Non-invasive prediction of sweet cherry soluble solids content using dielectric spectroscopy and down-sampling techniques by Kamil Sacilik, Necati Cetin, Burak Ozbey, Fernando Auat Cheein

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Detection of network intelligence features with the decision tree model by N. P. Sharaev, S. N. Petrov

“…The study was carried out to develop software module for detecting the features of network intelligence by machine learning methods.M e t h o d s . Analysis of open datasets of appropriate destination; formation of metrics characteristic of network intelligence; development of a dataset based on certain metrics; study of the effectiveness of machine learning methods for classification task.R e s u l t s .  …”
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Advances in sensor technologies for breast cancer detection: a comprehensive review of imaging and non-imaging approaches by Emmy Bhatti, Prabhpreet Kaur

“…Major improvements have been realized in the performance of sensors, which have been measured by parameters such as specificity, sensitivity, and reliability. …”
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Hybrid data-driven and physics-informed regularized learning of cyclic plasticity with neural networks by Stefan Hildebrand, Sandra Klinge

“…An extendable, efficient and explainable Machine Learning approach is proposed to represent cyclic plasticity and replace conventional material models based on the Radial Return Mapping algorithm. …”
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Data-efficient prediction of OLED optical properties enabled by transfer learning by Shin Jeong Min, Kim Sanmun, Menabde Sergey G., Park Sehong, Lee In-Goo, Kim Injue, Jang Min Seok

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A Hybrid Transfer Learning Approach to Teeth Diagnosis Using Orthopantomogram Radiographs by Ahmed Alabd-Aljabar, Zain Raisan, Mohammed Adnan, Salam Dhou

“…However, the hybrid framework combining AlexNet with a Support Vector Machine achieved an accuracy of 94%, and although it falls short of ViT in terms of performance, it comprises far fewer parameters. …”
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Data-driven discovery of the design rules for considering the curing deformation and the application on double-double composites by Yizhuo Gui, Hongwei Song, Jinglei Yang, Cheng Qiu

“…Two specific machine learning (ML) models were built. One is combined model of convolutional neural networks (CNN) and principle component analysis (PCA) technique for connecting the layup sequences and their corresponding PID. …”
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Researching Organic Solar Cells from the Perspective of Literature Big Data Analysis by Qing Wang, Zhixin Liu, Shengda Zhao, Yangjun Yan, Xinyi Li, Yajie Zhang, Xinghua Zhang

“…In this work, an intelligent machine learning method for literature structure delineation and information extraction is proposed. …”
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RSTHFS: A Rough Set Theory-Based Hybrid Feature Selection Method for Phishing Website Classification by Jahanggir Hossain Setu, Nabarun Halder, Ashraful Islam, M. Ashraful Amin

“…Despite advances in cybersecurity, accurately detecting phishing websites remains challenging due to the absence of universally accepted identification parameters. This study introduces a novel feature selection method, Rough Set Theory-based Hybrid Feature Selection (RSTHFS), to enhance phishing website detection using Machine Learning (ML) techniques. …”
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Toward a User-Accessible Spectroscopic Sensing Platform for Beverage Recognition Through K-Nearest Neighbors Algorithm by Luca Montaina, Elena Palmieri, Ivano Lucarini, Luca Maiolo, Francesco Maita

“…Through optimization of both the sensor configuration and KNN parameters, we identified a reduced set of four wavelengths that achieves over 96% classification accuracy across a diverse range of common beverages. …”
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Two-Step CNN Framework for Text Line Recognition in Camera-Captured Images by Yulia S. Chernyshova, Alexander V. Sheshkus, Vladimir V. Arlazarov

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Personalized learning in hybrid education by Alaa O. Khadidos, Hariprasath Manoharan, Adil O. Khadidos, Mohammad N. Alanazi, Fuhid Alanazi, Shitharth Selvarajan

“…The test results clearly demonstrate that the PLA outperforms the conventional method, not only with the parameters mentioned above but also in terms of cost-effectiveness using master and slave controllers.…”
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Bayesian surrogate assisted neural network model to predict the hydrogen storage in 9-ethylcarbazole by Ahsan Ali, Mohammad Usman, Hafiz Muhammad Ali, Uzair Sajjad, Md. Abdul Aziz, M. Nasiruzzaman Shaikh

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Application of artificial intelligence in three aspects of landslide risk assessment: A comprehensive review by Rongjie He, Wengang Zhang, Jie Dou, Nan Jiang, Huaixian Xiao, Jiawen Zhou

“…The generalization ability, sampling training strategies, and hyper-parameters optimization of these models are crucial and should be carefully considered. …”
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Evaluation of agriculture land transformations with socio-economic influences on wheat demand and supply for food sustainability by Danish Raza, Hong Shu, Muhsan Ehsan, Hong Fan, Kamal Abdelrahman, Hasnat Aslam, Abdul Quddoos, Rana Waqar Aslam, Majid Nazeer, Mohammed S. Fnais, Azeem Sardar

“…First, by incorporating more than three decades of satellite data (1990–2022) and different Landsat missions with machine learning algorithms, high-confidence classes were defined for different land features, including cropland. …”
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Hyperparameter optimisation in deep learning from ensemble methods: applications to proton structure by Juan Cruz-Martinez, Aron Jansen, Gijs van Oord, Tanjona R Rabemananjara, Carlos M R Rocha, Juan Rojo, Roy Stegeman

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Enhancing Pneumonia Diagnosis Through AI Interpretability: Comparative Analysis of Pixel-Level Interpretability and Grad-CAM on X-ray Imaging With VGG19 by Mohammad Ennab, Hamid Mcheick

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Sharing is CAIRing: Characterizing principles and assessing properties of universal privacy evaluation for synthetic tabular data by Tobias Hyrup, Anton Danholt Lautrup, Arthur Zimek, Peter Schneider-Kamp

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Benchmarking Diffusion Annealing-Based Bayesian Inverse Problem Solvers by Evan Scope Crafts, Umberto Villa

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Escalate Prognosis of Parkinson’s Disease Employing Wavelet Features and Artificial Intelligence from Vowel Phonation by Rumana Islam, Mohammed Tarique

“…For classification purposes, two popular machine learning models, namely, support vector machine (SVM) and k-nearest neighbors (kNNs), are trained to distinguish patients with PD. …”
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