Nature-Inspired Multi-Level Thresholding Integrated with CNN for Accurate COVID-19 and Lung Disease Classification in Chest X-Ray Images by Wafa Gtifa, Ayoub Mhaouch, Nasser Alsharif, Turke Althobaiti, Anis Sakly

“…<b>Methods</b>: The approach combines multi-level thresholding with the advanced metaheuristic optimization algorithms animal migration optimization (AMO), electromagnetism-like optimization (EMO), and the harmony search algorithm (HSA) to enhance image segmentation. A convolutional neural network (CNN) is then employed to classify segmented images into COVID-19, viral pneumonia, or normal categories. …”
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Artificial Intelligence in Patch Testing: Comprehensive Review of Current Applications and Future Prospects in Dermatology by Hilary S Tang, Joseph Ebriani, Matthew J Yan, Shannon Wongvibulsin, Mehdi Farshchian

“…Most studies employed convolutional neural networks (CNN) for image analysis, with accuracy rates ranging from 90.1% to 99.5%. …”
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Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human by Jucheol Moon, Pratik Jadhav, Sangtae Choi

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A novel approach for the detection of brain tumor and its classification via end-to-end vision transformer - CNN architecture by K. Chandraprabha, L. Ganesan, K. Baskaran

“…The report presents a vision transformer that can analyze brain tumors utilizing the Convolution Neural Network framework. The study’s goal is to create an image that can distinguish malignant tumors in the brain. …”
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Urban street network morphology classification through street-block based graph neural networks and multi-model fusion by Yang Liu, Qingsheng Guo, Chuanbang Zheng

“…To address this, we propose a novel fusion model that integrates three submodels: our proposed street-block graph neural network (SBGNet), a convolutional neural network (CNN) using ResNet-34, and a multi-layer perceptron (MLP). …”
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A Comprehensive Review on Unsupervised Domain Adaptation for 3D Segmentation and Reconstruction in CT Urography Imaging by Shreya, Sushanth, Dasharathraj K. Shetty, Shreepathy Ranga Bhatta, Nikita Panwar

“…The review gives an in-depth look at the current research on how an unsupervised domain adaptation or translation method can be used with 2D networks, especially for accurate kidney segmentation in urographic images. …”
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Fault Diagnosis Method of Permanent Magnet Synchronous Motor Demagnetization and Eccentricity Based on Branch Current by Zhiqiang Wang, Shangru Shi, Xin Gu, Zhezhun Xu, Huimin Wang, Zhen Zhang

“…Finally, to further improve diagnostic accuracy, a cascaded convolutional neural network based on dilated convolutional layers and multi-scale convolutional layers is designed as the diagnostic model. …”
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A multi-dimensional data-driven ship roll prediction model based on VMD-PCA and IDBO-TCN-BiGRU-Attention by Huifeng Wang, Jianchuan Yin, Jianchuan Yin, Nini Wang, Lijun Wang, Lijun Wang

“…The core of the model combines temporal convolutional networks (TCNs) and bidirectional gated recurrent units (BiGRUs). …”
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A Review on Glaucoma Disease Detection Using Computerized Techniques by Faizan Daud, Rakhshanda Imtiaz, Hussain Ahmad Madni, Haroon Ahmed Khan, Tariq M. Khan, Mohammad A. U. Khan, Syed Saud Naqvi

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Emerging Technologies for Precision Crop Management Towards Agriculture 5.0: A Comprehensive Overview by Mohamed Farag Taha, Hanping Mao, Zhao Zhang, Gamal Elmasry, Mohamed A. Awad, Alwaseela Abdalla, Samar Mousa, Abdallah Elshawadfy Elwakeel, Osama Elsherbiny

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Histopathological-based brain tumor grading using 2D-3D multi-modal CNN-transformer combined with stacking classifiers by Naira Elazab, Fahmi Khalifa, Wael Gab Allah, Mohammed Elmogy

“…An efficient method of learning hierarchical patterns within the tissue is the 2D-3D hybrid convolution neural network (CNN), which extracts contextual and spatial features. …”
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Comparative Analysis of Machine Learning Approaches for Fetal Movement Detection with Linear Acceleration and Angular Rate Signals by Lucy Spicher, Carrie Bell, Kathleen H. Sienko, Xun Huan

“…Random forest (RF), bi-directional long short-term memory (BiLSTM), and convolutional neural network (CNN) models were trained using hand-engineered features, time series data, and time–frequency spectrograms, respectively. …”
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A Systematic Review of the Current State of Transfer Learning Accelerated CNN-Based Plant Leaf Disease Classification by David J. Richter, Md Ilias Bappi, Shivani Sanjay Kolekar, Kyungbaek Kim

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Automated detection of craters on the lunar surface using deep learning: A review with insights from Chandrayaan-2 TMC-2 data by Mimansa Sinha, Sanchita Paul

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3D densely connected CNN with multi-scale receptive fields and hybrid loss for brain tumor segmentation by Fatemehzahra Adib, Maryam Amirmazlaghani, Mohammad Rahmati

“…This paper presents a 3D convolutional neural network (CNN) model to automatically segment brain tumors from MRI scans. …”
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Artificial Intelligence in the Diagnostic Use of Transcranial Doppler and Sonography: A Scoping Review of Current Applications and Future Directions by Giuseppe Miceli, Maria Grazia Basso, Elena Cocciola, Antonino Tuttolomondo

“…Machine learning, deep learning, and convolutional neural network algorithms have been effectively utilized in the analysis of TCD and TCCD data for several conditions. …”
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Author name disambiguation based on heterogeneous graph neural network. by Ge Wang, Zikai Sun, Weiyang Hu, MengHuan Cai

“…With the dramatic increase in the number of published papers and the continuous progress of deep learning technology, the research on name disambiguation is at a historic peak, the number of paper authors is increasing every year, and the situation of authors with the same name is intensifying, therefore, it is a great challenge to accurately assign the newly published papers to their respective authors. The current mainstream methods for author disambiguation are mainly divided into two methods: feature-based clustering and connection-based clustering, but none of the current mainstream methods can efficiently deal with the author name disambiguation problem, For this reason, this paper proposes the author name ablation method based on the relational graph heterogeneous attention neural network, first extract the semantic and relational information of the paper, use the constructed graph convolutional embedding module to train the splicing to get a better feature representation, and input the constructed network to get the vector representation. …”
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Memristor-Based Artificial Neural Networks for Hardware Neuromorphic Computing by Boyan Jin, Zhenlong Wang, Tianyu Wang, Jialin Meng

“…Various neural network architectures, including convolutional, recurrent, and spiking models, are discussed, highlighting the advantages of integrating memristors for in-memory computing and parallel processing. …”
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FPGA-QNN: Quantized Neural Network Hardware Acceleration on FPGAs by Mustafa Tasci, Ayhan Istanbullu, Vedat Tumen, Selahattin Kosunalp

“…Recently, convolutional neural networks (CNNs) have received a massive amount of interest due to their ability to achieve high accuracy in various artificial intelligence tasks. …”
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Assessment of Driver Stress using Multimodal wereable Signals and Self-Attention Networks by Pavan Kaveti, Ganapathy Nagarajan

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