Human activity recognition: an approach 2D CNN-LSTM to sequential image representation and processing of inertial sensor data by Wallace Camacho Carlos, Alessandro Copetti, Luciano Bertini, Leonard Barreto Moreira, Otávio de Souza Martins Gomes

“…This approach facilitated the effective exploration of data input and neural network architectures. By utilizing the recent history of movements as input for the models, this study evaluated the impact of this methodology on HAR using two adapted architectures: 2D convolutional neural networks combined with long short-term memory layers (2D CNN-LSTM) and standalone 2D convolutional neural networks (2D CNN). …”
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SVDD: SAR Vehicle Dataset Construction and Detection by Dan Gao, Xiaofang Wu, Zhijin Wen, Yue Xu, Zhengchao Chen

“…With the advent of high-quality SAR images and the rapid development of computing technology, the object detection algorithms based on convolution neural network have attracted a lot of attention in the field of SAR object detection. …”
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A Node-Regulated Deflection Routing Framework for Contention Minimization by Bakhe Nleya, Andrew Mutsvangwa

“…This is by way of regulated deflection routing (rDr) in which neural network agents are utilized in reinforcing the deflection route choices at core nodes. …”
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A Correlation Analysis-Based Structural Load Estimation Method for RC Beams Using Machine Vision and Numerical Simulation by Chun Zhang, Yinjie Zhao, Guangyu Wu, Han Wu, Hongli Ding, Jian Yu, Ruoqing Wan

“…Subsequently, a deep neural network (DNN) is trained as a FEM surrogate model to quickly predict the structural strain response by considering material uncertainties. …”
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Forward model emulator for atmospheric radiative transfer using Gaussian processes and cross validation by O. Lamminpää, J. Susiluoto, J. Hobbs, J. McDuffie, A. Braverman, H. Owhadi

“…In contrast with artificial neural network (ANN)-based methods, it is interpretable, and its efficiency is based on learning a kernel in an engineered and expressive family of kernels.…”
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Neural-field-based image reconstruction for bioluminescence tomography by Xuanxuan Zhang, Xu Cao, Jiulou Zhang, Lin Zhang, Guanglei Zhang

“…The proposed NF-based method establishes a transformation between the coordinate of an arbitrary spatial point and the source value of the point with a relatively light-weight multilayer perceptron, which has remarkable computational efficiency. Another simple neural network composed of two fully connected layers and a 1D convolutional layer is used to generate the neural features. …”
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Application of deep learning for technological parameter optimization of laser shock peening of Ti-6Al-4V alloy by Mikhail Verezhak, Aleksei Vshivkov, Elena Gachegova, Maria Bartolomei, Alexander Mayer, Sathya Swaroop

“…The results of research on determination of the optimal configuration of the deep learning model showed that when using sinusoidal activation function of the neural network with 4 hidden layers and the number of neurons 10, the best level of accuracy in solving the problem is achieved. …”
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Vibration Images-Driven Fault Diagnosis Based on CNN and Transfer Learning of Rolling Bearing under Strong Noise by Hongwei Fan, Ceyi Xue, Xuhui Zhang, Xiangang Cao, Shuoqi Gao, Sijie Shao

“…In this paper, aiming at the vibration image samples of rolling bearing affected by strong noise, the convolutional neural network- (CNN-) and transfer learning- (TL-) based fault diagnosis method is proposed. …”
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Implementation and Testing of V2I Communication Strategies for Emergency Vehicle Priority and Pedestrian Safety in Urban Environments by Federica Oliva, Enrico Landolfi, Giovanni Salzillo, Alfredo Massa, Simone Mario D’Onghia, Alfredo Troiano

“…The second scenario enhances pedestrian safety by alerting drivers, through the same application, about the presence of pedestrians detected at crosswalks by a traffic sensor equipped with neural network capabilities. Both scenarios were tested at two distinct intelligent intersections in Lioni, Avellino, Italy, and demonstrated notable effectiveness. …”
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Deep learning-enhanced defects detection for printed circuit boards by Van-Truong Nguyen, Xuan-Thuc Kieu, Duc-Tuan Chu, Xiem HoangVan, Phan Xuan Tan, Tuyen Ngoc Le

“…., a type of convolutional neural network (CNN)) model. The proposed algorithm is tested in three different lighting conditions: low light, normal light, and high light conditions. …”
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Design of a Novel Fractional Whale Optimization-Enhanced Support Vector Regression (FWOA-SVR) Model for Accurate Solar Energy Forecasting by Abdul Wadood, Hani Albalawi, Aadel Mohammed Alatwi, Hafeez Anwar, Tariq Ali

“…The FWOA-SVR model is comprehensively evaluated against traditional SVR, Long Short-Term Memory (LSTM), and Backpropagation Neural Network (BPNN) models using training, validation, and testing datasets. …”
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Speed Distribution Prediction of Freight Vehicles on Mountainous Freeway Using Deep Learning Methods by Yuren Chen, Yu Chen, Bo Yu

“…Meanwhile, the models were mostly developed based on the regression method, which is inconsistent with natural driving process. Recurrent neural network (RNN) is a distinctive type of deep learning method to capture the temporary dependency in behavioral research. …”
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Utilizing MRAMs With Low Resistance and Limited Dynamic Range for Efficient MAC Accelerator by Sateesh, Kaustubh Chakarwar, Shubham Sahay

“…Spintronics based magnetic memory devices can emulate synaptic behavior efficiently and are hailed as one of the most promising candidates for realizing compact and ultra-energy efficient neural network accelerators. Although ultra-dense magnetic memories with multi-bit capability (MLC) were proposed recently, their application in hybrid CMOS-non-volatile memory accelerators is limited due to their low dynamic range (memory window) and high cell currents (ON/OFF-state resistance in ∼kΩ). …”
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Comparison of In Silico Tools for Splice-Altering Variant Prediction Using Established Spliceogenic Variants: An End-User’s Point of View by Woori Jang, Joonhong Park, Hyojin Chae, Myungshin Kim

“…Therefore, we evaluated the performance of 8 in silico tools (Splice Site Finder, MaxEntScan, Splice-site prediction by neural network, GeneSplicer, Human Splicing Finder, SpliceAI, Splicing Predictions in Consensus Elements, and SpliceRover) using 114 NF1 spliceogenic variants, experimentally validated at the mRNA level. …”
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Solar Irradiance Prediction for Zaria Town Using Different Machine Learning Models by Ibrahim Abdulwahab, Sulaiman Haruna Sulaiman, Umar Musa, Ibrahim Abdullahi Shehu, Abdullahi Kakumi Musa, Ismaila Mahmud, Mohammed Musa, Abdullahi Abubakar, Abdulrahman Olaniyan

“…Four machine learning models (artificial neural network (ANN), decision tree (DT), random forest (RF), and gradient boost tree (GBT).) were used to predict and compare actual and anticipated solar radiation values. …”
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Nonlinear memristive computational spectrometer by Xin Li, Jie Wang, Feilong Yu, Jin Chen, Xiaoshuang Chen, Wei Lu, Guanhai Li

“…Additionally, we integrate this dynamic modulation with a specialized nonlinear neural network tailored to address the memristor’s inherent nonlinear photoresponse. …”
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Multimodal Adaptive Identity-Recognition Algorithm Fused with Gait Perception by Changjie Wang, Zhihua Li, Benjamin Sarpong

“…First, in combination with the collected gait information of individuals from triaxial accelerometers on smartphones, the collected information is preprocessed, and multimodal fusion is used with the existing standard datasets to yield a multimodal synthetic dataset; then, with the multimodal characteristics of the collected biological gait information, a Convolutional Neural Network based Gait Recognition (CNN-GR) model and the related scheme for the multimodal features are developed; at last, regarding the proposed CNN-GR model and scheme, a unimodal gait feature identity single-gait feature identification algorithm and a multimodal gait feature fusion identity multimodal gait information algorithm are proposed. …”
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Adaptive algorithms for change point detection in financial time series by Alexander Musaev, Dmitry Grigoriev, Maxim Kolosov

“…The work highlights the potential for future advancements in neural network applications and multi-expert decision systems, further enhancing predictive accuracy in volatile environments.…”
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An Improved Deep Learning Network Structure for Multitask Text Implication Translation Character Recognition by Xiaoli Ma, Hongyan Xu, Xiaoqian Zhang, Haoyong Wang

“…The coarse filter is based on some simple morphological features and stroke width features, and the fine filter is trained by a two-recognition convolutional neural network. The remaining character candidate regions are merged into horizontal or multidirectional character strings through the graph model. …”
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Winter Wheat Yield Prediction Using Satellite Remote Sensing Data and Deep Learning Models by Hongkun Fu, Jian Lu, Jian Li, Wenlong Zou, Xuhui Tang, Xiangyu Ning, Yue Sun

“…By adjusting the key parameters of the Convolutional Neural Network (CNN) with IGWO, the prediction accuracy is significantly enhanced. …”
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