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Mardewi, Mardewi, and Supriyadi La Wungo. "Klasifikasi Liver Cirrhosis Menggunakan Teknik Ensemble: Studi Perbandingan Model Boosted Tree, Bagged Tree, dan Rusboosted Tree." Journal of System and Computer Engineering (JSCE) 5, no. 2 (2024): 219–25. http://dx.doi.org/10.61628/jsce.v5i2.1302.
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Penyakit liver cirrhosis, sebagai penyakit hati kronis yang signifikan, menunjukkan peningkatan prevalensi global yang memerlukan pendekatan pencegahan yang lebih efektif. Dalam upaya meningkatkan deteksi dini dan manajemen pasien, penelitian ini mengusulkan pengembangan model prediksi risiko liver cirrhosis menggunakan teknologi machine learning, khususnya dengan membandingkan kinerja tiga model ensemble tree: Ensemble Boosted Tree, Ensemble Bagged Tree, dan Ensemble RUSBoosted Tree. Dengan memanfaatkan data klinis dan laboratorium dari pasien dewasa dengan riwayat atau risiko cirrhosis, penelitian ini menghasilkan temuan bahwa Ensemble Bagged Tree mencapai akurasi tertinggi sebesar 71%, diikuti oleh Ensemble Boosted Tree (67.2%) dan Ensemble RUSBoosted Tree (66%). Analisis variabel klinis dan laboratorium memberikan wawasan lebih lanjut tentang kontribusi faktor-faktor yang paling signifikan dalam prediksi risiko. Hasil penelitian ini memberikan landasan untuk pengembangan alat prediksi risiko liver cirrhosis yang lebih canggih, mendukung visi pencegahan yang lebih personalisasi dan efektif dalam manajemen penyakit hati.
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Florence, David Onyirimba, Deme Abraham, Dadik Bibu Gideon, et al. "Performance Evaluation of Machine Learning Models For Cervical Cancer Prediction." RA JOURNAL OF APPLIED RESEARCH 08, no. 11 (2022): 821–28. https://doi.org/10.5281/zenodo.7359642.
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ABSTRACT Cervical cancer is exclusively an anatomy of the female genitals involving the cervix and is the common cancer type that appears in all age women groups and the most common cause of death associated with cancer in gynecological practice, yet it is almost completely preventable if precancerous lesions are identified and treated promptly. The need to develop a quick, cheap and efficient method to diagnose a precursor lesion in an environment with high burden of the diseases with a view of reducing the burden of the disease motivated the need to apply Machine Learning (ML) technique towards cancer prediction. The primary objective of the study was to develop a ML model that can predict the occurrence of cervical cancer with a higher degree of accuracy. The cervical cancer dataset used in this study was obtained from Jos University Teaching Hospital (JUTH) and Aids Prevention Initiative in Nigeria (APIN). Several ML techniques were considered which includes Ensemble Bagged Tree, Fine Gaussian SVM, Cubic SVM, Fine Tree, Quadratic SVM, Medium Gaussian SVM, Ensemble Boosted Tree, Ensemble Rusboosted Tree, Medium Tree, Linear SVM, Corase Gaussian SVM and Coarse Tree algorithm. The study shows that Ensemble Bagged Tree and Fine Gaussian SVM gives a higher cervical cancer predictive accuracy of 99.7 percent and 99.6 percent respectively as the best performing predictive models, followed by Cubic SVM and Fine Tree with 98 percent and Fine Tree with 96.6 percent cervical cancer predictive accuracy respectively. The performance evaluation shows that Ensemble Bagged Tree and Fine Gaussian SVM perform excellently well in distinguishing and predicting the cervical classes correctly with the best prediction accuracy.
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Dr. A. Shaji George. "Handwriting Recognition Implementation: A Machine Learning Approach." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 02 (2025): 144–49. https://doi.org/10.47392/irjaem.2025.0025.
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Handwritten text recognition, also referred to as handwritten character recognition, is a field of study that combines model recognition, computer vision, and artificial intelligence. In order to translate handwritten letters into relevant text and computer commands in real time, handwriting recognition systems use pattern matching. The properties of photographs and touch-screen devices can be acquired, detected, and converted into a machine-readable form by an algorithm that recognizes handwriting. An ensemble of bagged classification trees is one way to accomplish this. A bagged classification tree is an ensemble learning technique that helps to increase the efficiency and accuracy of machine learning algorithms by lowering the variance of a prediction model and addressing bias-variance trade-offs. The standard Kaggle digits dataset from (0-9) was utilised in this study to identify handwritten digits using a bagged classification method. And with an accuracy level of 0.8371, we finally came to a conclusion about the importance of the bagged classification strategy.
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Dagogo-George, Tamunopriye Ene, Hammed Adeleye Mojeed, Abdulateef Oluwagbemiga Balogun, Modinat Abolore Mabayoje, and Shakirat Aderonke Salihu. "Tree-based homogeneous ensemble model with feature selection for diabetic retinopathy prediction." Jurnal Teknologi dan Sistem Komputer 8, no. 4 (2020): 297–303. http://dx.doi.org/10.14710/jtsiskom.2020.13669.
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Diabetic Retinopathy (DR) is a condition that emerges from prolonged diabetes, causing severe damages to the eyes. Early diagnosis of this disease is highly imperative as late diagnosis may be fatal. Existing studies employed machine learning approaches with Support Vector Machines (SVM) having the highest performance on most analyses and Decision Trees (DT) having the lowest. However, SVM has been known to suffer from parameter and kernel selection problems, which undermine its predictive capability. Hence, this study presents homogenous ensemble classification methods with DT as the base classifier to optimize predictive performance. Boosting and Bagging ensemble methods with feature selection were employed, and experiments were carried out using Python Scikit Learn libraries on DR datasets extracted from UCI Machine Learning repository. Experimental results showed that Bagged and Boosted DT were better than SVM. Specifically, Bagged DT performed best with accuracy 65.38 %, f-score 0.664, and AUC 0.731, followed by Boosted DT with accuracy 65.42 %, f-score 0.655, and AUC 0.724 when compared to SVM (accuracy 65.16 %, f-score 0.652, and AUC 0.721). These results indicate that DT's predictive performance can be optimized by employing the homogeneous ensemble methods to outperform SVM in predicting DR.
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Nsaif, Younis M., Molla Shahadat Hossain Hossain Lipu, Aini Hussain, Afida Ayob, Yushaizad Yusof, and Muhammad Ammirrul A. M. Zainuri. "A New Voltage Based Fault Detection Technique for Distribution Network Connected to Photovoltaic Sources Using Variational Mode Decomposition Integrated Ensemble Bagged Trees Approach." Energies 15, no. 20 (2022): 7762. http://dx.doi.org/10.3390/en15207762.
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The increasing integration of renewable sources into distributed networks results in multiple protection challenges that would be insufficient for conventional protection strategies to tackle because of the characteristics and functionality of distributed generation. These challenges include changes in fault current throughout various operating modes, different distribution network topologies, and high-impedance faults. Therefore, the protection and reliability of a photovoltaic distributed network relies heavily on accurate and adequate fault detection. The proposed strategy utilizes the Variational Mode Decomposition (VMD) and ensemble bagged trees method to tackle these problems in distributed networks. Primarily, VMD is used to extract intrinsic mode functions from zero-, positive-, and negative-sequence components of a three-phase voltage signal. Next, the acquired intrinsic mode functions are supplied into the ensemble bagged trees mechanism for detecting fault events in a distributed network. Under both radial and mesh-soft normally open-point (SNOP) topologies, the outcomes are investigated and compared in the customarily connected and the island modes. Compared to four machine learning mechanisms, including linear discriminant, linear support vector mechanism (SVM), cubic SVM and ensemble boosted tree, the ensemble bagged trees mechanism (EBTM) has superior accuracy. Furthermore, the suggested method relies mainly on local variables and has no communication latency requirements. Therefore, fault detection using the proposed strategy is reasonable. The simulation outcomes show that the proposed strategy provides 100 percent accurate symmetrical and asymmetrical fault diagnosis within 1.25 milliseconds. Moreover, this approach accurately identifies high- and low-impedance faults.
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Saeed, Mustafa, Sheikh, Jumani, and Mirjat. "Ensemble Bagged Tree Based Classification for Reducing Non-Technical Losses in Multan Electric Power Company of Pakistan." Electronics 8, no. 8 (2019): 860. http://dx.doi.org/10.3390/electronics8080860.
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Non-technical losses (NTLs) have been a major concern for power distribution companies (PDCs). Billions of dollars are lost each year due to fraud in billing, metering, and illegal consumer activities. Various studies have explored different methodologies for efficiently identifying fraudster consumers. This study proposes a new approach for NTL detection in PDCs by using the ensemble bagged tree (EBT) algorithm. The bagged tree is an ensemble of many decision trees which considerably improves the classification performance of many individual decision trees by combining their predictions to reach a final decision. This approach relies on consumer energy usage data to identify any abnormality in consumption which could be associated with NTL behavior. The key motive of the current study is to provide assistance to the Multan Electric Power Company (MEPCO) in Punjab, Pakistan for its campaign against energy stealers. The model developed in this study generates the list of suspicious consumers with irregularities in consumption data to be further examined on-site. The accuracy of the EBT algorithm for NTL detection is found to be 93.1%, which is considerably higher compared to conventional techniques such as support vector machine (SVM), k-th nearest neighbor (KNN), decision trees (DT), and random forest (RF) algorithm.
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Khade, H. S., A. D. Patange, S. S. Pardeshi, and R. Jegadeeshwaran. "Design of bagged tree ensemble for carbide coated inserts fault diagnosis." Materials Today: Proceedings 46 (2021): 1283–89. http://dx.doi.org/10.1016/j.matpr.2021.02.128.
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Amalina, I., A. Saidatul, C. Y. Fook, and R. F. Navea. "Performance Analysis Between Feature Extraction and Fusion in Familiar and Unfamiliar Typing Biometric Authentication." Journal of Physics: Conference Series 2071, no. 1 (2021): 012041. http://dx.doi.org/10.1088/1742-6596/2071/1/012041.
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Abstract The brain signals recorded by EEG devices are largely developed in for biometric authentication purposes. Those signals are very informative and reliable to be classified using signal processing. In this paper, the feature extraction and feature fusion are further studied to observe their performance towards the typing tasks. The signals are pre-processed to eliminate the unwanted noise present in the signals. The feature extraction method such as Welch’s method, Burg’s method and Yule Walk’s method are applied to extract the mean, median, standard deviation and variance in the data. Nonlinear feature such as fuzzy entropy is also been extracted. The extracted features are further classified by using k-Nearest Neighbour (k-NN), Random Forest (RF) and Ensemble Bagged Tree (EBT). The performance of feature extraction and feature fusion through concatenation are recorded and compared. For comparison, the feature fusion shows a better performance accuracy rather than feature extraction. The highest percentage accuracy was produced by Burg’s method for frontal-parietal lobes feature fusion which is 95.94% using Ensemble Bagged Tree (EBT).
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Saeed, Muhammad Salman, Mohd. Wazir Mustafa, Usman Ullah Sheikh, Attaullah Khidrani, and Mohd Norzali Haji Mohd. "THEFT DETECTION IN POWER UTILITIES USING ENSEMBLE OF CHAID DECISION TREE ALGORITHM." Science Proceedings Series 2, no. 2 (2020): 161–65. http://dx.doi.org/10.31580/sps.v2i2.1480.
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Theft of electricity and fraud in energy consumption billing are the primary concerns for Distribution System Operators . Because of those illegal activities, it is believed that billions of dollars are wasted each year. DSOs around the world continue to use conventional time consuming and inefficient methods for non-technical loss detection, particularly in underdeveloped countries . This research work attempts to solve the problems as mentioned above by designing an effective model for detecting electricity theft to classify fraudster customers in a power delivery system. The key motivation for this current study is to support the DSOs in their fight against the stealing of electricity. Initially, the proposed method uses the monthly energy customer consumption data obtained from Multan Electric Power Company (MEPCO) Pakistan to segregate fraudsters and honest customers. The Bagged Chi - square Automatic Interaction Detection (CHAID) based decision tree algorithm is then used to classify the honest and fraudster consumers.Furthermore, based on the mentioned metrics, the performance superiority of the Bagged CHAID-based NTL detection method is validated by comparing its efficacy with that of few well-known state-of-the-art machine learning algorithms such as Artificial Neural Network (ANN), Support Vector Machine (SVM),) Logistic Regression (LR), Discriminant Analysis and Bayesian Network (BN).
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Javaid, Haider Ali, Mohsin Islam Tiwana, Ahmed Alsanad, et al. "Classification of Hand Movements Using MYO Armband on an Embedded Platform." Electronics 10, no. 11 (2021): 1322. http://dx.doi.org/10.3390/electronics10111322.
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The study proposed the classification and recognition of hand gestures using electromyography (EMG) signals for controlling the upper limb prosthesis. In this research, the EMG signals were measured through an embedded system by wearing a band of MYO gesture control. In order to observe the behavior of these change movements, the EMG data was acquired from 10 healthy subjects (five male and five females) performing four upper limb movements. After extracting EMG data from MYO, the supervised classification approach was applied to recognize the different hand movements. The classification was performed with a 5-fold cross-validation technique under the supervision of Quadratic discriminant analysis (QDA), support vector machine (SVM), random forest, gradient boosted, ensemble (bagged tree), and ensemble (subspace K-Nearest Neighbors) classifier. The execution of these classifiers shows the overall accuracy of 83.9% in the case of ensemble (bagged tree) which is higher than other classifiers. Additionally, in this research an embedded system-based classification approach of hand movement was used for designing an upper limb prosthesis. This approach is different than previous techniques as MYO is used with an external Bluetooth module and different libraries that make its movement and performance boundless. The results of this study also inferred the operations which were easy for hand recognition and can be used for developing a powerful, efficient, and flexible prosthetic design in the future.
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de Paola, Elisa, Roberto Camussi, Fabio Gasparetti, et al. "Predicting Wall Pressure Fluctuations on Aerospace Launchers Through Machine Learning Approaches." Aerospace 11, no. 12 (2024): 972. http://dx.doi.org/10.3390/aerospace11120972.
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Artificial intelligence (AI) can be used to optimize the prediction of pressure fluctuations over the external surfaces of aerospace launchers and minimize the number of wind tunnel tests. In the present research, various machine learning (ML) techniques capable of predicting the acoustic load were tested and validated. The methods included decision trees, Gaussian Process Regression (GPR), Support Vector Machines (SVMs), artificial neural networks (ANNs), linear regression, and ensemble methods such as bagged and boosted trees. These algorithms were trained using experimental data from an extensive wind tunnel test campaign conducted to support the design of a VEGA (Advanced Generation European Vehicle) launcher vehicle and provide wall pressure fluctuations in many configurations. The main objective of this study was to identify, among several algorithms, the most suitable method able to process such complex databases efficiently and to provide reliable predictions. Different statistical indices, including the root mean square error (RMSE), the mean square error (MSE), and a correlation coefficient (R-squared), were employed to evaluate the performance of the ML methods. Among all the methods, the bagged tree algorithm outperformed the others, providing the most accurate predictions, with low RMSE and high R-squared values across all test cases. Other methods, such as the ANNs and GPR, exhibited higher errors, indicating their reduced suitability for this dataset. The results demonstrate that ensemble decision tree methods are highly effective in predicting acoustic loads, offering reliable predictions, even for configurations outside the training database. These findings support the application of ML-based models to optimize experimental campaigns and enhance the design of aerospace launch vehicles.
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Mustamin, Nurul Fathanah, Ariyani Buang, Firman Aziz, and Nur Hamdani Nur. "Ensemble Techniques Based Risk Classification for Maternal Health During Pregnancy." ILKOM Jurnal Ilmiah 16, no. 2 (2024): 190–97. http://dx.doi.org/10.33096/ilkom.v16i2.2005.190-197.
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This research focuses on the critical aspect of maternal health during pregnancy, emphasizing the need for early detection and intervention to address potential risks to both mothers and infants. Leveraging various classification methods, including Naïve Bayes, decision trees, and ensemble learning techniques, the study investigates the prediction of childbirth potential and pregnancy risks. The research begins with data collection, followed by preprocessing to clean and prepare the data, including handling missing values and normalization. Next, cross-validation is performed to ensure model robustness. Five ensemble techniques are used for risk classification: Ensemble Boosted Trees, which enhances the performance of decision trees; Ensemble Bagged Trees, which combines predictions from decision trees trained on different subsets of data; Ensemble Subspace Discriminant, which applies discriminant analysis on random subspaces; Ensemble Subspace KNN, which uses K-Nearest Neighbors (KNN) within random subspaces; and Ensemble RUS Boosted Trees. Key variables such as maternal age, height, Hb levels, blood pressure, and previous pregnancy history are considered in these analyses. Additionally, the study introduces Ensemble Learning based on Classification Trees, revealing significant improvements in accuracy compared to cost-sensitive learning approaches. The comparison of methods, including Naïve Bayes and K-Nearest Neighbor, provides insights into their respective performances, with ensemble techniques demonstrating their potential. The proposed ensemble learning techniques, namely Ensemble Boosted Trees, Ensemble Bagging Trees, Ensemble Subspace Discriminant, Ensemble Subspace KNN, and Ensemble RUS Boosted Trees, are systematically evaluated in classifying pregnancy risks based on a comprehensive dataset of 1014 records. The results showcase Ensemble Bagging Trees as a standout performer, with an accuracy of 85.6%, indicating robust generalization and effectiveness in clinical risk assessment compared to traditional methods such as Decision Tree (61.54% accuracy), K-Nearest Neighbor (74.48%), Ensemble Learning based on Cost-Sensitive Learning (73%), Ensemble Learning based on Classification Tree (76%), Gaussian Naïve Bayes (82.6%), Multinomial Naïve Bayes (84.8%), and Bernoulli Naïve Bayes (84.8%). Ensemble Bagging Trees achieved the highest accuracy proving to be more effective than the other methods. However, the study emphasizes the need for continuous refinement and adaptation of ensemble methods, considering both accuracy and interpretability, for successful deployment in healthcare decision-making. These findings contribute valuable insights into optimizing pregnancy risk classification models, paving the way for improved maternal and infant healthcare outcomes.
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Abraham, Shiny, Chau Huynh, and Huy Vu. "Classification of Soils into Hydrologic Groups Using Machine Learning." Data 5, no. 1 (2019): 2. http://dx.doi.org/10.3390/data5010002.
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Hydrologic soil groups play an important role in the determination of surface runoff, which, in turn, is crucial for soil and water conservation efforts. Traditionally, placement of soil into appropriate hydrologic groups is based on the judgement of soil scientists, primarily relying on their interpretation of guidelines published by regional or national agencies. As a result, large-scale mapping of hydrologic soil groups results in widespread inconsistencies and inaccuracies. This paper presents an application of machine learning for classification of soil into hydrologic groups. Based on features such as percentages of sand, silt and clay, and the value of saturated hydraulic conductivity, machine learning models were trained to classify soil into four hydrologic groups. The results of the classification obtained using algorithms such as k-Nearest Neighbors, Support Vector Machine with Gaussian Kernel, Decision Trees, Classification Bagged Ensembles and TreeBagger (Random Forest) were compared to those obtained using estimation based on soil texture. The performance of these models was compared and evaluated using per-class metrics and micro- and macro-averages. Overall, performance metrics related to kNN, Decision Tree and TreeBagger exceeded those for SVM-Gaussian Kernel and Classification Bagged Ensemble. Among the four hydrologic groups, it was noticed that group B had the highest rate of false positives.
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Rahman, Muhammad Muhitur, Md Shafiullah, Md Shafiul Alam, et al. "Decision Tree-Based Ensemble Model for Predicting National Greenhouse Gas Emissions in Saudi Arabia." Applied Sciences 13, no. 6 (2023): 3832. http://dx.doi.org/10.3390/app13063832.
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Greenhouse gas (GHG) emissions must be precisely estimated in order to predict climate change and achieve environmental sustainability in a country. GHG emissions are estimated using empirical models, but this is difficult since it requires a wide variety of data and specific national or regional parameters. In contrast, artificial intelligence (AI)-based methods for estimating GHG emissions are gaining popularity. While progress is evident in this field abroad, the application of an AI model to predict greenhouse gas emissions in Saudi Arabia is in its early stages. This study applied decision trees (DT) and their ensembles to model national GHG emissions. Three AI models, namely bagged decision tree, boosted decision tree, and gradient boosted decision tree, were investigated. Results of the DT models were compared with the feed forward neural network model. In this study, population, energy consumption, gross domestic product (GDP), urbanization, per capita income (PCI), foreign direct investment (FDI), and GHG emission information from 1970 to 2021 were used to construct a suitable dataset to train and validate the model. The developed model was used to predict Saudi Arabia’s national GHG emissions up to the year 2040. The results indicated that the bagged decision tree has the highest coefficient of determination (R2) performance on the testing dataset, with a value of 0.90. The same method also has the lowest root mean square error (0.84 GtCO2e) and mean absolute percentage error (0.29 GtCO2e), suggesting that it exhibited the best performance. The model predicted that GHG emissions in 2040 will range between 852 and 867 million tons of CO2 equivalent. In addition, Shapley analysis showed that the importance of input parameters can be ranked as urbanization rate, GDP, PCI, energy consumption, population, and FDI. The findings of this study will aid decision makers in understanding the complex relationships between the numerous drivers and the significance of diverse socioeconomic factors in defining national GHG inventories. The findings will enhance the tracking of national GHG emissions and facilitate the concentration of appropriate activities to mitigate climate change.
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Hajian, Gelareh, Behnam Behinaein, Ali Etemad, and Evelyn Morin. "Bagged tree ensemble modelling with feature selection for isometric EMG-based force estimation." Biomedical Signal Processing and Control 78 (September 2022): 104012. http://dx.doi.org/10.1016/j.bspc.2022.104012.
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Alazemi, Fahd, Asmaa Alazmi, Mubarak Alrumaidhi, and Nick Molden. "Predicting Fuel Consumption and Emissions Using GPS-Based Machine Learning Models for Gasoline and Diesel Vehicles." Sustainability 17, no. 6 (2025): 2395. https://doi.org/10.3390/su17062395.
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The transportation sector plays a vital role in enabling the movement of people, goods, and services, but it is also a major contributor to energy consumption and greenhouse gas emissions. Accurate modeling of fuel consumption and pollutant emissions is critical for effective transportation management and environmental sustainability. This study investigates the use of real-world driving data from gasoline and diesel vehicles to model fuel consumption and exhaust emissions (CO2 and NOx). The models were developed using ensemble bagged and decision tree algorithms with inputs derived from both vehicle speed and GPS speed data. The results demonstrate high predictive accuracy, with the ensemble bagged model consistently outperforming the decision tree model across all datasets. Notably, GPS speed-based models showed comparable performance to vehicle speed-based models, indicating the feasibility of using GPS data for real-time predictions. Furthermore, the combined gasoline and diesel engine dataset improved the accuracy of CO2 emission predictions, while the gasoline-only dataset yielded the highest accuracy for fuel consumption. These findings underscore the potential of integrating GPS-based machine learning models into Intelligent Transportation Systems (ITS) to enhance real-time monitoring and policymaking. Future research should explore the inclusion of heavy-duty vehicles, additional pollutants, and advanced modeling techniques to further improve predictive capabilities.
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Raza, Ahmad, Mohsin Ali, Muhammad Khurram Ehsan, and Ali Hassan Sodhro. "Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach." Sensors 23, no. 17 (2023): 7456. http://dx.doi.org/10.3390/s23177456.
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The rapid technological advancements in the current modern world bring the attention of researchers to fast and real-time healthcare and monitoring systems. Smart healthcare is one of the best choices for this purpose, in which different on-body and off-body sensors and devices monitor and share patient data with healthcare personnel and hospitals for quick and real-time decisions about patients’ health. Cognitive radio (CR) can be very useful for effective and smart healthcare systems to send and receive patient’s health data by exploiting the primary user’s (PU) spectrum. In this paper, tree-based algorithms (TBAs) of machine learning (ML) are investigated to evaluate spectrum sensing in CR-based smart healthcare systems. The required data sets for TBAs are created based on the probability of detection (Pd) and probability of false alarm (Pf). These data sets are used to train and test the system by using fine tree, coarse tree, ensemble boosted tree, medium tree, ensemble bagged tree, ensemble RUSBoosted tree, and optimizable tree. Training and testing accuracies of all TBAs are calculated for both simulated and theoretical data sets. The comparison of training and testing accuracies of all classifiers is presented for the different numbers of received signal samples. Results depict that optimizable tree gives the best accuracy results to evaluate the spectrum sensing with minimum classification error (MCE).
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Tsang, Long, Biao He, Ahmad Safuan A. Rashid, Abduladheem Turki Jalil, and Mohanad Muayad Sabri Sabri. "Predicting the Young’s Modulus of Rock Material Based on Petrographic and Rock Index Tests Using Boosting and Bagging Intelligence Techniques." Applied Sciences 12, no. 20 (2022): 10258. http://dx.doi.org/10.3390/app122010258.
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Rock deformation is considered one of the essential rock properties used in designing and constructing rock-based structures, such as tunnels and slopes. This study applied two well-established ensemble techniques, including boosting and bagging, to the artificial neural networks and decision tree methods for predicting the Young’s modulus of rock material. These techniques were applied to a dataset comprising 45 data samples from a mountain range in Malaysia. The final input variables of these models, including p-wave velocity, interlocking coarse-grained crystals of quartz, dry density, and Mica, were selected through a likelihood ratio test. In total, six models were developed: standard artificial neural networks, boosted artificial neural networks, bagged artificial neural networks, classification and regression trees, extreme gradient boosting trees (as a boosted decision tree), and random forest (as a bagging decision tree). The performance of these models was appraised utilizing correlation coefficient (R), mean absolute error (MAE), and lift chart. The findings of this study showed that, firstly, extreme gradient boosting trees outperformed all models developed in this study; secondly, boosting models outperformed the bagging models.
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Masoodi, Faheem Syeed, Iram Abrar, and Alwi M. Bamhdi. "An Effective Intrusion Detection System Using Homogeneous Ensemble Techniques." International Journal of Information Security and Privacy 16, no. 1 (2022): 1–18. http://dx.doi.org/10.4018/ijisp.2022010112.
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In this work, homogeneous ensemble techniques, namely bagging and boosting were employed for intrusion detection to determine the intrusive activities in network by monitoring the network traffic. Simultaneously, model diversity was enhanced as numerous algorithms were taken into account, thereby leading to an increase in the detection rate Several classifiers, i.e., SVM, KNN, RF, ETC and MLP) were used in case of bagging approach. Likewise, tree-based classifiers have been employed for boosting. The proposed model was tested on NSL-KDD dataset that was initially subjected to preprocessing. Accordingly, ten most significant features were identified using decision tree and recursive feature elimination method. Furthermore, the dataset was divided into five subsets, each one them being subjected to training, and the final results were obtained based on majority voting. Experimental results proved that the model was effective for detecting intrusive activities. Bagged ETC and boosted RF outperformed all the other classifiers with an accuracy of 99.123% and 99.309%, respectively.
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Widasari, Edita Rosana, Koichi Tanno, and Hiroki Tamura. "Automatic Sleep Disorders Classification Using Ensemble of Bagged Tree Based on Sleep Quality Features." Electronics 9, no. 3 (2020): 512. http://dx.doi.org/10.3390/electronics9030512.
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Sleep disorder is a medical disease of the sleep patterns, which commonly suffered by the elderly. Sleep disorders diagnosis and treatment are considered to be challenging due to a time-consuming and inconvenient process for the patient. Moreover, the use of Polysomnography (PSG) in sleep disorder diagnosis is a high-cost process. Therefore, we propose an efficient classification method of sleep disorder by merely using electrocardiography (ECG) signals to simplify the sleep disorders diagnosis process. Different from many current related studies that applied a five-minute epoch to observe the main frequency band of the ECG signal, we perform a pre-processing technique that suitable for the 30-seconds epoch of the ECG signal. By this simplification, the proposed method has a low computational cost so that suitable to be implemented in an embedded hardware device. Structurally, the proposed method consists of five stages: (1) pre-processing, (2) spectral features extraction, (3) sleep stage detection using the Decision-Tree-Based Support Vector Machine (DTB-SVM), (4) assess the sleep quality features, and (5) sleep disorders classification using ensemble of bagged tree classifiers. We evaluate the effectiveness of the proposed method in the task of classifying the sleep disorders into four classes (insomnia, Sleep-Disordered Breathing (SDB), REM Behavior Disorder (RBD), and healthy subjects) from the 51 patients of the Cyclic Alternating Pattern (CAP) sleep data. Based on experimental results, the proposed method presents 84.01% of sensitivity, 94.17% of specificity, 86.27% of overall accuracy, and 0.70 of Cohen’s kappa. This result indicates that the proposed method able to reliably classify the sleep disorders merely using the 30-seconds epoch ECG in order to address the issue of a multichannel signal such as the PSG.
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Wang, Jiatong, Tiantian Zhu, Shan Liang, R. Karthiga, K. Narasimhan, and V. Elamaran. "Binary and Multiclass Classification of Histopathological Images Using Machine Learning Techniques." Journal of Medical Imaging and Health Informatics 10, no. 9 (2020): 2252–58. http://dx.doi.org/10.1166/jmihi.2020.3124.
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Background and Objective: Breast cancer is fairly common and widespread form of cancer among women. Digital mammogram, thermal images of breast and digital histopathological images serve as a major tool for the diagnosis and grading of cancer. In this paper, a novel attempt has been proposed using image analysis and machine learning algorithm to develop an automated system for the diagnosis and grading of cancer. Methods: BreaKHis dataset is employed for the present work where images are available with different magnification factor namely 40×, 100×, 200×, 400× and 200× magnification factor is utilized for the present work. Accurate preprocessing steps and precise segmentation of nuclei in histopathology image is a necessary prerequisite for building an automated system. In this work, 103 images from benign and 103 malignant images are used. Initially color image is reshaped to gray scale format by applying Otsu thresholding, followed by top hat, bottom hat transform in preprocessing stage. The threshold value selected based on Ridler and calvard algorithm, extended minima transform and median filtering is applied for doing further steps in preprocessing. For segmentation of nuclei distance transform and watershed are used. Finally, for feature extraction, two different methods are explored. Result: In binary classification benign and malignant classification is done with the highest accuracy rate of 89.7% using ensemble bagged tree classifier. In case of multiclass classification 5-class are taken which are adenosis, fibro adenoma, tubular adenoma, mucinous carcinoma and papillary carcinoma the combination of multiclass classification gives the accuracy of 88.1% using ensemble subspace discriminant classifier. To the best of author’s knowledge, it is the first made in a novel attempt made for binary and multiclass classification of histopathology images. Conclusion: By using ensemble bagged tree and ensemble subspace discriminant classifiers the proposed method is efficient and outperform the state of art method in the literature.
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Laksono, Pringgo Widyo, Takahide Kitamura, Joseph Muguro, Kojiro Matsushita, Minoru Sasaki, and Muhammad Syaiful Amri bin Suhaimi. "Minimum Mapping from EMG Signals at Human Elbow and Shoulder Movements into Two DoF Upper-Limb Robot with Machine Learning." Machines 9, no. 3 (2021): 56. http://dx.doi.org/10.3390/machines9030056.
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This research focuses on the minimum process of classifying three upper arm movements (elbow extension, shoulder extension, combined shoulder and elbow extension) of humans with three electromyography (EMG) signals, to control a 2-degrees of freedom (DoF) robotic arm. The proposed minimum process consists of four parts: time divisions of data, Teager–Kaiser energy operator (TKEO), the conventional EMG feature extraction (i.e., the mean absolute value (MAV), zero crossings (ZC), slope-sign changes (SSC), and waveform length (WL)), and eight major machine learning models (i.e., decision tree (medium), decision tree (fine), k-Nearest Neighbor (KNN) (weighted KNN, KNN (fine), Support Vector Machine (SVM) (cubic and fine Gaussian SVM), Ensemble (bagged trees and subspace KNN). Then, we compare and investigate 48 classification models (i.e., 47 models are proposed, and 1 model is the conventional) based on five healthy subjects. The results showed that all the classification models achieved accuracies ranging between 74–98%, and the processing speed is below 40 ms and indicated acceptable controller delay for robotic arm control. Moreover, we confirmed that the classification model with no time division, with TKEO, and with ensemble (subspace KNN) had the best performance in accuracy rates at 96.67, recall rates at 99.66, and precision rates at 96.99. In short, the combination of the proposed TKEO and ensemble (subspace KNN) plays an important role to achieve the EMG classification.
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Turabieh, Hamza, and Ahmad S. Alghamdi. "Hybrid Machine Learning Classifiers for Indoor User Localization Problem." International Journal of Innovative Technology and Exploring Engineering 10, no. 3 (2021): 49–53. http://dx.doi.org/10.35940/ijitee.c8375.0110321.
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Wi-Fi technology is now everywhere either inside or outside buildings. Using Wi-fi technology introduces an indoor localization service(s) (ILS). Determining indoor user location is a hard and complex problem. Several applications highlight the importance of indoor user localization such as disaster management, health care zones, Internet of Things applications (IoT), and public settlement planning. The measurements of Wi-Fi signal strength (i.e., Received Signal Strength Indicator (RSSI)) can be used to determine indoor user location. In this paper, we proposed a hybrid model between a wrapper feature selection algorithm and machine learning classifiers to determine indoor user location. We employed the Minimum Redundancy Maximum Relevance (mRMR) algorithm as a feature selection to select the most active access point (AP) based on RSSI values. Six different machine learning classifiers were used in this work (i.e., Decision Tree (DT), Support Vector Machine (SVM), k-nearest neighbors (kNN), Linear Discriminant Analysis (LDA), Ensemble-Bagged Tree (EBaT), and Ensemble Boosted Tree (EBoT)). We examined all classifiers on a public dataset obtained from UCI repository. The obtained results show that EBoT outperforms all other classifiers based on accuracy value/
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Almaliki, Abdulrazak H., Abdessamed Derdour, and Enas Ali. "Air Quality Index (AQI) Prediction in Holy Makkah Based on Machine Learning Methods." Sustainability 15, no. 17 (2023): 13168. http://dx.doi.org/10.3390/su151713168.
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Makkah draws millions of visitors during Hajj and Ramadan, establishing itself as one of Saudi Arabia’s most bustling cities. The imperative lies in maintaining pristine air quality and comprehending diverse air pollutants to effectively manage and model air pollution. Given the capricious and variably spatiotemporal nature of pollution, predicting air quality emerges as a notably intricate endeavor. In this study, we confronted this challenge head-on by harnessing sophisticated machine learning techniques, encompassing the fine decision tree (FDT), ensemble boosted tree (EBOT), and ensemble bagged tree (EBAT). These advanced methodologies were enlisted to project air quality index (AQI) levels, focusing specifically on the Makkah region. Constructed and trained on air quality data spanning 2016 to 2018, our forecast models unearthed noteworthy insights. The outcomes revealed that EBOT exhibited unparalleled accuracy at 97.4%, astutely predicting 75 out of 77 samples. On the other hand, FDT and EBAT achieved accuracies of 96.1% and 94.8%, respectively. Consequently, the EBOT model emerges as the epitome of reliability, showcasing its prowess in forecasting the air quality index. We believe that the insights garnered from this research possess universal applicability, extending their potential to regions worldwide.
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Hamza, Turabieh, and S. Alghamdi Ahmad. "Hybrid Machine Learning Classifiers for Indoor User Localization Problem." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 3 (2021): 49–53. https://doi.org/10.35940/ijitee.C8375.0110321.
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Wi-Fi technology is now everywhere either inside or outside buildings. Using Wi-fi technology introduces an indoor localization service(s) (ILS). Determining indoor user location is a hard and complex problem. Several applications highlight the importance of indoor user localization such as disaster management, health care zones, Internet of Things applications (IoT), and public settlement planning. The measurements of Wi-Fi signal strength (i.e., Received Signal Strength Indicator (RSSI)) can be used to determine indoor user location. In this paper, we proposed a hybrid model between a wrapper feature selection algorithm and machine learning classifiers to determine indoor user location. We employed the Minimum Redundancy Maximum Relevance (mRMR) algorithm as a feature selection to select the most active access point (AP) based on RSSI values. Six different machine learning classifiers were used in this work (i.e., Decision Tree (DT), Support Vector Machine (SVM), k-nearest neighbors (kNN), Linear Discriminant Analysis (LDA), Ensemble-Bagged Tree (EBaT), and Ensemble Boosted Tree (EBoT)). We examined all classifiers on a public dataset obtained from UCI repository. The obtained results show that EBoT outperforms all other classifiers based on accuracy value.
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Nguyen, Kieu Anh, Walter Chen, Bor-Shiun Lin, and Uma Seeboonruang. "Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements." ISPRS International Journal of Geo-Information 10, no. 1 (2021): 42. http://dx.doi.org/10.3390/ijgi10010042.
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Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging, (b) boosting, and (c) stacking. The bagging method in this study refers to bagged multivariate adaptive regression splines (bagged MARS) and random forest (RF), and the boosting method includes Cubist and gradient boosting machine (GBM). Finally, the stacking method is an ensemble method that uses a meta-model to combine the predictions of base models. This study used RF and GBM as the meta-models, decision tree, linear regression, artificial neural network, and support vector machine as the base models. The dataset used in this study was sampled using stratified random sampling to achieve a 70/30 split for the training and test data, and the process was repeated three times. The performance of six ensemble methods in three categories was analyzed based on the average of three attempts. It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). This result was confirmed by the spatial comparison of the absolute differences (errors) between model predictions and observations using GBM and RF in the study area. In summary, the results show that as a group, the bagging method and the boosting method performed equally well, and the stacking method was third for the erosion pin dataset considered in this study.
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Doğan, Ferdi, Saadin Oyucu, Derya Betul Unsal, Ahmet Aksöz, and Majid Vafaeipour. "Impact of Environmental Conditions on Renewable Energy Prediction: An Investigation Through Tree-Based Community Learning." Applied Sciences 15, no. 1 (2025): 336. https://doi.org/10.3390/app15010336.
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The real-time prediction of energy production is essential for effective energy management and planning. Forecasts are essential in various areas, including the efficient utilization of energy resources, the provision of energy flexibility services, decision-making amidst uncertainty, the balancing of supply and demand, and the optimization of online energy systems. This study examines the use of tree-based ensemble learning models for renewable energy production prediction, focusing on environmental factors such as temperature, pressure, and humidity. The study’s primary contribution lies in demonstrating the effectiveness of the bagged trees model in reducing overfitting and achieving higher accuracy compared to other models, while maintaining computational efficiency. The results indicate that less sophisticated models are inadequate for accurately representing complex datasets. The results evaluate the effectiveness of machine learning methods in delivering valuable insights for energy sectors managing environmental conditions and predicting renewable energy sources
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ياسر الجناحي, ياسر الجناحي. "دراسة وتحليل مرضى Covid-19 باستخدام طرق التعلم الآلي". journal of King Abdulaziz University Computing and Information Technology Sciences 10, № 1 (2021): 37–47. http://dx.doi.org/10.4197/comp.10-1.2.
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. أنظمة التعلم الآلي (Machine Learning) في الرعاية الصحية تستخدم للتعرف على الأمراض وتشخيصها باستخدام بيانات المريض. وقد أدى استخدام أنظمة التعلم الآلي في التكنولوجيا إلى إصلاح وتحسين الرعاية الصحية، من خلال الكشف التلقائي عن الأمراض وتشخيصها، والتي بدورها تحسن صحة المريض وتنقذ الأرواح. لذلك، في هذه الدراسة، تم استخدام خوارزميات التعلم الآلي للتنبؤ بوفاة المرضى وتعافيهم. وباستخدام عدة خوارزميات سيتم توقع وفاة أو تعافي المرضى. وقد أعطت خوارزميات الـ Naïve Bayes و Bagged Trees أفضل معدلات أداء بنسبة 79? و 77? على التوالي. ومع ذلك، من حيث الدقة، أظهرت خوارزميات تصنيف الشجرة المتوسطة (MediumTree)(ensemble method Boosted Tree) والشجرة المجموعة المعززة دقة 89?. وأخيرًا أظهرت هذه الدراسة أن استخدام تقنية التعلم الآلي يمكن أن تنبه مقدمي الرعاية الصحية لتقديم علاج أسرع لمرضى فيروس كورونا عالي الخطورة (COVID-19) مما يساعد في إنقاذ الأرواح وتحسن جودة خدمة الرعاية الصحية.
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M, Sowmiya, Banu Rekha B, and Malar E. "Ensemble classifiers with hybrid feature selection approach for diagnosis of coronary artery disease." Scientific Temper 14, no. 03 (2023): 726–34. http://dx.doi.org/10.58414/scientifictemper.2023.14.3.24.
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Coronary artery disease (CAD) is a common type of cardiovascular disease with a high mortality rate worldwide. As symptoms may not be recognized until, after the cardiac attack, early diagnosis and treatment are critical to lowering mortality. The proposed study focuses on the creation of an intelligent ensemble system for the accurate detection of CAD. This paper presents the hybrid feature selection method based on Lasso, random forest-based boruta, and recursive feature elimination methods. The significance of a feature is determined by the score each approach provides. Machine learning techniques such as random forest, support vector machine, K-nearest neighbor, logistic regression, decision tree, and Naive Bayes are developed as base classifiers. Then, ensemble techniques like bagging and boosting models are created using base classifiers. The Z-Alizadeh Sani dataset was used to build and test the model. The bagged random forest model achieved 97.6% accuracy and 100% recall. The CatBoost model achieved 97.7% accuracy and 99.0% recall. Compared to traditional classifiers, the ensemble models achieved higher accuracy and can be used to assist clinicians in diagnosing coronary artery disease
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Saravanakumar, C., and N. Usha Bhanu. "Speed Efficient Fast Fourier Transform for Signal Processing of Nucleotides to Detect Diabetic Retinopathy Using Machine Learning." Journal of Medical Imaging and Health Informatics 12, no. 1 (2022): 27–34. http://dx.doi.org/10.1166/jmihi.2022.3922.
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Diabetic Retinopathy (DR) is a complicated disease of diabetes, which specifically affects the retina. The human-intensive analysis mechanism of DR infected retina are likely to diagnose wrongly compared to computer-intensive diagnosis systems. In this paper, in order to aid the computer based approach for the diagnosis of DR, a model based on machine learning algorithm is proposed. The nucleotides of the human retina are processed with the help of signal processing methodologies. A speed efficient Fast Fourier transform is proposed to work out the FFT of huge amount of samples with higher pace. The improvement in speed is achieved in 98% of the samples. The prediction parameters, derived from these samples are utilized to classify the healthy retina sequence and an infected retina. In this study, Fine Tree, KNN Fine, Weighted KNN, Ensemble Bagged Trees and Ensemble Subspace KNN classifiers are employed to build the models. The simulated results using MATLAB software show that the accuracy is 98% which is better than image processing based methods which were used earlier. The performance parameters such as sensitivity and specificity are determined for each model. The faithfulness of the model is studied by deriving the ROC Curve.
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Plesinger, Filip, Petr Nejedly, Ivo Viscor, Josef Halamek, and Pavel Jurak. "Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG." Physiological Measurement 39, no. 9 (2018): 094002. http://dx.doi.org/10.1088/1361-6579/aad9ee.
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Zhang, Shengli, Qianhao Yu, Haoran He, et al. "iDHS-DSAMS: Identifying DNase I hypersensitive sites based on the dinucleotide property matrix and ensemble bagged tree." Genomics 112, no. 2 (2020): 1282–89. http://dx.doi.org/10.1016/j.ygeno.2019.07.017.
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Mishra, Praveen Kumar, Anamika Yadav, and Mohammad Pazoki. "A Novel Fault Classification Scheme for Series Capacitor Compensated Transmission Line Based on Bagged Tree Ensemble Classifier." IEEE Access 6 (2018): 27373–82. http://dx.doi.org/10.1109/access.2018.2836401.
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Dadhich, Ajay, Jaideep Patel, Rovin Tiwari, Richa Verma, Pratha Mishra, and Jay Kumar Jain. "A flexible analytic wavelet transform and ensemble bagged tree model for electroencephalogram-based meditative mind-wandering detection." Healthcare Analytics 5 (June 2024): 100286. http://dx.doi.org/10.1016/j.health.2023.100286.
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Tarafder, Sreeza, Nasreen Badruddin, Norashikin Yahya, and Arbi Haza Nasution. "Drowsiness Detection Using Ocular Indices from EEG Signal." Sensors 22, no. 13 (2022): 4764. http://dx.doi.org/10.3390/s22134764.
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Drowsiness is one of the main causes of road accidents and endangers the lives of road users. Recently, there has been considerable interest in utilizing features extracted from electroencephalography (EEG) signals to detect driver drowsiness. However, in most of the work performed in this area, the eyeblink or ocular artifacts present in EEG signals are considered noise and are removed during the preprocessing stage. In this study, we examined the possibility of extracting features from the EEG ocular artifacts themselves to perform classification between alert and drowsy states. In this study, we used the BLINKER algorithm to extract 25 blink-related features from a public dataset comprising raw EEG signals collected from 12 participants. Different machine learning classification models, including the decision tree, the support vector machine (SVM), the K-nearest neighbor (KNN) method, and the bagged and boosted tree models, were trained based on the seven selected features. These models were further optimized to improve their performance. We were able to show that features from EEG ocular artifacts are able to classify drowsy and alert states, with the optimized ensemble-boosted trees yielding the highest accuracy of 91.10% among all classic machine learning models.
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Jamali, Ali, Masoud Mahdianpari, Brian Brisco, Jean Granger, Fariba Mohammadimanesh, and Bahram Salehi. "Comparing Solo Versus Ensemble Convolutional Neural Networks for Wetland Classification Using Multi-Spectral Satellite Imagery." Remote Sensing 13, no. 11 (2021): 2046. http://dx.doi.org/10.3390/rs13112046.
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Wetlands are important ecosystems that are linked to climate change mitigation. As 25% of global wetlands are located in Canada, accurate and up-to-date wetland classification is of high importance, nationally and internationally. The advent of deep learning techniques has revolutionized the current use of machine learning algorithms to classify complex environments, specifically in remote sensing. In this paper, we explore the potential and possible limitations to be overcome regarding the use of ensemble deep learning techniques for complex wetland classification and discusses the potential and limitation of various solo convolutional neural networks (CNNs), including DenseNet, GoogLeNet, ShuffleNet, MobileNet, Xception, Inception-ResNet, ResNet18, and ResNet101 in three different study areas located in Newfoundland and Labrador, Canada (i.e., Avalon, Gros Morne, and Grand Falls). Moreover, to improve the classification accuracies of wetland classes of bog, fen, marsh, swamp, and shallow water, the results of the three best CNNs in each study area is fused using three supervised classifiers of random forest (RF), bagged tree (BTree), Bayesian optimized tree (BOT), and one unsupervised majority voting classifier. The results suggest that the ensemble models, in particular BTree, have a valuable role to play in the classification of wetland classes of bog, fen, marsh, swamp, and shallow water. The ensemble CNNs show an improvement of 9.63–19.04% in terms of mean producer’s accuracy compared to the solo CNNs, to recognize wetland classes in three different study areas. This research indicates a promising potential for integrating ensemble-based learning and deep learning for operational large area land cover, particularly complex wetland type classification.
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Raha, Shrinwantu, Shasanka Kumar Gayen, and Sayan Deb. "Harnessing Machine Learning and Ensemble Models for Tourism Potential Zone Prediction for the Assam State of India." Journal of Advanced Geospatial Science & Technology 4, no. 2 (2024): 29–78. https://doi.org/10.11113/jagst.v4n2.92.
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Although several popular tourist destinations exist in Assam, India, its charm remains enigmatic. This research was aimed at predicting the tourism potential zone (TPZ) for the state of Assam using five machine learning models (i.e., Conditional Inference Tree, Bagged CART, Random Forest, Random Forest with Conditional Inference Tree, and Gradient Boosting models) and one ensemble model. A 5-step methodology was implemented to conduct this research. First, a tourism inventory database was prepared using Google Earth Imagery, and a rapid field investigation was performed using the global positioning system and nonparticipant observation technique. A total of 365 tourism points were present in the inventory, 70% (224 points) of which were used for the training set and 30% (124 points) for the validation set. Tourism conditioning factors, such as relief, aspect, viewshed, forest area, wetland, coefficient of variation of rainfall, reserve forest, population density, population growth rate, literacy rate, and road–railway density, were used as independent variables in the modeling process. The TPZ was predicted using the above machine learning models, and finally, a new TPZ ensemble model was proposed by combining all the models. The result showed that all machine learning models performed well in terms of prediction accuracy, and the ensemble model outperformed other models by achieving the highest area under the curve (97.6%), Kappa (0.82), and accuracy (0.93) values. The findings from this research using machine learning and ensemble methods can provide accurate and significant information for decision-makers to develop tourism in the region.
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Tanthanathewin, Rinrada, Warissaporn Wongrattanapipat, Tin Tin Khaing, and Pakinee Aimmanee. "Automatic exudate and aneurysm segmentation in OCT images using UNET++ and hyperreflective-foci feature based bagged tree ensemble." PLOS ONE 19, no. 5 (2024): e0304146. http://dx.doi.org/10.1371/journal.pone.0304146.
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Diabetic retinopathy’s signs, such as exudates (EXs) and aneurysms (ANs), initially develop from under the retinal surface detectable from optical coherence tomography (OCT) images. Detecting these signs helps ophthalmologists diagnose DR sooner. Detecting and segmenting exudates (EXs) and aneurysms (ANs) in medical images is challenging due to their small size, similarity to other hyperreflective regions, noise presence, and low background contrast. Furthermore, the scarcity of public OCT images featuring these abnormalities has limited the number of studies related to the automatic segmentation of EXs and ANs, and the reported performance of such studies has not been satisfactory. This work proposes an efficient algorithm that can automatically segment these anomalies by improving key steps in the process. The potential area where these hyper-reflective EXs and ANs occur was scoped by our method using a deep-learning U-Net++ program. From this area, the candidates for EX-AN were segmented using the adaptive thresholding method. Nine features based on appearances, locations, and shadow markers were extracted from these candidates. They were trained and tested using bagged tree ensemble classifiers to obtain only EX-AN blobs. The proposed method was tested on a collection of a public dataset comprising 80 images with hand-drawn ground truths. The experimental results showed that our method could segment EX-AN blobs with average recall, precision, and F1-measure as 87.9%, 86.1%, and 87.0%, respectively. Its F1-measure drastically outperformed two comparative methods, binary thresholding and watershed (BT-WS) and adaptive thresholding with shadow tracking (AT-ST), by 78.0% and 82.1%, respectively.
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Enas, Enas, Ahmed M. Dinar, Mazin Abed .., and Bourair AL AL-Attar. "Improving Loan Status Prediction Accuracy with Generative Adversarial Networks: Addressing Data Scarcity and Bias." Journal of Intelligent Systems and Internet of Things 13, no. 1 (2024): 251–58. http://dx.doi.org/10.54216/jisiot.130118.
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A precise and reliable loan status prediction is of the essence for financial institutions, However, the lack of real-world data and biases within that data can greatly impact the accuracy of machine learning models. Another challenge faced by loan status prediction models is class imbalance, where one category (such as approved loans) is much more common than another (such as defaulted loans), leading to skewed predictions towards the majority class. This study inspects Generative Adversarial Networks (GANs) to augment the data and improve the machine learning models’ performance. Several machine learning (ML) models including but not limited to Support Vector Machines (SVM) and ensemble bagged trees were employed on a Kaggle loan dataset (380 samples). Baseline training and testing accuracies were 86.9% and 86.3% (SVM) and 84.5% and 82.1% (ensemble). ActGAN (Activating Generative Networks) was then utilized to generate synthetic data points for both accepted and rejected loans. Retraining the models with new augmented data showed remarkable improvements: SVM accuracies for training and testing rose to 94.4% and 93.4%, while ensemble models achieved 97.4% and 95.8%, respectively. Other ML models were also explored such as KNN, Decision tree and logistic Regression and showed promising results in terms of accuracy as compared to the state of art. These findings put forward that GAN-based data augmentation can enhance the performance of loan status prediction. Future research could explore GAN’s impact of different architectures and assess the general applicability of this approach.
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Enas, Enas, Ahmed M. Dinar, Mazin Abed .., and Bourair AL AL-Attar. "Improving Loan Status Prediction Accuracy with Generative Adversarial Networks: Addressing Data Scarcity and Bias." Journal of Intelligent Systems and Internet of Things 13, no. 1 (2024): 225–33. http://dx.doi.org/10.54216/jisiot.130116.
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A precise and reliable loan status prediction is of the essence for financial institutions, However, the lack of real-world data and biases within that data can greatly impact the accuracy of machine learning models. Another challenge faced by loan status prediction models is class imbalance, where one category (such as approved loans) is much more common than another (such as defaulted loans), leading to skewed predictions towards the majority class. This study inspects Generative Adversarial Networks (GANs) to augment the data and improve the machine learning models’ performance. Several machine learning (ML) models including but not limited to Support Vector Machines (SVM) and ensemble bagged trees were employed on a Kaggle loan dataset (380 samples). Baseline training and testing accuracies were 86.9% and 86.3% (SVM) and 84.5% and 82.1% (ensemble). ActGAN (Activating Generative Networks) was then utilized to generate synthetic data points for both accepted and rejected loans. Retraining the models with new augmented data showed remarkable improvements: SVM accuracies for training and testing rose to 94.4% and 93.4%, while ensemble models achieved 97.4% and 95.8%, respectively. Other ML models were also explored such as KNN, Decision tree and logistic Regression and showed promising results in terms of accuracy as compared to the state of art. These findings put forward that GAN-based data augmentation can enhance the performance of loan status prediction. Future research could explore GAN’s impact of different architectures and assess the general applicability of this approach.
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Amaireh, Anas, Yan (Rockee) Zhang, Pak Wai Chan, and Dusan Zrnic. "A Novel Approach for Improving Cloud Liquid Water Content Profiling with Machine Learning." Remote Sensing 17, no. 11 (2025): 1836. https://doi.org/10.3390/rs17111836.
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Accurate prediction of Cloud Liquid Water Content (CLWC) is critical for understanding and forecasting weather phenomena, particularly in regions with complex microclimates. This study integrates high-resolution ERA5 climatic data from the European Centre for Medium-Range Weather Forecasts (ECMWF) with radiosonde observations from the Hong Kong area to address data accuracy and resolution challenges. Machine learning (ML) models—specifically Fine Tree regressors—were employed to interpolate radiosonde data, resolving temporal and spatial discrepancies and enhancing data coverage. A metaheuristic algorithm was also applied for data cleansing, significantly improving correlations between input features (temperature, pressure, and humidity) and CLWC. The methodology was tested across multiple ML algorithms, with ensemble models such as Bagged Trees demonstrating superior predictive accuracy and robustness. The approach substantially improved CLWC profile reliability, outperforming traditional methods and addressing the nonlinear complexities of atmospheric data. Designed for scalability, this methodology extends beyond Hong Kong’s unique conditions, offering a flexible framework for improving weather prediction models globally. By advancing CLWC estimation techniques, this work contributes to enhanced weather forecasting and atmospheric science in diverse climatic regions.
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Adnan, A., A. M. Yolanda, and F. Natasya. "A Comparison of Bagging and Boosting on Classification Data: Case Study on Rainfall Data in Sultan Syarif Kasim II Meteorological Station in Pekanbaru." Journal of Physics: Conference Series 2049, no. 1 (2021): 012053. http://dx.doi.org/10.1088/1742-6596/2049/1/012053.
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Abstract A frequent way for classification data is using a machine learning algorithm alongside ensemble methods like bagging and boosting. In earlier studies, these two algorithms have shown to be very accurate. The aim of this research is to discover performance of bagging and boosting to classify rainfall data obtained at the Sultan Syarif Kasim II Meteorological Station in Pekanbaru from 1 January 2018 until 31 July 2021. Rainfall data are classified into two categories: rainy and non-rainy. The parameters are average temperature, average humidity, sunshine duration, wind direction at maximum speed, and average wind speed. For comparison, this study developed Stochastic Gradient Boosting with Gradient Boosting Modelling and C5.0 from boosting, as well as Bagged Classification and Regression Tree (CART) and Random Forest from bagging. In order to generate reliable conclusions, each algorithm is run 30 times with repeated cross validation. The result demonstrates that Stochastic Gradient Boosting with Gradient Boosting Modelling is the best algorithm based on average accuracy.
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Peng, Lele, Shubin Zheng, Qianwen Zhong, Xiaodong Chai, and Jianhui Lin. "A novel bagged tree ensemble regression method with multiple correlation coefficients to predict the train body vibrations using rail inspection data." Mechanical Systems and Signal Processing 182 (January 2023): 109543. http://dx.doi.org/10.1016/j.ymssp.2022.109543.
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Abu Al-Haija, Qasem, and Mu’awya Al-Dala’ien. "ELBA-IoT: An Ensemble Learning Model for Botnet Attack Detection in IoT Networks." Journal of Sensor and Actuator Networks 11, no. 1 (2022): 18. http://dx.doi.org/10.3390/jsan11010018.
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Due to the prompt expansion and development of intelligent systems and autonomous, energy-aware sensing devices, the Internet of Things (IoT) has remarkably grown and obstructed nearly all applications in our daily life. However, constraints in computation, storage, and communication capabilities of IoT devices has led to an increase in IoT-based botnet attacks. To mitigate this threat, there is a need for a lightweight and anomaly-based detection system that can build profiles for normal and malicious activities over IoT networks. In this paper, we propose an ensemble learning model for botnet attack detection in IoT networks called ELBA-IoT that profiles behavior features of IoT networks and uses ensemble learning to identify anomalous network traffic from compromised IoT devices. In addition, our IoT-based botnet detection approach characterizes the evaluation of three different machine learning techniques that belong to decision tree techniques (AdaBoosted, RUSBoosted, and bagged). To evaluate ELBA-IoT, we used the N-BaIoT-2021 dataset, which comprises records of both normal IoT network traffic and botnet attack traffic of infected IoT devices. The experimental results demonstrate that our proposed ELBA-IoT can detect the botnet attacks launched from the compromised IoT devices with high detection accuracy (99.6%) and low inference overhead (40 µ-seconds). We also contrast ELBA-IoT results with other state-of-the-art results and demonstrate that ELBA-IoT is superior.
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Puri, Digambar Vithhalbuwa, Sanjay Nalbalwar, Anil Nandgaonkar, and Abhay Wagh. "Alzheimer’s disease detection from optimal EEG channels and Tunable Q-Wavelet Transform." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (2022): 1420. http://dx.doi.org/10.11591/ijeecs.v25.i3.pp1420-1428.
Full textAbstract:
Alzheimer’s disease (AD) is a non-curable neuro-degenerative disorder that has no cure to date. However, it can be delayed through daily activity assessment using a robust Electroencephalogram (EEG) based system at an early stage. A selection tech- nique using a Shannon entropy to signal energy ratio is proposed to select optimal EEG channels for AD detection. A threshold for channel selection is calculated using the best detection accuracy during backward elimination. The selected EEG channels are decomposed using Tunable Q-wavelet transform (TQWT) into nine different sub- bands (SBs). Four features: Katz’s fractal dimension, Tsallis entropy, Relyi’s entropy, and kurtosis are extracted for each SB. These features are used to train and test sup- port vector machine, k-nearest neighbor, Ensemble bagged tree (EBT), decision tree, and neural network for detecting AD patients from normal subjects. 16-channel EEG signals from 12 AD and 11 normal subjects recorded using the 10-20 electrode place- ment method are used for evaluation. Ten optimized channels are selected, resulting in 32.5% compression. The experimental results of the proposed method showed promis- ing classification accuracy of 96.20% with the seventh SB features and EBT classifier. The significance of these features was inspected by using the Kruskal-Wallis test.
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Digambar, Puri1, Nalbalwar2 Sanjay, Nandgaonkar2 Anil, and Wagh3 Abhay. "Alzheimer's disease detection from optimal electroencephalogram channels and tunable Q-wavelet transform." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (2022): 1420–28. https://doi.org/10.11591/ijeecs.v25.i3.pp1420-1428.
Full textAbstract:
Alzheimer’s disease (AD) is a non-curable neuro-degenerative disorder that has no cure to date. However, it can be delayed through daily activity assessment using a robust electroencephalogram (EEG) based system at an early stage. A selection technique using a Shannon entropy to signal energy ratio is proposed to select optimal EEG channels for AD detection. A threshold for channel selection is calculated using the best detection accuracy during backward elimination. The selected EEG channels are decomposed using tunable Q-wavelet transform (TQWT) into nine different subbands (SBs). Four features: Katz’s fractal dimension, Tsallis entropy, Relyi’s entropy, and kurtosis are extracted for each SB. These features are used to train and test support vector machine, k-nearest neighbor, ensemble bagged tree (EBT), decision tree, and neural network for detecting AD patients from normal subjects. 16-channel EEG signals from 12 AD and 11 normal subjects recorded using the 10-20 electrode placement method are used for evaluation. Ten optimized channels are selected, resulting in 32.5% compression. The experimental results of the proposed method showed promising classification accuracy of 96.20% with the seventh SB features and EBT classifier. The significance of these features was inspected by using the Kruskal-Wallis test.
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Lepine, Julien, and Vincent Rouillard. "Evaluation of Shock Detection Algorithm for Road Vehicle Vibration Analysis." Vibration 1, no. 2 (2018): 220–38. http://dx.doi.org/10.3390/vibration1020016.
Full textAbstract:
The ability to characterize shocks which occur during road transport is a vital prerequisite for the design of optimized protective packaging, which can assist in reducing cost and waste related to products and good transport. Many methods have been developed to detect shocks buried in road vehicle vibration signals, but none has yet considered the nonstationary nature of vehicle vibration and how, individually, they fail to accurately detect shocks. Using machine learning, several shock detection methods can be combined, and the reliability and accuracy of shock detection can also be improved. This paper presents how these methods can be integrated into four different machine learning algorithms (Decision Tree, k-Nearest Neighbors, Bagged Ensemble, and Support Vector Machine). The Pseudo-Energy Ratio/Fall-Out (PERFO) curve, a novel classification assessment tool, is also introduced to calibrate the algorithms and compare their detection performance. In the context of shock detection, the PERFO curve has an advantage over classical assessment tools, such as the Receiver Operating Characteristic (ROC) curve, as it gives more importance to high-amplitude shocks.
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Covaciu, Florina-Dorina, Camelia Berghian-Grosan, Ariana Raluca Hategan, Dana Alina Magdas, Adriana Dehelean, and Gabriela Cristea. "Machine Learning Approach to Comparing Fatty Acid Profiles of Common Food Products Sold on Romanian Market." Foods 12, no. 23 (2023): 4237. http://dx.doi.org/10.3390/foods12234237.
Full textAbstract:
Food composition issues represent an increasing concern nowadays, in the context of diverse food commodity varieties. The contents and types of fatty acids are a constant preoccupation among consumers because of their reflections of nutrition and health problems. This study aims to find the best tool for the rapid and reliable identification of similarities and differences among several food items from a fatty acid profile perspective. An acknowledged GC-FID method was considered, while, for a better interpretation of the analytical results, machine learning algorithms were used. It was possible to develop a recognition model able to simultaneously differentiate, with an accuracy of 79.3%, nine product types using the bagged tree ensemble model. The low number of samples or some similarities among the classes could be responsible for the wrong assignments that occurred, especially in the biscuit, wafer and instant soup classes. Better accuracies values of 95, 86.1, and 97.8% were obtained when the products were grouped into three categories: (1) sunflower oil, mayonnaise, margarine, and cream cheese; (2) biscuits, cookies, margarine, and wafers; and (3) sunflower oil, chips, and instant soup.
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Thamizhvani, T. R., Syed Uzma Farheen, R. J. Hemalatha, and A. Josephin Arockia Dhivya. "CLASSIFICATION OF PROGRESSIVE STAGES OF ALZHEIMER’S DISEASE IN MRI HIPPOCAMPAL REGION." Biomedical Engineering: Applications, Basis and Communications 32, no. 06 (2020): 2050050. http://dx.doi.org/10.4015/s1016237220500507.
Full textAbstract:
Alzheimer’s disease (AD) is a type of neuronal brain disorder that is degenerative and results in memory loss, skills and cognitive changes. The primary diagnostic tests for the disorder are defined to be total brain atrophy and hippocampal atrophy. Early diagnosis is significant and the design of automatic systems is necessary for this disorder. A potential biomarker for AD is described using a hippocampal magnetic resonance imaging volumetry system that possesses certain limitations. This paper aims to analyze the transition of stages from normal cognition to different forms that ultimately leads to Alzheimer’s disease. The magnetic resonance imaging (MRI) images of different stages are derived from the standard database for the segregation of hippocampal region. Later, the morphological and radiomic features are extracted from the hippocampal regions of different stages, since the hippocampus plays a major role in memory. Classification of extracted features was performed using machine learning algorithms like ensemble tree classifiers. The classification results based on performance parameters specify that the bagged tree classifier is more efficient. The 4-way classification has an accuracy of 95.6% indicating certain misclassification between the two classes MCI and PMCI. To categorize these two classes, a 2-way classification is described that has an accuracy of 98.6%. With these results, an effective method is defined for the analysis and identification of the different progressive stages of Alzheimer’s disease.
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Marchesi, Claudio, Monika Rani, Stefania Federici, Matteo Lancini, and Laura Eleonora Depero. "Evaluating chemometric strategies and machine learning approaches for a miniaturized near-infrared spectrometer in plastic waste classification." Acta IMEKO 12, no. 2 (2023): 1–7. http://dx.doi.org/10.21014/actaimeko.v12i2.1531.
Full textAbstract:
Optimizing the sorting of plastic waste plays a crucial role in improving the recycling process. In this contribution, we report on a comparative study of multiple machine learning and chemometric approaches to categorize a data set derived from the analysis of plastic waste performed with a handheld spectrometer working in the Near-Infrared (NIR) spectral range. Conducting a cost-effective NIR study requires identifying appropriate techniques to improve commodity identification and categorization. Chemometric techniques, such as Principal Component Analysis (PCA) and Partial Least Squares - Discriminant Analysis (PLS - DA), and machine learning techniques such as Support- Vector Machines (SVM), fine tree, bagged tree, and ensemble learning were compared. Various pre-treatments were tested on the collected NIR spectra. In particular, Standard Normal Variate (SNV) and Savitzky-Golay derivatives as signal pre-processing tools were compared with feature selection techniques such as multiple Gaussian Curve Fit based on Radial Basis Functions (RBF). Furthermore, results were combined into a single predictor by using a likelihood-based aggregation formula. Predictive performances of the tested models were compared in terms of classification parameters such as Non-Error Rate (NER) and Sensitivity (Sn) with the analysis of the confusion matrices, giving a broad overview and a rational means for the selection of the approach in the analysis of NIR data for plastic waste sorting.