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Karimi-Rizvandi, Sadegh, Hamid Valipoori Goodarzi, Javad Hatami Afkoueieh, et al. "Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms." Water 13, no. 5 (2021): 658. http://dx.doi.org/10.3390/w13050658.
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Owing to the reduction of surface-water resources and frequent droughts, the exploitation of groundwater resources has faced critical challenges. For optimal management of these valuable resources, careful studies of groundwater potential status are essential. The main goal of this study was to determine the optimal network structure of a Bayesian network (BayesNet) machine-learning model using three metaheuristic optimization algorithms—a genetic algorithm (GA), a simulated annealing (SA) algorithm, and a Tabu search (TS) algorithm—to prepare groundwater-potential maps. The methodology was applied to the town of Baghmalek in the Khuzestan province of Iran. For modeling, the location of 187 springs in the study area and 13 parameters (altitude, slope angle, slope aspect, plan curvature, profile curvature, topography wetness index (TWI), distance to river, distance to fault, drainage density, rainfall, land use/cover, lithology, and soil) affecting the potential of groundwater were provided. In addition, the statistical method of certainty factor (CF) was utilized to determine the input weight of the hybrid models. The results of the OneR technique showed that the parameters of altitude, lithology, and drainage density were more important for the potential of groundwater compared to the other parameters. The results of groundwater-potential mapping (GPM) employing the receiver operating characteristic (ROC) area under the curve (AUC) showed an estimation accuracy of 0.830, 0.818, 0.810, and 0.792, for the BayesNet-GA, BayesNet-SA, BayesNet-TS, and BayesNet models, respectively. The BayesNet-GA model improved the GPM estimation accuracy of the BayesNet-SA (4.6% and 7.5%) and BayesNet-TS (21.8% and 17.5%) models with respect to the root mean square error (RMSE) and mean absolute error (MAE), respectively. Based on metric indices, the GA provides a higher capability than the SA and TS algorithms for optimizing the BayesNet model in determining the GPM.
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Sagar, A. S. M. Sharifuzzaman, Jawad Tanveer, Yu Chen, et al. "BayesNet: Enhancing UAV-Based Remote Sensing Scene Understanding with Quantifiable Uncertainties." Remote Sensing 16, no. 5 (2024): 925. http://dx.doi.org/10.3390/rs16050925.
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Remote sensing stands as a fundamental technique in contemporary environmental monitoring, facilitating extensive data collection and offering invaluable insights into the dynamic nature of the Earth’s surface. The advent of deep learning, particularly convolutional neural networks (CNNs), has further revolutionized this domain by enhancing scene understanding. However, despite the advancements, traditional CNN methodologies face challenges such as overfitting in imbalanced datasets and a lack of precise uncertainty quantification, crucial for extracting meaningful insights and enhancing the precision of remote sensing techniques. Addressing these critical issues, this study introduces BayesNet, a Bayesian neural network (BNN)-driven CNN model designed to normalize and estimate uncertainties, particularly aleatoric and epistemic, in remote sensing datasets. BayesNet integrates a novel channel–spatial attention module to refine feature extraction processes in remote sensing imagery, thereby ensuring a robust analysis of complex scenes. BayesNet was trained on four widely recognized unmanned aerial vehicle (UAV)-based remote sensing datasets, UCM21, RSSCN7, AID, and NWPU, and demonstrated good performance, achieving accuracies of 99.99%, 97.30%, 97.57%, and 95.44%, respectively. Notably, it has showcased superior performance over existing models in the AID, NWPU, and UCM21 datasets, with enhancements of 0.03%, 0.54%, and 0.23%, respectively. This improvement is significant in the context of complex scene classification of remote sensing images, where even slight improvements mark substantial progress against complex and highly optimized benchmarks. Moreover, a self-prepared remote sensing testing dataset is also introduced to test BayesNet against unseen data, and it achieved an accuracy of 96.39%, which showcases the effectiveness of the BayesNet in scene classification tasks.
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Qiu, Jiaosheng. "Research on the Relationship between Intelligent Analysis and Weight of Keywords in English Test Questions." Wireless Communications and Mobile Computing 2022 (April 6, 2022): 1–11. http://dx.doi.org/10.1155/2022/3480746.
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With the rapid development of educational informationization, Internet of Things, and other technologies, English education has been paid special attention to, and all aspects such as educational model, learning behavior, teaching philosophy, and teaching evaluation have been greatly influenced by educational informationization. Based on the experience of practical education, this paper explores and studies the connotation and characteristics of English test questions and its influence and application on modern English test questions. This paper constructs a systematic method to extract keywords from English test questions. It perfects the fair and reasonable index of English keywords, establishes the weight system, discusses the relationship of keywords, and conducts academic research on vocabulary, word frequency and word position, emphatically adopts BayesNet algorithm to extract keywords, and realizes the evaluation system of English test keywords based on intelligent analysis and weight relationship. The results show that (1) selecting the calculation method and weight relationship suitable for the text system to carry out intelligent analysis, the weight ratio exceeds 65%; that is, the text keyword retrieval is successful. (2) The average accuracy (%), average recall (%) and average F -measure (%) in weighted names are almost less than 70%. Only the BayesNet algorithm has 72.3% weight analysis in keyword extraction in reading comprehension. (3) KEA algorithm, PAT TERR algorithm, and BayesNet algorithm take 0-2.8 s, 0-2.6 s, and 0-2.1 s, respectively, and the BayesNet algorithm takes the shortest time. The calculation time of users is greatly saved. (4) According to the calculation results of CPT model, the sum of the weights of the three algorithms is equal to 1, and the BayesNet algorithm is dominant in extracting keywords with a weight analysis of 0.529 in verb translation.
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Jang, Hyoyoung, Changbeom Yu, Jimin Seo, et al. "Probabilistic Prediction and Causal Analysis with BayesNet for Vehicle Brake Discs." Journal of Society for e-Business Studies 29, no. 3 (2024): 77–91. http://dx.doi.org/10.7838/jsebs.2024.29.3.077.
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Kumar, Munish, Payal Chhabra, and Naresh Kumar Garg. "An efficient content based image retrieval system using BayesNet and K-NN." Multimedia Tools and Applications 77, no. 16 (2018): 21557–70. http://dx.doi.org/10.1007/s11042-017-5587-8.
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Hu, Yan, Guangya Zhou, Chi Zhang, et al. "Identify Compounds' Target Against Alzheimer's Disease Based on In-Silico Approach." Current Alzheimer Research 16, no. 3 (2019): 193–208. http://dx.doi.org/10.2174/1567205016666190103154855.
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Background: Alzheimer's disease swept every corner of the globe and the number of patients worldwide has been rising. At present, there are as many as 30 million people with Alzheimer's disease in the world, and it is expected to exceed 80 million people by 2050. Consequently, the study of Alzheimer’s drugs has become one of the most popular medical topics. Methods: In this study, in order to build a predicting model for Alzheimer’s drugs and targets, the attribute discriminators CfsSubsetEval, ConsistencySubsetEval and FilteredSubsetEval are combined with search methods such as BestFirst, GeneticSearch and Greedystepwise to filter the molecular descriptors. Then the machine learning algorithms such as BayesNet, SVM, KNN and C4.5 are used to construct the 2D-Structure Activity Relationship(2D-SAR) model. Its modeling results are utilized for Receiver Operating Characteristic curve(ROC) analysis. Results: The prediction rates of correctness using Randomforest for AChE, BChE, MAO-B, BACE1, Tau protein and Non-inhibitor are 77.0%, 79.1%, 100.0%, 94.2%, 93.2% and 94.9%, respectively, which are overwhelming as compared to those of BayesNet, BP, SVM, KNN, AdaBoost and C4.5. Conclusion: In this paper, we conclude that Random Forest is the best learner model for the prediction of Alzheimer’s drugs and targets. Besides, we set up an online server to predict whether a small molecule is the inhibitor of Alzheimer's target at http://47.106.158.30:8080/AD/. Furthermore, it can distinguish the target protein of a small molecule.
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Sarkar, Alok, Md Maniruzzaman, Mohammad Ashik Alahe, and Mohiuddin Ahmad. "An Effective and Novel Approach for Brain Tumor Classification Using AlexNet CNN Feature Extractor and Multiple Eminent Machine Learning Classifiers in MRIs." Journal of Sensors 2023 (March 8, 2023): 1–19. http://dx.doi.org/10.1155/2023/1224619.
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A brain tumor is an uncontrolled malignant cell growth in the brain, which is denoted as one of the deadliest types of cancer in people of all ages. Early detection of brain tumors is needed to get proper and accurate treatment. Recently, deep learning technology has attained much attraction to the physicians for the diagnosis and treatment of brain tumors. This research presents a novel and effective brain tumor classification approach from MRIs utilizing AlexNet CNN for separating the dataset into training and test data along with extracting the features. The extracted features are then fed to BayesNet, sequential minimal optimization (SMO), Naïve Bayes (NB), and random forest (RF) classifiers for classifying brain tumors as no-tumor, glioma, meningioma, and pituitary tumors. To evaluate our model’s performance, we have utilized a publicly available Kaggle dataset. This paper demonstrates ROC, PRC, and cost curves for realizing classification performance of the models; also, performance evaluating parameters, such as accuracy, sensitivity, specificity, false positive rate, false negative rate, precision, f-measure, kappa statistics, MCC, ROC area, and PRC area, have been calculated for four testing options: the test data itself, cross-validation fold (CVF) 4, CVF 10, and percentage split (PS) 34% of the test data. We have achieved 88.75%, 98.15%, 86.25% and 100% of accuracy using the AlexNet CNN+BayesNet, AlexNet CNN+SMO, AlexNet CNN+NB, and AlexNet CNN+RF models, respectively, for the test data itself. The results imply that our approach is outstanding and very effective.
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Pawar, Kiran S., and Babasaheb J. Mohite. "Performance analysis of UKM-IDS20 dataset on machine learning algorithms." Journal of Statistics and Management Systems 27, no. 5 (2024): 997–1008. http://dx.doi.org/10.47974/jsms-1296.
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Machine learning algorithms are essential in classification and regression because they might a significant outcome on the accuracy of the classifier. It decreases the total of features of the traffic records. The algorithms adaptively improve their performance, reducing the processor and memory norms. This work proposes the machine learning algorithms on the available well-known mathematically proven classifiers. The proposed system for intrusion detection is tested and validated on UKM-IDS20 datasets with the suite of the classifiers. The system uses the BayesNet, Lazy Kstar, NaiveBayes, NavieBayesMultinomialText, NavieBayesUpdateable, Function LibSVM, Function SGDText, and Function Voted Perception classifier from the Bayes and function-based classifier to detect intrusion detection. The BayesNet classifier performed the higher accuracy of 99.9845% with 0.27 seconds to build the model on the dataset. The proposed system also served on the rule-based classifiers to achieve the accuracy, F1 score and built up a time to detect the intrusion detection on UKM-IDS20 datasets. The proposed system uses the rulebased machine learning algorithms JripOneR, Decision Table, Furia, PART, and ZeroR classifiers for network intrusion detection. The Furia rule-based classifier achieves the higher accuracy, F1 score, and Recall of 99.969%, 99.9775%, and 99.9888%, respectively, with 46 features of the UKMIDS20 dataset. The proposed framework achieve a superior accurateness of 99.9845% and an F1 score of 99.9887% with 1.6 second model built-up time with a Random Forest tree-based classifier on the UKMIDS20 dataset used in NIDS. The system applied on filter-based algorithms info gain (IG) with top ranked and threshold model to analyze the performance.
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Rubio Delgado, Elayne, Lisbeth Rodríguez-Mazahua, José Antonio Palet Guzmán, et al. "Analysis of Medical Opinions about the Nonrealization of Autopsies in a Mexican Hospital Using Association Rules and Bayesian Networks." Scientific Programming 2018 (2018): 1–21. http://dx.doi.org/10.1155/2018/4304017.
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This research identifies the factors influencing the reduction of autopsies in a hospital of Veracruz. The study is based on the application of data mining techniques such as association rules and Bayesian networks in data sets obtained from opinions of physicians. We analyzed, for the exploration and extraction of the knowledge, algorithms like Apriori, FPGrowth, PredictiveApriori, Tertius, J48, NaiveBayes, MultilayerPerceptron, and BayesNet, all of them provided by the API of WEKA. To generate mining models and present the new knowledge in natural language, we also developed a web application. The results presented in this study are those obtained from the best-evaluated algorithms, which have been validated by specialists in the field of pathology.
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Obaidullah, Sk Md, Anamika Mondal, Nibaran Das, and Kaushik Roy. "Script Identification from Printed Indian Document Images and Performance Evaluation Using Different Classifiers." Applied Computational Intelligence and Soft Computing 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/896128.
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Identification of script from document images is an active area of research under document image processing for a multilingual/ multiscript country like India. In this paper the real life problem of printed script identification from official Indian document images is considered and performances of different well-known classifiers are evaluated. Two important evaluating parameters, namely, AAR (average accuracy rate) and MBT (model building time), are computed for this performance analysis. Experiment was carried out on 459 printed document images with 5-fold cross-validation. Simple Logistic model shows highest AAR of 98.9% among all. BayesNet and Random Forest model have average accuracy rate of 96.7% and 98.2% correspondingly with lowest MBT of 0.09 s.
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Alaba, O. B., E. O. Taiwo, and O. A. Abass. "Data mining algorithm for development of a predictive model for mitigating loan risk in Nigerian banks." Journal of Applied Sciences and Environmental Management 25, no. 9 (2021): 1613–16. http://dx.doi.org/10.4314/jasem.v25i9.11.
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The focus of this paper is on the development of data mining algorithm for developing of predictive loan risk model for Nigerian banks. The model classifies and predicts the risk involved in granting loans to customers as either good or bad loan by collecting data based on J48 decision tree, BayesNet and Naïve Bayes algorithms for a period of ten (10) years (2010 2019) from using structured questionnaire. The formulation and simulation of the predictive model were carried out using Waikato Environment for Knowledge Analysis (WEKA) software. The performance of the three algorithms for predicting loan risk was done based on accuracy and error rate metrics. The study revealed that J48 decision tree model is the most efficient of all the three models.
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Guo, Hai, Jinghua Yin, Jingying Zhao, Yuanyuan Liu, Lei Yao, and Xu Xia. "An Automatic Detection Method of Nanocomposite Film Element Based on GLCM and Adaboost M1." Advances in Materials Science and Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/205817.
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An automatic detection model adopting pattern recognition technology is proposed in this paper; it can realize the measurement to the element of nanocomposite film. The features of gray level cooccurrence matrix (GLCM) can be extracted from different types of surface morphology images of film; after that, the dimension reduction of film can be handled by principal component analysis (PCA). So it is possible to identify the element of film according to the Adaboost M1 algorithm of a strong classifier with ten decision tree classifiers. The experimental result shows that this model is superior to the ones of SVM (support vector machine), NN and BayesNet. The method proposed can be widely applied to the automatic detection of not only nanocomposite film element but also other nanocomposite material elements.
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Abraham, Bejoy, and Madhu S. Nair. "Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier." Biocybernetics and Biomedical Engineering 40, no. 4 (2020): 1436–45. http://dx.doi.org/10.1016/j.bbe.2020.08.005.
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Praditya, Ni Wayan Priscila. "Prediksi Kualitas Red Wine dan White Wine Menggunakan Data Mining." Journal Software, Hardware and Information Technology 3, no. 2 (2023): 25–33. http://dx.doi.org/10.24252/shift.v3i2.90.
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Data mining is a technique used in business intelligence or artificial intelligence capable of classifying and clustering data based on the nature and correlation of the data sets used. The methods commonly used in data mining are C45, K-Means, Apriori Decision Tree, KNN, LSTM, Naive Bayesian, etc. In this study, the method used is the Decision Tree method which aims to classify the quality of red wine and white wine. The results of this study indicate that the prediction of red wine has a precision of 61.1%, recall of 60.7%, f-measure of 60.3%, and an average accuracy of 60.7%, while white wine has a precision of 58.2%, recall of 58.7%, f-measure 58.4%, and 58.7% accuracy. The method used in this study also shows that the Decision Tree can outperform other previously applied methods, namely Lib-SVM, BayesNet, and Multi Perceptron.
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P, Kiran Kumar, and Suneetha Rani R. "Blood Cancer Classification Based on Microarray Data." Knowledge Transactions on Applied Machine Learning 02, no. 04 (2024): 09–17. http://dx.doi.org/10.59567/ktaml.v2.04.02.
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This study delves into the classification of microarray data from patient samples—a critical step in deciphering disease mechanisms and improving patient stratification. Motivated by the potential of microarray technology to measure thousands of gene expression levels simultaneously, this research addresses the analytical challenges posed by the high dimensionality of such data. Employing eight distinct machine learning classifiers (J48, Random Forest, Random Tree, JRip, OneR, k-NN, SMO, and BayesNet), the study compares their performance using accuracy, precision, recall, F-measure, and ROC metrics. The results shed light on each classifier’s efficacy in handling complex microarray data and inform their application in clinical genomics. Despite offering valuable insights, the findings are subject to the limitations of microarray technology, including noise and variability in gene expression measurements. Ultimately, this research provides practical recommendations for leveraging machine learning classifiers in clinical genomics, thereby contributing to the progress of personalized medicine.
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Obaidullah, S. K., K. C. Santosh, Chayan Halder, Nibaran Das, and Kaushik Roy. "Word-Level Multi-Script Indic Document Image Dataset and Baseline Results on Script Identification." International Journal of Computer Vision and Image Processing 7, no. 2 (2017): 81–94. http://dx.doi.org/10.4018/ijcvip.2017040106.
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Document analysis research starves from the availability of public datasets. Without publicly available dataset, one cannot make fair comparison with the state-of-the-art methods. To bridge this gap, in this paper, the authors propose a word-level document image dataset of 13 different Indic languages from 11 official scripts. It is composed of 39K words that are equally distributed i.e., 3K words per language. For a baseline results, five different classifiers: multilayer perceptron (MLP), fuzzy unordered rule induction algorithm (FURIA), simple logistic (SL), library for linear classifier (LibLINEAR) and bayesian network (BayesNet) classifiers are used with three state-of-the-art features: spatial energy (SE), wavelet energy (WE) and the Radon transform (RT), including their possible combinations. The authors observed that MLP provides better results when all features are used, and achieved the bi-script accuracy of 99.24% (keeping Roman common), 98.38% (keeping Devanagari common) and tri-script accuracy of 98.19% (keeping both Devanagari and Roman common).
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Li, Yanjuan, Zhengnan Zhao, and Zhixia Teng. "i4mC-EL: Identifying DNA N4-Methylcytosine Sites in the Mouse Genome Using Ensemble Learning." BioMed Research International 2021 (May 29, 2021): 1–11. http://dx.doi.org/10.1155/2021/5515342.
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As one of important epigenetic modifications, DNA N4-methylcytosine (4mC) plays a crucial role in controlling gene replication, expression, cell cycle, DNA replication, and differentiation. The accurate identification of 4mC sites is necessary to understand biological functions. In the paper, we use ensemble learning to develop a model named i4mC-EL to identify 4mC sites in the mouse genome. Firstly, a multifeature encoding scheme consisting of Kmer and EIIP was adopted to describe the DNA sequences. Secondly, on the basis of the multifeature encoding scheme, we developed a stacked ensemble model, in which four machine learning algorithms, namely, BayesNet, NaiveBayes, LibSVM, and Voted Perceptron, were utilized to implement an ensemble of base classifiers that produce intermediate results as input of the metaclassifier, Logistic. The experimental results on the independent test dataset demonstrate that the overall rate of predictive accurate of i4mC-EL is 82.19%, which is better than the existing methods. The user-friendly website implementing i4mC-EL can be accessed freely at the following.
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Ajdinović, Nadina, Semina Nurkić, Jasmina Baraković Husić, and Sabina Baraković. "Recognition of traffic generated by WebRTC communication." Science, Engineering and Technology 1, no. 1 (2021): 15–20. http://dx.doi.org/10.54327/set2021/v1.i1.8.
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Network traffic recognition serves as a basic condition for network operators to differentiate and prioritize traffic for a number of purposes, from guaranteeing the Quality of Service (QoS), to monitoring safety, as well as monitoring and detecting anomalies. Web Real-Time Communication (WebRTC) is an open-source project that enables real-time audio, video, and text communication among browsers. Since WebRTC does not include any characteristic pattern for semantically based traffic recognition, this paper proposes models for recognizing traffic generated during WebRTC audio and video communication based on statistical characteristics and usage of machine learning in Weka tool. Five classification algorithms have been used for model development, such as Naive Bayes, J48, Random Forest, REP tree, and Bayes Net. The results show that J48 and BayesNet have the best performances in this experimental case of WebRTC traffic recognition. Future work will be focused on comparison of a wide range of machine learning algorithms using a large enough dataset to improve the significance of the results.
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Nguyen, Hai Thanh, Katrin Franke, and Slobodan Petrovic. "Improving Effectiveness of Intrusion Detection by Correlation Feature Selection." International Journal of Mobile Computing and Multimedia Communications 3, no. 1 (2011): 21–34. http://dx.doi.org/10.4018/jmcmc.2011010102.
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In this paper, the authors propose a new feature selection procedure for intrusion detection, which is based on filter method used in machine learning. They focus on Correlation Feature Selection (CFS) and transform the problem of feature selection by means of CFS measure into a mixed 0-1 linear programming problem with a number of constraints and variables that is linear in the number of full set features. The mixed 0-1 linear programming problem can then be solved by using branch-and-bound algorithm. This feature selection algorithm was compared experimentally with the best-first-CFS and the genetic-algorithm-CFS methods regarding the feature selection capabilities. Classification accuracies obtained after the feature selection by means of the C4.5 and the BayesNet over the KDD CUP’99 dataset were also tested. Experiments show that the authors’ method outperforms the best-first-CFS and the genetic-algorithm-CFS methods by removing much more redundant features while keeping the classification accuracies or getting better performances.
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Safara, Fatemeh, Shyamala Doraisamy, Azreen Azman, Azrul Jantan, and Sri Ranga. "Wavelet Packet Entropy for Heart Murmurs Classification." Advances in Bioinformatics 2012 (November 25, 2012): 1–6. http://dx.doi.org/10.1155/2012/327269.
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Heart murmurs are the first signs of cardiac valve disorders. Several studies have been conducted in recent years to automatically differentiate normal heart sounds, from heart sounds with murmurs using various types of audio features. Entropy was successfully used as a feature to distinguish different heart sounds. In this paper, new entropy was introduced to analyze heart sounds and the feasibility of using this entropy in classification of five types of heart sounds and murmurs was shown. The entropy was previously introduced to analyze mammograms. Four common murmurs were considered including aortic regurgitation, mitral regurgitation, aortic stenosis, and mitral stenosis. Wavelet packet transform was employed for heart sound analysis, and the entropy was calculated for deriving feature vectors. Five types of classification were performed to evaluate the discriminatory power of the generated features. The best results were achieved by BayesNet with 96.94% accuracy. The promising results substantiate the effectiveness of the proposed wavelet packet entropy for heart sounds classification.
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Ajdinović, Nadina, Semina Nurkić, Husić Jasmina Baraković, and Sabina Baraković. "Recognition of traffic generated by WebRTC communication." Science, Engineering and Technology 1, no. 1 (2021): 15–20. https://doi.org/10.54327/set2021/v1.i1.8.
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Network traffic recognition serves as a basic condition for network operators to differentiate and prioritize traffic for a number of purposes, from guaranteeing the Quality of Service (QoS), to monitoring safety, as well as monitoring and detecting anomalies. Web Real-Time Communication (WebRTC) is an open-source project that enables real-time audio, video, and text communication among browsers. Since WebRTC does not include any characteristic pattern for semantically based traffic recognition, this paper proposes models for recognizing traffic generated during WebRTC audio and video communication based on statistical characteristics and usage of machine learning in Weka tool. Five classification algorithms have been used for model development, such as Naive Bayes, J48, Random Forest, REP tree, and Bayes Net. The results show that J48 and BayesNet have the best performances in this experimental case of WebRTC traffic recognition. Future work will be focused on comparison of a wide range of machine learning algorithms using a large enough dataset to improve the significance of the results.
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Naeem, Samreen, Aqib Ali, Jamal Abdul Nasir, et al. "Automated Corn Seed Fusarium Disease Classification System Using Hybrid Feature Space and Conventional Machine Learning Techniques." Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences 58, no. 2 (2021): 1–10. http://dx.doi.org/10.53560/ppasa(58-2)692.
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The purpose of this learning is to detect the Corn Seed Fusarium Disease using Hybrid Feature Space and Conventional machine learning (ML) approaches. A novel machine learning approach is employed for the classification of a total of six types of corn seed are collected which contain Infected Fusarium (moniliforme, graminearum, gibberella, verticillioides, kernel) as well as healthy corn seed, based on a multi-feature dataset, which is the grouping of geometric, texture and histogram features extracted from digital images. For each corn seed image, a total of twenty-five multi-features have been developed on every area of interest (AOI), sizes (50 × 50), (100 × 100), (150 × 150), and (200 × 200). A total of seven optimized features were selected by using a machine learning-based algorithm named “Correlation-based Feature Selection”. For experimentation, “Random forest”, “BayesNet” and “LogitBoost” have been employed using an optimized multi-feature user-supplied dataset divided with 70% training and 30 % testing. A comparative analysis of three ML classifiers RF, BN, and LB have been used and a considerably very high classification ratio of 96.67 %, 97.22 %, and 97.78 % have been achieved respectively when the AOI size (200×200) have been deployed to the classifiers.
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Kwon, Ohbyung, Yun Seon Kim, Namyeon Lee, and Yuchul Jung. "When Collective Knowledge Meets Crowd Knowledge in a Smart City: A Prediction Method Combining Open Data Keyword Analysis and Case-Based Reasoning." Journal of Healthcare Engineering 2018 (October 3, 2018): 1–15. http://dx.doi.org/10.1155/2018/7391793.
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One of the significant issues in a smart city is maintaining a healthy environment. To improve the environment, huge amounts of data are gathered, manipulated, analyzed, and utilized, and these data might include noise, uncertainty, or unexpected mistreatment of the data. In some datasets, the class imbalance problem skews the learning performance of the classification algorithms. In this paper, we propose a case-based reasoning method that combines the use of crowd knowledge from open source data and collective knowledge. This method mitigates the class imbalance issues resulting from datasets, which diagnose wellness levels in patients suffering from stress or depression. We investigate effective ways to mitigate class imbalance issues in which the datasets have a higher proportion of one class over another. The results of this proposed hybrid reasoning method, using a combination of crowd knowledge extracted from open source data (i.e., a Google search, or other publicly accessible source) and collective knowledge (i.e., case-based reasoning), were that it performs better than other traditional methods (e.g., SMO, BayesNet, IBk, Logistic, C4.5, and crowd reasoning). We also demonstrate that the use of open source and big data improves the classification performance when used in addition to conventional classification algorithms.
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Li, Xinghang, Valentina L. Kouznetsova, and Igor F. Tsigelny. "miRNA in Machine-Learning-Based Diagnostics of Oral Cancer." Biomedicines 12, no. 10 (2024): 2404. http://dx.doi.org/10.3390/biomedicines12102404.
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Background: MicroRNAs (miRNAs) are crucial regulators of gene expression, playing significant roles in various cellular processes, including cancer pathogenesis. Traditional cancer diagnostic methods, such as biopsies and histopathological analyses, while effective, are invasive, costly, and require specialized skills. With the rising global incidence of cancer, there is a pressing need for more accessible and less invasive diagnostic alternatives. Objective: This research investigates the potential of machine-learning (ML) models based on miRNA attributes as non-invasive diagnostic tools for oral cancer. Methods and Tools: We utilized a comprehensive methodological framework involving the generation of miRNA attributes, including sequence characteristics, target gene associations, and cancer-specific signaling pathways. Results: The miRNAs were classified using various ML algorithms, with the BayesNet classifier demonstrating superior performance, achieving an accuracy of 95% and an area under receiver operating characteristic curve (AUC) of 0.98 during cross-validation. The model’s effectiveness was further validated using independent datasets, confirming its potential clinical utility. Discussion: Our findings highlight the promise of miRNA-based ML models in enhancing early cancer detection, reducing healthcare burdens, and potentially saving lives. Conclusions: This study paves the way for future research into miRNA biomarkers, offering a scalable and adaptable diagnostic approach for various cancers.
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Özdemir, Abdulkadir, Uğur Yavuz, and Fares Abdulhafidh Dael. "Performance evaluation of different classification techniques using different datasets." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 3584. http://dx.doi.org/10.11591/ijece.v9i5.pp3584-3590.
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<span>Nowadays data mining become one of the technologies that paly major effect on business intelligence. However, to be able to use the data mining outcome the user should go through many process such as classified data. Classification of data is processing data and organize them in specific categorize to be use in most effective and efficient use. In data mining one technique is not applicable to be applied to all the datasets. This paper showing the difference result of applying different techniques on the same data. This paper evaluates the performance of different classification techniques using different datasets. In this study four data classification techniques have chosen. They are as follow, BayesNet, NaiveBayes, Multilayer perceptron and J48. The selected data classification techniques performance tested under two parameters, the time taken to build the model of the dataset and the percentage of accuracy to classify the dataset in the correct classification. The experiments are carried out using Weka 3.8 software. The results in the paper demonstrate that the efficiency of Multilayer Perceptron classifier in overall the best accuracy performance to classify the instances, and NaiveBayes classifiers were the worst outcome of accuracy to classifying the instance for each dataset.</span>
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Abdulkadir, Özdemir, Yavuz Uğur, and Abdulhafidh Dael Fares. "Performance evaluation of different classification techniques using different datasets." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 5 (2019): 3584–90. https://doi.org/10.11591/ijece.v9i5.pp3584-3590.
Full textAbstract:
Nowadays data mining become one of the technologies that paly major effect on business intelligence. However, to be able to use the data mining outcome the user should go through many processes such as classified data. Classification of data is processing data and organize them in specific categorize to be use in most effective and efficient use. In data mining one technique is not applicable to be applied to all the datasets. Many data users wasting a lot of time trying many classification techniques in order to find the most an appropriate technique to be used. This paper showing the difference result of applying different techniques on the same data. This paper evaluates the performance of different classification techniques using different datasets. In this study four data classification techniques have chosen. They are as follow, BayesNet, NaiveBayes, Multilayer perceptron and J48. The selected data classification techniques performance tested under two parameters, the time taken to build the model of the dataset and the percentage of accuracy to classify the dataset in the correct classification. The experiments are carried out using Weka 3.8 software. The results in the paper demonstrate that the efficiency of Multilayer Perceptron classifier in overall the best accuracy performance to classify the instances, and NaiveBayes classifiers were the worst outcome of accuracy to classifying the instance for each dataset.
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Nazari, Leyla, Vida Ghotbi, Mohammad Nadimi, and Jitendra Paliwal. "A Novel Machine-Learning Approach to Predict Stress-Responsive Genes in Arabidopsis." Algorithms 16, no. 9 (2023): 407. http://dx.doi.org/10.3390/a16090407.
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This study proposes a hybrid gene selection method to identify and predict key genes in Arabidopsis associated with various stresses (including salt, heat, cold, high-light, and flagellin), aiming to enhance crop tolerance. An open-source microarray dataset (GSE41935) comprising 207 samples and 30,380 genes was analyzed using several machine learning tools including the synthetic minority oversampling technique (SMOTE), information gain (IG), ReliefF, and least absolute shrinkage and selection operator (LASSO), along with various classifiers (BayesNet, logistic, multilayer perceptron, sequential minimal optimization (SMO), and random forest). We identified 439 differentially expressed genes (DEGs), of which only three were down-regulated (AT3G20810, AT1G31680, and AT1G30250). The performance of the top 20 genes selected by IG and ReliefF was evaluated using the classifiers mentioned above to classify stressed versus non-stressed samples. The random forest algorithm outperformed other algorithms with an accuracy of 97.91% and 98.51% for IG and ReliefF, respectively. Additionally, 42 genes were identified from all 30,380 genes using LASSO regression. The top 20 genes for each feature selection were analyzed to determine three common genes (AT5G44050, AT2G47180, and AT1G70700), which formed a three-gene signature. The efficiency of these three genes was evaluated using random forest and XGBoost algorithms. Further validation was performed using an independent RNA_seq dataset and random forest. These gene signatures can be exploited in plant breeding to improve stress tolerance in a variety of crops.
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Kurnia, Muhammad Farhan, Siti Sumiati Solihat, Gut Windarsih, and Didi Usmadi. "IDENTIFIKASI OTOMATIS LIMA JENIS RESAK (Vatica spp.) BERDASARKAN BEBERAPA KARAKTER MORFOLOGI DAUN DAN ALGORITMA PEMBELAJARAN MESIN." Buletin Kebun Raya 26, no. 1 (2023): 26–37. http://dx.doi.org/10.55981/bkr.2023.740.
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Resak (Vatica spp.) merupakan salah satu marga yang termasuk dalam suku tumbuhan berkayu, Diperocarpaceae, dengan beberapa jenis di antaranya termasuk jenis terancam. Kemampuan identifikasi jenis dengan benar merupakan salah satu aspek yang penting dalam upaya konservasi. Penelitian ini bertujuan untuk mengetahui karakter morfologi daun resak, kemiripan antar jenis, dan performa dari lima algoritma pembelajaran mesin dalam mengidentifikasi otomatis jenis resak. Karakter morfologi yang diukur adalah warna, ukuran, bentuk, dan tekstur daun dari lima jenis Vatica spp. koleksi Kebun Raya Bogor. Perbedaan nilai rata-rata setiap karakter morfologi dianalisis menggunakan analisis sidik ragam dan uji Tukey. Keragaman dan kemiripan morfologi dianalisis menggunakan analisis komponen utama dan analisis kluster. Identifikasi otomatis dilakukan menggunakan lima algoritma pembelajaran mesin, yaitu BayesNet, K-Nearest Neighbor, Artificial Neural Network, Random Forest, dan Support Vector Machine. Hasil analisis menunjukkan bahwa karakter morfologi daun (warna, ukuran, bentuk, dan tekstur) pada kelima jenis resak mempunyai perbedaan yang signifikan. Semua karakter morfologi secara signifikan mempengaruhi perbedaan penciri daun resak. Pada tingkat kemiripan 80%, kelima jenis resak dikelompokkan menjadi tiga kluster, yaitu kluster I (V. granulata, V. pauciflora, dan V. venulosa), kluster II (V. bantamensis), dan kluster III (V. rassak). Algoritma pembelajaran mesin yang terbaik dalam melakukan identifikasi otomatis jenis resak menggunakan karakter morfologi daun adalah K-Nearest Neighbor dengan nilai overall accuracy 0,92, koefisien Kappa 0,90, rata-rata precision 0,93, dan rata-rata recall 0,92.
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Díaz, Álvarez Josefa, Jordi Matías-Guiu, Martín María Nieves Cabrera, et al. "Genetic Algorithms for Optimized Diagnosis of Alzheimer's Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging." Frontiers in Aging Neuroscience 13 (February 3, 2022): 13. https://doi.org/10.3389/fnagi.2021.708932.
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Genetic algorithms have a proven capability to explore a large space of solutions and deal with very large numbers of input features. We hypothesized that the application of these algorithms to 18F-Fluorodeoxyglucose Positron Emission Tomography (FDG- PET) may help in the diagnosis of Alzheimer’s disease (AD) and Frontotemporal Dementia (FTD) by selecting the most meaningful features and automating diagnosis. We aimed to develop algorithms for the three main issues in the diagnosis: discrimination between patients with AD or FTD and healthy controls (HC), the differential diagnosis between behavioural FTD (bvFTD) and AD, and differential diagnosis between primary progressive aphasia (PPA) variants. Genetic algorithms, customized with K-Nearest Neighbor and BayesNet Naives as the fitness function, were developed and compared with Principal Component Analysis (PCA). K-fold cross-validation within the same sample and external validation with ADNI-3 samples were performed. External validation was performed for the algorithms distinguishing AD and HC. Our study supports the use of FDG-PET imaging, which allowed a very high accuracy rate for the diagnosis of AD, FTD, and related disorders. Genetic algorithms identified the most meaningful features with the minimum set of features, which may be relevant for the automated assessment of brain FDG-PET images. Overall, our study contributes to the development of an automated and optimized diagnosis of neurodegenerative disorders using brain metabolism.
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Torres-Quezada, Yulissa. "Minería de datos para determinar los factores más influyentes en la ocurrencia de siniestros de tránsito en Ecuador en el año 2020." CEDAMAZ 11, no. 2 (2021): 124–32. http://dx.doi.org/10.54753/cedamaz.v11i2.1181.
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Actualmente, la ocurrencia de siniestros de tránsito representa un problema de salud pública a nivel nacional y regional, ocasionando pérdidas humanas, además de que cada día va en aumento a nivel mundial, es por ello que resulta fundamental e importante plantear un estudio que permita determinar cuáles son los factores que ocasionan la ocurrencia de los siniestros de tránsito. En este trabajo de investigación se aplica minería de datos para determinar los factores más influyentes en la ocurrencia de siniestros de tránsito en Ecuador en el año 2020, esto se llevó a cabo empleando cinco fases de la metodología Knowledge Discovery in Databases (KDD) constituida por: búsqueda de información, obtención de datos, depuración de la base de datos, aplicación de técnicas de minería de datos e interpretación y presentación de resultados, estas, utilizadas para el descubrimiento de patrones ocultos en el conjunto de datos, el cual fue recolectado por la Agencia Nacional de Tránsito (ANT) y tiene un total de 418 variables y 16972 registros de eventos registrados sobre siniestros de tránsito en Ecuador. Se aplicaron siete técnicas de minería de datos, tales como: CHAID, CHAID Exhaustivo, CRT, Perceptrón Multicapa, Función de Base Radial, Naive Bayes y BayesNet. El algoritmo CHAID Exhaustivo fue el que obtuvo los mejores resultados con el cual se identificó los patrones más importantes en los datos y se evaluó las posibles asociaciones entre las variables recogidas. Finalmente, se determinó que el factor humano es el factor más influyente con una probabilidad de ocurrencia del 69,64%.
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Prabakaran, Senthil, Ramalakshmi Ramar, Irshad Hussain, et al. "Predicting Attack Pattern via Machine Learning by Exploiting Stateful Firewall as Virtual Network Function in an SDN Network." Sensors 22, no. 3 (2022): 709. http://dx.doi.org/10.3390/s22030709.
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Decoupled data and control planes in Software Defined Networks (SDN) allow them to handle an increasing number of threats by limiting harmful network links at the switching stage. As storage, high-end servers, and network devices, Network Function Virtualization (NFV) is designed to replace purpose-built network elements with VNFs (Virtualized Network Functions). A Software Defined Network Function Virtualization (SDNFV) network is designed in this paper to boost network performance. Stateful firewall services are deployed as VNFs in the SDN network in this article to offer security and boost network scalability. The SDN controller’s role is to develop a set of guidelines and rules to avoid hazardous network connectivity. Intruder assaults that employ numerous socket addresses cannot be adequately protected by these strategies. Machine learning algorithms are trained using traditional network threat intelligence data to identify potentially malicious linkages and probable attack targets. Based on conventional network data (DT), Bayesian Network (BayesNet), Naive-Bayes, C4.5, and Decision Table (DT) algorithms are used to predict the target host that will be attacked. The experimental results shows that the Bayesian Network algorithm achieved an average prediction accuracy of 92.87%, Native–Bayes Algorithm achieved an average prediction accuracy of 87.81%, C4.5 Algorithm achieved an average prediction accuracy of 84.92%, and the Decision Tree algorithm achieved an average prediction accuracy of 83.18%. There were 451 k login attempts from 178 different countries, with over 70 k source IP addresses and 40 k source port addresses recorded in a large dataset from nine honeypot servers.
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Tripathi, Nandita, Michael Oakes, and Stefan Wermter. "A Scalable Meta-Classifier Combining Search and Classification Techniques for Multi-Level Text Categorization." International Journal of Computational Intelligence and Applications 14, no. 04 (2015): 1550020. http://dx.doi.org/10.1142/s1469026815500200.
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Nowadays, documents are increasingly associated with multi-level category hierarchies rather than a flat category scheme. As the volume and diversity of documents grow, so do the size and complexity of the corresponding category hierarchies. To be able to access such hierarchically classified documents in real-time, we need fast automatic methods to navigate these hierarchies. Today’s data domains are also very different from each other, such as medicine and politics. These distinct domains can be handled by different classifiers. A document representation system which incorporates the inherent category structure of the data should also add useful semantic content to the data vectors and thus lead to better separability of classes. In this paper, we present a scalable meta-classifier to tackle today’s problem of multi-level data classification in the presence of large datasets. To speed up the classification process, we use a search-based method to detect the level-1 category of a test document. For this purpose, we use a category–hierarchy-based vector representation. We evaluate the meta-classifier by scaling to both longer documents as well as to a larger category set and show it to be robust in both cases. We test the architecture of our meta-classifier using six different base classifiers (Random forest, C4.5, multilayer perceptron, naïve Bayes, BayesNet (BN) and PART). We observe that even though there is a very small variation in the performance of different architectures, all of them perform much better than the corresponding single baseline classifiers. We conclude that there is substantial potential in this meta-classifier architecture, rather than the classifiers themselves, which successfully improves classification performance.
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Das, Sarkar Snigdha Sarathi, Subangkar Karmaker Shanto, Masum Rahman, et al. "BayesBeat." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 1 (2022): 1–21. http://dx.doi.org/10.1145/3517247.
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Smartwatches or fitness trackers have garnered a lot of popularity as potential health tracking devices due to their affordable and longitudinal monitoring capabilities. To further widen their health tracking capabilities, in recent years researchers have started to look into the possibility of Atrial Fibrillation (AF) detection in real-time leveraging photoplethysmography (PPG) data, an inexpensive sensor widely available in almost all smartwatches. A significant challenge in AF detection from PPG signals comes from the inherent noise in the smartwatch PPG signals. In this paper, we propose a novel deep learning based approach, BayesBeat that leverages the power of Bayesian deep learning to accurately infer AF risks from noisy PPG signals, and at the same time provides an uncertainty estimate of the prediction. Extensive experiments on two publicly available dataset reveal that our proposed method BayesBeat outperforms the existing state-of-the-art methods. Moreover, BayesBeat is substantially more efficient having 40--200X fewer parameters than state-of-the-art baseline approaches making it suitable for deployment in resource constrained wearable devices.
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Joshi, Ankur, Sukanya Sharma, N. V. M. Rao, and A. K. Vaish. "Usage of Machine Learning Algorithm Models to Predict Operational Efficiency Performance of Selected Banking Sectors of India." International Journal of Emerging Technology and Advanced Engineering 12, no. 6 (2022): 105–14. http://dx.doi.org/10.46338/ijetae0622_14.
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—It was an attempt to predict the impact of NPAs in the selected public (SBI, BoI, BoB, BoM, CBoI, AB, CB, AlB,) and private (AxB, ICB, HDFCB and KB) banking sectors from 2008 to 2019. The data was also used to predict operational performance efficiency of these banking sectors after extracting through machine learning (ML) algorithm models and statistical interpretation of prediction accuracy by using WEKA tool. We used different models viz. NaiveBayes (NB), BayesNet (BN), logistic regression (LgR), Sequential minimal optimization of Support Vector Machine regression (SMOreg), Linear Logistic Regression (SL), Classification via Regression (CR), LogitBoost (LB); Logistic Model Tree (LMT), Random Forest & Random tree (RF & RT), Pruned & unpruned decision tree C4 (J48), and Class implementing minimal cost-complexity pruning (Cart) related to 15 attributes viz. GNPA, NNPA, GDP, CPI, PSL, TL, STA, GDP-1, RR, CPI-1, TE, TP and USTA as numeric as well as Banks, Year, GNPA>6, and GNPA>7, as nominal categories of dataset where overall performance accuracy was determined. The algorithm model classification predicted the highest values were for LB (78.47%) and Cart (74.30%) followed by J48 (73.61%), CR (72.91%) and LMT (69.44%) and lowest value in SMO (34.72%) as per 10-fold cross validation test. Additionally, these predicted results may have valuable implications for Indian banking sectors. We evaluated the operational efficiency as cumulative performance for 12 banking sectors as per assumed cut off values of GNPA. It may be varied with other independent variables like credit risk parameters, etc. It is suggested in future to study with parameters of deposit collection and investment to determine credit risk of these banking sectors. Keywords—Indian banking sectors, Machine learning models, Non-performing assets, Operational efficiency, WEKA tool
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Barata, Pedro C., Umang Swami, Adam Kessel, et al. "Landscape of circulating tumor DNA (ctDNA) abnormalities in advanced prostate cancer (aPCa): Distinctions in African American (AA) versus Caucasian (Ca) patients." Journal of Clinical Oncology 39, no. 6_suppl (2021): 156. http://dx.doi.org/10.1200/jco.2021.39.6_suppl.156.
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156 Background: AA have substantially higher prostate cancer incidence rates, are diagnosed at a younger age and with a more advanced stage as compared to Ca. However, after adjusting for known prognostic factors, AA have an increased overall survival. We hypothesized that these differences might be due to the underlying changes in the genomic landscape which can be revealed by liquid biopsy. Methods: Real world comprehensive genomic profiling of ctDNA from aPCa patients from two institutions. The first ctDNA results as reported by Guardant 360 panel (Redwood City, CA) were included. Association between genetic mutation and gene were tested using Barnard’s test. To account for multiple testing, we used Benjamini-Hochberg’s False Discovery Rate adjustment across all tests to determine thresholds for false discovery rates. Same analysis was performed using a Bayesian Network Machine learning approach. Results: Overall, 361 patients with aPCa (81 AA and 280 Ca) were included in the analysis. Pathogenic genomic alterations were found in 87.0% of the cases, more frequently TP53 (42.4%), AR (34.1%), PIK3CA (13.9%), BRAF (12.7%), NF1 (10.8%) and MYC (10.0%). Targetable alterations of interest included DNA repair genes [BRCA 2 (7.8%), BRCA 1 (4.4%), ATM (6.4%), CDK12 (2.2%)], PIK3CA/mTOR/AKT (19.1%), PTEN (3.3%) and NTRK (1.9%). MSI-high was found in 4 patients. AA as compared to Ca had a significantly higher prevalence of CDK12 (20.7% vs. 3.8%, p=0.016) and GNA11 mutations (3.7% vs. 0.4%, p=0.0225). BayesNet analysis also supported these results (table). Conclusions: In this dataset, liquid biopsy of ctDNA was useful for genetic characterization of aPCa and reveal differences in the molecular phenotype of AA and Ca in aPCa with potential clinical implications. These findings support ongoing research on the clinical utility of non-invasive genotyping and therapeutic response monitoring with a focus on AA population. [Table: see text]
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Mahiddin, Normadiah, Zulaiha Ali Othman, and Nur Arzuar Abdul Rahim. "Interrelated Decision-Making Model for Diabetes." Asia-Pacific Journal of Information Technology and Multimedia 10, no. 02 (2021): 170–86. http://dx.doi.org/10.17576/apjitm-2021-1002-12.
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Diabetes is one of the growing chronic diseases. Proper treatment is needed to produce its effects. Past studies have proposed an Interrelated Decision-making Model (IDM) as an intelligent decision support system (IDSS) solution for healthcare. This model can provide accurate results in determining the treatment of a particular patient. Therefore, the purpose of this study is to develop a diabetic IDM to see the increased decision-making accuracy with the IDM concept. The IDM concept allows the amount of data to increase with the addition of data records at the same level of care, and the addition of data records and attributes from the previous or subsequent levels of care. The more data or information, the more accurate a decision can be made. Data were developed to make diagnostic predictions for each stage of care in the development of type 2 diabetes. The development of data for each stage of care was confirmed by specialists. However, the experiments were performed using simulation data for two stages of care only. Four data sets of different sizes were provided to view changes in forecast accuracy. Each data set contained 2 data sets of primary care level and secondary care level with 4 times the change of the number of attributes from 25 to 58 and the number of records from 300 to 11,000. Data were developed to predict the level of diabetes confirmed by specialist doctors. The experimental results showed that on average, the J48 algorithm showed the best model (99%) followed by Logistics (98%), RandomTree (95%), NaiveBayes Updateable (93%), BayesNet (84%) and AdaBoostM1 (67%). Ratio analysis also showed that the accuracy of the forecast model has increased up to 49%. The MAPKB model for the care of diabetes is designed with data change criteria dynamically and is able to develop the latest dynamic prediction models effectively.v
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Gadžo, Amra, Mirza Suljić, Adisa Jusufović, Slađana Filipović, and Erna Suljić. "Data mining approach in detecting inaccurate financial statements in government-owned enterprises." Croatian operational research review 16, no. 1 (2025): 1–15. https://doi.org/10.17535/crorr.2025.0001.
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he study aims to assess the capability of various data mining techniques in detecting inaccurate financial statements of government-owned enterprises operating in the Federation of Bosnia and Herzegovina (FBiH). Inaccurate financial statements indicate potential financial fraud. Prediction models of four classification algorithms (J48, KNN, MLP, and BayesNet) were examined using a dataset comprising 200 audited financial statements from government-owned enterprises under the supervision of the Audit Office of the Institutions in the Federation of Bosnia and Herzegovina. The results obtained through data mining analysis reveal that a dataset encompassing seven balance sheet items provides the most comprehensive depiction of financial statement quality. These seven attributes are: opening entry of accounts receivable, profit (loss) at the end of the period, operating assets at the end of the period, accounts receivable at the end of the period, opening entry of operating assets, short term financial investments at the end of the period, and opening entry of short-term financial investments. By employing these seven attributes, the MLP algorithm was implemented to construct the most precise predictive model, achieving a 76% accurate classification rate for financial statements. Leveraging the identified attributes, a mathematical model could potentially be formulated to effectively predict financial statements of government-owned enterprises in FBiH. This, in turn, could considerably facilitate the process of selecting GOEs for inclusion in the annual work plan of state auditors. Presently, due to resource constraints, government-owned enterprises in FBiH do not undergo regular annual scrutiny by state auditors, with only 10 to 15 such enterprises being subject to audits each year. The results of this research can also be beneficial to both the public and the Financial Intelligence Agency in the FBiH. The paper contributes to filling the gap in the literature regarding the applied methodology, particularly in the part concerning the attributes used in the research.
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Taamneh, Madhar M., Salah Taamneh, Ahmad H. Alomari, and Musab Abuaddous. "Analyzing the Effectiveness of Imbalanced Data Handling Techniques in Predicting Driver Phone Use." Sustainability 15, no. 13 (2023): 10668. http://dx.doi.org/10.3390/su151310668.
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Distracted driving leads to a significant number of road crashes worldwide. Smartphone use is one of the most common causes of cognitive distraction among drivers. Available data on drivers’ phone use presents an invaluable opportunity to identify the main factors behind this behavior. Machine learning (ML) techniques are among the most effective techniques for this purpose. However, the potential and usefulness of these techniques are limited, due to the imbalance of available data. The majority class of instances collected is for drivers who do not use their phones, while the minority class is for those who do use their phones. This paper evaluates two main approaches for handling imbalanced datasets on driver phone use. These methods include oversampling and undersampling. The effectiveness of each method was evaluated using six ML techniques: Multilayer Perceptron (MLP), Support Vector Machine (SVM), Naive Bayes (NB), Bayesian Network (BayesNet), J48, and ID3. The proposed methods were also evaluated on three Deep Learning (DL) models: Arch1 (5 hidden layers), Arch2 (10 hidden layers), and Arch3 (15 hidden layers). The data used in this document were collected through a direct observation study to explore a set of human, vehicle, and road surface characteristics. The results showed that all ML methods, as well as DL methods, achieved balanced accuracy values for both classes. ID3, J48, and MLP methods outperformed the rest of the ML methods in all scenarios, with ID3 achieving slightly better accuracy. The DL methods also provided good performances, especially for the undersampling data. The results also showed that the classification methods performed best on the undersampled data. It was concluded that road classification has the highest impact on cell phone use, followed by driver age group, driver gender, vehicle type, and, finally, driver seatbelt usage.
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Alci, Aysun, Fatih Ikiz, Necim Yalcin, et al. "Prediction of Clavien Dindo Classification ≥ Grade III Complications After Epithelial Ovarian Cancer Surgery Using Machine Learning Methods." Medicina 61, no. 4 (2025): 695. https://doi.org/10.3390/medicina61040695.
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Background and Objectives: Ovarian cancer surgery requires multiple radical resections with a high risk of complications. The aim of this single-centre, retrospective study was to determine the best method for predicting Clavien–Dindo grade ≥ III complications using machine learning techniques. Material and Methods: The study included 179 patients who underwent surgery at the gynaecological oncology department of Antalya Training and Research Hospital between January 2015 and December 2020. The data were randomly split into training set n = 134 (75%) and test set n = 45 (25%). We used 49 predictors to develop the best algorithm. Mean absolute error, root mean squared error, correlation coefficients, Mathew’s correlation coefficient, and F1 score were used to determine the best performing algorithm. Cohens’ kappa value was evaluated to analyse the consistency of the model with real data. The relationship between these predicted values and the actual values were then summarised using a confusion matrix. True positive (TP) rate, False positive (FP) rate, precision, recall, and Area under the curve (AUC) values were evaluated to demonstrate clinical usability and classification skills. Results: 139 patients (77.65%) had no morbidity or grade I-II CDC morbidity, while 40 patients (22.35%) had grade III or higher CDC morbidity. BayesNet was found to be the most effective prediction model. No dominant parameter was observed in the Bayesian net importance matrix plot. The true positive (TP) rate was 76%, false positive (FP) rate was 15.6%, recall rate (sensitivity) was 76.9%, and overall accuracy was 82.2% A receiver operating characteristic (ROC) analysis was performed to estimate CDC grade ≥ III. AUC was 0.863 with a statistical significance of p < 0.001, indicating a high degree of accuracy. Conclusions: The Bayesian network model achieved the highest accuracy compared to all other models in predicting CDC Grade ≥ III complications following epithelial ovarian cancer surgery.
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Khazova, Anastasiya B. "Automatic Detection of Gender Identity: the Phenomenon of Russian Women's Prose." NSU Vestnik. Series: Linguistics and Intercultural Communication 18, no. 1 (2020): 22–32. http://dx.doi.org/10.25205/1818-7935-2020-18-1-22-32.
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The article deals with the method of automatic detection of authors’ gender identity on the material of fiction prose of 1980–2000. During this period, there is a special construct, called “women’s prose”, which is characterized by a special genre and stylistic originality. We set ourselves the task to find out whether the concept of “women’s prose” refers only to the non-text reality or is clearly reflected at the level of language. We have collected corpus of texts 1980–2000 and conducted that identified the most effective machine learning algorithms for the classification of male and female prose. This research focuses on methods for automatically determining the gender identity of authors on the material of prose from 1960 to 2000. The purpose of this work is to identify optimal methods for automatically determining the gender identity of the authors. The objectives of this study include highlighting the grammatical and stylistic features of prose from 1960 to 2000 and, in particular, women's prose and texts of 18th – 19th centuries; tracing the changes in the distribution of usage different parts of speech and punctuation for a specified period and conducting an experiment to identify the most effective algorithm for the classification of literary texts by using machine learning. The analysis revealed that women and men often use in their texts the following parts of speech: nouns, verbs, prepositions, pronominal nouns, conjunctions, and adjectives that reflects the specific artistic style. In addition, analysis was made of the use of the most commonly used punctuation marks from the given list: question mark, exclamation point, comma, colon, semicolon, period, comma. It has been observed that women are more actively using the means of punctuation as a means of expression in modern literature: the share of the use of exclamation, question marks and commas the writers is much higher than the value obtained through the analysis of men’s texts. The work also contains an analysis of the distribution of parts of speech and punctuation of literary texts of men and women of 18th – 19th centuries. We performed experiment to identify the most effective algorithm for determining the gender identity of the author. It was found that the most effective classifiers of literature are the implementation of algorithms as BayesNet and SMO.
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Barbaros, Emre, Seyit Hökelek, Hasan Ak, and Nurten Filiz Ak. "Bayesyen Dikine Hız Çözücü: BRaVe." Turkish Journal of Astronomy and Astrophysics 6, Special Issue: UAK2024 Proc. (2025): 338–43. https://doi.org/10.55064/tjaa.1598320.
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Çift yıldızların zamana bağlı radyal hız eğrilerinin analizi, bileşenlerin fiziksel parametrelerinin doğrudan belirlenmesine olanak sağlar. En doğru yörünge modellemesi elde etmek amacıyla geliştirdiğimiz BRaVe programı, yörünge parametrelerini ve hatalarını yüksek duyarlılıkla elde etmektedir. BRaVe, kendi içerisinde Bayes istatistiğine dayanan bir optimizasyon rutini içerir. Bu rutin sayesinde ele alınan çift yıldız sisteminin temel yörünge parametreleri en hassas şekilde elde edilebilmesi hedeflenmektedir. BRaVe kodu için referans yıldız olarak Capella A yıldızı seçilmiştir. Polarbase veri arşivinden alınan yüksek çözünürlüklü tayflardan CCF yöntemi ile radyal hızlar hesaplanmış ve yörünge parametreleri BRaVe aracılığı ile çözülmüştür. Bu çalışmada elde edilen sonuçlar, literatür ile kıyaslanarak paylaşılmıştır.
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Mao, Runjun, Chengdong Cao, James Jing Yue Qian, Jiufan Wang, and Yunpeng Liu. "Mixture of Gaussian Processes Based on Bayesian Optimization." Journal of Sensors 2022 (September 15, 2022): 1–10. http://dx.doi.org/10.1155/2022/7646554.
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This paper gives a detailed introduction of implementing mixture of Gaussian process (MGP) model and develops its application for Bayesian optimization (BayesOpt). The paper also develops techniques for MGP in finding its mixture components and introduced an alternative gating network based on the Dirichlet distributions. BayesOpt using the resultant MGP model significantly outperforms the one based on Gaussian process regression in terms of optimization efficiency in the test on tuning the hyperparameters in common machine learning algorithms. This indicates the success of the methods, implying a promising future of wider application for MGP model and the BayesOpt based on it.
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Han, Yunkun, and Zhanwen Han. "Bayesian analysis of galaxy spectral energy distributions with BayeSED." Proceedings of the International Astronomical Union 8, S295 (2012): 312. http://dx.doi.org/10.1017/s1743921313005140.
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AbstractIn Han & Han (2012), we have preliminarily built BayeSED and applied it to a sample of hyperluminous infrared galaxies. The physically reasonable results obtained from Bayesian model comparison and parameter estimation show that BayeSED could be a useful tool for understanding the nature of complex systems, such as dust obscured starburst-AGN composite galaxies, from decoding their complex SEDs. In this contribution, we present a more rigorous test of BayeSED by making a mock catalog from model SEDs with the value of all parameters to be known in advance.
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Shim, Heejung, and Bret Larget. "BayesCAT: Bayesian co-estimation of alignment and tree." Biometrics 74, no. 1 (2017): 270–79. http://dx.doi.org/10.1111/biom.12640.
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Lee, Michael D. "BayesSDT: Software for Bayesian inference with signal detection theory." Behavior Research Methods 40, no. 2 (2008): 450–56. http://dx.doi.org/10.3758/brm.40.2.450.
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Karaman, Emre, Mogens S. Lund, and Guosheng Su. "Multi-trait single-step genomic prediction accounting for heterogeneous (co)variances over the genome." Heredity 124, no. 2 (2019): 274–87. http://dx.doi.org/10.1038/s41437-019-0273-4.
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Abstract Widely used genomic prediction models may not properly account for heterogeneous (co)variance structure across the genome. Models such as BayesA and BayesB assume locus-specific variance, which are highly influenced by the prior for (co)variance of single nucleotide polymorphism (SNP) effect, regardless of the size of data. Models such as BayesC or GBLUP assume a common (co)variance for a proportion (BayesC) or all (GBLUP) of the SNP effects. In this study, we propose a multi-trait Bayesian whole genome regression method (BayesN0), which is based on grouping a number of predefined SNPs to account for heterogeneous (co)variance structure across the genome. This model was also implemented in single-step Bayesian regression (ssBayesN0). For practical implementation, we considered multi-trait single-step SNPBLUP models, using (co)variance estimates from BayesN0 or ssBayesN0. Genotype data were simulated using haplotypes on first five chromosomes of 2200 Danish Holstein cattle, and phenotypes were simulated for two traits with heritabilities 0.1 or 0.4, assuming 200 quantitative trait loci (QTL). We compared prediction accuracy from different prediction models and different region sizes (one SNP, 100 SNPs, one chromosome or whole genome). In general, highest accuracies were obtained when 100 adjacent SNPs were grouped together. The ssBayesN0 improved accuracies over BayesN0, and using (co)variance estimates from ssBayesN0 generally yielded higher accuracies than using (co)variance estimates from BayesN0, for the 100 SNPs region size. Our results suggest that it could be a good strategy to estimate (co)variance components from ssBayesN0, and then to use those estimates in genomic prediction using multi-trait single-step SNPBLUP, in routine genomic evaluations.
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Fleming, John E., Ines Pont Sanchis, Oscar Lemmens, et al. "From dawn till dusk: Time-adaptive bayesian optimization for neurostimulation." PLOS Computational Biology 19, no. 12 (2023): e1011674. http://dx.doi.org/10.1371/journal.pcbi.1011674.
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Stimulation optimization has garnered considerable interest in recent years in order to efficiently parametrize neuromodulation-based therapies. To date, efforts focused on automatically identifying settings from parameter spaces that do not change over time. A limitation of these approaches, however, is that they lack consideration for time dependent factors that may influence therapy outcomes. Disease progression and biological rhythmicity are two sources of variation that may influence optimal stimulation settings over time. To account for this, we present a novel time-varying Bayesian optimization (TV-BayesOpt) for tracking the optimum parameter set for neuromodulation therapy. We evaluate the performance of TV-BayesOpt for tracking gradual and periodic slow variations over time. The algorithm was investigated within the context of a computational model of phase-locked deep brain stimulation for treating oscillopathies representative of common movement disorders such as Parkinson’s disease and Essential Tremor. When the optimal stimulation settings changed due to gradual and periodic sources, TV-BayesOpt outperformed standard time-invariant techniques and was able to identify the appropriate stimulation setting. Through incorporation of both a gradual “forgetting” and periodic covariance functions, the algorithm maintained robust performance when a priori knowledge differed from observed variations. This algorithm presents a broad framework that can be leveraged for the treatment of a range of neurological and psychiatric conditions and can be used to track variations in optimal stimulation settings such as amplitude, pulse-width, frequency and phase for invasive and non-invasive neuromodulation strategies.
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Bailly, L., B. Giusiano, E. Mariné-Barjoan, J. F. Michiels, J. P. Daurès, and C. Pradier. "Capture-Recapture en Bayesien : sélection des modèles, pondération et hétérogénéité individuelle." Revue d'Épidémiologie et de Santé Publique 60 (September 2012): S70. http://dx.doi.org/10.1016/j.respe.2012.06.095.
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Martínez, Carlos Alberto, Kshitij Khare, and Mauricio A. Elzo. "On the Bayesness, minimaxity and admissibility of point estimators of allelic frequencies." Journal of Theoretical Biology 383 (October 2015): 106–15. http://dx.doi.org/10.1016/j.jtbi.2015.07.031.
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ŞENEL, Kerem, Mesut ÖZDİNÇ, Selcen ÖZTÜRKCAN, and Ahmet AKGÜL. "Instantaneous R for COVID-19 in Turkey: Estimation by Bayesian Statistical Inference." Turkiye Klinikleri Journal of Medical Sciences 40, no. 2 (2020): 127–31. http://dx.doi.org/10.5336/medsci.2020-76462.
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