Hybrid generative adversarial network based on frequency and spatial domain for histopathological image synthesis by Qifeng Liu, Tao Zhou, Chi Cheng, Jin Ma, Marzia Hoque Tania

“…Abstract Background Due to the complexity and cost of preparing histopathological slides, deep learning-based methods have been developed to generate high-quality histological images. …”
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An Enhanced LSTM Approach for Detecting IoT-Based DDoS Attacks Using Honeypot Data by Arjun Kumar Bose Arnob, M. F. Mridha, Mejdl Safran, Md Amiruzzaman, Md. Rajibul Islam

“…The contribution of this paper will be an addition to the deep learning techniques applied for the solution of intrusion detection systems (IDS), which will also allow the building and implementation of more efficient security mechanisms in IoT environments.…”
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A novel hybrid model by integrating TCN with TVFEMD and permutation entropy for monthly non-stationary runoff prediction by Huifang Wang, Xuehua Zhao, Qiucen Guo, Xixi Wu

“…Addressing this need, an ensemble deep learning model was hereby developed to forecast monthly runoff. …”
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Distributed training of foundation models for ophthalmic diagnosis by Sina Gholami, Fatema-E Jannat, Atalie Carina Thompson, Sally Shin Yee Ong, Jennifer I. Lim, Theodore Leng, Hamed Tabkhivayghan, Minhaj Nur Alam

“…Here we propose a distributed deep learning framework that integrates self-supervised and domain-adaptive federated learning to enhance the detection of eye diseases from optical coherence tomography images. …”
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An overlapping sliding window and combined features based emotion recognition system for EEG signals by Shruti Garg, Rahul Kumar Patro, Soumyajit Behera, Neha Prerna Tigga, Ranjita Pandey

“….; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.…”
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Sequence-variable attention temporal convolutional network for volcanic lithology identification based on well logs by Hanlin Feng, Zitong Zhang, Chunlei Zhang, Chengcheng Zhong, Qiaoyu Ma

“…Compared with machine learning and deep learning methods, the SVA-TCN demonstrates a remarkable accuracy of 99.00%, surpassing the accuracy of the comparison methods by 0.37–17.69%. …”
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Artificial intelligence in kidney transplantation: a 30-year bibliometric analysis of research trends, innovations, and future directions by Ying Jia He, Pin Lin Liu, Tao Wei, Tao Liu, Yi Fei Li, Jing Yang, Wen Xing Fan

“…Key research themes include AI-driven advancements in donor matching, deep learning for post-transplant monitoring, and machine learning algorithms for personalized immunosuppressive therapies. …”
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Acute ischemic stroke lesion segmentation in non-contrast CT images using 3D convolutional neural networks by A.V. Dobshik, S.K. Verbitskiy, I.A. Pestunov, K.M. Sherman, Yu.N. Sinyavskiy, A.A. Tulupov, V.B. Berikov

“…In this paper, an automatic algorithm aimed at volumetric segmentation of acute ischemic stroke lesion in non-contrast computed tomography brain 3D images is proposed. Our deep-learning approach is based on the popular 3D U-Net convolutional neural network architecture, which was modified by adding the squeeze-and-excitation blocks and residual connections. …”
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INTEGRATING AI IN THE DIAGNOSIS AND THERAPY OF MAXILLARY BONE DEFICIENCIES by Capatina Andreea, Asaftei Oana, Tibeica Andreea, Agop-Forna Doriana, Shokraei Gholamreza, Norina Forna

“…Results The application of AI in these studies has yielded impressive results, indicating that deep learning models can reliably identify periodontal bone loss. …”
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Encrypted traffic identification method based on deep residual capsule network with attention mechanism by Guozhen SHI, Kunyang LI, Yao LIU, Yongjian YANG

“…With the improvement of users’ security awareness and the development of encryption technology, encrypted traffic has become an important part of network traffic, and identifying encrypted traffic has become an important part of network traffic supervision.The encrypted traffic identification method based on the traditional deep learning model has problems such as poor effect and long model training time.To address these problems, the encrypted traffic identification method based on a deep residual capsule network (DRCN) was proposed.However, the original capsule network was stacked in the form of full connection, which lead to a small model coupling coefficient and it was impossible to build a deep network model.The DRCN model adopted the dynamic routing algorithm based on the three-dimensional convolutional algorithm (3DCNN) instead of the fully-connected dynamic routing algorithm, to reduce the parameters passed between each capsule layer, decrease the complexity of operations, and then build the deep capsule network to improve the accuracy and efficiency of recognition.The channel attention mechanism was introduced to assign different weights to different features, and then the influence of useless features on the recognition results was reduced.The introduction of the residual network into the capsule network layer and the construction of the residual capsule network module alleviated the gradient disappearance problem of the deep capsule network.In terms of data pre-processing, the first 784byte of the intercepted packets was converted into images as input of the DRCN model, to avoid manual feature extraction and reduce the labor cost of encrypted traffic recognition.The experimental results on the ISCXVPN2016 dataset show that the accuracy of the DRCN model is improved by 5.54% and the training time of the model is reduced by 232s compared with the BLSTM model with the best performance.In addition, the accuracy of the DRCN model reaches 94.3% on the small dataset.The above experimental results prove that the proposed recognition scheme has high recognition rate, good performance and applicability.…”
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Leveraging Quantum LSTM for High-Accuracy Prediction of Viral Mutations by Prashanth Choppara, Bommareddy Lokesh

“…The one-hot encoding technique is a standard technique in machine learning for encoding protein sequences into data that can be used in neural networks.The proposed QLSTM outperformed existing deep learning architectures such as the Attention-Augmented Convolutional Neural Network (AACNN), Stacked Recurrent Neural Network (Stacked RNN), Retention Network (RetNet), and Bidirectional Long Short Term Memory (BiLSTM). …”
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Ion channel classification through machine learning and protein language model embeddings by Ghazikhani Hamed, Butler Gregory

“…These results not only highlight the power of integrating protein language models with deep learning for ion channel classification but also underscore the importance of using up-to-date, comprehensive datasets in bioinformatics tasks. …”
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QTFN: A General End-to-End Time-Frequency Network to Reveal the Time-Varying Signatures of the Time Series by Tao Chen, Yang Jiao, Lei Xie, Hongye Su

“…Guided by classic TFD theory, the design of this deep learning architecture is heuristic, which firstly generates various basis functions through data-driven. …”
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Artificial Intelligence-Based Classification of Chest X-Ray Images into COVID-19 and Other Infectious Diseases by Arun Sharma, Sheeba Rani, Dinesh Gupta

“…The present study is aimed at creating efficient deep learning models, trained with chest X-ray images, for rapid screening of COVID-19 patients. …”
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Presegmenter Cascaded Framework for Mammogram Mass Segmentation by Urvi Oza, Bakul Gohel, Pankaj Kumar, Parita Oza

“…Accurate segmentation of breast masses in mammogram images is essential for early cancer diagnosis and treatment planning. Several deep learning (DL) models have been proposed for whole mammogram segmentation and mass patch/crop segmentation. …”
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Improved Localization and Recognition of Handwritten Digits on MNIST Dataset with ConvGRU by Yalin Wen, Wei Ke, Hao Sheng

“…Video location prediction for handwritten digits presents unique challenges in computer vision due to the complex spatiotemporal dependencies and the need to maintain digit legibility across predicted frames, while existing deep learning-based video prediction models have shown promise, they often struggle with preserving local details and typically achieve clear predictions for only a limited number of frames. …”
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Cloud and IoT based smart agent-driven simulation of human gait for detecting muscles disorder by Sina Saadati, Abdolah Sepahvand, Mohammadreza Razzazi

“…The results are then provided to medical and clinical experts to aid in differentiating between healthy and unhealthy muscles and for further investigation. Additionally, a deep learning-based ensemble framework is proposed to assist in the analysis of the simulation results, offering both accuracy and interpretability. …”
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Analysis of Multidimensional Clinical and Physiological Data with Synolitical Graph Neural Networks by Mikhail Krivonosov, Tatiana Nazarenko, Vadim Ushakov, Daniil Vlasenko, Denis Zakharov, Shangbin Chen, Oleg Blyus, Alexey Zaikin

“…To apply Geometric Deep Learning we propose a synolitic or ensemble graph representation of the data, a universal method that transforms any multidimensional dataset into a network, utilising only class labels from training data. …”
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Comparative Analysis of YOLOv8 and HSV Methods for Traffic Density Measurement by Prof. I Gede Pasek Suta Wijaya, Muhamad Nizam Azmi, Ario Yudo Husodo

“…In contrast, the YOLOv8 segmentation method utilizes a deep learning approach to detect and segment vehicles, providing potentially more precise measurements. …”
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Dual Generative Network with Discriminative Information for Generalized Zero-Shot Learning by Tingting Xu, Ye Zhao, Xueliang Liu

“…Nowadays, with the promotion of deep learning technology, the performance of zero-shot learning has been greatly improved. …”
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