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1

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, pene
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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) techniq
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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 classificati
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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 cla
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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 Vari
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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 re
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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.
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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
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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 p
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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 ext
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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 r
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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 fe
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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 decisio
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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
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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
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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
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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. Ac
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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
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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
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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 (me
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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 wrappe
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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 b
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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 wrappe
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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
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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
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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)(
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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 tec
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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 pac
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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 stud
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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
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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 positi
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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 segmentat
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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 mac
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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 mac
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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 metaheurist
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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 maxi
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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 botne
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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.

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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 f
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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.

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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
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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.

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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
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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.

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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 possib
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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.

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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
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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.

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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 - D
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