Design of an Iterative Method for Malware Detection Using Autoencoders and Hybrid Machine Learning Models by Rijvan Beg, R. K. Pateriya, Deepak Singh Tomar

“…In the evolving cyber threat landscape, one of the most visible and pernicious challenges is malware activity detection and analysis. …”
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COMPARISON OF POROSITY PREDICTION FROM SEISMIC DATA IN THE F3 BLOCK, NETHERLANDS USING MACHINE LEARNING by Urip Nurwijayanto Prabowo, Sudarmaji Sudarmaji, Jarot Setyowiyoto, Sismanto Sismanto

“…Both generators utilize a convolutional neural network-gated recurrent unit network (CNN-GRU). …”
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355 Validation of an artificial intelligence Algorithm for predicting diagnosis-related groups in a community health system by Angela Muhanga, King Jason, Xsolis Mueller, Jennifer L. Sanger, Julie M. Bartlett, Brian N. Manz, James W. Spiten, Matthew J. Overgaard, Shauna M. Wang Hanyin

“…This algorithm, a 1D convolutional neural network, predicts DRGs based on clinical documentation. …”
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Optimizing the automated recognition of individual animals to support population monitoring by Tijmen A. deLorm, Catharine Horswill, Daniella Rabaiotti, Robert M. Ewers, Rosemary J. Groom, Jessica Watermeyer, Rosie Woodroffe

“…The process of selecting suitable images was automated using convolutional neural networks that crop individuals from images, filter out unsuitable images, separate left and right flanks, and remove image backgrounds. …”
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The evaluation model of engineering practice teaching with complex network analytic hierarchy process based on deep learning by Xianlong Han, Xiaohui Chen

“…Then, the real university curriculum content, teaching resources, and virtual student data are organically integrated, and two deep learning algorithms, Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), are introduced. RNN is used to capture time series information, and CNN is used to extract spatial features. …”
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APD-BayNet: Jakarta Air Quality Index Prediction Using Bayesian Optimized Tabnet by Raey Faldo, Satria Mandala, Rina Pudji Astuti, Ary Setijadi Prihatmanto, Mohd Soperi Mohd Zahid

“…Jakarta, the capital of Indonesia, has consistently ranked among the world’s most polluted cities. Various machine learning-based studies have attempted to predict AQI levels in Jakarta, demonstrating promising results. …”
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Differential gray matter correlates and machine learning prediction of abuse and internalizing psychopathology in adolescent females by Sara A. Heyn, Taylor J. Keding, Josh Cisler, Katie McLaughlin, Ryan J. Herringa

“…Abstract Childhood abuse represents one of the most potent risk factors for the development of psychopathology during childhood, accounting for 30–60% of the risk for onset. …”
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High-precision monitoring and prediction of mining area surface subsidence using SBAS-InSAR and CNN-BiGRU-attention model by Mingfei Zhu, Xuexiang Yu, Hao Tan, Jiajia Yuan, Kai Chen, Shicheng Xie, Yuchen Han, Wenjiang Long

“…This study addresses these limitations by proposing a novel mining subsidence monitoring and prediction method based on Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and the Convolutional Neural Network—Bidirectional Gated Recurrent Unit—Attention (CNN-BiGRU-Attention) model. …”
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Deep learning-based automated segmentation and quantification of the dural sac cross-sectional area in lumbar spine MRI by George Ghobrial, Christian Roth

“…Advances in deep learning, particularly convolutional neural networks (CNNs) like the U-Net architecture, have demonstrated significant potential in the analysis of medical images. …”
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Advanced predictive machine and deep learning models for round-ended CFST column by Feng Shen, Ishan Jha, Haytham F. Isleem, Walaa J.K. Almoghayer, Mohammad Khishe, Mohamed Kamel Elshaarawy

“…Using an extensive dataset of 200 CFST stub column tests, this research evaluates three machine learning (ML) models – LightGBM, XGBoost, and CatBoost – and three deep learning (DL) models – Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM). …”
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Durability prediction of sustainable marine concrete under freeze-thaw cycles using multi-objective machine learning models by Aïssa Rezzoug, Ali H. AlAteah, Sadiq Alinsaif, Sahar A. Mostafa

“…Four machine learning techniques were utilized: a convolutional neural network (CNN), a genetic algorithm with optimized artificial neural network (GA-ANN), an adaptive neuro-fuzzy inference system (ANFIS), and multi-objective optimization (MOO). …”
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Continuous Estimation of Hand Kinematics From Electromyographic Signals Based on Power-and Time-Efficient Transformer Deep Learning Network by Chuang Lin, Chunxiao Zhao, Jianhua Zhang, Chen Chen, Ning Jiang, Dario Farina, Weiyu Guo

“…RNN series models, Convolution series models, and Transformer series models were used as reference models for comparison. …”
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DOA Estimation by Feature Extraction Based on Parallel Deep Neural Networks and MRMR Feature Selection Algorithm by Ashwaq Neaman Hassan Al-Tameemi, Mahmood Mohassel Feghhi, Behzad Mozaffari Tazehkand

“…In parallel, the proposed model extracts spatial and temporal features using a convolution neural network (CNN) and long short-term memory (LSTM). …”
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Small Target Detection Algorithm for UAV Aerial Images Based on Improved YOLOv7-tiny by ZHANG Guanghua, LI Congfa, LI Gangying, LU Weidang

“…Based on the YOLOv7-tiny network, the LeakyReLU activation function in the convolution block CBL is replaced by the SiLU activation function. …”
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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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Dual attention mechanisms with patch-level significance embedding for ischemic stroke classification in brain CT images by Mahesh Anil Inamdar, Anjan Gudigar, U. Raghavendra, Massimo Salvi, Nithin Raj, J. Pooja, Ajay Hegde, Girish R. Menon, U. Rajendra Acharya

“…Stroke is currently a major contributor to disability and mortality across the globe, with ischemic stroke being the most predominant subtype. Accurate and timely diagnosis is critical for effective treatment. …”
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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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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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Land Surface Temperature Super-Resolution With a Scale-Invariance-Free Neural Approach: Application to MODIS by Romuald Ait-Bachir, Carlos Granero-Belinchon, Aurelie Michel, Julien Michel, Xavier Briottet, Lucas Drumetz

“…The main contribution of this work is the introduction of a Scale-Invariance-Free approach for training neural network (NN) models, and the implementation of two NN models, called Scale-Invariance-Free Convolutional Neural Network for Super-Resolution (SIF-CNN-SR) for the super-resolution of MODIS LST products. …”
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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

“…The distance to fault is measured using the fault probability volume estimated by a pre-trained convolutional neural network (CNN). We evaluate the effectiveness of this data-driven approach employing two tree-ensemble models, eXtreme Gradient Boosting (XGBoost) and Random Forest, to estimate the volumetric fracture intensity (P32) in the wells. …”
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