Review of Different Types of Neural Network Architectures by T. L. Makosso, A. Almaktoof, K. Abo-Al-Ez

“…Five mains' architectures and their applications and gaps are presented in this paper. The different architectures are: feed-forward, Convolutional and, recurrent neural networks, Auto encoder and generational encoders and Deep reinforcement learning architecture. …”
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Dynamic Range Expansion Using Cumulative Histogram Learning for High Dynamic Range Image Generation by Hanbyol Jang, Kihun Bang, Jinseong Jang, Dosik Hwang

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PDSDC: Progressive Spatiotemporal Difference Capture Network for Remote Sensing Change Detection by YeKai Cui, Peng Duan, Jinjiang Li

“…To address these issues, this article proposes a progressive spatiotemporal difference capture network. This framework effectively mitigates information degradation and multiscale tradeoffs in feature fusion through a multipath optimization mechanism. …”
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Development and evaluation of machine learning models for premixed flame classification in different hydrogen-natural gas proportions using images and audio by Pedro Narvaez, Alejandro Lopez, Jousef E. Karam, Alejandro Restrepo, Andrés A. Amell

“…This study presents a novel approach for the automatic classification of flames in different hydrogen-natural gas mixtures using machine learning techniques. …”
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Change-Guided Difference Interaction Attention Network for Remote Sensing Change Detection by Canbin Hu, Sida Du, Hongyun Chen, Xiaokun Sun, Kailun Liu

“…To address these challenges, we propose the change-guided difference interaction attention network (CGDIANet). …”
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3D-CNN detection of systemic symptoms induced by different Potexvirus infections in four Nicotiana benthamiana genotypes using leaf hyperspectral imaging by Rizos-Theodoros Chadoulis, Ioannis Livieratos, Ioannis Manakos, Theodore Spanos, Zeinab Marouni, Christos Kalogeropoulos, Constantine Kotropoulos

“…In this study, the use of 3D Convolutional Neural Networks (3D-CNNs) was explored to detect presymptomatic viral infections in the model plant Nicotiana benthamiana L. and assess the generalization of these models across different plant genotypes. …”
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Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning. by Mizuho Nishio, Osamu Sugiyama, Masahiro Yakami, Syoko Ueno, Takeshi Kubo, Tomohiro Kuroda, Kaori Togashi

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Multitemporal Difference and Dynamic Optimization Framework for Multiscale Motion Satellite Video Super-Resolution by Qian Zhao, Youming Guo, Lei Min, Changhui Rao

“…Based on the extracted temporal difference data, we further develop a temporal differences-guided dynamic routing optimization module (T-DROM) to extract multiscale motion information. …”
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Classification of Different Age Groups of People by Using Deep Learning by Bülent Turan, Özkan İnik

“…DL modelsare designed using different numbers of these layers. In this study, people aredivided into 12 classes according to age groups. …”
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A Time–Frequency-Based Data-Driven Approach for Structural Damage Identification and Its Application to a Cable-Stayed Bridge Specimen by Naiwei Lu, Yiru Liu, Jian Cui, Xiangyuan Xiao, Yuan Luo, Mohammad Noori

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Enhancing geometric modeling in convolutional neural networks: limit deformable convolution by Wei Wang, Yuanze Meng, Han Li, Guiyong Chang, Shun Li, Chenghong Zhang

“…In the subsequent work, we perform lightweight work on the limit deformable convolution and design three kinds of LDBottleneck to adapt to different scenarios. …”
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Rapid and non-destructive classification of rice seeds with different flavors: an approach based on HPFasterNet by Helong Yu, Helong Yu, Zhenyang Chen, Shaozhong Song, Shaozhong Song, Shaozhong Song, Chunyan Qi, Junling Liu, Chenglin Yang

“…Rice is an important part of the food supply, its different varieties in terms of quality, flavor, nutritional value, and other aspects of the differences, directly affect the subsequent yield and economic benefits. …”
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Cyberbullying Detection in Arabic Text Using Different Deep Learning Approaches by Humera Shaziya

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Structural differences between human and mouse neurons and their implementation in generative AIs by Rino Saiga, Kaede Shiga, Yo Maruta, Chie Inomoto, Hiroshi Kajiwara, Naoya Nakamura, Yu Kakimoto, Yoshiro Yamamoto, Masahiro Yasutake, Masayuki Uesugi, Akihisa Takeuchi, Kentaro Uesugi, Yasuko Terada, Yoshio Suzuki, Viktor Nikitin, Vincent De Andrade, Francesco De Carlo, Yuichi Yamashita, Masanari Itokawa, Soichiro Ide, Kazutaka Ikeda, Ryuta Mizutani

“…Abstract Mouse and human brains have different functions that depend on their neuronal networks. …”
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Feature Coding and Graph via Transformer: Different Granularities Classification for Aircraft by Jianghao Rao, Senlin Qin, Zongyan An, Jianlin Zhang, Qiliang Bao, Zhenming Peng

“…Thanks to the ever-evolving nature of the convolutional neural network (CNN), it has become easier to distinguish and recognize different types of aircraft. …”
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The Convolution on Time Scales by Martin Bohner, Gusein Sh. Guseinov

“…As an extensive example, we consider the q-difference equations case.…”
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Convolution Smooth: A Post-Training Quantization Method for Convolutional Neural Networks by Yongyuan Chen, Zhendao Wang

“…However, existing methods often apply different quantization strategies to activations and weights, without considering their interplay. …”
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Fault Diagnosis for Rolling Bearings Under Complex Working Conditions Based on Domain-Conditioned Adaptation by Xu Zhang, Gaoquan Gu

“…The approach first constructs a multi-scale self-calibrating convolutional neural network to aggregate input signals across different scales, adaptively establishing long-range spatial and inter-channel dependencies at each spatial location, thereby enhancing feature modeling under noisy conditions. …”
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Recurrent neural network based turbo decoding algorithms for different code rates by Shridhar B. Devamane, Rajeshwari L. Itagi

“…BER performance of both is compared with that of a convolutional Viterbi decoder in awgn channel. Both the structures are studied for different input data-lengths and code rates.…”
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Accurately Identifying Different Ripening Stages of Strawberry Fruits in Complex Agricultural Scenarios by Xuefeng Ren, Yang Gan, Huan Liu, Yongming Chen, Ping Lin

“…Second, a receptive field coordinate attention convolutional mechanism is incorporated into the backbone network to enhance parameter sharing of convolutional kernels. …”
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