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Girolami, Mark, and Simon Rogers. "Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors." Neural Computation 18, no. 8 (2006): 1790–817. http://dx.doi.org/10.1162/neco.2006.18.8.1790.
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It is well known in the statistics literature that augmenting binary and polychotomous response models with gaussian latent variables enables exact Bayesian analysis via Gibbs sampling from the parameter posterior. By adopting such a data augmentation strategy, dispensing with priors over regression coefficients in favor of gaussian process (GP) priors over functions, and employing variational approximations to the full posterior, we obtain efficient computational methods for GP classification in the multiclass setting.1 The model augmentation with additional latent variables ensures full a posteriori class coupling while retaining the simple a priori independent GP covariance structure from which sparse approximations, such as multiclass informative vector machines (IVM), emerge in a natural and straightforward manner. This is the first time that a fully variational Bayesian treatment for multiclass GP classification has been developed without having to resort to additional explicit approximations to the nongaussian likelihood term. Empirical comparisons with exact analysis use Markov Chain Monte Carlo (MCMC) and Laplace approximations illustrate the utility of the variational approximation as a computationally economic alternative to full MCMC and it is shown to be more accurate than the Laplace approximation.
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Chatzis, Sotirios P. "A latent variable Gaussian process model with Pitman–Yor process priors for multiclass classification." Neurocomputing 120 (November 2013): 482–89. http://dx.doi.org/10.1016/j.neucom.2013.04.029.
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Zhao, Qibin, Liqing Zhang, and Andrzej Cichocki. "A Tensor-Variate Gaussian Process for Classification of Multidimensional Structured Data." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 1041–47. http://dx.doi.org/10.1609/aaai.v27i1.8568.
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As tensors provide a natural and efficient representation of multidimensional structured data, in this paper, we consider probabilistic multinomial probit classification for tensor-variate inputs with Gaussian processes (GP) priors placed over the latent function. In order to take into account the underlying multimodes structure information within the model, we propose a framework of probabilistic product kernels for tensorial data based on a generative model assumption. More specifically, it can be interpreted as mapping tensors to probability density function space and measuring similarity by an information divergence. Since tensor kernels enable us to model input tensor observations, the proposed tensor-variate GP is considered as both a generative and discriminative model. Furthermore, a fully variational Bayesian treatment for multiclass GP classification with multinomial probit likelihood is employed to estimate the hyperparameters and infer the predictive distributions. Simulation results on both synthetic data and a real world application of human action recognition in videos demonstrate the effectiveness and advantages of the proposed approach for classification of multiway tensor data, especially in the case that the underlying structure information among multimodes is discriminative for the classification task.
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Cho, Wanhyun, Sangkyoon Kim, and Soonyoung Park. "New Inference for a Multiclass Gaussian Process Classification Model using a Variational Bayesian EM Algorithm and Laplace Approximation." IEIE Transactions on Smart Processing and Computing 4, no. 4 (2015): 202–8. http://dx.doi.org/10.5573/ieiespc.2015.4.4.202.
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Kamau, J. N., P. K. Hinga, and S. I. Kamau. "Support Vector Machine Kernel Model Calibration for Optimal Accuracy in Automatic Pineapple Slices Classification." International Research Journal of Innovations in Engineering and Technology 06, no. 09 (2022): 01–8. http://dx.doi.org/10.47001/irjiet/2022.609001.
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Sorting pineapple can be automated with use of computer vision. The unique challenge with the pineapple slices is variability of the fruit slices color, ripeness and texture due to varying environmental parameters and fruit types. The most common types of pineapple fruit are smooth Caen and MD2. Currently the pineapple industries sort the slices manually using casual workers. Before commencement of a typical production shift, there is startup shift where machine are cleaned, prepared and calibrated for the production. Fruit slices are also sampled and processed to simulated actual production. A mock sorting is done to help guide the worker for the expected sorting for the five categories i.e: fancy ¾, fancy ½, choice, broken and reject. To achieve a fully automated sorting process there is a need to calibrate machine model and capture the day to day variability of fruit color, ripeness and texture. In this paper we propose to use an analytical method to calibrate the Support Vector Machine (SVM) with Gaussian radial basis function (RBF) for optimal sigma and box constraint (C). A compelling feature of the proposed algorithm is that it does not require an optimization search, making the selection process simpler and more computationally efficient. The proposed algorithm achieves the highest accuracy when used with the Gaussian multiclass SVM, as demonstrated by experimental results on three real-world datasets.
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Rekha, S. N., Aruna Jeyanthy P., and Devaraj D. "Relevance vector machine based fault classification in wind energy conversion system." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 3 (2019): 1506–13. https://doi.org/10.11591/ijece.v9i3.pp1506-1513.
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This Paper is an attempt to develop the multiclass classification in the Benchmark fault model applied on wind energy conversion system using the relevance vector machine (RVM). The RVM could apply a structural risk minimization by introducing a proper kernel for training and testing. The Gaussian Kernel is used for this purpose. The classification of sensor, process and actuators faults are observed and classified in the implementation. Training different fault condition and testing is carried out using the RVM implementation carried out using Matlab on the Wind Energy Conversion System (WECS). The training time becomes important while the training is carried out in a bigger WECS, and the hardware feasibility is prime while the testing is carried out on an online fault detection scenario. Matlab based implementation is carried out on the benchmark model for the fault detection in the WECS. The results are compared with the previously implemented fault detection technique and found to be performing better in terms of training time and hardware feasibility.
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Adi Pratama, I. Putu, Ery Setiyawan Jullev Atmadji, Dwi Amalia Purnamasar, and Edi Faizal. "Evaluating the Performance of Voting Classifier in Multiclass Classification of Dry Bean Varieties." Indonesian Journal of Data and Science 5, no. 1 (2024): 23–29. http://dx.doi.org/10.56705/ijodas.v5i1.124.
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This study explores the application of a voting classifier, integrating Decision Tree, Logistic Regression, and Gaussian Naive Bayes models, for the multiclass classification of dry bean varieties. Utilizing a dataset comprising 13,611 images of dry bean grains, captured through a high-resolution computer vision system, we extracted 16 features to train and test the classifier. Through a rigorous 5-fold cross-validation process, we assessed the model's performance, focusing on accuracy, precision, recall, and F1-score metrics. The results demonstrated significant variability in the classifier's performance across different data subsets, with accuracy rates fluctuating between 31.23% and 96.73%. This variability highlights the classifier's potential under specific conditions while also indicating areas for improvement. The research contributes to the agricultural informatics field by showcasing the effectiveness and challenges of using ensemble learning methods for crop variety classification, a crucial task for enhancing agricultural productivity and food security. Recommendations for future research include exploring additional features to improve model generalization, extending the dataset for broader applicability, and comparing the voting classifier's performance with other ensemble methods or advanced machine learning models. This study underscores the importance of machine learning in advancing agricultural classification tasks, paving the way for more efficient and accurate crop sorting and grading processes.
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N., Rekha S., P. Aruna Jeyanthy, and D. Devaraj. "Relevance vector machine based fault classification in wind energy conversion system." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 3 (2019): 1506. http://dx.doi.org/10.11591/ijece.v9i3.pp1506-1513.
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<p>This Paper is an attempt to develop the multiclass classification in the Benchmark fault model applied on wind energy conversion system using the relevance vector machine (RVM). The RVM could apply a structural risk minimization by introducing a proper kernel for training and testing. The Gaussian Kernel is used for this purpose. The classification of sensor, process and actuators faults are observed and classified in the implementation. Training different fault condition and testing is carried out using the RVM implementation carried out using Matlab on the Wind Energy Conversion System (WECS). The training time becomes important while the training is carried out in a bigger WECS, and the hardware feasibility is prime while the testing is carried out on an online fault detection scenario. Matlab based implementation is carried out on the benchmark model for the fault detection in the WECS. The results are compared with the previously implemented fault detection technique and found to be performing better in terms of training time and hardware feasibility.</p>
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Wu, Zhiyong, Xiangqian Ding, and Guangrui Zhang. "A Novel Method for Classification of ECG Arrhythmias Using Deep Belief Networks." International Journal of Computational Intelligence and Applications 15, no. 04 (2016): 1650021. http://dx.doi.org/10.1142/s1469026816500218.
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In this paper, a novel approach based on deep belief networks (DBN) for electrocardiograph (ECG) arrhythmias classification is proposed. The construction process of ECG classification model consists of two steps: features learning for ECG signals and supervised fine-tuning. In order to deeply extract features from continuous ECG signals, two types of restricted Boltzmann machine (RBM) including Gaussian–Bernoulli and Bernoulli–Bernoulli are stacked to form DBN. The parameters of RBM can be learned by two training algorithms such as contrastive divergence and persistent contrastive divergence. A suitable feature representation from the raw ECG data can therefore be extracted in an unsupervised way. In order to enhance the performance of DBN, a fine-tuning process is carried out, which uses backpropagation by adding a softmax regression layer on the top of the resulting hidden representation layer to perform multiclass classification. The method is then validated by experiments on the well-known MIT-BIH arrhythmia database. Considering the real clinical application, the inter-patient heartbeat dataset is divided into two sets and grouped into four classes (N, S, V, F) following the recommendations of AAMI. The experiment results show our approach achieves better performance with less feature learning time than traditional hand-designed methods on the classification of ECG arrhythmias.
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Hosenie, Zafiirah, Robert Lyon, Benjamin Stappers, Arrykrishna Mootoovaloo, and Vanessa McBride. "Imbalance learning for variable star classification." Monthly Notices of the Royal Astronomical Society 493, no. 4 (2020): 6050–59. http://dx.doi.org/10.1093/mnras/staa642.
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ABSTRACT The accurate automated classification of variable stars into their respective subtypes is difficult. Machine learning–based solutions often fall foul of the imbalanced learning problem, which causes poor generalization performance in practice, especially on rare variable star subtypes. In previous work, we attempted to overcome such deficiencies via the development of a hierarchical machine learning classifier. This ‘algorithm-level’ approach to tackling imbalance yielded promising results on Catalina Real-Time Survey (CRTS) data, outperforming the binary and multiclass classification schemes previously applied in this area. In this work, we attempt to further improve hierarchical classification performance by applying ‘data-level’ approaches to directly augment the training data so that they better describe underrepresented classes. We apply and report results for three data augmentation methods in particular: Randomly Augmented Sampled Light curves from magnitude Error (RASLE), augmenting light curves with Gaussian Process modelling (GpFit) and the Synthetic Minority Oversampling Technique (SMOTE). When combining the ‘algorithm-level’ (i.e. the hierarchical scheme) together with the ‘data-level’ approach, we further improve variable star classification accuracy by 1–4 per cent. We found that a higher classification rate is obtained when using GpFit in the hierarchical model. Further improvement of the metric scores requires a better standard set of correctly identified variable stars, and perhaps enhanced features are needed.
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Kim, Hyeong-Joo, Kevin Bagas Arifki Mawuntu, Tae-Woong Park, Hyeong-Soo Kim, Jun-Young Park, and Yeong-Seong Jeong. "Spatial Autocorrelation Incorporated Machine Learning Model for Geotechnical Subsurface Modeling." Applied Sciences 13, no. 7 (2023): 4497. http://dx.doi.org/10.3390/app13074497.
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Machine learning models for spatial prediction have been applied in various types of research. However, spatial relation has not been fully considered in modeling, since the Cartesian coordinates of the observed points are directly employed as the location information for machine learning features. This study presents a machine learning modeling process which incorporates spatial autocorrelation for geotechnical subsurface modeling. A new set of features called the Euclidean distance field (EDF) was generated based on the distance between the query points and the observed boreholes in order to incorporate spatial autocorrelation into the machine learning model. Principal component analysis (PCA) was performed to reduce the increasing dimensionality of the dataset caused by the EDF features. Optimized machine learning models based on several popular algorithms (Support Vector Machine, Gaussian Process Regression, Artificial Neural Network, and k-Nearest Neighbor) were employed for predicting several geotechnical information as the targets. The results showed that the optimized machine learning models constructed with the EDF modeling approach generate a slightly lower Root Mean Square Error (RMSE) score compared to the model with the direct XY coordinate approach by 0.041, 0.046, 1.302, and 1.561 for ground surface elevation, groundwater level, SPT-N value, and percent finer than 0.075 mm sieve, respectively. Both modeling approaches performed well for USCS-based soil classification with the EDF model having slightly improved classification accuracy by 0.72%. Furthermore, the model can perform balance multiclass classification as indicated by the >95% precision, recall, f1-score, and balanced accuracy score. These results indicate that spatial autocorrelation has a noticeable effect. Hence, it needs to be considered to improve the overall performance of spatial machine learning modeling. Comparison of geotechnical subsurface predictions generated based on different machine learning algorithms showed that the selection of the best-performing model based only on the lowest prediction error is not appropriate for spatial prediction modeling. Therefore, thorough analysis of the predicted data by visualization is necessary in the selection process for spatial prediction modeling.
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Panchawagh, Suhrud. "NIMG-02. DEVELOPING A RADIOMIC HIERARCHICAL GAUSSIAN PROCESS BOOSTING MODEL TO PREDICT PRIMARY TUMOR ORIGIN FROM MULTICENTRIC LONGITUDINAL MRI DATA OF BRAIN METASTASES." Neuro-Oncology 26, Supplement_8 (2024): viii195. http://dx.doi.org/10.1093/neuonc/noae165.0769.
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Abstract Focal neurologic deficits due to brain metastases might be the initial presentation in patients with cancer. Common approaches to investigate the occult primary malignancy include a whole-body PET-CT, whole-body MRI, studying tumor markers, or histopathological examination. Most of these modalities are expensive, time-consuming, invasive, and delay treatment. The field of radiomics leverages quantitative features extracted from images to improve the decision-making process in clinical practices. Although not easily understandable, these features are proxies for the tumor’s shape, contour, texture, and intensity patterns. They can help understand tumor heterogeneity, predict tumor behavior, and assess treatment response. We propose a robust radiomic workflow using longitudinal MRI data from 914 high-resolution imaging studies from 12 different centers (Ocaña-Tienda et al. (2023), Ramakrishnan et al. (2023), Wang et al. (2023)) to predict the origin of brain metastases from brain MRI scans. After using ensemble feature selection methods, we trained three fixed-effects and one mixed-effects multiclass classification model to account for longitudinal data. Fixed-effects logistic regression and support vector kernel models performed the worst (AUC 0.74 and 0.75, respectively), while the random forest performed better (AUC 0.97). The mixed-effects hierarchical Gaussian process boosting (GPBoost) model performed the best with an accuracy of 85.8%, precision of 85.4%, sensitivity of 85.8%, specificity of 95.7%, and an AUC of 0.98. In one-versus-rest classification, the GPBoost model identified the primary tumor as melanoma with the highest accuracy (96.1%) and non-small cell lung cancer with the lowest accuracy (89.0%). Developing such models holds great promise in reducing patient and institutional costs, improving time to effective treatment, and enhancing overall survival rates through personalized therapy approaches. In the future, we hope that incorporating newer patient data and refining the model to improve external validity would enable more accurate and generalizable predictions, leading to better clinical outcomes across diverse populations.
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Hertono, Gatot Fatwanto, Ridho Kresna Wattimena, Gabriella Aileen Mendrofa, and Bevina Desjwiandra Handari. "Classifying Coal Mine Pillar Stability Areas with Multiclass SVM on Ensemble Learning Models." Journal of Engineering and Technological Sciences 56, no. 1 (2024): 95–109. http://dx.doi.org/10.5614/j.eng.technol.sci.2024.56.1.8.
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Pillars are key structural components in coal mining. The safety requirements of underground coal mines are non-negotiable. Accurately classifying the areas of pillar stability helps ensure safety in coal mines. This study aimed to classify new pillar stability categories and their stability areas. The multiclass support vector machine (SVM) method was implemented with two types of kernel functions (polynomial and radial basis function (RBF) kernels) on pillar stability data with four new categories: failed or intact, either with or without an appropriate safety factor. This classification uses three basic ensemble learning models: Artificial Neural Network-Backpropagation Rectified Linear Unit, Artificial Neural Network-Backpropagation Exponential Linear Unit, and Artificial Neural Network-Backpropagation Gaussian Error Linear Unit. The results with four data proportions and ten experiments had an average accuracy and standard deviation of 92.98% and 0.56%-1.64% respectively. The accuracies of the multiclass SVM method using the polynomial kernel and the RBF kernel with Bayesian parameter optimization to classify the areas of pillar stability were 91% and 92%, respectively. The multiclass SVM method with the RBF kernel captured 96.6% of potentially dangerous pillars. The visualization of classification areas showed that areas with intact pillars may also have failed pillars.
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Clottey, Richard Nunoo, Winfred Yaokumah, and Justice Kwame Appati. "Modelling and Evaluation of Network Intrusion Detection Systems Using Machine Learning Techniques." International Journal of Intelligent Information Technologies 17, no. 4 (2021): 1–19. http://dx.doi.org/10.4018/ijiit.289971.
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This study aims at modelling and evaluating the performance of machine learning techniques on a recent network intrusion dataset. Five machine learning algorithms, which include K-Nearest Neighbour (KNN), Support Vector Machines (SVM), Voting Ensemble, Random Forest, and XGBoost, have been utilized in the development of the network intrusion detection models. The proposed models are tested using the UNSW_NB15 dataset. Three different K values are used for model with KNN algorithm and two different kernels are utilized in the development of the model with SVM. The best detection accuracy of the model developed with KNN was 84.9% with a K value of 9, the SVM model with the best accuracy is developed with the Gaussian kernel and obtained an accuracy of 83% and the Voting Ensemble achieved 83.4% accuracy. Random Forest model achieved accuracies of 90.2% and 70.8% for binary classification and multiclass classification respectively. Finally, XGBoost model also achieves accuracies of 85% and 51.77% for binary and multiclass classification respectively.
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Segera, Davies, Mwangi Mbuthia, and Abraham Nyete. "Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis." BioMed Research International 2019 (December 16, 2019): 1–11. http://dx.doi.org/10.1155/2019/4085725.
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Determining an optimal decision model is an important but difficult combinatorial task in imbalanced microarray-based cancer classification. Though the multiclass support vector machine (MCSVM) has already made an important contribution in this field, its performance solely depends on three aspects: the penalty factor C, the type of kernel, and its parameters. To improve the performance of this classifier in microarray-based cancer analysis, this paper proposes PSO-PCA-LGP-MCSVM model that is based on particle swarm optimization (PSO), principal component analysis (PCA), and multiclass support vector machine (MCSVM). The MCSVM is based on a hybrid kernel, i.e., linear-Gaussian-polynomial (LGP) that combines the advantages of three standard kernels (linear, Gaussian, and polynomial) in a novel manner, where the linear kernel is linearly combined with the Gaussian kernel embedding the polynomial kernel. Further, this paper proves and makes sure that the LGP kernel confirms the features of a valid kernel. In order to reveal the effectiveness of our model, several experiments were conducted and the obtained results compared between our model and other three single kernel-based models, namely, PSO-PCA-L-MCSVM (utilizing a linear kernel), PSO-PCA-G-MCSVM (utilizing a Gaussian kernel), and PSO-PCA-P-MCSVM (utilizing a polynomial kernel). In comparison, two dual and two multiclass imbalanced standard microarray datasets were used. Experimental results in terms of three extended assessment metrics (F-score, G-mean, and Accuracy) reveal the superior global feature extraction, prediction, and learning abilities of this model against three single kernel-based models.
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Yang, Xiaofeng. "Transmit Antenna Selection for Sum-Rate Maximization with Multiclass Scalable Gaussian Process Classification." International Journal of Antennas and Propagation 2023 (July 29, 2023): 1–7. http://dx.doi.org/10.1155/2023/3547030.
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Antenna selection techniques are extensively applied to reduce hardware cost and power consumption in multiple-input multiple-output (MIMO) systems. This paper proposed a low-cost antenna selection method for system sum-rate maximization based on multiclass scalable Gaussian process classification (SGPC) which is capable to perform analytical inference and is scalable for massive data. Simulation results show that the average sum-rate obtained by SGPC is 1. 9 bps/Hz more than that obtained by conventional optimization driven user-centric antenna selection (UCAS) algorithm and 1 bps/Hz more than that obtained by the up-to-date learning scheme based on a deep neural network (DNN) when signal-to-noise ratio (SNR) is 10 dB, the number of total antennas at BS is 6, the number of selected antennas is 4, and the number of single-antenna users is 4. The superiority of SGPC over UCAS and DNN is more obvious as SNR, the number of selected antennas, or the number of users increases.
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Sagar, K. Manoj. "MultiClass Text Classification Using Support Vector Machine." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–10. http://dx.doi.org/10.55041/ijsrem27465.
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Support vector machine (SVM) was initially designed for binary classification .To solve multi-class problems of support vector machines (SVM) more efficiently, a novel framework, which we call class-incremental learning (CIL) CIL reuses the old models of the classifier and learns only one binary sub-classifier with an additional phase of feature selection when a new class comes. In text classification, where computers sort text documents into categories, keeping up with new information can be tricky. Traditional methods need lots of retraining to adapt. However, Incremental Learning for multi-class Support Vector Machines (SVMs) offers a solution. It lets us update the model with new data while remembering what it learned before .In this project, we'll explore how Incremental Learning makes multi-class SVMs better at handling changing data and even learning about new categories as they appear .There is a problem in addressing the challenge of integrating new classes while maintaining classification accuracy on existing and new classes .The main goal of this project is to create a method that effectively adapts the MC- SVM to evolving data distributions while minimizing the impact on previously learned classes and optimising resource utilization. Keywords— feature extraction, support vector machine, multi class incremental learning, Gaussian kernel.
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A. Tamilmani, Et al. "An Ensemble Framework Approach to Crop Type Prediction Using Feature Selection and Multiclass Classification." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 1179–89. http://dx.doi.org/10.17762/ijritcc.v11i9.9027.
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Crop type classification plays a crucial role in modern agriculture, aiding in yield prediction, resource management, and land-use planning. This paper presents a comprehensive framework for crop type classification utilizing a combination of feature selection techniques, robust classification Algorithm, and a Support Vector Machine (SVM)-based multiclass classification approach. The proposed framework begins with a novel feature selection process that identifies the most relevant attributes from the Agricultural Data and Rainfall data. This feature selection step is essential for reducing data dimensionality, enhancing classification accuracy, and improving model interpretability. Following feature selection, a state-of-the-art multiclass classification strategy based on Support Vector Machines is employed. SVMs are known for their capability to handle high-dimensional data and have demonstrated superior performance in various classification tasks. In this framework, SVMs are adapted to handle multiclass crop type classification efficiently. The model is trained on the selected features and optimized using hyperparameter tuning techniques to ensure robust performance.
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Tufail, Ahsan Bin, Inam Ullah, Wali Ullah Khan, et al. "Diagnosis of Diabetic Retinopathy through Retinal Fundus Images and 3D Convolutional Neural Networks with Limited Number of Samples." Wireless Communications and Mobile Computing 2021 (November 17, 2021): 1–15. http://dx.doi.org/10.1155/2021/6013448.
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Diabetic retinopathy (DR) is a worldwide problem associated with the human retina. It leads to minor and major blindness and is more prevalent among adults. Automated screening saves time of medical care specialists. In this work, we have used different deep learning (DL) based 3D convolutional neural network (3D-CNN) architectures for binary and multiclass (5 classes) classification of DR. We have considered mild, moderate, no, proliferate, and severe DR categories. We have deployed two artificial data augmentation/enhancement methods: random weak Gaussian blurring and random shift along with their combination to accomplish these tasks in the spatial domain. In the binary classification case, we have found the performance of 3D-CNN architecture trained by deploying combined augmentation methods to be the best, while in the multiclass case, the performance of model trained without augmentation is the best. It is observed that the DL algorithms working with large volumes of data may achieve better performances as compared to the methods working with small volumes of data.
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Uddin, Jia, Myeongsu Kang, Dinh V. Nguyen, and Jong-Myon Kim. "Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/814593.
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This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D) texture features and a multiclass support vector machine (MCSVM). The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns), and extracts these texture features by generating the dominant neighborhood structure (DNS) map. The principal component analysis (PCA) is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA) multiclass support vector machines (MCSVMs) to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environments.
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Khemlapure, Venkatesh, Ashwini Patil, Nikita Chavan, and Nisha Mali. "Product Defect Detection Using Deep Learning." International Journal of Intelligent Systems and Applications 16, no. 4 (2024): 39–54. http://dx.doi.org/10.5815/ijisa.2024.04.03.
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To maximize production efficiency, product quality control is paying more attention to the quick and reliable automated quality visual inspection. Product defect detection is a critical part of the inspection process. Manual defect detection has a lot of flaws that can be overcome using a deep learning approach. In this paper we have proposed and implemented the deep learning models to detect defects in the manufactured product. Two types of classification, i.e., binary and multiclass classification, is done using CNN, AlexNet, and YOLO algorithms. For the binary classification which is just used to check whether there is a defect in the product, we have proposed three different architectures of CNN, out of which the third CNN model gave 99.44% and 97.49% for training and testing, respectively. We also tested the AlexNet model and got accuracy of 97.6%. And for the multiclass classification that is used for identification of type(s) of defects, the YOLOv8 model is proposed and implemented, which gives better results by attaining a remarkable accuracy of 98.7% for multiclass classification. We also designed and developed the Android Application, which is used on the field for defect detection in the manufacturing industry.
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Nurrahman, Fathu, Hari Wijayanto, Aji Hamim Wigena, and Nunung Nurjanah. "PRE-PROCESSING DATA ON MULTICLASS CLASSIFICATION OF ANEMIA AND IRON DEFICIENCY WITH THE XGBOOST METHOD." BAREKENG: Jurnal Ilmu Matematika dan Terapan 17, no. 2 (2023): 0767–74. http://dx.doi.org/10.30598/barekengvol17iss2pp0767-0774.
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Anemia and iron deficiency are health problems in Indonesia and globally. In Multiclass Classification, data problems often occur, such as missing data, too many variables, and unbalanced data. Then pre-processing data will be carried out using MissForest imputation, Boruta featuring selection, and SMOTE to help improve the performance of the classification model in predicting a particular class. After the data pre-processing process is carried out, classification modeling will be carried out using the XGBoost algorithm. It was found that when pre-processing the data could improve the performance of the model in predicting multiclass classification for cases of anemia and iron deficiency in women in Indonesia by 0.815 for the accuracy value and 0.9693 for the AUC value
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He, Yu, Xiaofan Dong, Theodore E. Simos, et al. "A bio-inspired weights and structure determination neural network for multiclass classification: Applications in occupational classification systems." AIMS Mathematics 9, no. 1 (2023): 2411–34. http://dx.doi.org/10.3934/math.2024119.
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<abstract><p>Undoubtedly, one of the most common machine learning challenges is multiclass classification. In light of this, a novel bio-inspired neural network (NN) has been developed to address multiclass classification-related issues. Given that weights and structure determination (WASD) NNs have been acknowledged to alleviate the disadvantages of conventional back-propagation NNs, such as slow training pace and trapping in a local minimum, we developed a bio-inspired WASD algorithm for multiclass classification problems (BWASDC) by using the metaheuristic beetle antennae search (BAS) algorithm to enhance the WASD algorithm's learning process. The BWASDC's effectiveness is then evaluated through applications in occupational classification systems. It is important to mention that systems of occupational classification serve as a fundamental indicator of occupational exposure. For this reason, they are highly significant in social science research. According to the findings of four occupational classification experiments, the BWASDC model outperformed some of the most modern classification models obtainable through MATLAB's classification learner app on all fronts.</p></abstract>
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Zhao, Lijun, Qingsheng Li, and Bingbing Li. "SAR Target Recognition via Monogenic Signal and Gaussian Process Model." Mathematical Problems in Engineering 2022 (September 13, 2022): 1–7. http://dx.doi.org/10.1155/2022/3086486.
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The monogenic signal and Gaussian process model are applied to synthetic aperture radar (SAR) target recognition. The monogenic signal is used to extract the features of the SAR image. The Gaussian process model is a statistical learning algorithm based on the Bayesian theory, which constructs a classification model by combining the kernel function and the probability judgement. Compared with the traditional classification model, the Gaussian process model can obtain higher classification efficiency and accuracy. During the implementation, the monogenic feature vector of the SAR image is used as the input, and the target label is used as the output to train the Gaussian process model. For the test sample to be classified, the target label is determined by calculating the posterior probability of each class using the Gaussian process model. In the experiments, the validations are carried out under typical conditions based on the MSTAR dataset. According to the experimental results, the proposed method maintains the highest performance under the standard operating condition, depression angle differences, and noise corruption, which verifies its effectiveness and robustness.
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Diethe, Tom, and Mark Girolami. "Online Learning with (Multiple) Kernels: A Review." Neural Computation 25, no. 3 (2013): 567–625. http://dx.doi.org/10.1162/neco_a_00406.
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This review examines kernel methods for online learning, in particular, multiclass classification. We examine margin-based approaches, stemming from Rosenblatt's original perceptron algorithm, as well as nonparametric probabilistic approaches that are based on the popular gaussian process framework. We also examine approaches to online learning that use combinations of kernels—online multiple kernel learning. We present empirical validation of a wide range of methods on a protein fold recognition data set, where different biological feature types are available, and two object recognition data sets, Caltech101 and Caltech256, where multiple feature spaces are available in terms of different image feature extraction methods.
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Pavel, Marius Sorin, Simona Moldovanu, and Dorel Aiordachioaie. "On Classification of the Human Emotions from Facial Thermal Images: A Case Study Based on Machine Learning." Machine Learning and Knowledge Extraction 7, no. 2 (2025): 27. https://doi.org/10.3390/make7020027.
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(1) Background: This paper intends to accomplish a comparative study and analysis regarding the multiclass classification of facial thermal images, i.e., in three classes corresponding to predefined emotional states (neutral, happy and sad). By carrying out a comparative analysis, the main goal of the paper consists in identifying a suitable algorithm from machine learning field, which has the highest accuracy (ACC). Two categories of images were used in the process, i.e., images with Gaussian noise and images with “salt and pepper” type noise that come from two built-in special databases. An augmentation process was applied to the initial raw images that led to the development of the two databases with added noise, as well as the subsequent augmentation of all images, i.e., rotation, reflection, translation and scaling. (2) Methods: The multiclass classification process was implemented through two subsets of methods, i.e., machine learning with random forest (RF), support vector machines (SVM) and k-nearest neighbor (KNN) algorithms and deep learning with the convolutional neural network (CNN) algorithm. (3) Results: The results obtained in this paper with the two subsets of methods belonging to the field of artificial intelligence (AI), together with the two categories of facial thermal images with added noise used as input, were very good, showing a classification accuracy of over 99% for the two categories of images, and the three corresponding classes for each. (4) Discussion: The augmented databases and the additional configurations of the implemented algorithms seems to have had a positive effect on the final classification results.
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Das, Abhijit, and Pramod . "An Approach for Identifying Network Intrusion in an Automated Process Control Computer System." International Journal of Electrical and Electronics Research 10, no. 4 (2022): 1219–24. http://dx.doi.org/10.37391/ijeer.100472.
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Technology and networks have improved significantly in recent decades, and Internet services are now available in almost every business. It has become increasingly important to develop information security technology to identify the most recent attack as hackers are getting better at stealing information. The most important technology for security is an Intrusion Detection System (IDS) which employs machine learning and deep learning technique to identify network irregularities. To detect an unknown attack, we propose to use a new intrusion detection system using a deep neural network methodology which provides excellent performance to detect intrusion. This research focuses on an automated process control computer system that recognizes, records, analyzes, and correlates threats to online safety. In addition, two different methods are used to detect an attack (the binary classification and the multiclass classification). One of the most promising features of the proposed technique is its accuracy (98.99 percent with the multiclass classification and the binary classification). The proposed method's first step creates a model for a multiclass intrusion detection system based on CNN. FOA (Fruit Fly Optimization Algorithm) is used in the process's pre-training phase to address the class imbalance issue. Each batch is obtained during the training process using the resampling method following the resampling weights, which are the results of the pre-training procedure.
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Yang, Na, and Yongtao Zhang. "A Gaussian Process Classification and Target Recognition Algorithm for SAR Images." Scientific Programming 2022 (January 20, 2022): 1–10. http://dx.doi.org/10.1155/2022/9212856.
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Synthetic aperture Radar (SAR) uses the relative movement of the Radar and the target to pick up echoes of the detected area and image it. In contrast to optical imaging, SAR imaging systems are not affected by weather and time and can detect targets in harsh conditions. Therefore, the SAR image has important application value in military and civilian purposes. This paper introduces the classification of Gaussian process. Gaussian process classification is a probabilistic classification algorithm based on Bass frame. This is a complete probability expression. Based on Gaussian process and SAR data, Gaussian process classification algorithm for SAR images is studied in this paper. In this paper, we introduce the basic principle of Gaussian process, briefly analyze the basic theory of classification and the characteristics of SAR images, provide the evaluation index system of image classification, and give the SAR classification model of Gaussian process. Taking Laplace approximation as an example, several classification algorithms are introduced directly. Based on the two classifications, we propose an indirect multipurpose classification method and a multifunction classification method for two-pair two-Gaussian processes. The SAR image algorithm based on the two categories is relatively simple and achieves certain results.
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Abisoye, Opeyemi Aderiike, Rasheed Gbenga Jimoh, and Muhammed Uthman Mubashir Babatunde Uthman. "Ensemble Feed-Forward Neural Network and Support Vector Machine for Prediction of Multiclass Malaria Infection." Journal of Information and Communication Technology 21, No.1 (2021): 117–48. http://dx.doi.org/10.32890/jict2022.21.1.6.
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Globally, recent research are focused on developing appropriate and robust algorithms to provide a robust healthcare system that is versatile and accurate. Existing malaria models are plagued with low rate of convergence, overfitting, limited generalization due to restriction to binary cases prediction, and proneness to local minimum errors in finding reliable testing output due to complexity of features in the feature space, which is a black box in nature. This study adopted a stacking method of heterogeneous ensemble learning of Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms to predict multiclass, symptomatic, and climatic malaria infection. ANN produced 48.33 percent accuracy, 60.61 percent sensitivity, and 45.58 percent specificity. SVM with Gaussian kernel function gave better performance results of 85.60 percent accuracy, 84.06 percent sensitivity, and 86.09 percent specificity. Consequently, to improve prediction performance, a stacking method was introduced to ensemble SVM with ANN. The proposed ensemble malaria model was tuned on different thresholds at a threshold value of 0.60, the ensemble model gave an optimum accuracy of 99.86 percent, sensitivity 100 percent, specificity 98.68 percent, and mean square error 0.14. The ensemble model experimental results indicated that stacked multiple classifiers produced better results than a single model. This research demonstrated the efficiency of heterogeneous stacking ensemble model on effects of climatic variations on multiclass malaria infection classification. Furthermore, the model reduced complexity, overfitting, low rate of convergence, and proneness to local minimum error problems of multiclass malaria infection in comparison to previous related models.
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Ay, Fahrettin, Gökhan İnce, Mustafa E. Kamaşak, and K. Yavuz Ekşi. "Classification of pulsars with Dirichlet process Gaussian mixture model." Monthly Notices of the Royal Astronomical Society 493, no. 1 (2020): 713–22. http://dx.doi.org/10.1093/mnras/staa154.
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ABSTRACT Young isolated neutron stars (INSs) most commonly manifest themselves as rotationally powered pulsars that involve conventional radio pulsars as well as gamma-ray pulsars and rotating radio transients. Some other young INS families manifest themselves as anomalous X-ray pulsars and soft gamma-ray repeaters that are commonly accepted as magnetars, i.e. magnetically powered neutron stars with decaying super-strong fields. Yet some other young INSs are identified as central compact objects and X-ray dim isolated neutron stars that are cooling objects powered by their thermal energy. Older pulsars, as a result of a previous long episode of accretion from a companion, manifest themselves as millisecond pulsars and more commonly appear in binary systems. We use Dirichlet process Gaussian mixture model (DPGMM), an unsupervised machine learning algorithm, for analysing the distribution of these pulsar families in the parameter space of period and period derivative. We compare the average values of the characteristic age, magnetic dipole field strength, surface temperature, and transverse velocity of all discovered clusters. We verify that DPGMM is robust and provide hints for inferring relations between different classes of pulsars. We discuss the implications of our findings for the magnetothermal spin evolution models and fallback discs.
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Khabti, Joharah, Saad AlAhmadi, and Adel Soudani. "Optimal Channel Selection of Multiclass Motor Imagery Classification Based on Fusion Convolutional Neural Network with Attention Blocks." Sensors 24, no. 10 (2024): 3168. http://dx.doi.org/10.3390/s24103168.
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The widely adopted paradigm in brain–computer interfaces (BCIs) involves motor imagery (MI), enabling improved communication between humans and machines. EEG signals derived from MI present several challenges due to their inherent characteristics, which lead to a complex process of classifying and finding the potential tasks of a specific participant. Another issue is that BCI systems can result in noisy data and redundant channels, which in turn can lead to increased equipment and computational costs. To address these problems, the optimal channel selection of a multiclass MI classification based on a Fusion convolutional neural network with Attention blocks (FCNNA) is proposed. In this study, we developed a CNN model consisting of layers of convolutional blocks with multiple spatial and temporal filters. These filters are designed specifically to capture the distribution and relationships of signal features across different electrode locations, as well as to analyze the evolution of these features over time. Following these layers, a Convolutional Block Attention Module (CBAM) is used to, further, enhance EEG signal feature extraction. In the process of channel selection, the genetic algorithm is used to select the optimal set of channels using a new technique to deliver fixed as well as variable channels for all participants. The proposed methodology is validated showing 6.41% improvement in multiclass classification compared to most baseline models. Notably, we achieved the highest results of 93.09% for binary classes involving left-hand and right-hand movements. In addition, the cross-subject strategy for multiclass classification yielded an impressive accuracy of 68.87%. Following channel selection, multiclass classification accuracy was enhanced, reaching 84.53%. Overall, our experiments illustrated the efficiency of the proposed EEG MI model in both channel selection and classification, showing superior results with either a full channel set or a reduced number of channels.
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Lestari, Wulan Sri, Yuni Marlina Saragih, and Caroline Caroline. "MULTICLASS CLASSIFICATION FOR STUNTING PREDICTION USING DEEP NEURAL NETWORKS." JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) 10, no. 2 (2024): 386–93. http://dx.doi.org/10.33480/jitk.v10i2.5636.
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Stunting is a chronic nutritional issue that hinders child growth and leads to serious long-term health and developmental impacts, particularly in developing countries. Therefore, early and accurate prediction of stunting is crucial for implementing effective interventions. This research aims to develop a multiclass classification model based on Deep Neural Networks (DNNs) to predict stunting status. The model is trained using a comprehensive dataset that encompasses various health variables related to stunting. The research process includes data collection, data preprocessing, dataset splitting, and training and evaluation of the DNNs model. The model can classify stunting status into four categories: stunted, severely stunted, normal, and tall. Further analysis is conducted to evaluate the influence of various parameters on the model's performance, including dataset splitting ratios (80:20 and 70:30) and learning rates (0.001, 0.0001, and 0.00001). The results show that a learning rate of 0.0001 yields the highest prediction accuracy, at 93.64% and 93.83% for the two data-splitting schemes. This indicates that this learning rate has achieved an optimal balance between convergence speed and the model's generalization capability. Additionally, the developed DNNs model can identify complex patterns hidden within the data without being affected by noise. These findings confirm that appropriate parameter selection, particularly the dataset splitting ratio and learning rate, can significantly enhance the DNNs model's ability to identify complex data patterns.
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Febriantono, M. Aldiki, Sholeh Hadi Pramono, Rahmadwati Rahmadwati, and Golshah Naghdy. "Classification of multiclass imbalanced data using cost-sensitive decision tree C5.0." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 1 (2020): 65. http://dx.doi.org/10.11591/ijai.v9.i1.pp65-72.
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The multiclass imbalanced data problems in data mining were an interesting to study currently. The problems had an influence on the classification process in machine learning processes. Some cases showed that minority class in the dataset had an important information value compared to the majority class. When minority class was misclassification, it would affect the accuracy value and classifier performance. In this research, cost sensitive decision tree C5.0 was used to solve multiclass imbalanced data problems. The first stage, making the decision tree model uses the C5.0 algorithm then the cost sensitive learning uses the metacost method to obtain the minimum cost model. The results of testing the C5.0 algorithm had better performance than C4.5 and ID3 algorithms. The percentage of algorithm performance from C5.0, C4.5 and ID3 were 40.91%, 40, 24% and 19.23%.
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M., Aldiki Febriantono, Hadi Pramono Sholeh, Rahmadwati, and Naghdy Golshah. "Classification of multiclass imbalanced data using cost-sensitive decision tree C5.0." International Journal of Artificial Intelligence (IJ-AI) 9, no. 1 (2020): 65–72. https://doi.org/10.11591/ijai.v9.i1.pp65-72.
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The multiclass imbalanced data problems in data mining were interesting cases to study currently. The problems had an influence on the classification process in machine learning processes. Some cases showed that minority class in the dataset had an important information value compared to the majority class. When minority class was misclassification, it would affect the accuracy value and classifier performance. In this research, cost sensitive decision tree C5.0 was used to solve multiclass imbalanced data problems. The first stage, making the decision tree model uses the C5.0 algorithm then the cost sensitive learning uses the metacost method to obtain the minimum cost model. The results of testing the C5.0 algorithm had better performance than C4.5 and ID3 algorithms. The percentage of algorithm performance from C5.0, C4.5 and ID3 were 40.91%, 40, 24% and 19.23%.
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Sanchez-Gomez, Daniel, Carlos P. Odriozola Lloret, Ana Catarina Sousa, et al. "A supervised multiclass framework for mineral classification of Iberian beads." PLOS ONE 19, no. 7 (2024): e0302563. http://dx.doi.org/10.1371/journal.pone.0302563.
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Research on personal adornments depends on the reliable characterisation of materials to trace provenance and model complex social networks. However, many analytical techniques require the transfer of materials from the museum to the laboratory, involving high insurance costs and limiting the number of items that can be analysed, making the process of empirical data collection a complicated, expensive and time-consuming routine. In this study, we compiled the largest geochemical dataset of Iberian personal adornments (n = 1243 samples) by coupling X-ray fluorescence compositional data with their respective X-ray diffraction mineral labels. This allowed us to develop a machine learning-based framework for the prediction of bead-forming minerals by training and benchmarking 13 of the most widely used supervised algorithms. As a proof of concept, we developed a multiclass model and evaluated its performance on two assemblages from different Portuguese sites with current mineralogical characterisation: Cova das Lapas (n = 15 samples) and Gruta da Marmota (n = 10 samples). Our results showed that decisión-tres based classifiers outperformed other classification logics given the discriminative importance of some chemical elements in determining the mineral phase, which fits particularly well with the decision-making process of this type of model. The comparison of results between the different validation sets and the proof-of-concept has highlighted the risk of using synthetic data to handle imbalance and the main limitation of the framework: its restrictive class system. We conclude that the presented approach can successfully assist in the mineral classification workflow when specific analyses are not available, saving time and allowing a transparent and straightforward assessment of model predictions. Furthermore, we propose a workflow for the interpretation of predictions using the model outputs as compound responses enabling an uncertainty reduction approach currently used by our team. The Python-based framework is packaged in a public repository and includes all the necessary resources for its reusability without the need for any installation.
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Buchanan, James J., Michael D. Schneider, Robert E. Armstrong, Amanda L. Muyskens, Benjamin W. Priest, and Ryan J. Dana. "Gaussian Process Classification for Galaxy Blend Identification in LSST." Astrophysical Journal 924, no. 2 (2022): 94. http://dx.doi.org/10.3847/1538-4357/ac35ca.
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Abstract A significant fraction of observed galaxies in the Rubin Observatory Legacy Survey of Space and Time (LSST) will overlap at least one other galaxy along the same line of sight, in a so-called “blend.” The current standard method of assessing blend likelihood in LSST images relies on counting up the number of intensity peaks in the smoothed image of a blend candidate, but the reliability of this procedure has not yet been comprehensively studied. Here we construct a realistic distribution of blended and unblended galaxies through high-fidelity simulations of LSST-like images, and from this we examine the blend classification accuracy of the standard peak-finding method. Furthermore, we develop a novel Gaussian process blend classifier model, and show that this classifier is competitive with both the peak finding method as well as with a convolutional neural network model. Finally, whereas the peak-finding method does not naturally assign probabilities to its classification estimates, the Gaussian process model does, and we show that the Gaussian process classification probabilities are generally reliable.
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Seo, Sambu, and Klaus Obermayer. "Soft Learning Vector Quantization." Neural Computation 15, no. 7 (2003): 1589–604. http://dx.doi.org/10.1162/089976603321891819.
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Learning vector quantization (LVQ) is a popular class of adaptive nearest prototype classifiers for multiclass classification, but learning algorithms from this family have so far been proposed on heuristic grounds. Here, we take a more principled approach and derive two variants of LVQ using a gaussian mixture ansatz. We propose an objective function based on a likelihood ratio and derive a learning rule using gradient descent. The new approach provides a way to extend the algorithms of the LVQ family to different distance measure and allows for the design of “soft” LVQ algorithms. Benchmark results show that the new methods lead to better classification performance than LVQ 2.1. An additional benefit of the new method is that model assumptions are made explicit, so that the method can be adapted more easily to different kinds of problems.
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Jiang, Xinwei, Xiaoping Fang, Zhikun Chen, Junbin Gao, Junjun Jiang, and Zhihua Cai. "Supervised Gaussian Process Latent Variable Model for Hyperspectral Image Classification." IEEE Geoscience and Remote Sensing Letters 14, no. 10 (2017): 1760–64. http://dx.doi.org/10.1109/lgrs.2017.2734680.
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Li, Jinxing, Bob Zhang, and David Zhang. "Shared Autoencoder Gaussian Process Latent Variable Model for Visual Classification." IEEE Transactions on Neural Networks and Learning Systems 29, no. 9 (2018): 4272–86. http://dx.doi.org/10.1109/tnnls.2017.2761401.
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Li, Jinxing, Bob Zhang, Guangming Lu, Hu Ren, and David Zhang. "Visual Classification With Multikernel Shared Gaussian Process Latent Variable Model." IEEE Transactions on Cybernetics 49, no. 8 (2019): 2886–99. http://dx.doi.org/10.1109/tcyb.2018.2831457.
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Oyama, H., M. Yamakita, K. Sata, and A. Ohata. "Identification of Static Boundary Model Based on Gaussian Process Classification." IFAC-PapersOnLine 49, no. 11 (2016): 787–92. http://dx.doi.org/10.1016/j.ifacol.2016.08.115.
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Sovia, Nabila Ayunda, and Ni Wayan Surya Wardhani. "ENSEMBLE CNN WITH ADASYN FOR MULTICLASS CLASSIFICATION ON CABBAGE PESTS." BAREKENG: Jurnal Ilmu Matematika dan Terapan 18, no. 2 (2024): 1237–48. http://dx.doi.org/10.30598/barekengvol18iss2pp1237-1248.
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Image classification is a complex process influenced by various factors, one of which is the amount of image data. In the context of cabbage pest classification, data often exhibits a significant class imbalance, where certain pests are more prevalent than others. This imbalance can pose challenges during model training and evaluation, potentially leading to biases in favor of the majority pests and reduced accuracy in identifying and classifying the less common ones. This research aims to enhance the classification performance for multiclass data specific to cabbage pests. We propose an ensemble learning approach that combines Convolutional Neural Network (CNN), Support Vector Machine (SVM), and Bagging methods. To address the imbalance issue inherent in cabbage pest data, we employ the Adaptive Synthetic Sampling (ADASYN) resampling technique. The CNN acts as the primary image identifier and classifier for various cabbage pests. Subsequently, the CNN model is integrated into SVM and Bagging models to mitigate the challenges of imbalanced data in pest classification. The research outcomes demonstrate that our ensemble approach, in conjunction with the ADASYN resampling technique, achieves an impressive accuracy rate of 97%, signifying its potential for improved cabbage pest detection and classification.
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Mandjes, Michel, and Jaap Storm. "A Diffusion-Based Analysis of a Multiclass Road Traffic Network." Stochastic Systems 11, no. 1 (2021): 60–81. http://dx.doi.org/10.1287/stsy.2019.0065.
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This paper studies a stochastic model that describes the evolution of vehicle densities in a road network. It is consistent with the class of (deterministic) kinematic wave models, which describe traffic flows based on conservation laws that incorporate the macroscopic fundamental diagram (a functional relationship between vehicle density and flow). Our setup is capable of handling multiple types of vehicle densities, with general macroscopic fundamental diagrams, on a network with arbitrary topology. Interpreting our system as a spatial population process, we derive, under natural scaling, fluid, and diffusion limits. More specifically, the vehicle density process can be approximated with a suitable Gaussian process, which yield accurate normal approximations to the joint (in the spatial and temporal sense) vehicle density process. The corresponding means and variances can be computed efficiently. Along the same lines, we develop an approximation to the vehicles’ travel time distribution between any given origin and destination pair. Finally, we present a series of numerical experiments that demonstrate the accuracy of the approximations and illustrate the usefulness of the results.
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Zerrouki, Khadidja, Nadjia Benblidia, and Omar Boussaid. "Preprocessing multilingual text for the detection of extremism and radicalization in social networks using deep learning." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e11286. https://doi.org/10.54021/seesv5n2-594.
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Due to the lack of strict controls on social networks, extremist groups like ISIS, Al-Qaeda, and white supremacists have taken advantage of these platforms to spread their ideas, distribute harmful content, and recruit new members. The study of online extremism and radicalization is a multifaceted and intricate area of research. Although the majority of research in this field focuses on the analysis of data in a single language, there needs to be more studies on the analysis of multilingual data, specifically about detecting multi-ideology extremism in social media content. This research paper introduces the building of an artificial intelligence system that identifies instances of extremism and radicalization from data extracted from social networks. We utilize natural language processing (NLP) linguistic methods and text classification to process the textual data. Our study results show significant progress in multiclass multilingual text classification and the detection of extremism and radicalization within social networks. The Bi-LSTM (Bidirectional et al.) model demonstrates a binary classification accuracy of 97.33%, and the multiclass classification accuracy of the Transformer-based model, which employs the DistilBERT-multi (Distilled version of the Multilingual Bidirectional Encoder Representations from Transformers) pre-trained model, is 91.07%.
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Akbar, Muhamad, Siti Nurmaini, and Radiyati Umi Partan. "The deep convolutional networks for the classification of multi-class arrhythmia." Bulletin of Electrical Engineering and Informatics 13, no. 2 (2024): 1325–33. http://dx.doi.org/10.11591/eei.v13i2.6102.
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An arrhythmia is an irregular heartbeat. Many researchers in the AI field have carried out the automatic classification of arrhythmias, and the issue that has been widely discussed is imbalanced data. A popular technique for overcoming this problem is the synthetic minority oversampling technique (SMOTE) technique. In this paper, the author adds some sampling of data obtained from other datasets into the primary dataset. In this case, the main dataset is the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) arrhythmia database and an additional dataset from the MIT-BIH supraventricular arrhythmia database. The classification process is carried out with one-dimensional convolutional neural network model (1D-CNN) to perform multiclass and subject-class advancement of medical instrumentation (AAMII) classifications. The results obtained from this study are an accuracy of 99.10% for multiclass and 99.25% for subject-class.
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Ren, Ming, Chi Cheung, and Gao Xiao. "Gaussian Process Based Bayesian Inference System for Intelligent Surface Measurement." Sensors 18, no. 11 (2018): 4069. http://dx.doi.org/10.3390/s18114069.
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This paper presents a Gaussian process based Bayesian inference system for the realization of intelligent surface measurement on multi-sensor instruments. The system considers the surface measurement as a time series data collection process, and the Gaussian process is used as mathematical foundation to establish an inferring plausible model to aid the measurement process via multi-feature classification and multi-dataset regression. Multi-feature classification extracts and classifies the geometric features of the measured surfaces at different scales to design an appropriate composite covariance kernel and corresponding initial sampling strategy. Multi-dataset regression takes the designed covariance kernel as input to fuse the multi-sensor measured datasets with Gaussian process model, which is further used to adaptively refine the initial sampling strategy by taking the credibility of the fused model as the critical sampling criteria. Hence, intelligent sampling can be realized with consecutive learning process with full Bayesian treatment. The statistical nature of the Gaussian process model combined with various powerful covariance kernel functions offer the system great flexibility for different kinds of complex surfaces.
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Alsolai, Hadeel, Shahnawaz Qureshi, Syed Muhammad Zeeshan Iqbal, et al. "Employing a Long-Short-Term Memory Neural Network to Improve Automatic Sleep Stage Classification of Pharmaco-EEG Profiles." Applied Sciences 12, no. 10 (2022): 5248. http://dx.doi.org/10.3390/app12105248.
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An increasing problem in today’s society is the spiraling number of people suffering from various sleep disorders. The research results presented in this paper support the use of a novel method that employs techniques from the classification of sleep disorders for more accurate scoring. Applying this novel method will assist researchers with better analyzing subject profiles for recommending prescriptions or to alleviate sleep disorders. In biomedical research, the use of animal models is required to experimentally test the safety and efficacy of a drug in the pre-clinical stage. We have developed a novel LSTM Recurrent Neural Network to process Pharmaco-EEG Profiles of rats to automatically score their sleep–wake stages. The results indicate improvements over the current methods; for the case of combined channels, the model accuracy improved by 1% and 3% in binary or multiclass classifications, respectively, to accuracies of 93% and 82%. In the case of using a single channel, binary and multiclass LSTM models for identifying rodent sleep stages using single or multiple electrode positions for binary or multiclass problems have not been evaluated in prior literature. The results reveal that single or combined channels, and binary or multiclass classification tasks, can be applied in the automatic sleep scoring of rodents.
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Kukkar, Ashima, Rajni Mohana, Anand Nayyar, Jeamin Kim, Byeong-Gwon Kang, and Naveen Chilamkurti. "A Novel Deep-Learning-Based Bug Severity Classification Technique Using Convolutional Neural Networks and Random Forest with Boosting." Sensors 19, no. 13 (2019): 2964. http://dx.doi.org/10.3390/s19132964.
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The accurate severity classification of a bug report is an important aspect of bug fixing. The bug reports are submitted into the bug tracking system with high speed, and owing to this, bug repository size has been increasing at an enormous rate. This increased bug repository size introduces biases in the bug triage process. Therefore, it is necessary to classify the severity of a bug report to balance the bug triaging process. Previously, many machine learning models were proposed for automation of bug severity classification. The accuracy of these models is not up to the mark because they do not extract the important feature patterns for learning the classifier. This paper proposes a novel deep learning model for multiclass severity classification called Bug Severity classification to address these challenges by using a Convolutional Neural Network and Random forest with Boosting (BCR). This model directly learns the latent and highly representative features. Initially, the natural language techniques preprocess the bug report text, and then n-gram is used to extract the features. Further, the Convolutional Neural Network extracts the important feature patterns of respective severity classes. Lastly, the random forest with boosting classifies the multiple bug severity classes. The average accuracy of the proposed model is 96.34% on multiclass severity of five open source projects. The average F-measures of the proposed BCR and the existing approach were 96.43% and 84.24%, respectively, on binary class severity classification. The results prove that the proposed BCR approach enhances the performance of bug severity classification over the state-of-the-art techniques.
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Xie, Yurong, Di Wu, and Zhe Qiang. "An Improved Mixture Model of Gaussian Processes and Its Classification Expectation–Maximization Algorithm." Mathematics 11, no. 10 (2023): 2251. http://dx.doi.org/10.3390/math11102251.
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The mixture of experts (ME) model is effective for multimodal data in statistics and machine learning. To treat non-stationary probabilistic regression, the mixture of Gaussian processes (MGP) model has been proposed, but it may not perform well in some cases due to the limited ability of each Gaussian process (GP) expert. Although the mixture of Gaussian processes (MGP) and warped Gaussian process (WGP) models are dominant and effective for non-stationary probabilistic regression, they may not be able to handle general non-stationary probabilistic regression in practice. In this paper, we first propose the mixture of warped Gaussian processes (MWGP) model as well as its classification expectation–maximization (CEM) algorithm to address this problem. To overcome the local optimum of the CEM algorithm, we then propose the split and merge CEM (SMC EM) algorithm for MWGP. Experiments were done on synthetic and real-world datasets, which show that our proposed MWGP is more effective than the models used for comparison, and the SMCEM algorithm can solve the local optimum for MWGP.
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Oblitas, Jimy, and Jorge Ruiz. "Multivariate Analysis for the Classification of Chocolate According to its Percentage of Cocoa by Using Terahertz Time-Domain Spectroscopy (THz-TDS)." Proceedings 70, no. 1 (2020): 109. http://dx.doi.org/10.3390/foods_2020-08029.
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Terahertz time-domain spectroscopy is a useful technique for determining some physical characteristics of materials, and is based on selective frequency absorption of a broad-spectrum electromagnetic pulse. In order to investigate the potential of this technology to classify cocoa percentages in chocolates, the terahertz spectra (0.5–10 THz) of five chocolate samples (50%, 60%, 70%, 80% and 90% of cocoa) were examined. The acquired data matrices were analyzed with the MATLAB 2019b application, from which the dielectric function was obtained along with the absorbance curves, and were classified by using 24 mathematical classification models, achieving differentiations of around 93% obtained by the Gaussian SVM algorithm model with a kernel scale of 0.35 and a one-against-one multiclass method. It was concluded that the combined processing and classification of images obtained from the terahertz time-domain spectroscopy and the use of machine learning algorithms can be used to successfully classify chocolates with different percentages of cocoa.