Brain-inspired multimodal motion and fine-grained action recognition by Yuening Li, Xiuhua Yang, Changkui Chen

“…These methods struggle particularly with video data containing complex combinations of actions and subtle motion variations.MethodsTypically, they depend on handcrafted feature extractors or simple convolutional neural network (CNN) architectures, which makes effective multimodal fusion challenging. …”
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Multi-Resolution Wavelet-Transformed Image Analysis of Histological Sections of Breast Carcinomas by Hae-Gil Hwang, Hyun-Ju Choi, Byeong-Il Lee, Hye-Kyoung Yoon, Sang-Hee Nam, Heung-Kook Choi

“…In the classification step, we applied two types of classifiers to the extracted features, namely a statistics-based multivariate (discriminant) analysis and a neural network. Using features from second-level Haar transform wavelet images in combination with discriminant analysis, we obtained classification accuracies of 96.67 and 87.78% for the training and testing set (90 images each), respectively. …”
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ECGConVT: A Hybrid CNN and Vision Transformer Model for Enhanced 12-Lead ECG Images Classification by Mudassar Khalid, Charnchai Pluempitiwiriyawej, Abdulkadhem A. Abdulkadhem, Imran Afzal, Tien Truong

“…We propose ECGConVT framework that combines Convolutional Neural Network (CNN) module for extracting local features, and Vision Transformer (ViT) module for capturing global features. …”
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Optimized Prediction of Weapon Effectiveness in BVR Air Combat Scenarios Using Enhanced Regression Models by Andre R. Kuroswiski, Annie S. Wu, Angelo Passaro

“…For instance, Lasso regression, a PR method with regularization, achieves results that are 33% better and 2.1 times faster than the best artificial neural network-based solution. Our results challenge common assumptions in the literature about the complexity and feasibility of higher-order PR solutions, suggesting that they can be a compelling alternative for various challenges across domains. …”
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A Tuning Method for the Supplementary Voltage Controller of Dual-Side Grid Forming Converters in Distributed Storage Systems by Angel Perez-Basante, Asier Gildemuro, Ander Ordono, Salvador Ceballos, Eneko Unamuno, Jon Andoni Barrena

“…Real-time estimation of the optimum controller gains by making use of an artificial neural network is proposed. Simulation and experimental results are presented to validate the method.…”
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Quantifying Spatiotemporal Dynamics of Solar Radiation over the Northeast China Based on ACO-BPNN Model and Intensity Analysis by Xiangqian Li, Zhijun Tong, Enliang Guo, Xiaolong Luo

“…In this study, a new backpropagation neural network (BPNN) model optimized with an Ant Colony Optimization (ACO) algorithm was developed to generate the ACO-BPNN model, which had demonstrated superior performance for simulating solar radiation compared to traditional BPNN modelling, for Northeast China. …”
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Small Object Detection with Multiscale Features by Guo X. Hu, Zhong Yang, Lei Hu, Li Huang, Jia M. Han

“…The existing object detection algorithm based on the deep convolution neural network needs to carry out multilevel convolution and pooling operations to the entire image in order to extract a deep semantic features of the image. …”
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Multimodal Data Fusion for Depression Detection Approach by Mariia Nykoniuk, Oleh Basystiuk, Nataliya Shakhovska, Nataliia Melnykova

“…These networks were developed using convolutional neural network (CNN) layers to learn local patterns, a bidirectional LSTM (Bi-LSTM) to process sequences, and a self-attention mechanism to improve focus on key parts of the data. …”
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Asynchronous Wireless Signal Modulation Recognition Based on In-Phase Quadrature Histogram by Xu Zhang, Xi Hui, Pengwu Wan, Tengfei Hui, Xiongfei Li

“…Then, the feature parameters of this 2D image are extracted by radial basis function neural network (RBFNN) to complete the recognition of the modulation mode of the input signal. …”
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Industrial Robot Vibration Anomaly Detection Based on Sliding Window One-Dimensional Convolution Autoencoder by ZhiDan Zhong, Yao Zhao, AoYu Yang, HaoBo Zhang, DongHao Qiao, ZhiHui Zhang

“…First, the convolutional neural network and the autoencoder model are effectively integrated to construct a one-dimensional convolutional autoencoder model. …”
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Predicting the Adsorption Efficiency Using Machine Learning Framework on a Carbon-Activated Nanomaterial by Kalapala Prasad, V. Ravi Kumar, R. Suresh Kumar, A. S. Rajesh, Anjani Kumar Rai, Essam A. Al-Ammar, Saikh Mohammad Wabaidur, Amjad Iqbal, Dawit Kefyalew

“…The present study implements that the effectiveness of PCM adsorption on a carbon-activated nanomaterial was predicted using an artificial neural network, a machine learning technology. As a factor of adsorbent particle size, adsorbent dosage, training time, and starting concentrations, the adsorption capacity for each medicinal ingredient was examined. …”
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An Approach to Integrating Tactical Decision-Making in Industrial Maintenance Balance Scorecards Using Principal Components Analysis and Machine Learning by Néstor Rodríguez-Padial, Marta Marín, Rosario Domingo

“…In the proposed Custom Balance Scorecard design, an exploratory data phase is integrated with another analysis and prediction phase using Principal Component Analysis algorithms and Machine Learning that uses Artificial Neural Network algorithms. This new extension allows better control over the maintenance function of an industrial plant in the medium-term with a yearly horizon taken over monthly intervals which allows the measurement of the indicators of strategic productive areas and the discovery of hidden behavior patterns in work orders. …”
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SQL Injection Detection Based on Lightweight Multi-Head Self-Attention by Rui-Teng Lo, Wen-Jyi Hwang, Tsung-Ming Tai

“…This paper presents a novel neural network model for the detection of Structured Query Language (SQL) injection attacks for web applications. …”
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Variable-Parameter Impedance Control of Manipulator Based on RBFNN and Gradient Descent by Linshen Li, Fan Wang, Huilin Tang, Yanbing Liang

“…To address the computationally intensive nature of current hybrid force/position control methods, a variable-parameter impedance control method for manipulators, utilizing a gradient descent method and Radial Basis Function Neural Network (RBFNN), is proposed. This method employs a position-based impedance control structure that integrates iterative learning control principles with a gradient descent method to dynamically adjust impedance parameters. …”
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Intelligent Fault Diagnosis of Aeroengine Sensors Using Improved Pattern Gradient Spectrum Entropy by Huihui Li, Linfeng Gou, Hua Zheng, Huacong Li

“…A new intelligent fault diagnosis scheme combining improved pattern gradient spectrum entropy (IPGSE) and convolutional neural network (CNN) is proposed in this paper, aiming at the problem of poor fault diagnosis effect and real-time performance when CNN directly processes one-dimensional time series signals of aeroengine. …”
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Photovoltaic Array Fault Diagnosis and Localization Method Based on Modulated Photocurrent and Machine Learning by Yebo Tao, Tingting Yu, Jiayi Yang

“…It is determined that when utilizing a neural network algorithm, the fault identification speed meets measurement requirements (5800 obs/s), and the fault diagnosis accuracy is optimal (97.8%).…”
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Prediction and design optimization of mechanical properties for rubber fertilizer hose reinforced with helically wrapped nylon by Mengfan Wang, Lixin Zhang, Changxin Fu

“…For the first time, the Crested Porcupine Optimizer algorithm was used to improve the Generalized Regression Neural Network (CPO-GRNN) method to establish a surrogate model for predicting the mechanical properties of HWNR hoses, and it was compared with Response Surface Methodology (RSM). …”
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A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images by Jiang Xie, Jinzhu Wei, Huachan Shi, Zhe Lin, Jinsong Lu, Xueqing Zhang, Caifeng Wan

“…In this study, a novel convolutional neural network model with bimodal layer-wise feature fusion module (BLFFM) and temporal hybrid attention module (THAM) is proposed, which uses multistage bimodal ultrasound images as input for early prediction of the efficacy of neoadjuvant chemotherapy in locally advanced breast cancer (LABC) patients. …”
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CNN-Based Pupil Center Detection for Wearable Gaze Estimation System by Warapon Chinsatit, Takeshi Saitoh

“…This paper presents a convolutional neural network- (CNN-) based pupil center detection method for a wearable gaze estimation system using infrared eye images. …”
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Classification of NSCLC subtypes using lung microbiome from resected tissue based on machine learning methods by Pragya Kashyap, Kalbhavi Vadhi Raj, Jyoti Sharma, Naveen Dutt, Pankaj Yadav

“…Next, benchmarking was performed across six different supervised-classification algorithms viz. logistic-regression, naïve-bayes, random-forest, extreme-gradient-boost (XGBoost), k-nearest neighbor, and deep neural network. Noteworthy, XGBoost, with an accuracy of 76.25%, and AUROC (area-under-receiver-operating-characteristic) of 0.81 with 69% specificity and 76% sensitivity, outperform the other five classification algorithms using LDA-transformed features. …”
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