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Статті в журналах з теми "Hybrid machine learning models"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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1

Lok, Lai Kai, Vazeerudeen Abdul Hameed, and Muhammad Ehsan Rana. "Hybrid machine learning approach for anomaly detection." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 1016–24. https://doi.org/10.11591/ijeecs.v27.i2.pp1016-1024.

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This research aims to improve anomaly detection performance by developing two variants of hybrid models combining supervised and unsupervised machine learning techniques. Supervised models cannot detect new or unseen types of anomaly. Hence in variant 1, a supervised model that detects normal samples is followed by an unsupervised learning model to screen anomaly. The unsupervised model is weak in differentiating between noise and fraud. Hence in variant 2, the hybrid model incorporates an unsupervised model that detects anomaly is followed by a supervised model to validate an anomaly. Three d

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2

Pulicharla, Mohan Raja. "Hybrid Quantum-Classical Machine Learning Models: Powering the Future of AI." Journal of Science & Technology 4, no. 1 (2023): 40–65. http://dx.doi.org/10.55662/jst.2023.4102.

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The burgeoning field of machine learning has transformed numerous sectors, revolutionizing everything from image recognition to financial forecasting. However, classical machine learning algorithms often encounter limitations when dealing with complex, high-dimensional problems. This is where the nascent field of quantum machine learning (QML) emerges, offering a paradigm shift with its unique computational capabilities. By harnessing the principles of quantum mechanics, QML promises to solve problems intractable for classical methods, like simulating complex molecules or optimizing financial

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3

Rodrigues, Sandy, Gerhard Mütter, Helena Geirinhas Ramos, and F. Morgado-Dias. "Machine Learning Photovoltaic String Analyzer." Entropy 22, no. 2 (2020): 205. http://dx.doi.org/10.3390/e22020205.

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Photovoltaic (PV) system energy production is non-linear because it is influenced by the random nature of weather conditions. The use of machine learning techniques to model the PV system energy production is recommended since there is no known way to deal well with non-linear data. In order to detect PV system faults, the machine learning models should provide accurate outputs. The aim of this work is to accurately predict the DC energy of six PV strings of a utility-scale PV system and to accurately detect PV string faults by benchmarking the results of four machine learning methodologies kn

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4

Masrom, Suraya, Rahayu Abdul Rahman, Masurah Mohamad, Abdullah Sani Abd Rahman, and Norhayati Baharun. "Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 3 (2022): 1153. http://dx.doi.org/10.11591/ijai.v11.i3.pp1153-1163.

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This paper addresses the performances of machine learning classification models for the detection of tax avoidance problems. The machine learning models employed automated features selection with hybrid two metaheuristics algorithms namely particle swarm optimization (PSO) and genetic algorithm (GA). Dealing with a real dataset on the tax avoidance cases among companies in Malaysia, has created a stumbling block for the conventional machine learning models to achieve higher accuracy in the detection process as the associations among all of the features in the datasets are extremely low. This p

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5

Suraya, Masrom, Abdul Rahman Rahayu, Mohamad Masurah, Sani Abd Rahman Abdullah, and Baharun Norhayati. "Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms." International Journal of Artificial Intelligence (IJ-AI) 11, no. 3 (2022): 1153–63. https://doi.org/10.11591/ijai.v11.i3.pp1153-1163.

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Анотація:

This paper addresses the performances of machine learning classification models for the detection of tax avoidance problems. The machine learning models employed automated features selection with hybrid two metaheuristics algorithms namely particle swarm optimization (PSO) and genetic algorithm (GA). Dealing with a real dataset on the tax avoidance cases among companies in Malaysia, has created a stumbling block for the conventional machine learning models to achieve higher accuracy in the detection process as the associations among all of the features in the datasets are extremely low. This p

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6

Chou, Jui-Sheng, Chih-Fong Tsai, and Yu-Hsin Lu. "PROJECT DISPUTE PREDICTION BY HYBRID MACHINE LEARNING TECHNIQUES." Journal of Civil Engineering and Management 19, no. 4 (2013): 505–17. http://dx.doi.org/10.3846/13923730.2013.768544.

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This study compares several well-known machine learning techniques for public-private partnership (PPP) project dispute problems. Single and hybrid classification techniques are applied to construct models for PPP project dispute prediction. The single classification techniques utilized are multilayer perceptron (MLP) neural networks, decision trees (DTs), support vector machines, the naïve Bayes classifier, and k-nearest neighbor. Two types of hybrid learning models are developed. One combines clustering and classification techniques and the other combines multiple classification techniques.

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7

Chaubey, Mangalam. "Diabetes Mellitus Prediction using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 4786–90. http://dx.doi.org/10.22214/ijraset.2023.52755.

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Abstract: Diabetes is a chronic metabolic disorder affecting millions of people worldwide, and machine learning has shown great potential in predicting the disease using medical and demographic features from patient data. In this paper, we propose a hybrid model of Support Vector Machines (SVM) and XGBoost for diabetes prediction, which combines the strengths of both algorithms to achieve higher accuracy and better performance. We evaluate the proposed model using the Pima Indian diabetes dataset and compare its performance with other machine learning models. To improve the performance of the

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8

Kim, Yeonuk, Monica Garcia, T. Andrew Black, and Mark S. Johnson. "Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge." PLOS One 20, no. 7 (2025): e0328798. https://doi.org/10.1371/journal.pone.0328798.

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Physics-informed machine learning techniques have emerged to tackle challenges inherent in pure machine learning (ML) approaches. One such technique, the hybrid approach, has been introduced to estimate terrestrial evapotranspiration (ET), a crucial variable linking water, energy, and carbon cycles. A key advantage of these hybrid ET models is their improved performance, particularly under extreme conditions, compared to ET estimates relying solely on ML. However, the mechanisms driving their improved performance are not well understood. To address this gap, we developed six hybrid approaches

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9

Lok, Lai Kai, Vazeerudeen Abdul Hameed, and Muhammad Ehsan Rana. "Hybrid machine learning approach for anomaly detection." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 1016. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp1016-1024.

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Анотація:

This research aims to <span lang="EN-US">improve anomaly detection performance by developing two variants of hybrid models combining supervised and unsupervised machine learning techniques. Supervised models cannot detect new or unseen types of anomaly. Hence in variant 1, a supervised model that detects normal samples is followed by an unsupervised learning model to screen anomaly. The unsupervised model is weak in differentiating between noise and fraud. Hence in variant 2, the hybrid model incorporates an unsupervised model that detects anomaly is followed by a supervised model to val

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10

Sengaliappan, Dr M. "Flood Prediction using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41892.

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Due to urbanization and climate change, flooding has increased in frequency and severity, upsetting lives and seriously damaging property. Flood Susceptibility Modeling (FSM), which employs sophisticated machine learning approaches, helps identify flood-prone locations and the elements that contribute to these risks in order to solve this problem. This study explores hybrid FSM models that integrate the Index of Entropy (IOE) with Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF) to offer a dependable approach for flood prediction and prevention. To assess the predictive

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11

Giabbanelli, Philippe J. "Hybrid Models That Combine Machine Learning and Simulations." Computing in Science & Engineering 24, no. 5 (2022): 72–76. http://dx.doi.org/10.1109/mcse.2023.3238649.

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12

Bui, Quang-Thanh, Quang-Tuan Pham, Van-Manh Pham, et al. "Hybrid machine learning models for aboveground biomass estimations." Ecological Informatics 79 (March 2024): 102421. http://dx.doi.org/10.1016/j.ecoinf.2023.102421.

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13

Буханец, А. К., Д. А. Дудник, and О. В. Косникова. "Hybrid machine learning models for financial market analysis." Applied Economic Researches Journal, no. 1 (March 30, 2024): 27–35. http://dx.doi.org/10.47576/2949-1908.2024.1.1.003.

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В статье дается обзор исследовательской деятельности в сфере, связанной с гибридными моделями машинного обучения, которые применяются при анализе финансового рынка. Рассматриваются инновационные подходы, которые позволяют совмещать достоинства того или иного метода в обучении с точным прогнозированием и осуществлением управленческих решений на практике, с учетом сложности среды, где функционируют финансовые рынки. The article provides an overview of research activities in the field related to hybrid machine learning models that are used in financial market analysis. Innovative approaches are c

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14

Rahman, Abdur, Gulfam Khan, Afzal Azad, Ahmar Ejaz, and Maruti Maurya. "Detecting Fake News Using Hybrid Machine Learning Models." International Journal of Innovative Research in Computer Science and Technology 13, no. 3 (2025): 127–37. https://doi.org/10.55524/ijircst.2025.13.3.20.

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The increasing diffusion of misinformation in online media has raised alarm as a significant threat to information credibility and societal trust. The ease of disseminating false information across social media platforms, news websites, and digital forums has led to severe consequences, including political manipulation, financial fraud, and public misinformation. This research outlines a robust strategy for detecting misinformation using Natural Language Processing. To take the detection process a step further, the study analyses the implementation of ensemble models. Ensemble learning combine

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15

Gaurav Verma and Saiyed Tazen Ali. "Hybrid Models Integrating Tensor Learning with Conventional Machine Learning for Enhanced Predictive Analytics." International Journal of Web of Multidisciplinary Studies 1, no. 1 (2024): 9–18. https://doi.org/10.71366/ijwos234124.

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Modern data-driven applications require sophisticated analytical techniques capable of handling increasingly complex and high-dimensional datasets. While conventional machine learning models have achieved significant success in various domains, their performance often plateaus when confronted with large-scale, multi-modal, and correlated data. Tensor learning, which extends matrix-based representations to higher-order data structures, provides a powerful framework for modelling such data. However, standalone tensor methods can be challenging to integrate into existing machine learning pipeline

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16

Agarwalla, Arav. "Enhancing Stock Return Predictions: Comparing Machine Learning Methods with Traditional Financial Models." International Journal of Social Science and Economic Research 09, no. 11 (2024): 5215–28. https://doi.org/10.46609/ijsser.2024.v09i11.016.

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This paper aims to understand if machine learning models can enhance stock price predictions compared to that of traditional financial models. The paper covers traditional financial models such as Stochastic Discount Factor models, factor-based models, option pricing models, and behavioural models, and machine learning techniques like supervised learning, deep learning, and hybrid models. By summarizing the results of various papers, this review compares the predictive accuracy of these models. The review found that machine learning methods, deep learning and hybrid models, outperformed tradit

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17

Reddy, Viswanathan Ramasamy, Sukham Romen Singh, Elangovan Guruva Reddy, E. Punarselvam E. Punarselvam, and T. Vengatesh T. Vengatesh. "Machine Learning based Rainfall." Journal of Neonatal Surgery 14, no. 15S (2025): 1435–46. https://doi.org/10.63682/jns.v14i15s.3861.

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Predicting the amount of rain is important for many industries, including agriculture, water resource management, and disaster relief. The intricate spatiotemporal patterns of rainfall are often difficult for traditional technologies to adequately represent. By utilising historical data and meteorological variables, machine learning (ML) techniques present a viable method for improving rainfall prediction. Rainfall prediction tasks have been subjected to a variety of machine learning techniques, including as decision trees, random forests, support vector machines (SVM), and deep learning model

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18

Silahtaroğlu, Yenilmez Oğuz. "Machine Learning Integration in Econometric Models." Next Generation Journal for The Young Researchers 8, no. 1 (2024): 77. http://dx.doi.org/10.62802/8c33p210.

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The integration of machine learning (ML) into econometric models represents a transformative advancement in the field of econometrics, enabling researchers to tackle complex, high-dimensional datasets while maintaining the interpretability and rigor of traditional econometric approaches. This research investigates the synergies between machine learning and econometrics, focusing on how ML techniques can enhance model flexibility, predictive accuracy, and causal inference in economic analysis. By leveraging methods such as regularization, ensemble learning, and deep learning, the study explores

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19

Wang, Xiang, Mi Tian, Qiang Qin, and Jingwei Liang. "Hybridization of Machine Learning Algorithms and an Empirical Regression Model for Predicting Debris-Flow-Endangered Areas." Advances in Civil Engineering 2023 (November 6, 2023): 1–16. http://dx.doi.org/10.1155/2023/9465811.

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Accurate delineation of debris-flow-endangered areas (e.g., the maximum runout distance) is a necessary prerequisite for the debris-flow risk assessment and countermeasures design. Recently, machine-learning models have been proved to be an effective tool in predicting debris-flow parameters. However, existing machine-learning models are generally developed based on a very limited number of observation data, which may result in the predictive model overfitting or underfitting. How to develop a robust model for accurate forecasting of debris-flow-endangered areas still remains a difficult task.

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20

K S, Mr Keerthi, Ms Nandini C N, Ms Monisha Ganapati Moger, Ms Janani C G, and Mr Manuprasad A. "Intrusion Detection System Using Hybrid Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2024): 1–6. https://doi.org/10.55041/ijsrem41545.

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The project introduces a state-of-the-art Intrusion Detection System (IDS) utilizing Hybrid Machine Learning that addresses the limitations of traditional systems by integrating anomaly and signature-based detection models. The proposed solution integrates anomaly detection and signature-based models for comprehensive detection of known and unknown attacks. Advanced machine learning algorithms ensure high accuracy while Explainable AI (XAI) provides interpretability for detection decisions. Federated learning is utilized to preserve data privacy. Designed for real-time detection in IOT and clo

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21

Choudhary, Laxmi, and Jitendra Singh Choudhary. "Deep Learning Meets Machine Learning: A Synergistic Approach towards Artificial Intelligence." Journal of Scientific Research and Reports 30, no. 11 (2024): 865–75. http://dx.doi.org/10.9734/jsrr/2024/v30i112614.

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The evolution of artificial intelligence (AI) has progressed from rule-based systems to learning-based models, integrating machine learning (ML) and deep learning (DL) to tackle complex data-driven tasks. This review examines the synergy between ML, which utilizes algorithms like decision trees and support vector machines for structured data, and DL, which employs neural networks for processing unstructured data such as images and natural language. The combination of these paradigms through hybrid ML-DL models has enhanced prediction accuracy, scalability, and automation across domains like he

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22

Subramanian, Suresh, and Y. Angeline Christobel. "A Hybrid Machine Learning Model to Predict Heart Disease Accurately." Indian Journal of Science and Technology 15, no. 12 (2022): 527–34. http://dx.doi.org/10.17485/ijst/v15i12.104.

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23

Nosratabadi, Saeed, Amirhosein Mosavi, Puhong Duan, et al. "Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods." Mathematics 8, no. 10 (2020): 1799. http://dx.doi.org/10.3390/math8101799.

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This paper provides a comprehensive state-of-the-art investigation of the recent advances in data science in emerging economic applications. The analysis is performed on the novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a broad and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, is used to ensure the quality of the survey. Th

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24

Shakil, Farhan, Sadia Afrin, Abdullah Al Mamun, et al. "HYBRID MULTI-MODAL DETECTION FRAMEWORK FOR ADVANCED PERSISTENT THREATS IN CORPORATE NETWORKS USING MACHINE LEARNING AND DEEP LEARNING." International Journal of Computer Science & Information System 10, no. 02 (2025): 6–20. https://doi.org/10.55640/ijcsis/volume10issue02-02.

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This study addresses the challenge of detecting Advanced Persistent Threats (APTs) in corporate networks by developing a hybrid multi-modal detection framework. We combine traditional machine learning models, deep learning architectures, and transformer-based models to improve the detection of sophisticated and stealthy cyber threats. A comprehensive dataset, consisting of network traffic and event logs, was processed through rigorous data preprocessing, feature engineering, and model development. The results show that the hybrid ensemble model, integrating Gradient Boosting and Transformer-ba

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25

Aijaz, Iflah, and Parul Agarwal. "A Study on Time Series Forecasting using Hybridization of Time Series Models and Neural Networks." Recent Advances in Computer Science and Communications 13, no. 5 (2020): 827–32. http://dx.doi.org/10.2174/1573401315666190619112842.

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Introduction: Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) are leading linear and non-linear models in Machine learning respectively for time series forecasting. Objective: This survey paper presents a review of recent advances in the area of Machine Learning techniques and artificial intelligence used for forecasting different events. Methods: This paper presents an extensive survey of work done in the field of Machine Learning where hybrid models for are compared to the basic models for forecasting on the basis of error parameters like Mean Absolute

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26

PHAN, HAN THI NGOC, and ARJINA AKTER. "HYBRID MACHINE LEARNING APPROACH FOR ORAL CANCER DIAGNOSIS AND CLASSIFICATION USING HISTOPATHOLOGICAL IMAGES." International Journal of Medical Science and Dental Health 11, no. 01 (2025): 63–76. https://doi.org/10.55640/ijmsdh-11-01-08.

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Oral cancer remains a significant global health challenge, with early diagnosis crucial for improving patient outcomes. This study explores the integration of machine learning (ML) techniques in the detection and classification of oral cancer using histopathological images. A hybrid approach combining deep learning-based feature extraction (via pre-trained convolutional neural networks) and traditional handcrafted methods is proposed. The study uses a dataset of 10,000 annotated histopathological images, carefully preprocessed to enhance consistency and mitigate quality variations. Multiple ML

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27

Jitendra, Khatti, and Singh Grover Kamaldeep. "Prediction of Geotechnical Properties of Soil using Artificial Intelligence Framework." International Journal of Recent Technology and Engineering (IJRTE) 10, no. 4 (2021): 218–27. https://doi.org/10.35940/ijrte.D6625.1110421.

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The present research work is carried out to predict the geotechnical properties (consistency limits, OMC, and MDD) of soil using AI technologies, namely regression analysis (RA), support vector machine (SVM), Gaussian process regression (GPR), artificial neural networks (ANNs), and relevance vector machine (RVM). The models of machine learning (SVM, GPR), hybrid learning (RVM), and deep learning (ANNs) are constructed in MATLAB R2020a with different configurations. The models of RA are built using the Data Analysis Tool of Microsoft Excel 2019. The input parameters of AI models are gravel, san

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28

Qaid, Talal S., Hussein Mazaar, Mohammad Yahya H. Al-Shamri, Mohammed S. Alqahtani, Abeer A. Raweh, and Wafaa Alakwaa. "Hybrid Deep-Learning and Machine-Learning Models for Predicting COVID-19." Computational Intelligence and Neuroscience 2021 (August 3, 2021): 1–11. http://dx.doi.org/10.1155/2021/9996737.

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The COVID-19 pandemic has had a significant impact on public life and health worldwide, putting the world’s healthcare systems at risk. The first step in stopping this outbreak is to detect the infection in its early stages, which will relieve the risk, control the outbreak’s spread, and restore full functionality to the world’s healthcare systems. Currently, PCR is the most prevalent diagnosis tool for COVID-19. However, chest X-ray images may play an essential role in detecting this disease, as they are successful for many other viral pneumonia diseases. Unfortunately, there are common featu

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29

Anbananthen, Kalaiarasi Sonai Muthu, Mikail Bin Muhammad Azman Busst, Rajkumar Kannan, and Subarmaniam Kannan. "A Comparative Performance Analysis of Hybrid and Classical Machine Learning Method in Predicting Diabetes." Emerging Science Journal 7, no. 1 (2022): 102–15. http://dx.doi.org/10.28991/esj-2023-07-01-08.

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Diabetes mellitus is one of medical science’s most important research topics because of the disease’s severe consequences. High blood glucose levels characterize it. Early detection of diabetes is made possible by machine learning techniques with their intelligent capabilities to accurately predict diabetes and prevent its complications. Therefore, this study aims to find a machine learning approach that can more accurately predict diabetes. This study compares the performance of various classical machine learning models with the hybrid machine learning approach. The hybrid model includes the

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30

Granata, Francesco, Fabio Di Nunno, and Giuseppe Modoni. "Hybrid Machine Learning Models for Soil Saturated Conductivity Prediction." Water 14, no. 11 (2022): 1729. http://dx.doi.org/10.3390/w14111729.

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The hydraulic conductivity of saturated soil is a crucial parameter in the study of any engineering problem concerning groundwater. Hydraulic conductivity mainly depends on particle size distribution, soil compaction, and properties that influence aggregation and water retention. Generally, finding simple and accurate analytical equations between the hydraulic conductivity of soil and the characteristics on which it depends is a very hard task. Machine learning algorithms can provide excellent tools for tackling highly nonlinear regression problems. Additionally, hybrid models resulting from t

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31

Melesse, Assefa M., Khabat Khosravi, John P. Tiefenbacher, et al. "River Water Salinity Prediction Using Hybrid Machine Learning Models." Water 12, no. 10 (2020): 2951. http://dx.doi.org/10.3390/w12102951.

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Electrical conductivity (EC), one of the most widely used indices for water quality assessment, has been applied to predict the salinity of the Babol-Rood River, the greatest source of irrigation water in northern Iran. This study uses two individual—M5 Prime (M5P) and random forest (RF)—and eight novel hybrid algorithms—bagging-M5P, bagging-RF, random subspace (RS)-M5P, RS-RF, random committee (RC)-M5P, RC-RF, additive regression (AR)-M5P, and AR-RF—to predict EC. Thirty-six years of observations collected by the Mazandaran Regional Water Authority were randomly divided into two sets: 70% fro

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32

Erten, Gamze Erdogan, Karim Mokdad, Jed Nisenson, Gabriela Brandao, and Jeff Boisvert. "Human-AI interaction: Machine learning-based geostatistical hybrid models." Applied Soft Computing 182 (October 2025): 113580. https://doi.org/10.1016/j.asoc.2025.113580.

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33

Rozos, Evangelos. "Assessing Hydrological Simulations with Machine Learning and Statistical Models." Hydrology 10, no. 2 (2023): 49. http://dx.doi.org/10.3390/hydrology10020049.

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Machine learning has been used in hydrological applications for decades, and recently, it was proven to be more efficient than sophisticated physically based modelling techniques. In addition, it has been used in hybrid frameworks that combine hydrological and machine learning models. The concept behind the latter is the use of machine learning as a filter that advances the performance of the hydrological model. In this study, we employed such a hybrid approach but with a different perspective and objective. Machine learning was used as a tool for analyzing the error of hydrological models in

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34

Giroh, Himanshu, Vipin Kumar, and Gurdiyal Singh. "Improving the Performance of Hybrid Models Using Machine Learning and Optimization Techniques." International Journal of Membrane Science and Technology 10, no. 2 (2023): 3396–409. http://dx.doi.org/10.15379/ijmst.v10i2.3138.

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Hybrid models, which combine multiple machine learning algorithms or optimization techniques, have shown great promise in tackling complex real-world problems. The integration of diverse approaches can lead to enhanced performance, increased accuracy, and more robust predictions. In this paper, we explore various methods to improve the performance of hybrid models using machine learning and optimization techniques. We discuss the advantages of hybrid models, the challenges associated with their design and implementation, and present case studies to demonstrate their effectiveness in different

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Gad, Raghad Ahmed, and Ahmed Abdelhafeez. "Alzheimer's Disease Prediction using Hybrid Machine Learning Techniques." SciNexuses 1 (December 30, 2024): 174–83. https://doi.org/10.61356/j.scin.2024.1517.

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Artificial intelligence (AI) and machine learning (ML) have shown benefits in many domains during their growth, particularly considering the enormous amount of data generated recently. For quicker and more precise decision-making regarding illness projections, it might be more dependable. Models can be used to analyze and visualize diseases. The article compares several machine learning algorithms and hybrid machine learning models. A range of machine learning techniques is also available. The following techniques were tested: Random Forest, AdaBoost Classifier, Gaussian NB, Decision Tree, and

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Roggero, Alessandro, Jakub Filipek, Shih-Chieh Hsu, and Nathan Wiebe. "Quantum Machine Learning with SQUID." Quantum 6 (May 30, 2022): 727. http://dx.doi.org/10.22331/q-2022-05-30-727.

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In this work we present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems. The classical infrastructure is based on PyTorch and we provide a standardized design to implement a variety of quantum models with the capability of back-propagation for efficient training. We present the structure of our framework and provide examples of using SQUID in a standard binary classification problem from the popular MNIST dataset. In particular, we highlight the implications for scalability for gradient-based optimiza

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Moussaoui, Jallal-Eddine, Mehdi Kmiti, Khalid El Gholami, and Yassine Maleh. "A Systematic Review on Hybrid AI Models Integrating Machine Learning and Federated Learning." Journal of Cybersecurity and Privacy 5, no. 3 (2025): 41. https://doi.org/10.3390/jcp5030041.

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Cyber threats are growing in scale and complexity, outpacing the capabilities of traditional security systems. Machine learning (ML) models offer enhanced detection accuracy but often rely on centralized data, raising privacy concerns. Federated learning (FL), by contrast, enables decentralized model training but suffers from scalability and latency issues. Hybrid AI models, which integrate ML and FL techniques, have emerged as a promising solution to balance performance, privacy, and scalability in cybersecurity. This systematic review investigates the current landscape of hybrid AI models, e

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Aschepkov, Valeriy. "METHODS OF MACHINE LEARNING IN MODERN METROLOGY." Measuring Equipment and Metrology 85 (2024): 57–60. http://dx.doi.org/10.23939/istcmtm2024.01.057.

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In the modern world of scientific and technological progress, the requirements for the accuracy and reliability of measurements are becoming increasingly stringent. The rapid development of machine learning (ML) methods opens up perspectives for improving metrological processes and enhancing the quality of measurements. This article explores the potential application of ML methods in metrology, outlining the main types of ML models in automatic instrument calibration, analysis, and prediction of data. Attention is paid to the development of hybrid approaches that combine ML methods with tradit

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Schetakis, Nikolaos, Davit Aghamalyan, Michael Boguslavsky, Agnieszka Rees, Marc Rakotomalala, and Paul Robert Griffin. "Quantum Machine Learning for Credit Scoring." Mathematics 12, no. 9 (2024): 1391. http://dx.doi.org/10.3390/math12091391.

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This study investigates the integration of quantum circuits with classical neural networks for enhancing credit scoring for small- and medium-sized enterprises (SMEs). We introduce a hybrid quantum–classical model, focusing on the synergy between quantum and classical rather than comparing the performance of separate quantum and classical models. Our model incorporates a quantum layer into a traditional neural network, achieving notable reductions in training time. We apply this innovative framework to a binary classification task with a proprietary real-world classical credit default dataset

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Sanjeev Gour. "Hybrid Machine Learning for Disease Diagnosis: A Review of Case Studies and Performance Evaluation Using Multi-Source Data." Journal of Information Systems Engineering and Management 10, no. 36s (2025): 604–12. https://doi.org/10.52783/jisem.v10i36s.6537.

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The review paper discusses the development and assessment of hybrid machine learning frameworks for early disease diagnosis using multi-source clinical data. It highlights the importance of early disease forecasting in healthcare, as it allows for more efficient illness management, mitigating symptom severity, and decelerating disease development. Traditional disease prediction methods often face challenges, such as reliance on isolated data sources and the existence of imbalanced datasets. Hybrid machine learning models offer a robust approach to address these shortcomings by integrating the

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Nguyen, Vu, Binh Pham, Ba Vu, et al. "Hybrid Machine Learning Approaches for Landslide Susceptibility Modeling." Forests 10, no. 2 (2019): 157. http://dx.doi.org/10.3390/f10020157.

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This paper presents novel hybrid machine learning models, namely Adaptive Neuro Fuzzy Inference System optimized by Particle Swarm Optimization (PSOANFIS), Artificial Neural Networks optimized by Particle Swarm Optimization (PSOANN), and Best First Decision Trees based Rotation Forest (RFBFDT), for landslide spatial prediction. Landslide modeling of the study area of Van Chan district, Yen Bai province (Vietnam) was carried out with the help of a spatial database of the area, considering past landslides and 12 landslide conditioning factors. The proposed models were validated using different m

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Sreshta, Tella. "Automatic Detection of Genetic Diseases in Pediatric Age Using Pupillometry." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45715.

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Abstract— This paper introduces a comprehensive framework for automated disease detection using pupillometry data. Our approach establishes a robust pipeline that includes data preprocessing, feature extraction, and machine learning-based classification of patients based on their pupillary responses. We extract key features from both left and right pupil diameter measurements, such as maximum and minimum values, delta, channel height (CH), latency, and mean change velocity (MCV).To enhance classification accuracy, we train and evaluate multiple machine learning models, including Support Vector

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Maddala, Jeevan Babu, Bhargav Reddy Modugulla, Sahithi Amulya Pulusu, Sanjay Mannepalli, Praveen prakash Pamidimalla, and Rukhiya Khanam. "Heart Failure Prediction Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 1901–11. http://dx.doi.org/10.22214/ijraset.2024.59236.

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Abstract: Cardiovascular Disease (CVD) currently stands as the leading cause of death worldwide. Clinical data analytics encounter a significant challenge in accurately predicting cardiac disease. The healthcare industry generates vast volumes of raw data, necessitating its transformation into meaningful insights through machine learning techniques. The objective is to leverage machine learning models to improve the predictability of survival among cardiac patients. This study employs machine learning classifiers: Random Forest, Gradient Boosting classifier, Extra Tree Classifier, XG-Boost, Ad

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Malave, Sachin, Bharti Khemani, Rucha Kelkar, Urvi Balekundri, Shravani Bogar, and Areen Kolekar. "Improving Mental Health Diagnosis with Hybrid Ensemble Models: A Data-Driven Approach." EPJ Web of Conferences 328 (2025): 01036. https://doi.org/10.1051/epjconf/202532801036.

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In today's world, mental health conditions including stress, worry, and depression are more common, particularly among working adults and students. To stop these problems from getting worse, prompt detection and treatment are crucial. This study examines how emotional and behavioural indicators might be used to predict mental health issues using machine learning (ML) algorithms. A mental health dataset was used to train and assess a number of machines learning models, including Logistic Regression, K-Nearest Neighbours, Decision Tree, Random Forest, Gradient Boosting, XGBoost, and a Hybrid ens

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Yashwanth, Ch, D. Vikram Kumar, Divya S, and V. Swathi. "Applying Machine Learning to Identify Malicious Behavior." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–6. https://doi.org/10.55041/ijsrem39652.

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Malicious assaults, with an emphasis on URLs, are detected using a new technique that makes use of machine learning techniques. We use hybrid machine learning models in conjunction with ensemble approaches for Natural Language Processing (NLP). To extract pertinent information, we preprocess a dataset that includes both malicious and genuine URLs. We improve our models' accuracy and efficiency by using strategies like Grid Search Hyper Parameter Optimisation and Canopy feature selection. Evaluation measures that show the effectiveness of our method include precision, accuracy, recall, F1-score

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Chakraborty, Subhadeep. "Multi-Disease Detection using Hybrid Machine Learning." Scholars Journal of Engineering and Technology 10, no. 10 (2022): 271–78. http://dx.doi.org/10.36347/sjet.2022.v10i10.002.

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Machine Learning has a significant application in the detection of disease because of the automated process. Using machine learning models, the detection of disease can be done with higher effectiveness and with less error which may be seen in the context of computations made by humans. In this research, the detection of multiple diseases has been done with the application of machine learning. In this research context, three data have been selected namely Heart Disease Data (from UCI Repository), Liver Disease Data (from Kaggle Repository) and Diabetes Data (from Kaggle Repository). To detect

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Albattah, Waleed, and Saleh Albahli. "Intelligent Arabic Handwriting Recognition Using Different Standalone and Hybrid CNN Architectures." Applied Sciences 12, no. 19 (2022): 10155. http://dx.doi.org/10.3390/app121910155.

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Handwritten character recognition is a computer-vision-system problem that is still critical and challenging in many computer-vision tasks. With the increased interest in handwriting recognition as well as the developments in machine-learning and deep-learning algorithms, researchers have made significant improvements and advances in developing English-handwriting-recognition methodologies; however, Arabic handwriting recognition has not yet received enough interest. In this work, several deep-learning and hybrid models were created. The methodology of the current study took advantage of machi

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Shamim, Md Mahfuzul Islam, Abu Bakar bin Abdul Hamid, Tadiwa Elisha Nyamasvisva, and Najmus Saqib Bin Rafi. "Advancement of Artificial Intelligence in Cost Estimation for Project Management Success: A Systematic Review of Machine Learning, Deep Learning, Regression, and Hybrid Models." Modelling 6, no. 2 (2025): 35. https://doi.org/10.3390/modelling6020035.

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This systematic review investigates the integration of artificial intelligence (AI) in cost estimation within project management, focusing on its impact on accuracy and efficiency compared to traditional methods. This study synthesizes findings from 39 high-quality articles published between 2016 and 2024, evaluating various machine learning (ML), deep learning (DL), regression, and hybrid models in sectors such as construction, healthcare, manufacturing, and real estate. The results show that AI-powered approaches, particularly artificial neural networks (ANNs)—which constitute 26.33% of the

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Choudhary, Jyoti, Haresh Kumar Sharma, Pradeep Malik, and Saibal Majumder. "Price Forecasting of Crude Oil Using Hybrid Machine Learning Models." Journal of Risk and Financial Management 18, no. 7 (2025): 346. https://doi.org/10.3390/jrfm18070346.

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Crude oil is a widely recognized, indispensable global and national economic resource. It is significantly susceptible to the boundless fluctuations attributed to various variables. Despite its capacity to sustain the global economic framework, the embedded uncertainties correlated with the crude oil markets present formidable challenges that investors must diligently navigate. In this research, we propose a hybrid machine learning model based on random forest (RF), gated recurrent unit (GRU), conventional neural network (CNN), extreme gradient boosting (XGBoost), functional partial least squa

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Aman, Aman, and Rajender Singh Chhillar. "Comparative Analysis of Hybrid Machine Learning Models for Early- Stage Diabetes and Cardiovascular Disease Prediction." International Journal of Research Publication and Reviews 6, no. 4 (2025): 12479–84. https://doi.org/10.55248/gengpi.6.0425.15180.

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