Progressive Self-Prompting Segment Anything Model for Salient Object Detection in Optical Remote Sensing Images by Xiaoning Zhang, Yi Yu, Daqun Li, Yuqing Wang

“…With the continuous advancement of deep neural networks, salient object detection (SOD) in natural images has made significant progress. …”
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Security systems’ status with the use of technical means of video recording and video surveillance: international experience, perspectives for implementation in the activities of t... by V. A. Korshenko, V. V. Chumak, M. V. Mordvyntsev, D. V. Pashniev

“…It has been found out that such countries (EU, USA, China, Russia) install modern “smart” CCTV cameras, the information from which is sent to modern situational centers, where it is processed by using artificial intelligence, neural networks and cloud infrastructure. Certain types of cameras even have the ability to independently process the received information. …”
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MUNet: a novel framework for accurate brain tumor segmentation combining UNet and mamba networks by Lijuan Yang, Lijuan Yang, Qiumei Dong, Da Lin, Chunfang Tian, Xinliang Lü

“…However, existing models based on Transformers and Convolutional Neural Networks (CNNs) still have limitations in medical image processing. …”
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A Study on Canopy Volume Measurement Model for Fruit Tree Application Based on LiDAR Point Cloud by Na Guo, Ning Xu, Jianming Kang, Guohai Zhang, Qingshan Meng, Mengmeng Niu, Wenxuan Wu, Xingguo Zhang

“…During model construction, the study optimized the hyperparameters of partial least squares regression (PLSR), backpropagation (BP) neural networks, and gradient boosting decision trees (GBDT) to build canopy volume measurement models tailored to the dataset. …”
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Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review by Aijing Luo, Wei Chen, Hongtao Zhu, Wenzhao Xie, Xi Chen, Zhenjiang Liu, Zirui Xin

“…In terms of model type, deep learning, represented by convolutional neural networks, was most frequently applied (14/23, 61%). …”
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Investigating Maps of Science Using Contextual Proximity of Citations Based on Deep Contextualized Word Representation by Muhammad Roman, Abdul Shahid, Shafiullah Khan, Lisu Yu, Muhammad Asif, Yazeed Yasin Ghadi

“…We have, therefore, used contextual word representation, which is trained through deep neural networks. Deep models require massive data for generalizing the model, however, the existing state-of-the-art datasets don’t provide much information for the training models to get generalized. …”
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A deep learning approach for automatic 3D segmentation of hip cartilage and labrum from direct hip MR arthrography by Malin Kristin Meier, Ramon Andreas Helfenstein, Adam Boschung, Andreas Nanavati, Adrian Ruckli, Till D. Lerch, Nicolas Gerber, Bernd Jung, Onur Afacan, Moritz Tannast, Klaus A. Siebenrock, Simon D Steppacher, Florian Schmaranzer

“…Abstract The objective was to use convolutional neural networks (CNNs) for automatic segmentation of hip cartilage and labrum based on 3D MRI. …”
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Risk factors affecting polygenic score performance across diverse cohorts by Daniel Hui, Scott Dudek, Krzysztof Kiryluk, Theresa L Walunas, Iftikhar J Kullo, Wei-Qi Wei, Hemant Tiwari, Josh F Peterson, Wendy K Chung, Brittney H Davis, Atlas Khan, Leah C Kottyan, Nita A Limdi, Qiping Feng, Megan J Puckelwartz, Chunhua Weng, Johanna L Smith, Elizabeth W Karlson, Regeneron Genetics Center, Penn Medicine BioBank, Gail P Jarvik, Marylyn D Ritchie

“…Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. …”
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Generalizing the Brady-Yong Algorithm: Efficient Fast Hough Transform for Arbitrary Image Sizes by Danil D. Kazimirov, Ekaterina O. Rybakova, Vitalii V. Gulevskii, Arseniy P. Terekhin, Elena E. Limonova, Dmitry P. Nikolaev

“…The Hough (discrete Radon) transform (HT/DRT) is a digital image processing tool that has become indispensable in many application areas, ranging from general image processing to neural networks and X-ray computed tomography. The utilization of the HT in applied problems demands its computational efficiency and increased accuracy. …”
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Improving timing resolution of BGO for TOF-PET: a comparative analysis with and without deep learning by Francis Loignon-Houle, Nicolaus Kratochwil, Maxime Toussaint, Carsten Lowis, Gerard Ariño-Estrada, Antonio J. Gonzalez, Etiennette Auffray, Roger Lecomte

“…Deep learning, particularly convolutional neural networks (CNNs), can also enhance CTR by training with digitized waveforms. …”
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Cloud-edge hybrid deep learning framework for scalable IoT resource optimization by Umesh Kumar Lilhore, Sarita Simaiya, Yogesh Kumar Sharma, Anjani Kumar Rai, S. M. Padmaja, Khan Vajid Nabilal, Vimal Kumar, Roobaea Alroobaea, Hamed Alsufyani

“…The use of Graph Neural Networks (GNNs) improves the accuracy of resource representation, while reinforcement learning-based scheduling allows for seamless adaptation to changing workloads. …”
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Transauricular vagus nerve stimulation in preventing post-traumatic stress disorder in emergency trauma surgery patients in China: a study protocol for a multicenter, double-blind,... by Jun Zhang, Xin Yu, Gang Chen, Yu Li, Qi-Hong Shen, Xinru Lin, Tieshuai Liu, Yunyun Yu, Jingwen Liang

“…Transauricular vagus nerve stimulation (ta-VNS) modulates the autonomic nervous system by stimulating the nucleus tractus solitarius while affecting PTSD-related neural networks, including the prefrontal cortex, hippocampus and amygdala, potentially offering new options for PTSD prevention and treatment. …”
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Stock price prediction with attentive temporal convolution-based generative adversarial network by Ying Liu, Xiaohua Huang, Liwei Xiong, Ruyu Chang, Wenjing Wang, Long Chen

“…The advent of deep learning has led to substantial improvements in prediction accuracy, with various recurrent neural networks widely employed for representation learning from stock sequences. …”
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Housing Price Forecasting Using AI (LSTM) by Hossein Ziyadi, Erfan Salavati, Mohammad Mahdi Lotfi Heravi

“…Our model is based on the Recurrent Neural Networks. Due to its capability to preserve past information, the LSTM algorithm was implemented as a time series forecasting model. …”
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HEDDI-Net: heterogeneous network embedding for drug-disease association prediction and drug repurposing, with application to Alzheimer’s disease by Yin-Yuan Su, Hsuan-Cheng Huang, Yu-Ting Lin, Yi-Fang Chuang, Sheh-Yi Sheu, Chen-Ching Lin

“…Abstract Background The traditional process of developing new drugs is time-consuming and often unsuccessful, making drug repurposing an appealing alternative due to its speed and safety. Graph neural networks (GCNs) have emerged as a leading approach for predicting drug-disease associations by integrating drug and disease-related networks with advanced deep learning algorithms. …”
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Analysis of drought and extreme precipitation events in Thailand: trends, climate modeling, and implications for climate change adaptation by José Francisco de Oliveira-Júnior, David Mendes, Helder Dutra Porto, Kelvy Rosalvo Alencar Cardoso, José Augusto Ferreira Neto, Emannuel Bezerra Cavalcante da Silva, Marlúcia de Aquino Pereira, Monica Cristina Damião Mendes, Bernardo Bruno Dias Baracho, Punyawi Jamjareegulgarn

“…The climate indices used were Consecutive Dry Days (CDD), Maximum Number of Consecutive Summer Days (CSU), Consecutive Wet Days (CWD), Warm Spell Duration Index (WSDI), and Maximum Number of Consecutive Wet Days (WW) derived from simulations of an ensemble composed of six models from the Intergovernmental Panel on Climate Change (IPCC) via the Coupled Model Intercomparison Project Phase 6 (CMIP6) using Artificial Neural Networks (ANN) with the backpropagation method. …”
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Harnessing deep learning to detect bronchiolitis obliterans syndrome from chest CT by Mateusz Koziński, Doruk Oner, Jakub Gwizdała, Catherine Beigelman-Aubry, Pascal Fua, Angela Koutsokera, Alessio Casutt, Argyro Vraka, Michele De Palma, John-David Aubert, Horst Bischof, Christophe von Garnier, Sahand Jamal Rahi, Martin Urschler, Nahal Mansouri

“…Abstract Background Bronchiolitis Obliterans Syndrome (BOS), a fibrotic airway disease that may develop after lung transplantation, conventionally relies on pulmonary function tests (PFTs) for diagnosis due to limitations of CT imaging. Deep neural networks (DNNs) have not previously been used for BOS detection. …”
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A novel early stage drip irrigation system cost estimation model based on management and environmental variables by Masoud Pourgholam-Amiji, Khaled Ahmadaali, Abdolmajid Liaghat

“…Then, different machine learning models such as Multivariate Linear Regression, Support Vector Regression, Artificial Neural Networks, Gene Expression Programming, Genetic Algorithms, Deep Learning, and Decision Trees, were used to estimate the costs of each of the of the aforementioned sections. …”
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Comprehensive Evaluation and Error-Component Analysis of Four Satellite-Based Precipitation Estimates against Gauged Rainfall over Mainland China by Guanghua Wei, Haishen Lü, Wade T. Crow, Yonghua Zhu, Jianbin Su, Li Ren

“…Moreover, V06C and V06UC rainfall estimates are compared against the Precipitation Estimation from Remotely Sensed Imagery using Artificial Neural Networks (PERSIANN)-Climate Data Record (CDR) and the Climate Prediction Center morphing technique (CMORPH) gauge-satellite blended (BLD) products. …”
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Improving machine learning predictions to estimate fishing effort using vessel's tracking data by J. Samarão, A. Moreno, M.B. Gaspar, M.M. Rufino

“…We assessed seven supervised ML algorithms, including Logistic Regression, Ridge Classifier, Random Forest Classifier, K-Neighbours, Gradient Boosting Classifier, LinearSVC, Recurrent Neural Networks and XGBoost, using four case studies, from bivalve dredge and octopus pots and traps fisheries.First, in a preliminary statistical analysis between common error measures derived from the confusion matrix was decided to use accuracy, precision, and sensitivity as evaluation criteria. …”
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