8.473 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
Identification of potential biomarkers for lung cancer using integrated bioinformatics and machine learning approaches
…biomarkers for lung cancer by integrating bioinformatics analysis and machine learning (ML)-based approaches. Data were normalized using the robust multiarray average method and batch effect were corrected using the ComBat method. Differentially expressed genes were identified by the LIMMA approach…
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New solutions for antibiotic discovery: Prioritizing microbial biosynthetic space using ecology and machine learning
With the explosive increase in genome sequence data, perhaps the major challenge in natural-product-based drug discovery is the identification of gene clusters most likely to specify new chemistry and bioactivities. We discuss the challenges and state-of-the-art of antibiotic discovery based on…
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Machine learning-based anomaly detection and prediction in commercial aircraft using autonomous surveillance data
…modes of transportation, some accidents may occur. The creation of a machine learning model employing data from autonomous-reliant surveillance transmissions is essential for the detection and prediction of commercial aircraft accidents. This research included the development of abnormal…
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Analyzing and predicting global happiness index via integrated multilayer clustering and machine learning models
…a novel predictive framework that integrates unsupervised and supervised machine learning techniques to uncover the complex patterns underlying happiness scores across nations. Initially, we apply K-Means clustering to group countries based on similarities in their happiness patterns. For the first…
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Use of machine learning in predicting continuity of HIV treatment in selected Nigerian States
…by 2030, with interruptions in treatment (IIT) a significant challenge. Machine learning (ML) models can help HIV programs implement targeted interventions to improve the quality of care, develop effective early interventions, and provide insights into optimal resource allocation and program…
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Characterizing the role of early life factors in machine learning-based multimorbidity risk prediction
…Our methodological approach investigates the contribution of ELFs to machine-learning-based risk prediction models for comorbid populations, incorporating a wide set of early-life and proximal variables, with a special focus on prenatal and postnatal ELFs. To address the complexity of integrating…
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Seed quality drives grain yield in Ethiopian and Senegalese sorghum: Insights from machine learning
…diverse germplasm. To address this, we applied a phenotype-informed machine learning (PIML) framework to analyze nine phenotypic traits in 179 Ethiopian and Senegalese accessions. Using hierarchical clustering and oversampling with ADASYN, we achieved high classification accuracy (0.99) for…
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A machine learning approach for estimating forage maize yield and quality in NW Spain
…yield) for six cultivars during the 23-year period. LightGBM models (a machine learning technique) were used with the simulated forage maize yield, quality data, historical weather, soil, and management data to capture non-linear relationships in the data and to identify the most influential…
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Leveraging structure-informed machine learning for fast steric zipper propensity prediction across whole proteomes
…structure-based prediction of steric zipper propensity and the use of machine learning to accelerate the calculation of predictive models across many scientific areas. Leveraging these advances, we have developed a new approach for rapid proteome-wide assessment of zipper profiles that is informed…
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The potential of evaluating shape drawing using machine learning for predicting high autistic traits
Background: Children with high autistic traits often exhibit deficits in drawing, an important skill for social adaptability. Machine learning is a powerful technique for learning predictive models from movement data, so drawing processes and product characteristics can be objectively evaluated…
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Predicting depression severity using machine learning models: Insights from mitochondrial peptides and clinical factors
…with mitochondrial pathways remains elusive, recent advances in machine learning present an avenue for further investigation. This study employed advanced machine learning techniques to classify major depressive disorders based on clinical indicators and mitochondrial oxidative stress markers. Six…
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Forecasting acute childhood malnutrition in Kenya using machine learning and diverse sets of indicators
…Specifically, our objectives include: develop a spatio-temporal machine learning model to forecast acute malnutrition among children in Kenya using DHIS2 data, enhance forecasting capability by integrating external complementary indicators, such as publicly available satellite imagery-driven…
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Predicting Type 2 diabetes onset age using machine learning: A case study in KSA
The rising prevalence of Type 2 Diabetes (T2D) in Saudi Arabia presents significant healthcare challenges. Estimating the age at onset of T2D can aid early interventions, potentially reducing complications due to late diagnoses. This study, conducted at King Abdulaziz Medical University Hospital…
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Exploring the achievements and forecasting of SDG 3 using machine learning algorithms: Bangladesh perspective
…423.2 for U5MR, selected based on their lowest AIC values. Additionally, Machine Learning (ML) models, including Bidirectional Recurrent Neural Networks (BRNN) and Elastic Neural Networks (ENET), were employed for all the indicators. Results: ENET demonstrates superior performance compared to both…
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Machine learning and spatio-temporal analysis of meteorological factors on waterborne diseases in Bangladesh
…to 2020. Exploratory spatial analysis, spatial regression and tree-based machine learning models were utilized to analyze the data. Results: From 2017 and 2020, Bangladesh reported 73, 606 cholera, 38, 472 typhoid, 2, 510 hepatitis A and 1, 643 amoebiasis disease cases. Among the waterborne…
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Using statistical modelling and machine learning in detecting bone properties: A systematic review protocol
…accessibility. However, the emergence of artificial intelligence (AI) and machine learning (ML) has brought promising tools capable of analysing complex medical data to enhance the detection and prediction of osteoporosis-related bone properties. This systematic review protocol outlines the…
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An efficient detection of Sinkhole attacks using machine learning: Impact on energy and security
…in WSNs. SFlexCrypt stands out by seamlessly integrating advanced machine learning algorithms to achieve high-precision detection and effective mitigation of sinkhole attacks. Employing a dataset from Contiki-Cooja, SFlexCrypt has been rigorously tested, demonstrating a detection accuracy of 100%…
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Unsupervised machine learning identifies opioid taper reversal patterns in a longitudinal cohort (2008–2018)
…hypotheses. In our analyses, we show an application of unsupervised machine learning to identify hidden patterns in large data. Our aim was to use methods that did not impose any restrictions on the kind of patterns to be identified nor bias the approach to lead us to results that are well studied…
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Design optimization of high-sensitivity PCF-SPR biosensor using machine learning and explainable AI
…a broad RI range of 1.31 to 1.42. In addition to conventional methods, machine learning (ML) regression techniques were integrated to predict key optical properties, while explainable AI (XAI) methods, particularly Shapley Additive exPlanations (SHAP), were used to analyze model outputs and…
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Predicting mortality dynamics in cancer patients: A machine learning approach to pre-death events
…using raw laboratory values failed to do so. These findings suggest that machine learning-driven temporal analysis can reveal clinically meaningful state transitions that conventional approaches overlook, offering new insights into the heterogeneous nature of terminal disease progression. This…
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