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Search Results (9)

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Keywords = automated machine learning (AML)

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55 pages, 18379 KiB  
Article
Maritime Risk Assessment: A Cutting-Edge Hybrid Model Integrating Automated Machine Learning and Deep Learning with Hydrodynamic and Monte Carlo Simulations
by Egemen Ander Balas and Can Elmar Balas
J. Mar. Sci. Eng. 2025, 13(5), 939; https://doi.org/10.3390/jmse13050939 - 11 May 2025
Viewed by 905
Abstract
In this study, a Hybrid Maritime Risk Assessment Model (HMRA) integrating automated machine learning (AML) and deep learning (DL) with hydrodynamic and Monte Carlo simulations (MCS) was developed to assess maritime accident probabilities and risks. The machine learning models of Light Gradient Boosting [...] Read more.
In this study, a Hybrid Maritime Risk Assessment Model (HMRA) integrating automated machine learning (AML) and deep learning (DL) with hydrodynamic and Monte Carlo simulations (MCS) was developed to assess maritime accident probabilities and risks. The machine learning models of Light Gradient Boosting (LightGBM), XGBoost, Random Forest, and Multilayer Perceptron (MLP) were employed. Cross-validation of model architectures, calibrated baseline configurations, and hyperparameter optimization enabled predictive precision, producing generalizability. This hybrid model establishes a robust maritime accident probability prediction framework through a multi-stage methodology that ensembles learning architecture. The model was applied to İzmit Bay (in Türkiye), a highly jammed maritime area with dense traffic patterns, providing a complete methodology to evaluate and rank risk factors. This research improves maritime safety studies by developing an integrated, simulation-based decision-making model that supports risk assessment actions for policymakers and stakeholders in marine spatial planning (MSP). The potential spill of 20 barrels (bbl) from an accident between two tankers was simulated using the developed model, which interconnects HYDROTAM-3D and the MCS. The average accident probability in İzmit Bay was estimated to be 5.5 × 10−4 in the AML based MCS, with a probability range between 2.15 × 10−4 and 7.93 × 10−4. The order of the predictions’ magnitude was consistent with the Undersecretariat of the Maritime Affairs Search and Rescue Department accident data for İzmit Bay. The spill reaches the narrow strait of the inner basin in the first six hours. This study determines areas within the bay at high risk of accidents and advocates for establishing emergency response centers in these critical areas. Full article
(This article belongs to the Special Issue Recent Advances in Maritime Safety and Ship Collision Avoidance)
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21 pages, 504 KiB  
Article
Biomimicry-Inspired Automated Machine Learning Fit-for-Purpose Wastewater Treatment for Sustainable Water Reuse
by Vasileios Alevizos, Zongliang Yue, Sabrina Edralin, Clark Xu, Nikitas Gerolimos and George A. Papakostas
Water 2025, 17(9), 1395; https://doi.org/10.3390/w17091395 - 6 May 2025
Cited by 1 | Viewed by 843
Abstract
The growing global freshwater scarcity urgently requires innovative wastewater treatment technologies. This study hypothesized that biomimicry-inspired automated machine learning (AML) could effectively manage wastewater variability through adaptive processing techniques. Utilizing decentralized swarm intelligence, specifically the Respected Parametric Insecta Swarm (RPIS), the system demonstrated [...] Read more.
The growing global freshwater scarcity urgently requires innovative wastewater treatment technologies. This study hypothesized that biomimicry-inspired automated machine learning (AML) could effectively manage wastewater variability through adaptive processing techniques. Utilizing decentralized swarm intelligence, specifically the Respected Parametric Insecta Swarm (RPIS), the system demonstrated robust adaptability to fluctuating influent conditions, maintaining stable effluent quality without centralized control. Bio-inspired oscillatory control algorithms maintained stability under dynamic influent scenarios, while adaptive sensor feedback enhanced real-time responsiveness. Machine learning (ML) methods inspired by biological morphological evolution accurately classified influent characteristics (F1 score of 0.91), optimizing resource allocation dynamically. Significant reductions were observed, with chemical consumption decreasing by approximately 11% and additional energy usage declining by 14%. Furthermore, bio-inspired membranes with selective permeability substantially reduced fouling, maintaining minimal fouling for up to 30 days. Polynomial chaos expansions efficiently approximated complex nonlinear interactions, reducing computational overhead by approximately 35% through parallel processing. Decentralized swarm algorithms allowed the rapid recalibration of system parameters, achieving stable pathogen removal and maintaining effluent turbidity near 3.2 NTU (Nephelometric Turbidity Units), with total suspended solids consistently below 8 mg/L. Integrating biomimicry with AML thus significantly advances sustainable wastewater reclamation practices, offering quantifiable improvements critical for resource-efficient water management. Full article
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27 pages, 9234 KiB  
Article
Big Data in Leadership Studies: Automated Machine Learning Model to Predict Preferred Leader Behavior Across Cultures
by Erik Lankut, Gillian Warner-Søderholm, Ilan Alon and Inga Minelgaité
Businesses 2024, 4(4), 696-722; https://doi.org/10.3390/businesses4040039 - 18 Nov 2024
Viewed by 5027
Abstract
With global leadership as the new norm, discussion about followers’ preferred leader behaviors across cultures is growing in significance. This study proposes a comprehensive predictive model to explore significant preferred leadership factors, drawn from the Leader Behavior Description Questionnaire (LBDQXII), across cultures using [...] Read more.
With global leadership as the new norm, discussion about followers’ preferred leader behaviors across cultures is growing in significance. This study proposes a comprehensive predictive model to explore significant preferred leadership factors, drawn from the Leader Behavior Description Questionnaire (LBDQXII), across cultures using automated machine learning (AML). We offer a robust empirical measurement of culturally contingent leader behavior and entrepreneurship behaviors and provide a tool for assessing the cultural predictors of preferred leader behavior to minimize predictive errors, explore patterns in the data and make predictions in an empirically robust way. Hence, our approach fills a gap in the literature relating to applications of AML in leadership studies and contributes a novel empirical method to better predict leadership preferences. Cultural indicators from Global Leadership and Organizational Behavior (GLOBE) predict the likelihood of the preferred leader behaviors of “Role Assumption”, “Production Emphasis” and “Initiation of Structure”. Hofstede’s Long-Term/Short-Term Orientation is the most critical predictor of preferences for “Tolerance of Uncertainty” and “Initiation of Structure”, whereas the value of restraint impacts the likelihood of preferring leaders with skills in “Integration” and “Consideration”. Significant entrepreneurial values indicators have a significant impact on preferences for leaders focused on “Initiation of Structure”, “Production Emphasis” and “Predictive Accuracy”. Findings also support earlier studies that reveal age and gender significantly impact our preferences for specific leader behaviors. We discuss and offer conclusions to support our findings that foster development of global business managers and practitioners. Full article
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25 pages, 15916 KiB  
Review
Acute Myeloid Leukemia: Diagnosis and Evaluation by Flow Cytometry
by Feras Ally and Xueyan Chen
Cancers 2024, 16(22), 3855; https://doi.org/10.3390/cancers16223855 - 17 Nov 2024
Cited by 4 | Viewed by 6922
Abstract
With recent technological advances and significant progress in understanding the pathogenesis of acute myeloid leukemia (AML), the updated fifth edition WHO Classification (WHO-HAEM5) and the newly introduced International Consensus Classification (ICC), as well as the European LeukemiaNet (ELN) recommendations in 2022, require the [...] Read more.
With recent technological advances and significant progress in understanding the pathogenesis of acute myeloid leukemia (AML), the updated fifth edition WHO Classification (WHO-HAEM5) and the newly introduced International Consensus Classification (ICC), as well as the European LeukemiaNet (ELN) recommendations in 2022, require the integration of immunophenotypic, cytogenetic, and molecular data, alongside clinical and morphologic findings, for accurate diagnosis, prognostication, and guiding therapeutic strategies in AML. Flow cytometry offers rapid and sensitive immunophenotyping through a multiparametric approach and is a pivotal laboratory tool for the classification of AML, identification of therapeutic targets, and monitoring of measurable residual disease (MRD) post therapy. The association of immunophenotypic features and recurrent genetic abnormalities has been recognized and applied in informing further diagnostic evaluation and immediate therapeutic decision-making. Recently, the evolving role of machine learning models in assisting flow cytometric data analysis for the automated diagnosis and prediction of underlying genetic alterations has been illustrated. Full article
(This article belongs to the Special Issue Flow Cytometry of Hematological Malignancies)
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22 pages, 1367 KiB  
Article
Detection of GPS Spoofing Attacks in UAVs Based on Adversarial Machine Learning Model
by Lamia Alhoraibi, Daniyal Alghazzawi and Reemah Alhebshi
Sensors 2024, 24(18), 6156; https://doi.org/10.3390/s24186156 - 23 Sep 2024
Cited by 3 | Viewed by 4993
Abstract
Advancements in wireless communication and automation have revolutionized mobility systems, notably through autonomous vehicles and unmanned aerial vehicles (UAVs). UAV spatial coordinates, determined via Global Positioning System (GPS) signals, are susceptible to cyberattacks due to unencrypted and unauthenticated transmissions with GPS spoofing being [...] Read more.
Advancements in wireless communication and automation have revolutionized mobility systems, notably through autonomous vehicles and unmanned aerial vehicles (UAVs). UAV spatial coordinates, determined via Global Positioning System (GPS) signals, are susceptible to cyberattacks due to unencrypted and unauthenticated transmissions with GPS spoofing being a significant threat. To mitigate these vulnerabilities, intrusion detection systems (IDSs) for UAVs have been developed and enhanced using machine learning (ML) algorithms. However, Adversarial Machine Learning (AML) has introduced new risks by exploiting ML models. This study presents a UAV-IDS employing AML methodology to enhance the detection and classification of GPS spoofing attacks. The key contribution is the development of an AML detection model that significantly improves UAV system robustness and security. Our findings indicate that the model achieves a detection accuracy of 98%, demonstrating its effectiveness in managing large-scale datasets and complex tasks. This study emphasizes the importance of physical layer security for enhancing IDSs in UAVs by introducing a novel detection model centered on an adversarial training defense method and advanced deep learning techniques. Full article
(This article belongs to the Section Sensor Networks)
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19 pages, 3408 KiB  
Article
Automatic Detection of Acute Leukemia (ALL and AML) Utilizing Customized Deep Graph Convolutional Neural Networks
by Lida Zare, Mahsan Rahmani, Nastaran Khaleghi, Sobhan Sheykhivand and Sebelan Danishvar
Bioengineering 2024, 11(7), 644; https://doi.org/10.3390/bioengineering11070644 - 24 Jun 2024
Cited by 7 | Viewed by 3677
Abstract
Leukemia is a malignant disease that impacts explicitly the blood cells, leading to life-threatening infections and premature mortality. State-of-the-art machine-enabled technologies and sophisticated deep learning algorithms can assist clinicians in early-stage disease diagnosis. This study introduces an advanced end-to-end approach for the automated [...] Read more.
Leukemia is a malignant disease that impacts explicitly the blood cells, leading to life-threatening infections and premature mortality. State-of-the-art machine-enabled technologies and sophisticated deep learning algorithms can assist clinicians in early-stage disease diagnosis. This study introduces an advanced end-to-end approach for the automated diagnosis of acute leukemia classes acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML). This study gathered a complete database of 44 patients, comprising 670 ALL and AML images. The proposed deep model’s architecture consisted of a fusion of graph theory and convolutional neural network (CNN), with six graph Conv layers and a Softmax layer. The proposed deep model achieved a classification accuracy of 99% and a kappa coefficient of 0.85 for ALL and AML classes. The suggested model was assessed in noisy conditions and demonstrated strong resilience. Specifically, the model’s accuracy remained above 90%, even at a signal-to-noise ratio (SNR) of 0 dB. The proposed approach was evaluated against contemporary methodologies and research, demonstrating encouraging outcomes. According to this, the suggested deep model can serve as a tool for clinicians to identify specific forms of acute leukemia. Full article
(This article belongs to the Special Issue Monitoring and Analysis of Human Biosignals, Volume II)
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15 pages, 600 KiB  
Review
Integrating AI and ML in Myelodysplastic Syndrome Diagnosis: State-of-the-Art and Future Prospects
by Amgad Mohamed Elshoeibi, Ahmed Badr, Basel Elsayed, Omar Metwally, Raghad Elshoeibi, Mohamed Ragab Elhadary, Ahmed Elshoeibi, Mohamed Amro Attya, Fatima Khadadah, Awni Alshurafa, Ahmad Alhuraiji and Mohamed Yassin
Cancers 2024, 16(1), 65; https://doi.org/10.3390/cancers16010065 - 22 Dec 2023
Cited by 7 | Viewed by 3086
Abstract
Myelodysplastic syndrome (MDS) is composed of diverse hematological malignancies caused by dysfunctional stem cells, leading to abnormal hematopoiesis and cytopenia. Approximately 30% of MDS cases progress to acute myeloid leukemia (AML), a more aggressive disease. Early detection is crucial to intervene before MDS [...] Read more.
Myelodysplastic syndrome (MDS) is composed of diverse hematological malignancies caused by dysfunctional stem cells, leading to abnormal hematopoiesis and cytopenia. Approximately 30% of MDS cases progress to acute myeloid leukemia (AML), a more aggressive disease. Early detection is crucial to intervene before MDS progresses to AML. The current diagnostic process for MDS involves analyzing peripheral blood smear (PBS), bone marrow sample (BMS), and flow cytometry (FC) data, along with clinical patient information, which is labor-intensive and time-consuming. Recent advancements in machine learning offer an opportunity for faster, automated, and accurate diagnosis of MDS. In this review, we aim to provide an overview of the current applications of AI in the diagnosis of MDS and highlight their advantages, disadvantages, and performance metrics. Full article
(This article belongs to the Section Cancer Causes, Screening and Diagnosis)
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27 pages, 86576 KiB  
Article
UMAP Based Anomaly Detection for Minimal Residual Disease Quantification within Acute Myeloid Leukemia
by Lisa Weijler, Florian Kowarsch, Matthias Wödlinger, Michael Reiter, Margarita Maurer-Granofszky, Angela Schumich and Michael N. Dworzak
Cancers 2022, 14(4), 898; https://doi.org/10.3390/cancers14040898 - 11 Feb 2022
Cited by 15 | Viewed by 5726
Abstract
Leukemia is the most frequent malignancy in children and adolescents, with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) as the most common subtypes. Minimal residual disease (MRD) measured by flow cytometry (FCM) has proven to be a strong prognostic factor in [...] Read more.
Leukemia is the most frequent malignancy in children and adolescents, with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) as the most common subtypes. Minimal residual disease (MRD) measured by flow cytometry (FCM) has proven to be a strong prognostic factor in ALL as well as in AML. Machine learning techniques have been emerging in the field of automated MRD quantification with the objective of superseding subjective and time-consuming manual analysis of FCM-MRD data. In contrast to ALL, where supervised multi-class classification methods have been successfully deployed for MRD detection, AML poses new challenges: AML is rarer (with fewer available training data) than ALL and much more heterogeneous in its immunophenotypic appearance, where one-class classification (anomaly detection) methods seem more suitable. In this work, a new semi-supervised approach based on the UMAP algorithm for MRD detection utilizing only labels of blast free FCM samples is presented. The method is tested on a newly gathered set of AML FCM samples and results are compared to state-of-the-art methods. We reach a median F1-score of 0.794, while providing a transparent classification pipeline with explainable results that facilitates inter-disciplinary work between medical and technical experts. This work shows that despite several issues yet to overcome, the merits of automated MRD quantification can be fully exploited also in AML. Full article
(This article belongs to the Special Issue Bioinformatics, Big Data and Cancer)
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11 pages, 1959 KiB  
Article
Beyond the In-Practice CBC: The Research CBC Parameters-Driven Machine Learning Predictive Modeling for Early Differentiation among Leukemias
by Rana Zeeshan Haider, Ikram Uddin Ujjan, Najeed Ahmed Khan, Eloisa Urrechaga and Tahir Sultan Shamsi
Diagnostics 2022, 12(1), 138; https://doi.org/10.3390/diagnostics12010138 - 7 Jan 2022
Cited by 25 | Viewed by 5049
Abstract
A targeted and timely treatment can be a beneficial tool for patients with hematological emergencies (particularly acute leukemias). The key challenges in the early diagnosis of leukemias and related hematological disorders are their symptom-sharing nature and prolonged turnaround time as well as the [...] Read more.
A targeted and timely treatment can be a beneficial tool for patients with hematological emergencies (particularly acute leukemias). The key challenges in the early diagnosis of leukemias and related hematological disorders are their symptom-sharing nature and prolonged turnaround time as well as the expertise needed in reporting confirmatory tests. The present study made use of the potential morphological and immature fraction-related parameters (research items or cell population data) generated during complete blood cell count (CBC), through artificial intelligence (AI)/machine learning (ML) predictive modeling for early (at the pre-microscopic level) differentiation of various types of leukemias: acute from chronic as well as myeloid from lymphoid. The routine CBC parameters along with research CBC items from a hematology analyzer in the diagnosis of 1577 study subjects with hematological neoplasms were collected. The statistical and data visualization tools, including heat-map and principal component analysis (PCA,) helped in the evaluation of the predictive capacity of research CBC items. Next, research CBC parameter-driven artificial neural network (ANN) predictive modeling was developed to use the hidden trend (disease’s signature) by increasing the auguring accuracy of these potential morphometric parameters in differentiation of leukemias. The classical statistics for routine and research CBC parameters showed that as a whole, all study items are significantly deviated among various types of leukemias (study groups). The CPD parameter-driven heat-map gave clustering (separation) of myeloid from lymphoid leukemias, followed by the segregation (nodding) of the acute from the chronic class of that particular lineage. Furthermore, acute promyelocytic leukemia (APML) was also well individuated from other types of acute myeloid leukemia (AML). The PCA plot guided by research CBC items at notable variance vindicated the aforementioned findings of the CPD-driven heat-map. Through training of ANN predictive modeling, the CPD parameters successfully differentiate the chronic myeloid leukemia (CML), AML, APML, acute lymphoid leukemia (ALL), chronic lymphoid leukemia (CLL), and other related hematological neoplasms with AUC values of 0.937, 0.905, 0.805, 0.829, 0.870, and 0.789, respectively, at an agreeably significant (10.6%) false prediction rate. Overall practical results of using our ANN model were found quite satisfactory with values of 83.1% and 89.4.7% for training and testing datasets, respectively. We proposed that research CBC parameters could potentially be used for early differentiation of leukemias in the hematology–oncology unit. The CPD-driven ANN modeling is a novel practice that substantially strengthens the predictive potential of CPD items, allowing the clinicians to be confident about the typical trend of the “disease fingerprint” shown by these automated potential morphometric items. Full article
(This article belongs to the Special Issue Advances in Hematology Laboratory)
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