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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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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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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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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Code-Switching ASR for Low-Resource Indic Languages: A Hindi-Marathi Case Study by Hemant Palivela, Meera Narvekar, David Asirvatham, Shashi Bhushan, Vinay Rishiwal, Udit Agarwal

“…This work critically evaluates current methods and proposes improvements using modern deep-learning techniques to address the primary challenges in developing efficient ASR models for Hindi and Marathi. …”
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Temporal integration of ResNet features with LSTM for enhanced skin lesion classification by Sasmita Padhy, Sachikanta Dash, Naween Kumar, Shailendra Pratap Singh, Gyanendra Kumar, Poonam Moral

“…This research combines ResNet-50 for spatial feature extraction with Long Short-Term Memory (LSTM) networks for temporal analysis and introduces an innovative hybrid deep learning model, Residual network-Long Short-Term Memory (R-LSTM50). …”
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Text-Based Price Recommendation System for Online Rental Houses by Lujia Shen, Qianjun Liu, Gong Chen, Shouling Ji

“…In this paper, we analyzed the relationship between the description of each listing and its price, and proposed a text-based price recommendation system called TAPE to recommend a reasonable price for newly added listings. We used deep learning techniques (e.g., feedforward network, long short-term memory, and mean shift) to design and implement TAPE. …”
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Attention-based interactive multi-level feature fusion for named entity recognition by Yiwu Xu, Yun Chen

“…Recently, Deep Neural Networks (DNNs) have been extensively applied to NER tasks owing to the rapid development of deep learning technology. However, despite their advancements, these models fail to take full advantage of the multi-level features (e.g., lexical phrases, keywords, capitalization, suffixes, etc.) of entities and the dependencies between different features. …”
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A pediatric emergency prediction model using natural language process in the pediatric emergency department by Arum Choi, Chohee Kim, Jisu Ryoo, Jangyeong Jeon, Sangyeon Cho, Dongjoon Lee, Junyeong Kim, Changhee Lee, Woori Bae

“…Abstract This study developed a predictive model using deep learning (DL) and natural language processing (NLP) to identify emergency cases in pediatric emergency departments. …”
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New Heuristics Method for Malicious URLs Detection Using Machine Learning by Maher Kassem Hasan

“…., Further works on deep learning models emphasized their potentials. In our study, the optimized Random Forest model in our case showed the best performance, and its training accuracy was 99%, while validation accuracy was 90.5%, also logistic Regression and SVM achieved training accuracy was 89.31%, while validation accuracy was 90.5%. …”
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LASSO–MOGAT: a multi-omics graph attention framework for cancer classification by Fadi Alharbi, Aleksandar Vakanski, Murtada K. Elbashir, Mohanad Mohammed

“…This article introduces Least Absolute Shrinkage and Selection Operator–Multi-omics Gated Attention (LASSO–MOGAT), a novel graph-based deep learning framework that integrates messenger RNA, microRNA, and DNA methylation data to classify 31 cancer types. …”
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TBF-YOLOv8n: A Lightweight Tea Bud Detection Model Based on YOLOv8n Improvements by Wenhui Fang, Weizhen Chen

“…To solve the problem of the high computational complexity of deep learning detection models, we developed the Tea Bud DSCF-YOLOv8n (TBF-YOLOv8n)lightweight detection model. …”
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On the Effect of the Patient Table on Attenuation in Myocardial Perfusion Imaging SPECT by Tamino Huxohl, Gopesh Patel, Wolfgang Burchert

“…Abstract Background The topic of the effect of the patient table on attenuation in myocardial perfusion imaging (MPI) SPECT is gaining new relevance due to deep learning methods. Existing studies on this effect are old, rare and only consider phantom measurements, not patient studies. …”
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A novel knowledge distillation framework for enhancing small object detection in blurry environments with unmanned aerial vehicle-assisted images by Sayed Jobaer, Xue-song Tang, Yihong Zhang, Gaojian Li, Foysal Ahmed

“…Abstract Deep learning-based object detectors excel on mobile devices but often struggle with blurry images that are common in real-world scenarios, like unmanned aerial vehicle (UAV)-assisted images. …”
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Triple-attentions based salient object detector for strip steel surface defects by Li Zhang, Xirui Li, Yange Sun, Huaping Guo

“…Abstract Accurate detection of surface defects on strip steel is essential for ensuring strip steel product quality. Existing deep learning based detectors for strip steel surface defects typically strive to iteratively refine and integrate the coarse outputs of the backbone network, enhancing the models’ ability to express defect characteristics. …”
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