Online Purchase Behavior Prediction Model Based on Recurrent Neural Network and Naive Bayes by Chaohui Zhang, Jiyuan Liu, Shichen Zhang

“…At the same time, the existing online purchase behavior prediction models lack consideration of time series features. This paper combines the Recurrent Neural Network, which is more suitable for the commodity recommendation scenario of the e-commerce platform, with Naive Bayes, which is simple in logic and efficient in operation and constructs the online purchase behavior prediction model RNN-NB, which can consider the features of time series. …”
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SCENet: Small Kernel Convolution with Effective Receptive Field Network for Brain Tumor Segmentation by Bin Guo, Ning Cao, Ruihao Zhang, Peng Yang

“…An ASPP module is introduced to the bottleneck layer to enlarge the receptive field and can capture multi-scale detailed features. Furthermore, a large number of experiments were performed to evaluate the performance of our model on the BraTS2021 dataset. …”
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Empowering Beyond 5G Networks: An Experimental Assessment of Zero-Touch Management and Orchestration by Sergio Barrachina-Munoz, Farhad Rezazadeh, Luis Blanco, Slawomir Kuklinski, Engin Zeydan, Ashima Chawla, Lanfranco Zanzi, Francesco Devoti, Vasiliki Vlahodimitropoulou, Ioannis Chochliouros, Anne-Marie Bosneag, Sihem Cherrared, Luca Vettori, Josep Mangues-Bafalluy

“…For the first time, we evaluate its practicality in a 5G cloud-native testbed through a virtual reality (VR) streaming use case, supported by solutions such as federated learning-based CPU forecasting, anomaly detection, and deep reinforcement learning (DRL) for radio access network (RAN) optimization. …”
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The Challenge of Deep Learning for the Prevention and Automatic Diagnosis of Breast Cancer: A Systematic Review by Jhelly-Reynaluz Pérez-Núñez, Ciro Rodríguez, Luis-Javier Vásquez-Serpa, Carlos Navarro

“…Objectives: This review aims to evaluate several convolutional neural network (CNN) models applied to breast cancer detection, to identify and categorize CNN variants in recent studies, and to analyze their specific strengths, limitations, and challenges. …”
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An efficient hybrid Hopfield convolutional neural network for detecting spam bots in Twitter platform by A.V. Santhosh Kumar, N. Suresh Kumar, R. Kanniga Devi

“…This paper proposes a novel spam bot detection system for the Twitter social network that combines profile and tweet-based features. …”
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Fusion of Multimodal Audio Data for Enhanced Speaker Identification Using Kolmogorov-Arnold Networks by Aryaman Tamotia, Dhruv Shantu Karmokar, Rethi Komal, K. Khadar Nawas, A. Shahina, A. Nayeemulla Khan

“…In this paper, we discuss an efficient version of Kolmogorov-Arnold Network for audio-based speaker identification using three different types of audio inputs: bone conduction, air conduction, and throat microphone recordings, alongside evaluation on the publicly available LibriSpeech dataset to enhance reproducibility. …”
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Perbandingan Random Forest dan Convolutional Neural Network dalam Memprediksi Peralihan Pelanggan by Dewa Adji Kusuma, Atika Ratna Dewi, Andreas Rony Wijaya

“…This study compares Random Forest (RF) and Convolutional Neural Network (CNN) algorithms in predicting customer switching, using Correlation-based Feature Selection (CFS) and Recursive Feature Elimination (RFE) for data partitioning. …”
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Attentional dysfunction arises from right frontocentral and occipital network connectivity in Parkinson's disease by Isobel Timothea French, Kuo-Hsuan Chang, Wei-Kuang Liang, Wen-Sheng Chang, Yen-Shi Lo, Yi-Ru Wang, Mei-Ling Cheng, Norden E. Huang, Hsiu-Chuan Wu, Siew-Na Lim, Chiung-Mei Chen, Chi-Hung Juan

“…Objective: To utilize the novel Holo-Hilbert Spectral Analysis (HHSA) to reveal dynamic EEG features of the Flanker task in PD patients and healthy normal controls for differentiating and elucidating attentional network deficits in patients. …”
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MaskDGNets: Masked-attention guided dynamic graph aggregation network for event extraction. by Guangwei Zhang, Fei Xie, Lei Yu

“…We designed a reconstructed weighted loss function to supervise and adjust each module individually to ensure the optimal feature representation. Finally, the proposed MaskDGNets framework is evaluated on two baseline datasets, DuEE and CCKS2020. …”
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MultiChem: predicting chemical properties using multi-view graph attention network by Heesang Moon, Mina Rho

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Efficient guided inpainting of larger hole missing images based on hierarchical decoding network by Xiucheng Dong, Yaling Ju, Dangcheng Zhang, Bing Hou, Jinqing He

“…In this paper, a novel hierarchical decoding network for image inpainting is proposed. Firstly, the structural priors extracted from the encoding layer are utilized to guide the first decoding layer, while residual blocks are employed to extract deep-level image features. …”
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SVPDSA: Selective View Perception Data Synthesis With Annotations Using Lightweight Diffusion Network by S. Raghavendra, Vijayalakshmi, Vainidhi, S. K. Abhilash, Venu Madhav Nookala, P. V. Arun Kumar, Ramyashree

“…A decoder module is introduced to expand latent code features and generate labeled annotations for tasks such as semantic segmentation, instance segmentation, and depth estimation. …”
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Method for predicting cutter remaining life based on multi-scale cyclic convolutional network by Tao Li, Yinquan Yu, Chaoqiang Huang, Jinwen Yang, Yuanchang Zhong, Yong Hao

“…To extract the features with time-series information to predict the remaining cutter life more effectively, this article proposes a new deep neural network, which is named the multi-scale cyclic convolutional neural network. …”
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Providing a model of purchase intention and customer experience on customer behavior in virtual networks by Kazem Tolooee, Hossein budaghi Khajeh Noubar, Hossein Gharehbiglo, Mojtaba Ramazani

“…AbstractThe purpose of this research is to provide a model of purchase intention and experience of customer on customer behavior in virtual networks. The current research is applicable in terms of purpose, and descriptive-survey in terms of nature and method. …”
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Deep learning for network security: an Attention-CNN-LSTM model for accurate intrusion detection by Abdullah Mujawib Alashjaee

“…Hence, this study proposes a novel hybrid deep learning model, which associates Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), along with a self-attention method to highlight the utmost informative input features. …”
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FQDNet: A Fusion-Enhanced Quad-Head Network for RGB-Infrared Object Detection by Fangzhou Meng, Aoping Hong, Hongying Tang, Guanjun Tong

“…To enhance multi-modal feature fusion, we introduce a Channel Swap SCDown Block (CSSB) for initial feature interaction and a lightweight Spatial Channel Attention Fusion Module (SCAFM) to further refine the integration of complementary RGB-IR features. …”
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Enhancing Anomaly Detection in Attributed Networks Using Proximity Preservation and Advanced Embedding Techniques by Wasim Khan, Mohammad Ishrat, Mohammad Nadeem Ahmed, Shafiqul Abidin, Mohammad Husain, Mohd Izhar, Abu Taha Zamani, Mohammad Rashid Hussain, Arshad Ali

“…A key challenge is maintaining the integrity of both network and node feature structures during the embedding process. …”
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Novel cross-dimensional coarse-fine-grained complementary network for image-text matching by Meizhen Liu, Anis Salwa Mohd Khairuddin, Khairunnisa Hasikin, Weitong Liu

“…This module aggregates local features across different dimensions as well as within the same dimension to form coherent global features, thus preserving the semantic integrity of the information. …”
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Geometry-enhanced graph neural networks accelerate circRNA therapeutic target discovery by Zhen Li, Mingming Qi, Juyuan Huang, Wei Zhang, Xu Tan, Yifan Chen

“…G2CDA introduces torsion-based geometric encoding into the message propagation process of the circRNA-drug network. For each potential association, we construct local simplicial complexes, extract their geometric features, and integrate these features as adaptive weights during message propagation and aggregation. …”
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A novel temporal classification prototype network for few-shot bearing fault detection by Yanfei Liu, Ziang Du, Hao Zheng, Qian Zhang, Cheng Chen, Nana Wu

“…Subsequently, discrete data sample points are transformed into points within the feature space via our Enhanced Temporal Convolutional Network(ETCN). …”
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