Classification of Red Foxes: Logistic Regression and SVM with VGG-16, VGG-19, and Inception V3 by Brian Sabayu, Imam Yuadi

“…This study conducts an evaluation of three deep learning architectures: Inception V3, VGG-16, and VGG-19, utilizing images of red foxes. …”
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Multi-Modal Deep Learning for Lung Cancer Detection Using Attention-Based Inception-ResNet by Mohamed Hosny, Ibrahim A. Elgendy, Mousa Ahmad Albashrawi

“…However, these methods are time-intensive and highly susceptible to human error. Deep learning (DL) has emerged as a powerful alternative to autonomously identify complex patterns within radiological and histopathological images. …”
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The role of pharmacists in mitigating medication errors in the perioperative setting: a systematic review by Lina Naseralallah, Somaya Koraysh, May Alasmar, Bodoor Aboujabal

“…Methods PubMed, CINAHL, and Embase were searched from inception to September 2023. Studies were included if they tested a pharmacist-led intervention aimed at reducing medication errors in adult perioperative settings. …”
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Dietary intakes and nutritional issues in inborn errors of immunity: a systematic review by Macey Freer, Rani Bhatia, Rani Bhatia, Kahn Preece, Kahn Preece, Kirrilly M. Pursey, Kirrilly M. Pursey, Kirrilly M. Pursey

“…IntroductionInborn errors of immunity (IEI) are characterized by an inherited dysregulation or absence of immune system components that can manifest clinically in complications that predispose an individual to feeding difficulties or impaired swallowing, digestion, and absorption. …”
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A Strong Noise Reduction Network for Seismic Records by Tong Shen, Xuan Jiang, Wenzheng Rong, Lei Xu, Xianguo Tuo, Guili Peng

“…The network enhances the U-Net architecture with an improved inception module and coordinate attention (CA) mechanism, suppressing noise and enhancing signal clarity. …”
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Rice Variety Identification Based on Transfer Learning Architecture Using DENS-INCEP by Wahyudi Agustiono, Kurniawan Eka Permana, Caroline Chan, Deshinta Arrova Dewi, Moch. Miftachur Rifqi Al Husain

“…However, traditional manual methods of identification, relying on visual characteristics, are prone to human error, resulting in variety mixing, reduced quality, and higher costs. …”
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Automatic smart brain tumor classification and prediction system using deep learning by Qurat Ul Ain Ishfaq, Rozi Bibi, Abid Ali, Faisal Jamil, Yousaf Saeed, Rana Othman Alnashwan, Samia Allaoua Chelloug, Mohammed Saleh Ali Muthanna

“…The model’s precision, sensitivity, accuracy, f1-score, error rate, specificity, Y-index, balanced accuracy, geometric mean, and ROC are considered as performance metrics. …”
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Model Transformations Used in IT Project Initial Phases: Systematic Literature Review by Oksana Nikiforova, Kristaps Babris, Uldis Karlovs-Karlovskis, Marta Narigina, Andrejs Romanovs, Anita Jansone, Janis Grabis, Oscar Pastor

“…The paper emphasizes the critical importance of the initial phase in IT project development to avoid implementation errors. It argues that minimizing these errors can be achieved by developing project artifacts at the early stage using a model-driven engineering-based approach. …”
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Performance Comparison of Support Vector Machine (SVM) and k-Nearest Neighbors (kNN) in Verifying Material Orientation by Eldio Utama, Eko Rudiawan Jamzuri

“… In automated manufacturing, verifying material orientation is essential to ensure the product assembly proceeds without errors. For instance, in the beverage industry, incorrect orientation of materials, such as bottle caps, can lead to failures in the packaging process, resulting in improperly sealed bottles that may compromise product quality and safety. …”
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CoPaD-Mark: A Coded Parallelizable Deep Learning-Based Scheme for Robust Image Watermarking by Andy M. Ramos, Cecilio Pimentel, Daniel P. B. Chaves

“…The embedding layer employs a parallel structure with convolutional neural networks inspired by the Inception Net, while the extraction layer uses deformable convolutions. …”
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Magnitude and Characteristics of Adverse Drug Events in Saudi Arabia: A Systematic Review by Lina Alharbi, Duaa Almuraee, Lama AteeqAllah, Wareef Belal, Sheraz Ali

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Deep learning methods for clinical workflow phase-based prediction of procedure duration: a benchmark study by Emanuele Frassini, Teddy S. Vijfvinkel, Rick M. Butler, Maarten van der Elst, Benno H. W. Hendriks, John J. van den Dobbelsteen

“…InceptionTime achieves Mean Absolute Error (MAE) values below 5 min and Symmetric Mean Absolute Percentage Error (SMAPE) under 6% at 60-s sampling intervals. …”
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Deep learning based localisation and classification of gamma photon interactions in thick nanocomposite and ceramic monolithic scintillators by Mushen Shen, Ragy Abraham, Elise Cribbin, Harrison Gregor, Mitra Safavi-Naeini, Daniel Franklin

“…Across the evaluated materials, median total localisation error ranged from 0.58 mm to 2.91 mm with the CNN and 0.59 mm to 2.10 mm with InceptionNet, assuming 50% detector quantum efficiency. …”
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Generative autoencoder to prevent overregularization of variational autoencoder by YoungMin Ko, SunWoo Ko, YoungSoo Kim

“…If the reconstruction error is not sufficiently small to belong to the population, the generative model performance cannot be guaran-teed. …”
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Enhanced Occupational Safety in Agricultural Machinery Factories: Artificial Intelligence-Driven Helmet Detection Using Transfer Learning and Majority Voting by Simge Özüağ, Ömer Ertuğrul

“…The following neural networks were employed: MobileNetV2, ResNet50, DarkNet53, AlexNet, ShuffleNet, DenseNet201, InceptionV3, Inception-ResNetV2, and GoogLeNet. Subsequently, the extracted features were subjected to iterative neighborhood component analysis (INCA) for feature selection, after which they were classified using the k-nearest neighbor (kNN) algorithm. …”
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Prediction of Temperature Distribution with Deep Learning Approaches for SM1 Flame Configuration by Gökhan Deveci, Özgün Yücel, Ali Bahadır Olcay

“…In the secondary approach, similar to the first approach, studies were conducted with different deep learning models, namely Res-Net, Efficient Net, and Inception Net, to evaluate model performance. The U-Net model, which is well developed, stands out with its low error and small file size. …”
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EDGE DETECTION TECHNIQUE BASED ON BILATERAL FILTERING AND ITERATIVE THRESHOLD SELECTION ALGORITHM AND TRANSFER LEARNING FOR TRAFFIC SIGN RECOGNITION by Milind PARSE, Dhanya PRAMOD

“…The results show that the proposed algorithm achieves optimal Mean Square Error (MSE) and Root Mean Square Error (RMSE) error rates and has a better Signal to Noise Ratio (SNR) and Peak Signal to Noise Ratio (PSNR) ratio than the traditional edge detection algorithms. …”
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Urban tree species benchmark dataset for time series classificationEasyData - Data Terra by Clément Bressant, Romain Wenger, David Michéa, Anne Puissant

“…It supports direct integration into deep learning frameworks and includes three InceptionTime-based models trained on Sentinel-2, PlanetScope and both sources through a fusion architecture (Dual-InceptionTime). …”
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An Ensemble Fuzzy-Based Deep Learning Framework for Automatic Detection of Children with ADHD From EEG Signal by Jalil Manafian, Mehdi Fazli, Onur Ilhan, Subhiya Zeynalli, Sukaina Tuama Ghafel

“…Three transfer learning models, namely VGGNet, Inception V3, and Inception ResNet V2, are utilized in this task, enhanced with extra layers to catch data-specific attributes. …”
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Efficient Hybrid Deep Learning Model for Battery State of Health Estimation Using Transfer Learning by Jinling Ren, Misheng Cai, Dapai Shi

“…The experimental results show that the root mean square error (RMSE) of the model on the NCM and NMC datasets is 0.522% and 0.283%, respectively, with a coefficient of determination (R²) not less than 0.98 and mean absolute percentage error (MAPE) of 0.431% and 0.22%, respectively. …”
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