for finding brain tumors. Information about the aberrant tissue growth in the brain is discernible from the MRI scans. In numerous research papers, machine learning, and deep learning algorithms are used to detect brain tumors. It takes extremely little time to forecast a brain tumor when these algorithms are applied to MRI pictures, and better accuracy makes it easier to treat patients. The radiologist can make speedy decisi

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Al-Awadi, Jhan Yahya Rbat, Hadeel K. Aljobouri, and Ali M. Hasan. "MRI Brain Scans Classification Using Extreme Learning Machine on LBP and GLCM." International Journal of Online and Biomedical Engineering (iJOE) 19, no. 02 (2023): 134–49. http://dx.doi.org/10.3991/ijoe.v19i02.33987.

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The primary goal of this study is to predict the presence of a brain tumor using MRI brain images. These images are first pre-processed to remove the boundary borders and the undesired regions. Gray-Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern method (LBP) approaches are mixed for extracting multiple local and global features. The best features are selected using the ANOVA statistical approach, which is based on the largest variance. Then, the selected features are applied to many state of arts classifiers as well as to Extreme Learning Machine (ELM) neural network model, where t

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Venu, Vasantha Sandhya, Chellapilla V. K. N. S. N. Moorthy, Preeti S. Patil, Navnath D. Kale, Chetan Vikram Andhare, and Mukesh Kumar Tripathi. "Analyzing electroencephalogram signals with machine learning to comprehend online learning media." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1876. http://dx.doi.org/10.11591/ijeecs.v35.i3.pp1876-1885.

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<p>In E-learning, evaluating students' comprehension of lecture video content is significant. The surge in online platform usage due to the pandemic has been remarkable, but the pressing issue is that learning outcomes still need to match the growth. Addressing this, a scientific system that gauges the comprehensibility of lecture videos becomes crucial for the effective design of future courses. This research paper is based on a cognitive approach utilizing EEG signals to determine student's level of comprehension. The study involves the design, evaluation, and comparison of multiple ma

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Shivani, Vora. "Hcnnxgboost: A Hybrid Cnn-Xgboost Approach for Effective Emotion Detection in Textual Data." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 13, no. 10 (2024): 12–17. https://doi.org/10.35940/ijitee.J9959.13100924.

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<strong>Abstract: </strong>in recent years, emotional analysis has become a key focus in computational studies, driven by the need to understand societal sentiments. this exploration is motivated by its many promising applications, such as community well-being evaluation, human-computer interaction, suicide prevention, and personalized recommendations. even with progress in other areas like identifying expressions from facial cues and speech, the study of text-based emotion recognition remains a fascinating field of research because machines struggle to interpret context, especially compared t

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Alshammari, Khaznah, Shah Muhammad Hamdi, and Soukaina Filali Boubrahimi. "Identifying Flare-indicative Photospheric Magnetic Field Parameters from Multivariate Time-series Data of Solar Active Regions." Astrophysical Journal Supplement Series 271, no. 2 (2024): 39. http://dx.doi.org/10.3847/1538-4365/ad21e4.

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Abstract Photospheric magnetic field parameters are frequently used to analyze and predict solar events. Observation of these parameters over time, i.e., representing solar events by multivariate time-series (MVTS) data, can determine relationships between magnetic field states in active regions and extreme solar events, e.g., solar flares. We can improve our understanding of these events by selecting the most relevant parameters that give the highest predictive performance. In this study, we propose a two-step incremental feature selection method for MVTS data using a deep-learning model base

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Duceac (Covrig), Mădălina, Călin Gheorghe Buzea, Alina Pleșea-Condratovici, et al. "A Hybrid Ensemble Learning Framework for Predicting Lumbar Disc Herniation Recurrence: Integrating Supervised Models, Anomaly Detection, and Threshold Optimization." Diagnostics 15, no. 13 (2025): 1628. https://doi.org/10.3390/diagnostics15131628.

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Lumbar disc herniation (LDH) recurrence remains a pressing clinical challenge, with limited predictive tools available to support early identification and personalized intervention. Background: Predicting recurrence after lumbar disc herniation (LDH) remains clinically important but algorithmically difficult due to extreme class imbalance and low signal-to-noise ratio. Objective: This study proposes a hybrid machine learning framework that integrates supervised classifiers, unsupervised anomaly detection, and decision threshold tuning to predict LDH recurrence using routine clinical data. Meth

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Cao, Guojun, Xiaoyan Wei, and Jiangxia Ye. "Fireground Recognition and Spatio-Temporal Scalability Research Based on ICESat-2/ATLAS Vertical Structure Parameters." Forests 15, no. 9 (2024): 1597. http://dx.doi.org/10.3390/f15091597.

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In the ecological context of global climate change, ensuring the stable carbon sequestration capacity of forest ecosystems, which is among the most important components of terrestrial ecosystems, is crucial. Forest fires are disasters that often burn vegetation and damage forest ecosystems. Accurate recognition of firegrounds is essential to analyze global carbon emissions and carbon flux, as well as to discover the contribution of climate change to the succession of forest ecosystems. The common recognition of firegrounds relies on remote sensing data, such as optical data, which have difficu

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Pinki, Farhana Tazmim, Md Abdul Awal, Khondoker Mirazul Mumenin, et al. "HGSOXGB: Hunger-Games-Search-Optimization-Based Framework to Predict the Need for ICU Admission for COVID-19 Patients Using eXtreme Gradient Boosting." Mathematics 11, no. 18 (2023): 3960. http://dx.doi.org/10.3390/math11183960.

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Millions of people died in the COVID-19 pandemic, which pressured hospitals and healthcare workers into keeping up with the speed and intensity of the outbreak, resulting in a scarcity of ICU beds for COVID-19 patients. Therefore, researchers have developed machine learning (ML) algorithms to assist in identifying patients at increased risk of requiring an ICU bed. However, many of these studies used state-of-the-art ML algorithms with arbitrary or default hyperparameters to control the learning process. Hyperparameter optimization is essential in enhancing the classification effectiveness and

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Fjeldsted, Aaron P., Tyler J. Morrow, Clayton D. Scott, et al. "The Evaluation of Machine Learning Techniques for Isotope Identification Contextualized by Training and Testing Spectral Similarity." Journal of Nuclear Engineering 5, no. 3 (2024): 373–401. http://dx.doi.org/10.3390/jne5030024.

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Precise gamma-ray spectral analysis is crucial in high-stakes applications, such as nuclear security. Research efforts toward implementing machine learning (ML) approaches for accurate analysis are limited by the resemblance of the training data to the testing scenarios. The underlying spectral shape of synthetic data may not perfectly reflect measured configurations, and measurement campaigns may be limited by resource constraints. Consequently, ML algorithms for isotope identification must maintain accurate classification performance under domain shifts between the training and testing data.

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Shahi, T. B., C. Sitaula, and N. Paudel. "A Hybrid Feature Extraction Method for Nepali COVID-19-Related Tweets Classification." Computational Intelligence and Neuroscience 2022 (March 9, 2022): 1–11. http://dx.doi.org/10.1155/2022/5681574.

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COVID-19 is one of the deadliest viruses, which has killed millions of people around the world to this date. The reason for peoples’ death is not only linked to its infection but also to peoples’ mental states and sentiments triggered by the fear of the virus. People’s sentiments, which are predominantly available in the form of posts/tweets on social media, can be interpreted using two kinds of information: syntactical and semantic. Herein, we propose to analyze peoples’ sentiment using both kinds of information (syntactical and semantic) on the COVID-19-related twitter dataset available in t

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Imani, Mehdi, Ali Beikmohammadi, and Hamid Reza Arabnia. "Comprehensive Analysis of Random Forest and XGBoost Performance with SMOTE, ADASYN, and GNUS Under Varying Imbalance Levels." Technologies 13, no. 3 (2025): 88. https://doi.org/10.3390/technologies13030088.

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This study examines the efficacy of Random Forest and XGBoost classifiers in conjunction with three upsampling techniques—SMOTE, ADASYN, and Gaussian noise upsampling (GNUS)—across datasets with varying class imbalance levels, ranging from moderate to extreme (15% to 1% churn rate). Employing metrics such as F1 score, ROC AUC, PR AUC, Matthews Correlation Coefficient (MCC), and Cohen’s Kappa, this research provides a comprehensive evaluation of classifier performance under different imbalance scenarios, focusing on applications in the telecommunications domain. The findings highlight that tune

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Ghorbani, Aida, Amir Daneshvar, Ladan Riazi, and Reza Radfar. "Friend Recommender System for Social Networks Based on Stacking Technique and Evolutionary Algorithm." Complexity 2022 (August 31, 2022): 1–11. http://dx.doi.org/10.1155/2022/5864545.

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In recent years, social networks have made significant progress and the number of people who use them to communicate is increasing day by day. The vast amount of information available on social networks has led to the importance of using friend recommender systems to discover knowledge about future communications. It is challenging to choose the best machine learning approach to address the recommender system issue since there are several strategies with various benefits and drawbacks. In light of this, a solution based on the stacking approach was put out in this study to provide a buddy reco

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Nida, Nudrat, Muhammad Haroon Yousaf, Aun Irtaza, and Sergio A. Velastin. "Instructor Activity Recognition through Deep Spatiotemporal Features and Feedforward Extreme Learning Machines." Mathematical Problems in Engineering 2019 (April 30, 2019): 1–13. http://dx.doi.org/10.1155/2019/2474865.

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Human action recognition has the potential to predict the activities of an instructor within the lecture room. Evaluation of lecture delivery can help teachers analyze shortcomings and plan lectures more effectively. However, manual or peer evaluation is time-consuming, tedious and sometimes it is difficult to remember all the details of the lecture. Therefore, automation of lecture delivery evaluation significantly improves teaching style. In this paper, we propose a feedforward learning model for instructor’s activity recognition in the lecture room. The proposed scheme represents a video se

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Md. Sadiq Iqbal and Mohammod Abul Kashem. "A Machine Learning Framework for Identifying Sources of AI-Generated Text." Statistics, Optimization & Information Computing 13, no. 5 (2025): 2186–204. https://doi.org/10.19139/soic-2310-5070-2225.

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The rise of AI-generated text requires efficient identification methods to ascertain its origin. This research presents a comprehensive dataset derived from responses to various questions posed to AI models including ChatGPT, Gemini, DeepAI, and Bing, alongside human respondents. We meticulously preprocessed the dataset and utilized both manual methods such as Count Vector (CV), Bag of Words (BoW), and Hashing Vectorization (HV), as well as automated Deep Learning (DL) models like Bidirectional Encoder Representations from Transformers (BERT), Extreme Language understanding Network (XLNet), En

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