Federated learning-based hybrid convolutional recurrent neural network for multi-class intrusion detection in IoT networks by Prabu Selvam, P. Karthikeyan, S. Manochitra, A. V. L. N. Sujith, T. Ganesan, Rajaram Ayyasamy, Mohammed Shuaib, Shadab Alam, A. Rajendran

“…For multi-class intrusion detection in IoT networks, this study proposed an novel hybrid convolutional recurrent neural network (CRNN) model based on federated learning. …”
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Adaptive spatial-channel feature fusion and self-calibrated convolution for early maize seedlings counting in UAV images by Zhenyuan Sun, Zhenyuan Sun, Zhenyuan Sun, Zhi Yang, Zhi Yang, Yimin Ding, Yimin Ding, Boyan Sun, Boyan Sun, Saiju Li, Saiju Li, Zhen Guo, Zhen Guo, Lei Zhu, Lei Zhu

“…RC-Dino introduces two innovative components: a novel self-calibrating convolutional layer named RSCconv and an adaptive spatial feature fusion module called ASCFF. …”
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Deep Learning Techniques in DICOM Files Classification: A Systematic Review by Mabirizi, Vicent, Kawuma, Simon, Natumanya, Deborah, Wasswa, William

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Timing data visualization: tactical intent recognition and portable framework by SONG Yafei, LI Lemin, QUAN Wen, NI Peng, WANG Ke

“…By transforming time series into images, a robust and transferable tactical intent recognition framework was proposed, which integrated curve filtering technology and the EfficientNetV2 image recognition network. …”
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Timing data visualization: tactical intent recognition and portable framework by SONG Yafei, LI Lemin, QUAN Wen, NI Peng, WANG Ke

“…By transforming time series into images, a robust and transferable tactical intent recognition framework was proposed, which integrated curve filtering technology and the EfficientNetV2 image recognition network. …”
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A spatial scene reconstruction framework in emergency response scenario by Nan Zheng, Danhuai Guo

“…Finally, use graph convolutional neural networks to obtain scene knowledge graph embeddings that consider spatial constraints. …”
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Robust graph fusion and recognition framework for fingerprint and finger‐vein by Zhitao Wu, Hongxu Qu, Haigang Zhang, Jinfeng Yang

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A novel framework for segmentation of small targets in medical images by Longxuan Zhao, Tao Wang, Yuanbin Chen, Xinlin Zhang, Hui Tang, Fuxin Lin, Chunwang Li, Qixuan Li, Tao Tan, Dezhi Kang, Tong Tong

“…This framework leverages the established capacity of convolutional neural networks to acquire effective image representations. …”
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Application of Deep Learning Framework for Early Prediction of Diabetic Retinopathy by Fahad Mostafa, Hafiz Khan, Fardous Farhana, Md Ariful Haque Miah

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Development of interpretable intelligent frameworks for estimating river water turbidity by Amin Gharehbaghi, Salim Heddam, Saeid Mehdizadeh, Sungwon Kim

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DEFENDIFY: defense amplified with transfer learning for obfuscated malware framework by Rodrigo Castillo Camargo, Juan Murcia Nieto, Nicolás Rojas, Daniel Díaz-López, Santiago Alférez, Angel Luis Perales Gómez, Pantaleone Nespoli, Félix Gómez Mármol, Umit Karabiyik

“…The proposed framework was configured to test four Convolutional Neural Network architectures: ResNet18, ResNet34, EfficientNetB3, and EfficientNetV2S. …”
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Machine learning frameworks to accurately predict coke reactivity index by Ayat Hussein Adhab, Morug Salih Mahdi, Krunal Vaghela, Anupam Yadav, Jayaprakash B, Mayank Kundlas, Ankur Srivastava, Jayant Jagtap, Aseel Salah Mansoor, Usama Kadem Radi, Nasr Saadoun Abd, Samim Sherzod

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SOD-YOLO: A lightweight small object detection framework by Yunze Xiao, Nan Di

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Towards edge-collaborative, lightweight and privacy-preserving classification framework by Jinbo XIONG, Yongjie ZHOU, Renwan BI, Liang WAN, Youliang TIAN

“…Aiming at the problems of data leakage of perceptual image and computational inefficiency of privacy-preserving classification framework in edge-side computing environment, a lightweight and privacy-preserving classification framework (PPCF) was proposed to supports encryption feature extraction and classification, and achieve the goal of data transmission and computing security under the collaborative classification process of edge nodes.Firstly, a series of secure computing protocols were designed based on additive secret sharing.Furthermore, two non-collusive edge servers were used to perform secure convolution, secure batch normalization, secure activation, secure pooling and other deep neural network computing layers to realize PPCF.Theoretical and security analysis indicate that PPCF has excellent accuracy and proved to be security.Actual performance evaluation show that PPCF can achieve the same classification accuracy as plaintext environment.At the same time, compared with homomorphic encryption and multi-round iterative calculation schemes, PPCF has obvious advantages in terms of computational cost and communication overhead.…”
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Similarity based city data transfer framework in urban digitization by Haoxiang Wang, Xiaoping Che, Enyao Chang, Chenxin Qu, Ganghua Zhang, Zihan Zhou, Zhenlin Wei, Gengyu Lyu, Pengfei Li

“…Afterwards, we build an adaptation transfer learning framework to achieve data transfer within the same city cluster, which ensures the reliability of cross-city data transferring results. …”
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MolAttnNet: A Predictive Model for Organic Drug Solubility Based on Graph Convolutional Networks and Transformer-Attention by Chenxu Wang, Yijun Feng, Zhejie Xu, Xiaohui Xu, Bangguo Peng

“…The framework comprises three specialized modules: a Graph Convolutional Network for extracting local molecular structural features, a multi-granularity attention mechanism for capturing both local and global molecular dependencies, and an adaptive LSTM with chemically-informed forget gates for selective feature retention and noise attenuation. …”
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Classification of iPSC-derived cultures using convolutional neural networks to identify single differentiated neurons for isolation or measurement by Purva Patel, Lina K. Mohammed Ali, Uma Kaushik, Mallory Wright, Kaylee Green, Jason E. Waligorski, Colin L. Kremitzki, Graham W. Bachman, Serena N. Elia, William J. Buchser

“…Additionally, the 2-stage training framework can be used more broadly for cellular classification tasks. …”
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SAITI-DCGAN: Self-Attention Based Deep Convolutional Generative Adversarial Networks for Data Augmentation of Infrared Thermal Images by Zhichao Wu, Changyun Wei, Yu Xia, Ze Ji

“…To further expand its applicability, the proposed method is designed not only to address the specific needs of aluminum foil sealing but also to serve as a robust framework that can be adapted to a wide range of industrial defect detection tasks. …”
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Acceleration of Urdu Optical Character Recognition on Zynq UltraScale+ MPSoC Using Deep Convolutional Neural Network by Fauzia Yasir, Majida Kazmi

“…This study presents a hardware-accelerated Urdu OCR framework using a custom-designed Convolutional Neural Network (CNN) optimized for deployment on the Xilinx Zynq UltraScale+ MPSoC (ZCU104). …”
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Identifying key genetic variants in Alzheimer’s disease progression using Graph Convolutional Networks (GCN) and biological impact analysis by Belal A. Hamed, Heba Mamdouh Farghaly, Ahmed Omar, Tarek Abd El-Hafeez

“…We present a novel deep learning framework integrating Single Nucleotide Polymorphism (SNP) data with Graph Convolutional Networks (GCNs) to predict gene-disease relationships in AD. …”
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