High-resolution hybrid TDM-CDM MIMO automotive radar by Zakaria Benyahia, Mostafa Hefnawi, Mohamed Aboulfatah, Hassan Abdelmounim, Jamal Zbitou

“…On the other hand, the DL-based scheme utilizes the SqueezeNet deep convolutional neural network (DCNN), which treats the angle, range, and Doppler estimations of the extracted targets as a multi-label classification problem. …”
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Temperature dynamics and mechanical properties analysis of carbon fiber epoxy composites radiated by nuclear explosion simulated light source by Lin Yuan, Jun Li, Boyu Wang, Xin Zhang, Weijie Zhu, Yang Liu, Haiyang Zhang, Xiaoxiang Han

“…This investigation employed an artificial light source to replicate the effects of nuclear explosion radiation and utilized a physical information neural network (PINN) to examine the temperature evolution and corresponding changes in the mechanical properties of carbon fiber/epoxy composites (CFEC). …”
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Interactive trajectory prediction for autonomous driving based on Transformer by R. Xu, J. Li, S. Zhang, L. Li, H. Li, G. Ren, X. Tang

“…The rationale underlying this approach lies in its seamless fusion of scene context modeling and multi-modal prediction within a neural network architecture. At the heart of this innovative framework resides the multi-headed attention mechanism, ingeniously deployed in both the agent attention layer and the scene attention layer. …”
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Unfolder: fast localization and image rectification of a document with a crease from folding in half by A.M. Ershov, D.V. Tropin, E.E. Limonova, D.P. Nikolaev, V.V. Arlazarov

“…The Unfolder algorithm allowed for a recognition error rate of 0.33, which is better than the advanced neural network methods DocTr (0.44) and DewarpNet (0.57). …”
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Fault Diagnosis of Gearbox in Multiple Conditions Based on Fine-Grained Classification CNN Algorithm by Pengcheng Jiang, Hua Cong, Jing Wang, Dongsheng Zhang

“…The use of the convolutional neural network for fault diagnosis has been a common method of research in recent years. …”
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Fast and low‐power leading‐one detectors for energy‐efficient logarithmic computing by Mohammad Saeed Ansari, Shyama Gandhi, Bruce F. Cockburn, Jie Han

“…The Mitchell logarithmic multiplier and a neural network are considered to further illustrate the practicality of the proposed designs.…”
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A multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification by Cheng-Jiang Wei, Yan Tang, Yang-Bai Sun, Tie-Long Yang, Cheng Yan, Hui Liu, Jun Liu, Jing-Ning Huang, Ming-Han Wang, Zhen-Wei Yao, Ji-Long Yang, Zhi-Chao Wang, Qing-Feng Li

“…To address privacy concerns, we utilized a lightweight deep neural network suitable for hospital deployment. The final model achieved an accuracy of 85.71% for MPNST diagnosis in the validation cohort and 84.75% accuracy in the independent test set, outperforming another classic two-step model. …”
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Segmentation Algorithm of Magnetic Resonance Imaging Glioma under Fully Convolutional Densely Connected Convolutional Networks by Jie Dong, Yueying Zhang, Yun Meng, Tingxiao Yang, Wei Ma, Huixin Wu

“…This work focused on the application value of magnetic resonance imaging (MRI) image segmentation algorithm based on fully convolutional DenseNet neural network (FCDNN) in glioma diagnosis. In this work, based on the fully convolutional DenseNet algorithm, a new MRI image automatic semantic segmentation method cerebral gliomas semantic segmentation network (CGSSNet) was established and was applied to glioma MRI image segmentation by using the BraTS public dataset as research data. …”
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Urban Traffic Flow Forecasting Based on Graph Structure Learning by Guangyu Huo, Yong Zhang, Yimei Lv, Hao Ren, Baocai Yin

“…At the same time, the temporal convolution network captures the temporal correlation between a single time series. The graph neural network uses the graph for forecasting. Our model no longer relies on accurate graph priors and achieves better forecasting results than previous work. …”
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Real-Time Quality Monitoring and Anomaly Detection for Vision Sensors in Connected and Autonomous Vehicles by Elena Politi, Charalampos Davalas, Christos Chronis, George Dimitrakopoulos, Dimitrios Michail, Iraklis Varlamis

“…On this basis we adopt a two-stage approach to validate the performance of the proposed methods against a baseline Convolutional Neural Network (CNN) in a controlled low-criticality environment, as well as in more complex real-world scenarios. …”
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Collaborative forecasting management model for multi‐energy microgrid considering load response characterization by Huiyu Bao, Yi Sun, Jie Peng, Xiaorui Qian, Peng Wu

“…It proposes multiple iterations of data, fits the dynamic environment of MEMG by continuously improving the long short‐term memory (LSTM) neural network based on knowledge distillation (KD) architecture, and then optimizes the MEMG state space by considering the knowledge of load response characteristics, Furthermore, it combines multi‐agent deep deterministic policy gradient (MADDPG) with horizontal federated (hF) learning to co‐train multi‐MEMG, addressing the issues of training efficiency during co‐training. …”
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Intelligent Classification of Stable and Unstable Slope Conditions Based on Landslide Movement by Long Tsang, Ali Ghorbani, Seyed Mohammad Hossein Khatami, Dmitrii Ulrikh

“…Three models of Tree, Adaboost and artificial neural network (ANN) were developed for classification into two categories, stable and unstable. …”
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Building extraction from unmanned aerial vehicle imagery using Mask-RCNN (case study: Institut Teknologi Sepuluh Nopember, Surabaya) by Ramadhani Anisa, Alya Nurul Fitri

“…The Mask Region-based Convolutional neural network (Mask R-CNN) has shown recent improvements in object detection and extraction for updating data, which are superior to other methods. …”
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Memory-driven deep-reinforcement learning for autonomous robot navigation in partially observable environments by Estrella Montero, Nabih Pico, Mitra Ghergherehchi, Ho Seung Song

“…The proposed method takes the relative states of humans within a limited FoV and sensor range as input into the neural network. The model employs a bidirectional gated recurrent unit as a temporal function to strategically incorporate the previous context of input sequences and facilitate the assimilation of the observations. …”
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A data Mining Approach using CNN and LSTM to Predict Divorce before Marriage by Touba Torabipour, Safieh Siadat, Hosein Taghavi

“…In the current work, we proposed a method to predict divorce by combining a convolutional neural network (CNN) and long short-term memory (LSTM). …”
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Credit Card Fraud Detection through Parenclitic Network Analysis by Massimiliano Zanin, Miguel Romance, Santiago Moral, Regino Criado

“…We show how the inclusion of features extracted from the network data representation improves the score obtained by a standard, neural network-based classification algorithm and additionally how this combined approach can outperform a commercial fraud detection system in specific operation niches. …”
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Deteksi Dan Klasifikasi Hama Potato Beetle Pada Tanaman Kentang Menggunakan YOLOV8 by Daniel Geoffrey Manurung, Mohammad Ryan Pinasthika, Muhammad Azka Obila Vasya, Rania Aprilia Dwi Setya Putri, Agustinus Parasian Tampubolon, Rakan Fadhil Prayata, Septia Khoirin Nisa, Novanto Yudistira

“…Metode yang digunakan untuk mendeteksi dan mengklasifikasikan potato beetle adalah Convolutional Neural Network (CNN) yang merupakan algoritma jaringan syaraf tiruan yang efektif dalam pengolahan citra. …”
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Prediksi Kesiapan Sekolah Menggunakan Machine Learning Berbasis Kombinasi Adam dan Nesterov Momentum by Indah Mustika Rahayu, Ahmad Yusuf, Mujib Ridwan

“…Penelitian menggunakan algoritma Artificial Neural Network dengan metode optimasi kombinasi Adam dan Nesterov Momentum. …”
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Cloud-Based Framework for COVID-19 Detection through Feature Fusion with Bootstrap Aggregated Extreme Learning Machine by Amjad Rehman Khan, Tanzila Saba, Tariq Sadad, Seng-phil Hong

“…An extreme learning machine (ELM) is a neural network modification with a high capability for pattern recognition and classification problems for COVID-19 detection. …”
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A single flow detection enabled method for DDoS attacks in IoT based on traffic feature reconstruction and mapping by Lixia XIE, Bingdi YUAN, Hongyu YANG, Ze HU, Xiang CHENG, Liang ZHANG

“…To address the slow response time of existing detection modules to Internet of things (IoT) distributed denial of service (DDoS) attacks, their low feature differentiation, and poor detection performance, a single flow detection enabled method based on traffic feature reconstruction and mapping (SFDTFRM) was proposed.Firstly, SFDTFRM employed a queue to store previously arrived flow based on the first in, first out rule.Secondly, to address the issue of similarity between normal communication traffic of IoT devices and DDoS attack traffic, a multidimensional reconstruction neural network model more lightweight compared to the baseline model and a function mapping method were proposed.The modified model loss function was utilized to reconstruct the quantitative feature matrix of the queue according to the corresponding index, and transformed into a mapping feature matrix through the function mapping method, enhancing the differences between different types of traffic, including normal communication traffic of IoT devices and DDoS attack traffic.Finally, the frequency information was extracted using a text convolutional network and information entropy calculation and the machine learning classifier was employed for DDoS attack traffic detection.The experimental results on two benchmark datasets show that SFDTFRM can effectively detect different DDoS attacks, and the average metrics value of SFDTFRM is a maximum of 12.01% higher than other existing methods.…”
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