High Precision Piston Error Sensing of Segmented Telescope Based on Frequency Domain Filtering by Dequan Li, Dong Wang

“…The representation of feature image that reflects each submirror's piston error which obtained by this method is the same.Therefore, regardless of the number or the arrangement of submirrors, the single shallow convolutional neural network trained by any of the extracted submirror interference image dataset can be used to achieve high-precision detection of different submirror piston errors.Finally, simulation experiment results show the effectiveness of the proposed method.…”
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Optimization of Deep Neural Networks Using a Micro Genetic Algorithm by Ricardo Landa, David Tovias-Alanis, Gregorio Toscano

“…This work proposes the use of a micro genetic algorithm to optimize the architecture of fully connected layers in convolutional neural networks, with the aim of reducing model complexity without sacrificing performance. …”
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Exploring the Detection Accuracy of Concrete Cracks Using Various CNN Models by Mohammed Ameen Mohammed, Zheng Han, Yange Li

“…The performance of three different convolutional neural network (CNN) models was then evaluated. …”
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Enhanced Skin Lesion Classification Using Deep Learning, Integrating with Sequential Data Analysis: A Multiclass Approach by Azmath Mubeen, Uma N. Dulhare

“…In dermatological research, accurately identifying different types of skin lesions, such as nodules, is essential for early diagnosis and effective treatment. …”
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Comparative Study of CNN Architectures for Brain Tumor Classification Using MRI: Exploring GradCAM for Visualizing CNN Focus by Areli Chinga, Wilden Bendezu, Antonio Angulo

“…This study aims to refine the diagnosis of brain tumors using convolutional neural network algorithms. Currently, diagnostic accuracy is limited, therefore, our approach uses five different CNN architectures to accurately identify and classify affected brain regions, specifically glioma, meningioma, or pituitary tumors. …”
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DoS and DDoS Attack Detection in IoT Infrastructure using Xception Model with Explainability by Nelly Elsayed, Zag ElSayed, Ahmed Abdelgawad

“…We employed the Xception model with fine-tuning, and we achieved an average of 91% accuracy in detecting eleven different types of DoS and DDoS attacks, which is higher than the current state-of-the-art by 5% targeting the same task. …”
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Application analysis of computer vision and image recognition based on improved VGG16 network by Xuanzhang Zhu, Yafei Li

“…The proposed model achieves a recognition accuracy of 0.971 when recognizing images of different categories, significantly higher than other models. …”
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Implementation of Machine Vision Methods for Cattle Detection and Activity Monitoring by Roman Bumbálek, Tomáš Zoubek, Jean de Dieu Marcel Ufitikirezi, Sandra Nicole Umurungi, Radim Stehlík, Zbyněk Havelka, Radim Kuneš, Petr Bartoš

“…For training time, the fastest was 7:47:17, with a difference of 1:02:00 between the fastest and slowest times. …”
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Evaluation of deep learning models for RGB image-based detection of potato virus y strain symptoms (O, NO, and NTN) in potato plants by Charanpreet Singh, Gurjit S. Randhawa, Aitazaz A. Farooque, Yuvraj S. Gill, Lokesh Kumar KM, Mathuresh Singh, Khalil Al-Mughrabi

“…In this study, the use of these models for the detection of infected plants with different strains of PVY has been explored and extended. …”
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Classification of pulmonary diseases from chest radiographs using deep transfer learning. by Muneeba Shamas, Huma Tauseef, Ashfaq Ahmad, Ali Raza, Yazeed Yasin Ghadi, Orken Mamyrbayev, Kymbat Momynzhanova, Tahani Jaser Alahmadi

“…This paper has explored the effectiveness of Convolutional Neural Networks and transfer learning to improve the predictive outcomes of fifteen different pulmonary diseases using chest radiographs. …”
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Sines, transient, noise neural modeling of piano notes by Riccardo Simionato, Stefano Fasciani

“…The noise sub-module uses a learnable time-varying filter, and the transients are generated using a deep convolutional network. From singular notes, we emulate the coupling between different keys in trichords with a convolutional-based network. …”
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Application research of 3D virtual interactive technology in interactive teaching of arts and crafts by Mingqi Yao

“…The results show that when the noise standard deviation is 50, model 1 can achieve the target accuracy with only 82 iterations, while model 2 requires as many as 56 iterations. For images of different types and noise intensities, the average PSNR values of model 1 are 29.3dB, 30.5dB and 28.9dB, respectively. …”
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Functional connectivity in EEG: a multiclass classification approach for disorders of consciousness by Sreelakshmi Raveendran, Kala S, Ramakrishnan A G, Ramakrishnan A G, Raghavendra Kenchaiah, Jayakrushna Sahoo, Santhos Kumar, Farsana M K, Ravindranadh Chowdary Mundlamuri, Sonia Bansal, Binu V S, Subasree R

“…The extracted SWC metrics, mean, reflecting the stability of connectivity, and standard deviation, indicating variability, are analyzed to discern FC differences at the group level. Multiclass classification is attempted using various models of artificial neural networks that include different multilayer perceptrons (MLP), recurrent neural networks, long-short-term memory networks, gated recurrent units, and a hybrid CNN-LSTM model that combines convolutional neural networks (CNN) and long-short-term memory network to validate the discriminative power of these FC features. …”
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Exploratory development of human–machine interaction strategies for post-stroke upper-limb rehabilitation by Kang Xia, Xue-Dong Chang, Chong-Shuai Liu, Yu-Hang Yan, Han Sun, Yi-Min Wang, Xin-Wei Wang

“…For robot-in-charge and therapist-in-charge strategy, the desired and measured angle-time curve present good correlation, with low phase difference, which serve the purpose of real-time control. …”
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Timing data visualization: tactical intent recognition and portable framework by SONG Yafei, LI Lemin, QUAN Wen, NI Peng, WANG Ke

“…Curve filtering technology effectively reduced redundancy in numerous time-domain features, model parameters, and training time, an enhanced Gramian angular field (GAF) method was proposed to encode time series into images, enhancing the feature extraction capabilities of convolutional neural networks. The EfficientNetV2 network was adept at processing intent images and could serve as a pre-trained model, facilitating transfer learning across different systems. …”
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Timing data visualization: tactical intent recognition and portable framework by SONG Yafei, LI Lemin, QUAN Wen, NI Peng, WANG Ke

“…Curve filtering technology effectively reduced redundancy in numerous time-domain features, model parameters, and training time, an enhanced Gramian angular field (GAF) method was proposed to encode time series into images, enhancing the feature extraction capabilities of convolutional neural networks. The EfficientNetV2 network was adept at processing intent images and could serve as a pre-trained model, facilitating transfer learning across different systems. …”
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Classification of Hybrid and Peking Duck DOD Varieties Based on Feather Images Using CNN by Khoironi, I Wayan Rangga Pinastawa

“…Day Old Duck (DOD) hybrid duck is highly sought after in the rearing industry, and this crossbreeding process produces hybrid and Peking varieties with different feather color patterns. Sorting DOD is important to maintain the consistency of hybrid varieties, but the process requires high expertise and a long time. …”
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The Detection of Fake Text News using a Dense-based 1D-CNN Deep Learning Algorithm by Khalid Abood Kamel, Jumana Waleed

“…compared to the previous different methods to solve this problem, including some common ‎deep-learning methods. …”
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Rail Transit Prediction Based on Multi-View Graph Attention Networks by Li Wang, Xin Wang, Jiao Wang

“…Multiple nodes and various associations such as different types of stations and lines in rail transit always exist at the same time. …”
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Adaptive clustering federated learning via similarity acceleration by Suxia ZHU, Binke GU, Guanglu SUN

“…In order to solve the problem of model performance degradation caused by data heterogeneity in the federated learning process, it is necessary to consider personalizing in the federated model.A new adaptively clustering federated learning (ACFL) algorithm via similarity acceleration was proposed, achieving adaptive acceleration clustering based on geometric properties of local updates and the positive feedback mechanism during clients federated training.By dividing clients into different task clusters, clients with similar data distribution in the same cluster was cooperated to improve the performance of federated model.It did not need to determine the number of clusters in advance and iteratively divide the clients, so as to avoid the problems of high computational cost and slow convergence speed in the existing clustering federation methods while ensuring the performance of models.The effectiveness of ACFL was verified by using deep convolutional neural networks on commonly used datasets.The results show that the performance of ACFL is comparable to the clustered federated learning (CFL) algorithm, it is better than the traditional iterative federated cluster algorithm (IFCA), and has faster convergence speed.…”
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