Multisegment Mapping Network for Massive MIMO Detection by Yongzhi Yu, Jianming Wang, Limin Guo

“…This paper proposes a deep neural network for massive MIMO detection, named Multisegment Mapping Network (MsNet). …”
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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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Radar Jamming Recognition: Models, Methods, and Prospects by Zan Wang, Zhengwei Guo, Gaofeng Shu, Ning Li

“…Furthermore, the focus shifts to neural network-based methods, such as shallow neural network methods and deep neural network methods. In particular, restricted sample strategies are also discussed as potential future directions. …”
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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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Robust Face Detection and Identification under Occlusion using MTCNN and RESNET50 by Eiman Wahab, Wajeeha Shafique, Habiba Amir, Sameena Javed, Muhammad Marouf

“…Our project utilizes the power of deep learning model: Residual Network (ResNet50), the form of deep neural network architectures well-suited for the job of features extraction. …”
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2.5D Facial Personality Prediction Based on Deep Learning by Jia Xu, Weijian Tian, Guoyun Lv, Shiya Liu, Yangyu Fan

“…Our experimental results show that the deep neural network trained by large labeled datasets can reliably predict people’s multidimensional personality characteristics through 2.5D static facial contour images, and the prediction accuracy is better than the previous method using 2D images.…”
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Reconstruction of Three-Dimensional Porous Media Using Deep Transfer Learning by Yi Du, Jie Chen, Ting Zhang

“…Hence, a method for reconstructing porous media is presented by applying DTL to extract features from a training image (TI) of porous media to replace the process of scanning a TI for different patterns as in multiple-point statistical methods. The deep neural network is practically used to extract the complex features from the TI of porous media, and then, a reconstructed result can be obtained by transfer learning through copying these features. …”
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Application of Machine Learning in Multi-Directional Model to Follow Solar Energy Using Photo Sensor Matrix by P. Dhanalakshmi, V. Venkatesh, P. S. Ranjit, N. Hemalatha, S. Divyapriya, R. Sandhiya, Sumit Kushwaha, Asmita Marathe, Mekete Asmare Huluka

“…In this paper, we introduce a deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here. …”
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Accurate Recognition and Simulation of 3D Visual Image of Aerobics Movement by Wenhua Fan, Hyun Joo Min

“…A lot of results have been achieved by applying deep neural networks to the 3D visual image recognition of aerobics movements, but there are still many problems to be overcome. …”
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An Enhancement Deep Feature Extraction Method for Bearing Fault Diagnosis Based on Kernel Function and Autoencoder by Fengtao Wang, Bosen Dun, Xiaofei Liu, Yuhang Xue, Hongkun Li, Qingkai Han

“…Subsequently, a deep neural network is constructed with one KAE and multiple AEs to extract inherent features layer by layer. …”
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The Fault Diagnosis of Rolling Bearing Based on Improved Deep Forest by Xiwen Qin, Dingxin Xu, Xiaogang Dong, Xueteng Cui, Siqi Zhang

“…At present, the technology of intelligent identification of bearing mostly relies on deep neural network, which has high requirements for computer equipment and great effort in hyperparameter tuning. …”
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Gradient Enhancement Techniques and Motion Consistency Constraints for Moving Object Segmentation in 3D LiDAR Point Clouds by Fangzhou Tang, Bocheng Zhu, Junren Sun

“…In this paper, we introduce a novel deep neural network designed to enhance the performance of 3D LiDAR point cloud moving object segmentation (MOS) through the integration of image gradient information and the principle of motion consistency. …”
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A Semi-supervised Deep Learning Method for Cervical Cell Classification by Siqi Zhao, Yongjun He, Jian Qin, Zixuan Wang

“…Cervical cell classification is a key technology in the intelligent cervical cancer diagnosis system. Training a deep neural network-based classification model requires a large amount of data. …”
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Application of Improved Naive Bayesian-CNN Classification Algorithm in Sandstorm Prediction in Inner Mongolia by Li Tiancheng, Ren Qing-dao-er-ji, Qiu Ying

“…Convolutional neural network (CNN) is a deep neural network with convolution structure, which can automatically learn features from massive data. …”
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Similarity-Based Summarization of Music Files for Support Vector Machines by Jan Jakubik, Halina Kwaśnicka

“…Recent advancements in the area rely on the use of deep learning, which allows researchers to operate on a low-level description of the music. Deep neural network architectures can learn to build feature representations that summarize music files from data itself, rather than expert knowledge. …”
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DLD: An Optimized Chinese Speech Recognition Model Based on Deep Learning by Hong Lei, Yue Xiao, Yanchun Liang, Dalin Li, Heow Pueh Lee

“…To improve the performance of offline Chinese speech recognition, we propose a hybrid acoustic model of deep convolutional neural network, long short-term memory, and deep neural network (DCNN-LSTM-DNN, DLD). This model utilizes DCNN to reduce frequency variation and adds a batch normalization (BN) layer after its convolutional layer to ensure the stability of data distribution, and then use LSTM to effectively solve the gradient vanishing problem. …”
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A New Preprocessing Method for Diabetes and Biomedical Data Classification by Sarbast CHALO, İbrahim Berkan AYDİLEK

“…We present a method for the identification of diabetes that involves the training of the features of a deep neural network between five and 10 times using the cross-validation training mode. …”
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Vehicle Detection and Tracking Based on Improved YOLOv8 by Yunxiang Liu, Shujun Shen

“…Then we replaced the convolutional kernel with a dual convolutional kernel to construct a lightweight deep neural network. Subsequently, the Focaler-EIoU loss function is introduced to improve the accuracy. …”
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A Time-Aware CNN-Based Personalized Recommender System by Dan Yang, Jing Zhang, Sifeng Wang, XueDong Zhang

“…With the in-depth study and application of deep learning algorithms, deep neural network is gradually used in recommender systems. …”
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Application of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sands by Yuan Feng, Jongwan Eun, Seunghee Kim, Yong-Rak Kim

“…A fully connected deep neural network was utilized for training. This neural network model leverages automatic differentiation to apply the governing equations as constraints, based on the mathematical approximations established by the neural network itself. …”
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