A comprehensive investigation of morphological features responsible for cerebral aneurysm rupture using machine learning by Mostafa Zakeri, Amirhossein Atef, Mohammad Aziznia, Azadeh Jafari

“…To achieve this, we leverage a comprehensive dataset that incorporates clinical and morphological data extracted from 3D real geometries of previous patients. The aim of this research is to provide valuable insights that can help make informed decisions during the treatment process and potentially save the lives of future patients. …”
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Review of driver behaviour modelling for highway on‐ramp merging by Zine el abidine Kherroubi, Samir Aknine

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MODELS OF UNIVERSITY AUTONOMY IN EU COUNTRIES: COMPARATIVE ANALYSIS by Oksana Bulvinska

“…On the basis of a comparative analysis of common features and specifics of models of university autonomy, the author's classification is substantiated depending on three fundamental factors: relations between state authorities and universities; internal organizational and management structure of universities; as well as generalization of cases of certain national systems of higher education or groups of countries united by historical and regional features. …”

Management Model for the Construction’s Waste Use in Walls Manufacturing by BIKS Yuriy, LIALIUK Olena, ZERONG Wan, LIALIUK Andrii

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%diag_test: a generic SAS macro for evaluating diagnostic accuracy measures for multiple diagnostic tests by Jacques K. Muthusi, Peter W. Young, Frankline O. Mboya, Samuel M. Mwalili

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Requirement Analysis for Data-Driven Electroencephalography Seizure Monitoring Software to Enhance Quality and Decision Making in Digital Care Pathways for Epilepsy: A Feasibility... by Pantea Keikhosrokiani, Johanna Annunen, Jonna Komulainen-Ebrahim, Jukka Kortelainen, Mika Kallio, Päivi Vieira, Minna Isomursu, Johanna Uusimaa

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Diagnosis Retinal Disease by using Deep Learning Models by attallh salih, Manar Kashmoola

“…The concept uses deep CNN to learn a feature hierarchy from pixels to layers of classification retinal diseases. On the test set, the classifier accuracy is 65.60 % for a (1FE1C) Model, 86.81% for (2FE2C) Model, 96.00% for (3FE3C) Model, and 88.62% for (VGG16) Pre-Train Model. …”
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XSE-TomatoNet: An explainable AI based tomato leaf disease classification method using EfficientNetB0 with squeeze-and-excitation blocks and multi-scale feature fusion by Md Assaduzzaman, Prayma Bishshash, Md. Asraful Sharker Nirob, Ahmed Al Marouf, Jon G. Rokne, Reda Alhajj

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Liquid white box model as an explainable AI for surgery by Homer A. Riva-Cambrin, Rahul Singh, Sanju Lama, Garnette R. Sutherland

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A deep learning model for predicting systemic lupus erythematosus-associated epitopes by Jiale He, Zixia Liu, Xiaopo Tang

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Mapping dominant plant communities in the degraded Zoige swamp using Sentinel-1/2 imagery and its implications for vegetation restoration by Guoying Zhang, Chuanpeng Zhao, Mingming Jia, Rong Zhang, Hou Jiang, Zongming Wang

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Drought Detection in Satellite Imagery: A Layered Ensemble Machine Learning Approach by Muhammad Owais Raza, Naeem Ahmed Mahoto, Mana Saleh Al Reshan, Ali Alqazzaz, Adel Rajab, Asadullah Shaikh

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Integrating Machine Learning and Material Feeding Systems for Competitive Advantage in Manufacturing by Müge Sinem Çağlayan, Aslı Aksoy

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Neural Networks Method in modeling of the financial company’s performance by I. P. Kurochkina, I. I. Kalinin, L. A. Mamatova, E. B. Shuvalova

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Transfer Learning Fusion Approaches for Colorectal Cancer Histopathological Image Analysis by Houda Saif ALGhafri, Chia S. Lim

“…Our study presents three attention-based decision fusion models that combine pre-trained CNNs (Inception V3, Xception, and MobileNet) with a spatial attention mechanism to enhance feature extraction and focus on critical image regions. …”
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Comparison of different AI systems for diagnosing sepsis, septic shock, and cardiogenic shock: a retrospective study by Dirk Obmann, Philipp Münch, Bernhard Graf, Holger von Jouanne-Diedrich, York A. Zausig

“…The BNCs exhibited an accuracy of 87.6% to 91.5%. The CART model demonstrated a notable 91.6% accuracy when only three decision levels were used, whereas the intricate ANN model reached 90.5% accuracy. …”
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A Unified Deep-Domain Adaptation Framework: Advancing Feature Separability and Local Alignment by Pranav Kumar, Jimson Mathew, Rakesh Kumar Sanodiya, Avinash Kumar Chouhan, Rahul Reddy Bukkasamudram, Chandra Sai Teja Adhikarla

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Review of state-of-the-art and conceptual framework approaches in seaside port operations: berth allocation, and quay crane assignment and scheduling by Maria Boluda-Prieto, Ana Esteso, M.M.E., Angel Ortiz

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Drought Monitoring during Growth Rangeland Coverage, Ardabil Province by saideh eiyni, saeide eini

“…According to the monthly rainfall data, 4 synoptic stations of Ardebil province (Ardebil, Khalkhal, Meshgin Shahr and Parsabad Moghan) during the statistical period of 2016-1996 were used to calculate drought index (SEPI) index for 4 periods of 1, 3, 6 and 9 months. Landsat TM and OLI satellite imagery was also used to prepare landslide classification maps based on the maximum probability model and calculation of vegetation indices NDVI, EVI, SAVI and LAI. …”
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PV Module Soiling Detection Using Visible Spectrum Imaging and Machine Learning by Boris I. Evstatiev, Dimitar T. Trifonov, Katerina G. Gabrovska-Evstatieva, Nikolay P. Valov, Nicola P. Mihailov

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