Relevant bibliographies by topics / Modelli diagnostici ML

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Modelli diagnostici ML'

Author: Grafiati
Published: 10 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Modelli diagnostici ML"

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Bhore, Prof Priyanka. "Advancing Diagnostic Accuracy and Efficiency through Machine Learning Integration in Healthcare." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 1967–73. https://doi.org/10.22214/ijraset.2025.66707.

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Machine learning (ML) has the potential to transform healthcare by improving the accuracy and efficiency of medical diagnoses. This project showcases the use of ML in healthcare through a DenseNet121 model designed to classify chest X-ray images into four categories: Pneumonia, Atelectasis, Pneumothorax, and No Finding. Utilizing the DenseNet121 architecture, recognized for its strong feature extraction abilities, the model was trained on a dataset of chest X-ray images along with relevant metadata. The goal was to accurately identify these conditions, thereby assisting healthcare professional
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Özer, İlyas. "Utilizing Machine Learning for Enhanced Diagnosis and Management of Pediatric Appendicitis: A Multilayer Neural Network Approach." Aintelia Science Notes 2, no. 2 (2023): 18–24. https://doi.org/10.5281/zenodo.10473089.

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This study focuses on pediatric appendicitis, a leading cause of hospital admissions due to abdominal pain in children, characterized by a substantial risk of perforation, especially in younger patients. Traditional diagnostic methods, while effective, often lack specificity and are supplemented by varying laboratory and imaging techniques. This research introduces a novel application of machine learning (ML), specifically a multi-output neural network model, to address the complexities of diagnosing appendicitis, determining its severity, and guiding management strate
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Thamodharan, A. "Advanced Predictive Modeling for Early Detection of Diabetes Insipidus: Leveraging Machine Learning Algorithms to Enhance Diagnostic Accuracy and Personalized Treatment Pathways." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem03046.

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Abstract: Diabetes Insipidus (DI) is a rare disorder characterized by the inability to concentrate urine, leading to frequent urination and excessive thirst. Early detection of DI is crucial for timely treatment, as delayed diagnosis can result in complications such as dehydration, electrolyte imbalances, and kidney damage. This paper explores the application of advanced predictive modeling techniques, particularly machine learning (ML) algorithms, to enhance the early detection and diagnosis of Diabetes Insipidus. Traditional diagnostic approaches, such as water deprivation tests and serum os
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Ding, Yueheng. "Advances and Challenges in Machine Learning for Diabetes Prediction: A Comprehensive Review." Applied and Computational Engineering 109, no. 1 (2024): 75–80. http://dx.doi.org/10.54254/2755-2721/109/20241437.

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Abstract. Diabetes mellitus is a prevalent and severe metabolic disorder disease that poses significant health risks globally, leading to substantial healthcare burdens. Recent days, advancements in artificial intelligence (AI) have markedly enhanced the accuracy and efficiency of diabetes outcome predicted by machine learning (ML), offering a promising approach for early intervention and treatment. This paper evaluates several advanced ML models, including Random Forest (RF), Support Vector Machine (SVM), and Neural Networks techniques based on neural networks. Each model's strengths and limi
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Kadhim, Dhuha Abdalredha, and Mazin Abed Mohammed. "Advanced Machine Learning Models for Accurate Kidney Cancer Classification Using CT Images." Mesopotamian Journal of Big Data 2025 (January 10, 2025): 1–25. https://doi.org/10.58496/mjbd/2025/001.

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Kidney cancer, particularly renal cell carcinoma (RCC), poses significant challenges in early and accurate diagnosis due to the complexity of tumor characteristics in computerized tomography (CT) images. Traditional diagnostic approaches often struggle with variability in data and lack the precision required for effective clinical decision-making. This study aims to develop and evaluate machine learning (ML) models for the accurate classification of kidney cancer using CT images, focusing on improving diagnostic precision and addressing potential challenges of overfitting and dataset heterogen
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Al-Batah, Mohammad, Mowafaq Salem Alzboon, and Muhyeeddin Alqaraleh. "Superior Classification of Brain Cancer Types Through Machine Learning Techniques Applied to Magnetic Resonance Imaging." Data and Metadata 4 (January 1, 2025): 472. http://dx.doi.org/10.56294/dm2025472.

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Brain cancer remains one of the most challenging medical conditions due to its intricate nature and the critical functions of the brain. Effective diagnostic and treatment strategies are essential, particularly given the high stakes involved in early detection. Magnetic Resonance (MR) imaging has emerged as a crucial modality for the identification and monitoring of brain tumors, offering detailed insights into tumor morphology and behavior. Recent advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized the analysis of medical imaging, significantly enhancing
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Hasan, Aseel, and Mahdi Mazinani. "DETECTION OF KERATOCONUS DISEASE DEPENDING ON CORNEAL TOPOGRAPHY USING DEEP LEARNING." Kufa Journal of Engineering 16, no. 1 (2025): 463–78. https://doi.org/10.30572/2018/kje/160125.

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Keratoconus is a disease that ML has contributed much in its diagnosis and management. It is not a widely prevalent disease, with a research gap caused by the absence of standardized datasets for model training and evaluation. This work presents a novel dataset, which strengthens the CNN model's resilience and creates standards for assessing keratoconus diagnostic techniques. The research depends on data of patients examined at Jenna Ophthalmic Center in Baghdad. The proposed system works on three stages: pre-processing, feature extraction, and classification with machine learning algorithms i
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Patel, Sumir, Veysel Kocaman, Mehmet Burak Sayici, and Nikhil Patel. "Auto-machine learning for opportunistic thyroid nodule detection in lung cancer screening chest CT." Journal of Clinical Oncology 42, no. 16_suppl (2024): e13639-e13639. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.e13639.

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e13639 Background: Automated Machine Learning (Auto-ML) in medical imaging is a process that allows non-experts to utilize machine learning techniques, opening the door for non-coder physician-driven exploitation of the technology. Auto-ML was applied for opportunistic detection of thyroid nodules in the context of low-dose lung cancer screening chest CT, facilitated by an innovative platform integration. By leveraging scans originally intended for lung cancer screening, suspicious appearing asymptomatic thyroid nodules can also be screened for where technically feasible. Methods: CT scans fro
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Kumar Singh, Siddhanta, and Anand Sharma. "Revving up insights: machine learning-based classification of OBD II data and driving behavior analysis using g-force metrics." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 2188–97. https://doi.org/10.11591/eei.v14i3.9398.

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This research work uses machine learning (ML) approaches to classify on-board diagnostics II (OBD II) data and g-force measures to provide a thorough analysis of driving behavior. The research paper effectively demonstrates the classification of driving behaviours using OBD II and g-force data. Driving behaviours are analyzed by using ML algorithms such as random forest (RF), AdaBoost, and K-nearest neighbors (KNN). The analysis goes beyond a summary by discussing how OBD II data, g-force metrics, and the algorithms interrelate to classify ten distinct driving behaviors (e.g., weaving, swervin
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S, Suresh, and Dhanalakshmi S. "Tuberculosis prediction: performance analysis of machine ‎learning models for early diagnosis and screening using ‎symptom severity level data." International Journal of Basic and Applied Sciences 14, no. 1 (2025): 435–44. https://doi.org/10.14419/parmkr90.

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Tuberculosis (TB) remains a formidable issue for worldwide public health and calls for swift and exact diagnostic strategies to achieve the ‎best health results for those affected. A methodical machine learning (ML) sequence was diligently followed, featuring data preprocessing, ‎feature choice, encoding, and the training of the model in a logical order. A detailed investigation was performed on six unique machine ‎learning architectures, comprising the ANN, SVM, Decision Tree, Random Forest, XGBoost, and Logistic Regression, closely analyzing ‎their key performance measures essential for meas
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Dissertations / Theses on the topic "Modelli diagnostici ML"

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Navicelli, Andrea, Mario Tucci, and Filippo De Carlo. "Analisi ed applicazione di modelli diagnostici e prognostici per guasti e prestazioni di componenti di impianti industriali nell’era I4.0." Doctoral thesis, 2021. http://hdl.handle.net/2158/1234822.

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Il ruolo fondamentale che la manutenzione gioca nei costi di esercizio e nella produttività degli impianti industriali ha portato le aziende e i ricercatori a spostare il loro interesse su questo tema. L'ultima frontiera dell'innovazione in campo manutentivo, resa possibile anche dall'avvento della quarta rivoluzione industriale che promuove la sensorizzazione e l’interconnessione di tutti i macchinari di impianto, è la manutenzione predittiva. Essa mira ad ottenere una previsione accurata della vita utile dei componenti degli impianti industriali al fine di ottimizzare la schedulazione degli
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Book chapters on the topic "Modelli diagnostici ML"

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Jan, Saifullah, Aiman (83a409e8-2ed0-4d4e-a1d5-1ffcc121e8cb, Bilal Khan, and Muhammad Arshad. "Exploring COVID-19 Classification and Object Detection Strategies." In Deep Cognitive Modelling in Remote Sensing Image Processing. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2913-9.ch009.

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The overlapping imaging characteristics of COVID-19 viral pneumonia and non-COVID-19 viral pneumonia chest X-rays (CXRs) make differentiation difficult for radiologists. Machine learning (ML) has demonstrated promising outcomes in a range of medical sectors, enhancing diagnostic accuracy through its interaction with radiological tests. The potential contribution of ML models in assisting radiologists in discriminating COVID-19 from non-COVID-19 viral pneumonia from CXRs, on the other hand, deserves further examination and exploration. The goal of this study is to empirically assess ML models'
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Ramkumar, P., and Sivaprakash C. "Machine Learning Techniques for Automatic Diagnosis of Glaucoma Detection." In Advances in Healthcare Information Systems and Administration. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7888-5.ch005.

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Glaucoma is a primary cause of permanent blindness, and early and precise detection is essential to prevent significant visual loss. In the context of diagnosing glaucoma, this abstract focuses on three machine learning techniques: decision trees, linear regression, and support vector machines (SVM). Despite being primarily utilised for predictive modelling, linear regression has been modified to diagnose glaucoma by examining the correlation between continuous risk factors and diagnostic results, which helps identify high-risk patients early on. Because SVMs are good at handling high-dimensio
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Prasad, G. "Machine Learning-Based Solutions for Aerospace Engineering." In Innovative Machine Learning Applications in the Aerospace Industry. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7525-9.ch002.

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The incorporation of machine learning (ML) in aircraft engineering has transformed the design, analysis, and operation of intricate aerospace systems. This study examines the present and developing applications of machine learning techniques in critical domains like aircraft design optimisation, defect detection and diagnostics, flight control systems, and predictive maintenance. Utilising extensive information from simulations, sensors, and real-time operations, machine learning models facilitate more efficient decision-making, improved system reliability, and decreased operational costs. Mor
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Conference papers on the topic "Modelli diagnostici ML"

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Krzton, Karolina, Joanna Kolodziej, Adrian Widlak, Mateusz Nawrocki, and Jose Sigut. "Vulnerabilities Of Machine Learning Algorithms To Adversarial Attacks In Medical Images." In 39th ECMS International Conference on Modelling and Simulation. ECMS, 2025. https://doi.org/10.7148/2025-0255.

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Machine learning (ML) techniques have gained widespread adoption in medical image diagnosis. However, their susceptibility to adversarial attacks raises concerns regarding their reliability in clinical applications. This study investigates the robustness of two convolutional neural network architectures, ResNet50 and VGG16, against adversarial perturbations introduced via the Fast Gradient Signed Method (FGSM) and DeepFool algorithms. An experimental evaluation was conducted using medical imaging data from the Lung Image Database Consortium (LIDC-IDRI), comprising computed tomography (CT) imag
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korish, M., M. Ibrahim, L. Tealdi, and A. Al Hanaee. "Real-Time Production Optimization: A Machine Learning Approach to Virtual Flow Metering." In GOTECH. SPE, 2025. https://doi.org/10.2118/224561-ms.

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Abstract This study investigates the application of machine learning (ML) techniques for virtual flow metering (VFM) in oil wells. To develop a robust and accurate VFM model, Two Different Fields were tested for the new technique, one field offshore Egypt & the other field is onshore Iraq; This comprehensive dataset included detailed production data, well parameters, and operational information. Various ML algorithms, including Random Forest, Support Vector Regression, and Artificial Neural Networks, were rigorously tested and compared to identify the optimal model for VFM. The selected mo
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Hassani, H., A. Shahbazi, A. Yusifov, et al. "Calculation of Production Back Allocation Using Machine Learning Algorithms." In GOTECH. SPE, 2024. http://dx.doi.org/10.2118/219392-ms.

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Abstract The petroleum industry is reliant on precise and efficient back allocation, a process that calculates individual well production rates from shared facilities or multi-well platforms. Especially in matured facilities and legacy assets, traditional measurement techniques often fail to provide the necessary accuracy due to a lack of pre-installed flow meters and individual measurement mechanisms. Furthermore, these methods frequently require additional interventions, a factor that could potentially defer production, incur significant costs, and require extensive supply chain management.
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Kayode, Babatope O., Karl D. Stephen, and Abdullah Kaba. "Application of Data Science Algorithms to Establish a Novel Parameterization Approach for Static and Dynamic Models." In SPE Symposium: Leveraging Artificial Intelligence to Shape the Future of the Energy Industry. SPE, 2023. http://dx.doi.org/10.2118/214476-ms.

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Abstract Numerical simulation results are the basis of numerous oil and gas field developments. We based the numerical simulation models (or dynamic models) on 3D geological models. We constructed a geological model using core and log data obtained from wells as inputs to create a reservoir prototype. This paper describes the applications of artificial intelligence (AI) algorithms for parameterization of static and dynamic modeling processes. Accordingly, a hypothetical 3D geological model was created, and porosity and permeability were distributed using sequential Gaussian simulation. Then, P
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Reports on the topic "Modelli diagnostici ML"

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Malkinson, Mertyn, Irit Davidson, Moshe Kotler, and Richard L. Witter. Epidemiology of Avian Leukosis Virus-subtype J Infection in Broiler Breeder Flocks of Poultry and its Eradication from Pedigree Breeding Stock. United States Department of Agriculture, 2003. http://dx.doi.org/10.32747/2003.7586459.bard.

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Objectives 1. Establish diagnostic procedures to identify tolerant carrier birds based on a) Isolation of ALV-J from blood, b) Detection of group-specific antigen in cloacal swabs and egg albumen. Application of these procedures to broiler breeder flocks with the purpose of removing virus positive birds from the breeding program. 2. Survey the AL V-J infection status of foundation lines to estimate the feasibility of the eradication program 3. Investigate virus transmission through the embryonated egg (vertical) and between chicks in the early post-hatch period (horizontal). Establish a model
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99mTc SPECT-CT, Consensus QIBA Profile. Chair Yuni Dewaraja and Robert Miyaoka. Radiological Society of North America (RSNA)/Quantitative Imaging Biomarkers Alliance (QIBA), 2019. https://doi.org/10.1148/qiba/20191021.

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The quantification of 99mTc labeled biomarkers can add unique value in many different settings, ranging from clinical trials of investigation new drugs to the treatment of individual patients with marketed therapeutics. For example, goals of precision medicine include using companion radiopharmaceutical diagnostics as just-in-time, predictive biomarkers for selecting patients to receive targeted treatments, customizing doses of internally administered radiotherapeutics, and assessing responses to treatment. This Profile describes quantitative outcome measures that represent proxies of target c
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