An Explainable Bayesian TimesNet for Probabilistic Groundwater Level Prediction by Zechen Peng, Shaoxing Mo, Alexander Y. Sun, Jichun Wu, Xiankui Zeng, Miao Lu, Xiaoqing Shi

“…SHAP analysis reveals that historical GWLs are the most informative features, with meteorological predictors making secondary contributions. …”
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A Combined Deep Learning Method with Attention-Based LSTM Model for Short-Term Traffic Speed Forecasting by Pan Wu, Zilin Huang, Yuzhuang Pian, Lunhui Xu, Jinlong Li, Kaixun Chen

“…We investigate the relevant literature and found that although most methods can achieve good prediction performance with the complete sample data, when there is a certain missing rate in the database, it is difficult to maintain accuracy with these methods. …”
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Comparing efficiency of an attention-based deep learning network with contemporary radiological workflow for pulmonary embolism detection on CTPA: A retrospective study by Gagandeep Singh, Annie Singh, Tejasvi Kainth, Sudhir Suman, Nicole Sakla, Luke Partyka, Tej Phatak, Prateek Prasanna

“…Rational and objectives: Pulmonary embolism (PE) is the third most fatal cardiovascular disease in the United States. …”
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High-gamma and beta bursts in the left supramarginal gyrus can differentiate verbal memory states and performance by Shennan Aibel Weiss, Nicolás Sawczuk, Daniel Y. Rubinstein, Michael R. Sperling, Katrina Wendel-Mitoraj, Päivi Österman, René Dumay-Roscher, Charles B. Mikell, Sima Mofakham, Kelly Coulehan, Petar M. Djuric, Diego Fernandez Slezak, Juan Esteban Kamienkowski

“…To address over-training, we also trained and then tested the CNN on distinct datasets in four subjects. In most of these experiments CNN performed better than chance. …”
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A data-driven approach to predict fracture intensity using machine learning for presalt carbonate reservoirs: A feasibility study in the Mero Field, Santos Basin, Brazil by Eberton Rodrigues de Oliveira Neto, Fábio Júnior Damasceno Fernandes, Tuany Younis Abdul Fatah, Raquel Macedo Dias, Zoraida Roxana Tejada da Piedade, Antonio Fernando Menezes Freire, Wagner Moreira Lupinacci

“…Results from feature importance methods, such as permutation importance and Shapley Additive explanations (SHAP), highlight curvature as the most important feature, followed by distance to fault, Young's modulus (or P-Impedance), silica content, and Poisson's ratio. …”
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Automated Detection of Gastrointestinal Diseases Using Resnet50*-Based Explainable Deep Feature Engineering Model with Endoscopy Images by Veysel Yusuf Cambay, Prabal Datta Barua, Abdul Hafeez Baig, Sengul Dogan, Mehmet Baygin, Turker Tuncer, U. R. Acharya

“…This work aims to develop a novel convolutional neural network (CNN) named ResNet50* to detect various gastrointestinal diseases using a new ResNet50*-based deep feature engineering model with endoscopy images. …”
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DeepMiRBP: a hybrid model for predicting microRNA-protein interactions based on transfer learning and cosine similarity by Sasan Azizian, Juan Cui

“…The first component employs bidirectional long short-term memory (Bi-LSTM) neural networks to capture sequential dependencies and context within RNA sequences, attention mechanisms to enhance the model’s focus on the most relevant features and transfer learning to apply knowledge gained from a large dataset of RNA-protein binding sites to the specific task of predicting microRNA-protein interactions. …”
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Development of a deep learning model for automated detection of calcium pyrophosphate deposition in hand radiographs by Thomas Hügle, Elisabeth Rosoux, Guillaume Fahrni, Deborah Markham, Tobias Manigold, Fabio Becce

“…CPPD presence was then predicted using a convolutional neural network. We tested seven CPPD models, each with a different combination of sites out of TFCC, MCP-2 and MCP-3. …”
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Prevalence, associated risk factors and satellite imagery analysis in predicting soil-transmitted helminth infection in Nakhon Si Thammarat Province, Thailand by Jarawadee Muenjak, Jutarat Thongrod, Chanakan Choodamdee, Pongphan Pongpanitanont, Manachai Yingklang, Tongjit Thanchomnang, Sakhone Laymanivong, Penchom Janwan

“…Mono-infections predominated, with Trichuris trichiura (5.02%) and hookworm (3.49%) being the most frequent. Mixed infections accounted for 1.25%, primarily co-infections of hookworm with T. trichiura (0.94%) or Strongyloides stercoralis (0.31%). …”
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Hybrid Deep Learning for Survival Prediction in Brain Metastases Using Multimodal MRI and Clinical Data by Cristian Constantin Volovăț, Călin Gheorghe Buzea, Diana-Ioana Boboc, Mădălina-Raluca Ostafe, Maricel Agop, Lăcrămioara Ochiuz, Ștefan Lucian Burlea, Dragoș Ioan Rusu, Laurențiu Bujor, Dragoș Teodor Iancu, Simona Ruxandra Volovăț

“…Permutation feature importance highlighted edema-to-tumor ratio and enhancing tumor volume as the most informative predictors. Grad-CAM visualizations confirmed the model’s attention to anatomically and clinically relevant regions. …”
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Construction of a predictive model for the efficacy of anti-VEGF therapy in macular edema patients based on OCT imaging: a retrospective study by Tingting Song, Boyang Zang, Chui Kong, Xifang Zhang, Huihui Luo, Wenbin Wei, Zheqing Li

“…This model innovatively introduces group convolution and multiple convolutional kernels to handle multidimensional features based on traditional attention mechanisms for visual recognition tasks, while utilizing spatial pyramid pooling (SPP) to combine and extract the most useful features. …”
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Short-term and long-term inertia forecasting with low-inertia event prediction in IBR-integrated power systems using a deep learning approach by Santosh Diggikar, Arunkumar Patil, Katkar Siddhant Satyapal, Kunal Samad

“…Accurate inertia forecasting is essential for ensuring grid stability, particularly in systems such as the Great Britain (GB) power system, where inertia levels occasionally fall below critical thresholds. However, most traditional and online estimation techniques provide reactive inertia assessments, limiting their effectiveness for proactive grid management. …”
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Comparing acoustic representations for deep learning-based classification of underwater acoustic signals: A case study on orca (Orcinus orca) vocalizations by Fabio Frazao, Ruth Joy, Michael Dowd

“…This statement is also true for most other ways of representing acoustic information. …”
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A hybrid CNN-BILSTM deep learning framework for signal detection of a massive MIMONOMA system by Mohamed A. Abdelhamed, Mennatalla Samy, Bassem E. Elnaghi, Ahmed Magdy

“…Non-orthogonal multiple access (NOMA) has been proposed as a replacement for orthogonal multiple access (OMA) in 6G networks to reduce latency, improve throughput and increase data rates. However, the most common technique for detecting NOMA in receivers, known as successive interference cancellation (SIC), has limitations in error detection. …”
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Accurate estimation of permeability reduction resulted from low salinity water flooding in clay-rich sandstones by Xiaojuan Zhang, Muntadher Abed Hussein, Tarak Vora, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Farzona Alimova, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Ahmad Khalid

“…The results show that random forest and ensemble learning algorithms delivered the highest predictive accuracy, evidenced by the most substantial coefficient of determination (R2) and minimal error metrics. …”
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Laryngeal cancer diagnosis based on improved YOLOv8 algorithm by Xin Nie, Xueyan Zhang, Di Wang, Yuankun Liu, Lumin Xing, Wenjian Liu

“…Laryngeal cancer is the most common malignant tumor in the head and neck region. …”
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Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning by Jakub Horvath, Pavel Jedlicka, Marie Kratka, Zdenek Kubat, Eduard Kejnovsky, Matej Lexa

“…Previous experimental and sequence studies have provided only limited information about LTR structure and composition, mostly from model systems. To enhance our understanding of these key sequence modules, we focused on the contrasts between LTRs of various retrotransposon families and other genomic regions. …”
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Vibration Signal Analysis for Intelligent Rotating Machinery Diagnosis and Prognosis: A Comprehensive Systematic Literature Review by Ikram Bagri, Karim Tahiry, Aziz Hraiba, Achraf Touil, Ahmed Mousrij

“…In the context of fault detection, support vector machines (SVMs), convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, k-nearest neighbors (KNN), and random forests have been identified as the five most frequently employed algorithms. …”
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Diagnosis of osteosarcoma based on multimodal microscopic imaging and deep learning by Zihan Wang, Jinjin Wu, Chenbei Li, Bing Wang, Qingxia Wu, Lan Li, Huijie Wang, Chao Tu, Jianhua Yin

“…Osteosarcoma is the most common primary bone tumor with high malignancy. …”
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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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