HE-BiDet: A Hardware Efficient Binary Neural Network Accelerator for Object Detection in SAR Images by Dezheng Zhang, Zehan Liang, Rui Cen, Zhihong Yan, Rui Wan, Dong Wang

“…Convolutional Neural Network (CNN)-based Synthetic Aperture Radar (SAR) target detection eliminates manual feature engineering and improves robustness but suffers from high computational costs, hindering on-satellite deployment. …”
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Air quality prediction using stacked bi- long short-term memory and convolutional neural network in India by S Karkuzhali, Thendral Puyalnithi, R Nirmalan

“…This study focuses on advancing air quality prediction in India through the application of cutting-edge deep learning techniques, specifically the Stacked Bidirectional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) architecture. Through meticulous preprocessing - encompassing missing value handling, normalization, and temporal sequencing - the dataset is prepared for the Stacked Bi-LSTM and CNN hybrid model. …”
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Attention-driven multi-model architecture for unbalanced network traffic intrusion detection via extreme gradient boosting by Oluwadamilare Harazeem Abdulganiyu, Taha Ait Tchakoucht, Ahmed El Hilali Alaoui, Yakub Kayode Saheed

“…The effectiveness of our proposed system was evaluated by employing two datasets, one of which is the NSL-KDD, which exhibits a skewed distribution of network traffic favoring the majority class, and CSE-CIC-IDS2018 dataset, where approximately 83 % of the data consists of benign network traffic. …”
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Intercellular Communication Network of CellChat Uncovers Mechanisms of Kidney Fibrosis Based on Single-Cell RNA Sequencing by Lei Lei, Yun-Xiu Xiang, Mao-Lin Luo, Ze-Yu Zhang, Hong-Wei Wu, Chun Tang, Tian-Jiao Cui, Xue-Mei Zhang, Xiao-Hua Wang, Denis Delic, Thomas Klein, Yvonne Liu, Bernhard K. Krämer, Zhi-Hua Zheng, Yong-Ping Lu, Berthold Hocher, Ting Zhu

“…Kidney function was assessed by measuring blood urea nitrogen, serum creatinine, and urinary albumin-to-creatinine ratio. Renal fibrosis was evaluated histologically. Single-cell RNA sequencing (scRNA-seq) was performed to analyze intercellular communication networks and identify alterations in ligand-receptor pairs and signaling pathways involved in fibrosis. …”
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Event-Type Identification in Power Grids Using a Spectral Correlation Function-Aided Convolutional Neural Network by Ozgur Alaca, Ali Riza Ekti, Jhi-Young Joo, Nils Stenvig

“…This study introduces a novel event-type identification method, utilizing a Spectral Correlation Function (SCF)-aided Convolutional Neural Network (CNN). The proposed method employs a six-stage cascaded structure consisting of: (1) data collection, (2) clipping, (3) augmentation, (4) feature extraction (FE), (5) training, and (6) testing. …”
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Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data by Ke Niu, Jiayang Guo, Yijie Pan, Xin Gao, Xueping Peng, Ning Li, Hailong Li

“…Specifically, a multichannel deep attention neural network (DANN) was proposed by integrating multiple layers of neural networks, attention mechanism, and feature fusion to capture the interrelationships in multimodality data. …”
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Transferability Evaluation in Wi-Fi Intrusion Detection Systems Through Machine Learning and Deep Learning Approaches by Saud Yonbawi, Adil Afzal, Muhammad Yasir, Muhammad Rizwan, Natalia Kryvinska

“…This endeavor unravels a diverse performance landscape, demonstrating that the outstanding performance observed on a particular dataset doesn’t guarantee the transferability of features across dissimilar datasets nestled within different network environments. …”
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Evaluation of Flavor Type of Tobacco Blending Module: A Prediction Model Based on Near-Infrared Spectrum by Lin Wang, Yuhan Guan, Yaohua Zhang

“…Combining the power of XGBoost and deep learning, we constructed a flavor prediction model based on feature variables. The XGBoost model was utilized to extract essential information from the high-dimensional near-infrared spectra, while a convolutional neural network with an attention mechanism was employed to predict the flavor type of the modules. …”
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Evaluating the Performance of a Fake News Model on A Domain-Specific and Heterogeneous Dataset to Improve Detection by Georgina Obuandike, Emmy Danny Ajik, Faith Oluwatosin Echobu

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Intelligent Layout and Optimization of EV Charging Stations: Initial Configuration via Enhanced K-Means and Subsequent Refinement through Integrated GCN by H. Yang, M. Liu, J. Zou, R. Xu, J. Huang, P. Geng

“…Additionally, sensitivity analysis and ablation experiments based on Points of Interest (POI) data are conducted to evaluate the impact of various POI features on the layout of charging stations and to explore the contribution of different model components to classification performance.…”
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StyleGraph: A Heterogeneous Graph Neural Framework for Stylistic and Semantic Rumor Detection on Social Media by Haider Jaffar, Ali Mohades, Mohammad Ebrahim Shiri

“…StyleGraph was evaluated using Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and Cluster GCNs on the PHEME dataset under both 5-event and 9-event settings. …”
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Hybrid of DSR-GAN and CNN for Alzheimer disease detection based on MRI images by Sarah Oraby, Ahmed Emran, Basel El-Saghir, Saeed Mohsen

“…Abstract In this paper, we propose a deep super-resolution generative adversarial network (DSR-GAN) combined with a convolutional neural network (CNN) model designed to classify four stages of Alzheimer’s disease (AD): Mild Dementia (MD), Moderate Dementia (MOD), Non-Demented (ND), and Very Mild Dementia (VMD). …”
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FD²-YOLO: A Frequency-Domain Dual-Stream Network Based on YOLO for Crack Detection by Junwen Zhu, Jinbao Sheng, Qian Cai

“…Firstly, the model employs a dual backbone architecture, integrating edge and texture features in the frequency domain with semantic features in the spatial domain, to enhance the extraction of crack-related features. …”
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Differences in the relationships between interoceptive sensibility and self-objectification in women with high and low body dissatisfaction: A network analysis. by Akansha Mahesh Naraindas, Amy McInerney, Sonya Deschênes, Sarah Maeve Cooney

“…Self-objectification not only affects women's body image but is associated with diminished subjective awareness of, and attention towards, internal bodily states (i.e., interoceptive sensibility). This study uses network analysis and network comparison to investigate the relationships between features of interoceptive sensibility and self-objectification in women with high (N = 348) and low (N = 354) body dissatisfaction. …”
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Development and psychometric evaluation of the fear of medical imaging radiation scale (FOMIRS): insights from multimethod analysis by Lin-sen Feng, Si-rong She, Yuan-yuan Zhang, Jia-qi Xie, Zheng-jiao Dong, Ai Tang, Yin-zhu Li, Xiao-qian Wu, Qing Yang, Hao-yu Wang, San-bin Wang

“…Logistic regression analysis, network analysis, and structural equation modeling were employed to examine the relationships between the FOMIRS and related variables. …”
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Fusion of Sentiment and Market Signals for Bitcoin Forecasting: A SentiStack Network Based on a Stacking LSTM Architecture by Zhizhou Zhang, Changle Jiang, Meiqi Lu

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Network-based predictive models for artificial intelligence: an interpretable application of machine learning techniques in the assessment of depression in stroke patients by Wenwei Zuo, Xuelian Yang

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Method for compensating multi-time measurement data of distribution network based on alternating minimization matrix completion combined with VMD-ARIMA-LSTM by Xiaodan Yu, Ruijia Jiang, Xiaolong Jin, Hongjie Jia, Yunfei Mu, Wei Wei, Wanxin Tang

“…The results of state estimation are obtained through systematic component reconstruction. Comprehensive evaluations using IEEE 33-bus distribution network and actual distribution system measurement datasets demonstrate the framework's effectiveness in both multi-timescale data assimilation and state estimation accuracy under limited observability conditions.…”
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MGFNet: An MLP-dominated gated fusion network for semantic segmentation of high-resolution multi-modal remote sensing images by Kan Wei, JinKun Dai, Danfeng Hong, Yuanxin Ye

“…To address this issue, this paper proposes a multi-layer perceptron (MLP) dominated gate fusion network (MGFNet). MGFNet consists of three modules: a multi-path feature extraction network, an MLP-gate fusion (MGF) module, and a decoder. …”
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DGA malicious domain name identification based on XGBoost and particle swarm optimization algorithm by CHEN Zesheng, ZHOU Min, FENG Lichun, CHEN Weijie

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