Contrastive Dual-Pool Feature Adaption for Domain Incremental Remote Sensing Scene Classification by Yingzhao Shao, Yunsong Li, Xiaodong Han

“…Remote sensing image classification has achieved remarkable success in environmental monitoring and urban planning using deep neural networks (DNNs). However, the performance of these models is significantly impacted by domain shifts due to seasonal changes, varying atmospheric conditions, and different geographical locations. …”
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Components of brending and the application of artifical intelligence technologies in their implementation by Оleg Kovalchuk

“…It is emphasized that special attention needs to be paid to the implementation of the specified components of branding in the context of the active formation of digital information and communication tools, the development of neural networks and related artificial intelligence technologies, because they cause significant changes at various stages of branding, forming both additional challenges and obstacles, and and opening up new opportunities. …”
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Research on YOLOv5 Oracle Recognition Algorithm Based on Multi-Module Fusion by Xinhang Zhang, Zhenhua Ma, Yaru Zhang, Huiying Ru

“…C3-DAttention combines channel and spatial attention mechanisms to enhance feature extraction in deep convolutional neural networks. Detect_Efficient further improves the model’s detection and recognition capabilities. …”
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Classification of Dyslexia Among School Students Using Deep Learning by Alia Hussein, Ahmed Talib Abdulameer, Ali Abdulkarim, Husniza Husni, Dalia Al-Ubaidi

“…This study proposes a novel approach for diagnosing dyslexic children using spectrogram analysis and convolutional neural networks (CNNs). Spectrograms are visual representations of audio signals that provide detailed frequency and intensity information. …”
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Data-Driven Technology in Event-Based Vision by Ruolin Sun, Dianxi Shi, Yongjun Zhang, Ruihao Li, Ruoxiang Li

“…Thanks to the preponderance of deep learning techniques and the compatibility between bio-inspired spiking neural networks and event-based sensors, data-driven approaches have become a hot spot, which along with the dedicated hardware and datasets constitute an emerging field named event-based data-driven technology. …”
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Diagnosis and Detection of Alzheimer’s Disease Using Learning Algorithm by Gargi Pant Shukla, Santosh Kumar, Saroj Kumar Pandey, Rohit Agarwal, Neeraj Varshney, Ankit Kumar

“…After pre-processing, we proposed three learning algorithms for AD classification, that is random forest, XGBoost, and Convolution Neural Networks (CNN). Results are computed on dataset and show that it outperformed with exiting work in terms of accuracy is 97.57% and sensitivity is 97.60%.…”
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Automatic Identification Model for Landslide Disaster Using Remote Sensing Images Based on Improved Multiresunet by Zhenyu Zhao, Shucheng Tan, Qinghua Zhang, Hui Chen

“…Despite significant progress in semantic segmentation achieved by convolutional neural networks (CNNs), the local sensory field of CNNs poses difficulties in differentiating between landslides and bare surfaces. …”
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Exploiting the Quantum Advantage for Satellite Image Processing: Review and Assessment by Soronzonbold Otgonbaatar, Dieter Kranzlmuller

“…Our quantum resource estimation showed that quantum machine learning (QML) models, with a sufficient number of T-gates, provide the quantum advantage if and only if they generalize on unseen data points better than their classical counterparts deployed on the HPC system and they break the symmetry in their weights at each learning iteration like in conventional deep neural networks. We also estimated the quantum resources required for some QML models as an initial innovation. …”
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Right Amygdalar and Temporofrontal Activation During Autobiographic, But Not During Fictitious Memory Retrieval by Hans J. Markowitsch, Alexander Thiel, Mechthild Reinkemeier, Josef Kessler, Adem Koyuncu, Wolf-Dieter Heiss

“…Functional imaging was used to study the neural networks engaged in retrieving autobiographic and fictitious information of closely similar content. …”
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The Real-Time Prediction of Cracks and Wrinkles in Sheet Metal Forming According to Changes in Shape and Position of Drawbeads Based on a Digital Twin by Sarang Yi, Daeil Hyun, Seokmoo Hong

“…A digital twin was developed to predict the sheet metal forming process using Support Vector Machine, Random Forest, Gradient Boosting Machine, and Artificial Neural Networks. The machine learning models were trained using finite element analysis data corresponding to the position and bead force of drawbeads, enabling the real-time prediction of wrinkles and crack occurrences. …”
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Enhancing precision in multiple sclerosis lesion segmentation: A U-net based machine learning approach with data augmentation by Oezdemir Cetin, Berkay Canel, Gamze Dogali, Unal Sakoglu

“…The proposed algorithm employs Convolutional Neural Networks (CNNs) in the form of U-Net architecture, a renowned model for biomedical image segmentation. …”
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First-principles based data-driven strain engineering for ferroelectrics via active machine learning: A nonlinear piezoelectric constitutive equation by Susumu MINAMI, Yasuaki MARUYAMA, Yoshimasa ABE, Tomohiro NAKAYAMA, Takahiro SHIMADA

“…Here, we developed a technical framework that enables efficient exploration of physical properties in the vast strain space based on machine learning (i.e., artificial neural networks), active learning, and high-throughput first-principles calculation. …”
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A comparative performance analysis of machine learning models for compressive strength prediction in fly ash-based geopolymers concrete using reference data by Muhammad Kashif Anwar, Muhammad Ahmed Qurashi, Xingyi Zhu, Syyed Adnan Raheel Shah, Muhammad Usman Siddiq

“…Seven models such as multiple linear regression (MLR), artificial neural networks (ANNs), support vector machines (SVMs), K-nearest neighbor (KNNs), decision trees (DT), and ensemble methods combining DT with boosting and bootstrapping were employed. …”
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Minimum Description Length and Multi-Criteria Decision Analysis in Predictive Modeling by Petr Silhavy, Katerina Hlavackova-Schindler, Radek Silhavy

“…Various regression models and feed-forward neural networks were evaluated using criteria such as MAE, MAPE, RMSE, and Adjusted <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>. …”
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Enhancing Indoor mmWave Communication With ML-Based Propagation Models by Gustavo Adulfo Lopez-Ramirez, Alejandro Aragon-Zavala

“…We employ various ML models, including Artificial Neural Networks (ANNs), hybrid models integrating linear regression, ANNs, and Gaussian Processes, and Extreme Gradient Boosting (XGBoost), to predict and analyze the propagation loss in a controlled indoor setting. …”
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Transferability Evaluation in Wi-Fi Intrusion Detection Systems Through Machine Learning and Deep Learning Approaches by Saud Yonbawi, Adil Afzal, Muhammad Yasir, Muhammad Rizwan, Natalia Kryvinska

“…A comprehensive evaluation involving Multilayer Perceptron(MLP), and Convolutional Neural Networks (CNN) models has been executed, uncovering that CNN conspicuously outshines the MLP model.…”
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Increased Accuracy of Emotion Recognition in Individuals with Autism-Like Traits after Five Days of Magnetic Stimulations by Pingping Liu, Guixian Xiao, Kongliang He, Long Zhang, Xinqi Wu, Dandan Li, Chunyan Zhu, Yanghua Tian, Panpan Hu, Bensheng Qiu, Gong-Jun Ji, Kai Wang

“…Our findings indicated that iTBS of the rpSTS could improve emotion perception of ALT individuals by modulating associated neural networks. This stimulation protocol could be a vital therapeutic strategy for the treatment of ASD.…”
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Ensemble Deep Learning Object Detection Fusion for Cell Tracking, Mitosis, and Lineage by Imad Eddine Toubal, Noor Al-Shakarji, D. D. W. Cornelison, Kannappan Palaniappan

“…EDNet uses an ensemble approach for 2D cell detection that is deep-architecture-agnostic and achieves state-of-the-art performance surpassing single-model YOLO and FasterRCNN convolutional neural networks. EDNet detections are used in our M2Track multiobject tracking algorithm for tracking cells, detecting cell mitosis (cell division) events, and cell lineage graphs. …”
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A holistic research based on RSM and ANN for improving drilling outcomes in Al–Si–Cu–Mg (C355) alloy by Şenol Bayraktar, Cem Alparslan, Nurten Salihoğlu, Murat Sarıkaya

“…Statistical analyses of the effects of V and f on thrust force (Fz), surface roughness (Ra), and torque (Mz) were performed using Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and Analysis of Variance (ANOVA). …”
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Application of three-dimensional printing in the planning and execution of aortic aneurysm repair by Harshank Patel, Paul Choi, Jennifer C. Ku, Rosemary Vergara, Rafael Malgor, Dhiren Patel, Yong Li

“…This review examines the application of 3D printing and AI in complex aortic aneurysm repair, highlighting current clinical trends.MethodsAn exhaustive literature review was performed using keywords such as “3D printing,” “Artificial intelligence,” “Thoracoabdominal aneurysm,” “Abdominal aortic aneurysm,” “Aortic arch aneurysm,” “Endovascular repair,” and “Open repair” in PubMed and Google Scholar indexes up to June 2022.ResultsThis analysis included seven studies: four focused on 3D-printed phantoms for endovascular repair of various aortic pathologies (aortic arch, thoracoabdominal aorta, juxtarenal and pararenal aorta), one on open thoracoabdominal aneurysm repair using 3D-printed models for graft construction, and two on the use of convolutional neural networks, an AI-based technology, for the pre-processing of aortic computed tomography angiography images.ConclusionThe application of 3D printing and AI-based image pre-processing in the planning of complex aortic aneurysms offers several benefits, including enhanced patient and trainee education, more accurate fenestration placement, reduced surgical time and complications, and decreased surgeon stress.…”
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