A Cognitive Radio Spectrum Sensing Method for an OFDM Signal Based on Deep Learning and Cycle Spectrum by Guangliang Pan, Jun Li, Fei Lin

“…In this paper, we propose a novel spectrum sensing method based on deep learning and cycle spectrum, which applies the advantage of the convolutional neural network (CNN) in an image to the spectrum sensing of an orthogonal frequency division multiplex (OFDM) signal. …”
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Towards Transparent AI in Medicine: ECG-Based Arrhythmia Detection with Explainable Deep Learning by Oleksii Kovalchuk, Oleksandr Barmak, Pavlo Radiuk, Liliana Klymenko, Iurii Krak

“…Second, we proposed an arrhythmia classification method utilizing a modified convolutional neural network (CNN) architecture with additional convolutional and batch normalization layers. …”
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Prediction of Gas Chromatography-Mass Spectrometry Retention Times of Pesticide Residues by Chemometrics Methods by Elaheh Konoz, Amir H. M. Sarrafi, Alireza Feizbakhsh, Zahra Dashtbozorgi

“…A 6-7-1 back propagation artificial neural network (ANN) was used to improve the accuracy of the constructed model. …”
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Deciphering Design of Aggregation‐Induced Emission Materials by Data Interpretation by Junyi Gong, Ziwei Deng, Huilin Xie, Zijie Qiu, Zheng Zhao, Ben Zhong Tang

“…Furthermore, a conditional variational autoencoder and integrated gradient analysis are employed to examine the trained neural network model, thereby gaining insights into the relationship between the structural features encapsulated in the fingerprints and the macroscopic photophysical properties. …”
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MCF-DTI: Multi-Scale Convolutional Local–Global Feature Fusion for Drug–Target Interaction Prediction by Jihong Wang, Ruijia He, Xiaodan Wang, Hongjian Li, Yulei Lu

“…The model utilizes the SMILES representation of drugs and the sequence features of targets, employing a multi-scale convolutional neural network (MSCNN) with parallel shared-weight modules to extract features from the drug side. …”
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Real‐time recognition of human motions using multidimensional features in ultrawideband biological radar by Jinxiao Zhong, Liangnian Jin, Qiang Mao

“…A multidimensional features long short‐term memory (LSTM) neural network model is presented using multibranch network structure and high‐dimensional radar feature fusion, which can recognise motions of human in real time, even in the presence of occlusions. …”
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Deep-learning-assisted reconfigurable metasurface antenna for real-time holographic beam steering by Ma Hyunjun, Kim Jin-Soo, Choe Jong-Ho, Park Q-Han

“…The scattering equation from Born approximation is used as the decoder in training the neural network, and analytic Green’s function calculation is used to check the validity of Born approximation. …”
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Thermal Heating in ReRAM Crossbar Arrays: Challenges and Solutions by Kamilya Smagulova, Mohammed E. Fouda, Ahmed Eltawil

“…In addition, it reviews the available solutions designed to mitigate the impact of these challenges, including emerging temperature-resilient Deep Neural Network (DNN) training methods. Our work also provides a summary of the techniques and their advantages and limitations.…”
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Modulation of empathic abilities by the interplay between estrogen receptors and arginine vasopressin by Rui Du, Ting Liang, Guofang Lu

“…ERα and ERβ, pivotal in estrogen regulation, influence neurotransmitter dynamics and neural network activities, crucial for empathic development. …”
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Traffic Status Prediction of Arterial Roads Based on the Deep Recurrent Q-Learning by Wei Hao, Donglei Rong, Kefu Yi, Qiang Zeng, Zhibo Gao, Wenguang Wu, Chongfeng Wei, Biljana Scepanovic

“…The research results show that the prediction of the traffic delay index is within a reasonable interval, and it is significantly better than traditional prediction methods such as the LSTM, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), exponential smoothing method, and Back Propagation (BP) neural network, which shows that the model proposed in this paper has the feasibility of application.…”
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Evaluation of Cruise Ship Supply Logistics Service Providers with ANP-RBF by Liling Huang, Yong Tan, Xu Guan

“…We first establish an evaluation decision model for the interdependence and feedback relationship between the criteria by using analytic network process (ANP) for weight definition of each index; then, we use Super Decisions software to simulate the results, combine RBF neural network training and validation, and extract implicit knowledge and laws. …”
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Online multi‐object tracking based on time and frequency domain features by Mahbubeh Nazarloo, Meisam Yadollahzadeh‐Tabari, Homayun Motameni

“…The features are given for learning vector quantization, which is a supervised artificial neural network (ANN). It is used to classify the dataset. …”
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Forecasting of Energy Production for Photovoltaic Systems Based on ARIMA and ANN Advanced Models by Laurentiu Fara, Alexandru Diaconu, Dan Craciunescu, Silvian Fara

“…This article is dedicated to two forecasting models: (1) ARIMA (Autoregressive Integrated Moving Average) statistical approach to time series forecasting, using measured historical data, and (2) ANN (Artificial Neural Network) using machine learning techniques. The main contributions of the authors could be synthetized as follows: (1) analysis and discussion of the experimental and simulated results regarding solar radiation forecast, as well as energy production prediction and forecasting based on ARIMA and ANN models for two case studies: (a) laboratory BIPV system developed at the Polytechnic University of Bucharest and (b) large PV park placed in a specific site of the south of Romania. …”
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Fault Diagnosis of Power Transformers With Membership Degree by Enwen Li, Linong Wang, Bin Song

“…Though a high correct rate is reported with intelligent methods as artificial neural network, support vector machine, and so on, these methods are usually too complicated to be implemented practically on a wide range. …”
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High-integrated photonic tensor core utilizing high-dimensional lightwave and microwave multidomain multiplexing by Xiangyan Meng, Nuannuan Shi, Guojie Zhang, Junshen Li, Ye Jin, Shiyou Sun, Yichen Shen, Wei Li, Ninghua Zhu, Ming Li

“…Abstract The burgeoning volume of parameters in artificial neural network models has posed substantial challenges to conventional tensor computing hardware. …”
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A Robust License Plate Detection and Character Recognition Algorithm Based on a Combined Feature Extraction Model and BPNN by Fei Xie, Ming Zhang, Jing Zhao, Jiquan Yang, Yijian Liu, Xinyue Yuan

“…This paper has proposed a novel license plate detection and character recognition algorithm based on a combined feature extraction model and BPNN (Backpropagation Neural Network) which is adaptable in weak illumination and complicated backgrounds. …”
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Scheme Analysis for Enhancing Autonomous Driving Based on Computer Vision by Chen Xinyi, Luo Binbin, Xu Ziyue

“…Thesis adopted both literature review and technical analysis, focusing on recent developments in key technologies such as image processing, hybrid convolutional neural network (CNN)-transformer models, object detection, and multi-sensor fusion. …”
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A Prediction Model of Relativistic Electrons at Geostationary Orbit Using the EMD‐LSTM Network and Geomagnetic Indices by H. Zhang, H. R. Xu, G. S. Peng, Y. D. Qian, X. X. Zhang, G. L. Yang, C. Shen, Z. Li, J. W. Yang, Z. Q. Wang, F. He, C. L. Gu, M. B. Zhu

“…Abstract In this study, the Empirical Mode Decomposition algorithm (EMD) and the Long Short Term Memory neural network (LSTM) are combined into an EMD‐LSTM model, to predict the variation of the >2 MeV electron fluxes 1 day ahead. …”
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Oculomotor Plant Mathematical Model in Kalman Filter Form With Peak Velocity-Based Neural Pulse for Continuous Gaze Prediction by Dmytro Katrychuk, Dillon J. Lohr, Oleg V. Komogortsev

“…We additionally extend the prior work by evaluating the proposed model on the largest to date pool of 322 subjects against the naive zero displacement baseline and a long short-term memory (LSTM) neural network.…”
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Deep learning and explainable AI for classification of potato leaf diseases by Sarah M. Alhammad, Doaa Sami Khafaga, Walaa M. El-hady, Farid M. Samy, Khalid M. Hosny

“…Transfer learning enables the model to benefit from pre-trained neural network architectures and weights, enhancing its ability to learn meaningful representations from limited labeled data. …”
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