A Near-Infrared Imaging System for Robotic Venous Blood Collection by Zhikang Yang, Mao Shi, Yassine Gharbi, Qian Qi, Huan Shen, Gaojian Tao, Wu Xu, Wenqi Lyu, Aihong Ji

“…The success of this robotic approach is heavily dependent on the quality of vein imaging. In this paper, we develop a vein imaging device based on the simulation analysis of vein imaging parameters and propose a U-Net+ResNet18 neural network for vein image segmentation. …”
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A Two-Stage Method for Diagnosing COVID-19, Leveraging CNN, and Transfer Learning on CT Scan Images by Touba torabipour, Abolfazl Gandomi, Mohammad Ghanimi

“…Subsequently, a CNN neural network is constructed for image detection and categorization in the second stage. …”
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Delays in diagnosis and treatment of ATTR cardiac amyloidosis: A real‐world data analysis by Julia Vogel, Sophia Jura, Stephan Settelmeier, Florian Buehning, Tobias Lerchner, Alexander Carpinteiro, Tienush Rassaf, Lars Michel

“…Clinical, laboratory, and imaging data were analysed. Diagnostic timelines were compared across two periods (2018–2020 and 2021–2023). …”
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Design of a Classification Recognition Model for Bone and Muscle Anatomical Imaging Based on Convolutional Neural Network and 3D Magnetic Resonance by Ting Pan, Yang Yang

“…In this paper, we use convolutional neural networks to conduct in-depth research and analysis on the classification and recognition of bone and muscle anatomical imaging graphics of 3D magnetic resonance and design corresponding models for practical applications. …”
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A New Classification Method in Ultrasound Images of Benign and Malignant Thyroid Nodules Based on Transfer Learning and Deep Convolutional Neural Network by Weibin Chen, Zhiyang Gu, Zhimin Liu, Yaoyao Fu, Zhipeng Ye, Xin Zhang, Lei Xiao

“…With data augmentation as a training set, transfer learning with the trained GoogLeNet convolutional neural network was performed to extract image features. …”
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Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images by M. Kumar, S. K. Mishra, S. S. Sahu

“…Gaussian noise is one of the dominant noises, which degrades the quality of acquired Computed Tomography (CT) image data. It creates difficulties in pathological identification or diagnosis of any disease. …”
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Improving Disparity Consistency With Self-Refined Cost Volumes for Deep Learning-Based Satellite Stereo Matching by Jiyong Kim, Seoyeon Cho, Minkyung Chung, Yongil Kim

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Approximation of Time-Frequency Shift Equivariant Maps by Neural Networks by Dae Gwan Lee

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Analysis of the Urban Development Technologies of Ukek using Neural Networks (preliminary research results) by Singatulin Rustam A.

“…Complex patterns and associations between archaeological, historical, and geological data from different years were identified. The software tools included convolutional neural networks for image analysis, recurrent neural networks for time sequence analysis, and deep neural networks for complex classification, modeling, and verification tasks. …”
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Educational Behaviour Analysis Using Convolutional Neural Network and Particle Swarm Optimization Algorithm by Zhenjiang Dong

“…To improve the quality of online education, a comprehensive and effective analysis of educational behaviour is necessary. In this paper, we proposed a network model based on the ResNet50 network fused with a bilinear hybrid attention mechanism and proposed an adaptive pooling weight algorithm based on the average pooling algorithm for the problems of image feature extraction caused by traditional pooling algorithm such as mutilation and blurring. …”
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Artificial neural networks and remote sensing in the analysis of the highly variable Pampean shallow lakes by Graciela Canziani, Rosana Ferrati, Claudia Marinelli, Federico Dukatz

“…Using bands 2 and 4 of LandSat 5 TM and LandSat 7 ETM+ images andconstructing adequate artificial neural network models (ANN), aclassification of shallow lakes according to their turbidity is obtained.ANN models are also used to determine chlorophyll-a and total suspendedsolids concentrations from satellite image data. …”
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Sentiment Analysis of Tweets on Prakerja Card using Convolutional Neural Network and Naive Bayes by Pahlevi Wahyu Hardjita, Nurochman, Rahmat Hidayat

“…People’s comments on it can be useful information, and this research tries to analyze the sentiment regarding the Prakerja Card program using the Convolutional Neural Network and Naive Bayes methods. The main task in this sentiment analysis is analyzing the data and then classifying them into one of the following classes: positive, negative or neutral. …”
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Video Tactical Intelligence Analysis Method of Karate Competition Based on Convolutional Neural Network by Jun Zhong, Jian Xu

“…The performance of image classification technology based on deep network has been greatly improved, making computer vision enter the stage of industrialization and be gradually applied to many aspects of human work and life. …”
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Evaluating Deep Learning Architectures for Breast Tumor Classification and Ultrasound Image Detection Using Transfer Learning by Christopher Kormpos, Fotios Zantalis, Stylianos Katsoulis, Grigorios Koulouras

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Deep Learning-Based Classification of Canine Cataracts from Ocular B-Mode Ultrasound Images by Sanghyeon Park, Seokmin Go, Seonhyo Kim, Jaeho Shim

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Aided Greenway Design Approach Based on Internet Big Data and AIGC Fine-Tuning Model by Yifan WU, Lu MENG, Liang LI

“…The framework can be divided into four major processes: Network big data collection, intelligent evaluation of network big data, AIGC image fine-tuning model construction, and AI-aided design generation. 1) Network big data collection: Obtain datasets related to the required landscape architecture segmentation scenarios through online social platforms for evaluation and fine-tuning model training. 2) Intelligent evaluation of network big data: Analyze and categorize image data, and filter out the scenario images with excellent user evaluation based on text sentiment evaluation and subsidiary information analysis. 3) AIGC image fine-tuning model construction: Utilize the high-quality image dataset obtained in the previous stage to conduct fine-tuning model training based on a mature pre-trained general model, and inject relevant knowledge and experience from the sub-scenarios of landscape architecture in a cost-effective manner, thereby enhancing the model’s generative capabilities. 4) AI-aided design generation: Employ the fine-tuning model obtained through training to assist in generating scenario images according to the needs of design practice, and based on the intensity of control over the generated content, divide the aided scenario generation into “weakly controlled” and “strongly controlled” aided design scenarios. …”
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MAHGA: Multi-Aspect Heterogeneous Graph Analysis for Harmful Speech Detection on Social Networks by Ryo Yoshida, Soh Yoshida, Mitsuji Muneyasu

“…Deep neural networks demonstrate high accuracy in detecting harmful social media posts; however, conventional text-based methods often overlook critical contextual relationships among posts, users, and shared information. …”
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Optimizing Fingerprint Identification: CNNs With Raw Images Versus Handcrafted Features for Real-Time Systems by Shaik Salma, Tauheed Ahmed, Garimella Ramamurthy

“…This study investigates the balance between accuracy and computational efficiency(thereby speed) by comparing two approaches: training a Convolutional Neural Network (CNN) with raw fingerprint images and training a CNN using handcrafted fingerprint features. …”
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Artificial Intelligence in Advancing Algal Bioactive Ingredients: Production, Characterization, and Application by Bingbing Guo, Xingyu Lu, Xiaoyu Jiang, Xiao-Li Shen, Zihao Wei, Yifeng Zhang

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Preliminary Electroencephalography-Based Assessment of Anxiety Using Machine Learning: A Pilot Study by Katarzyna Mróz, Kamil Jonak

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