Multi-branch network for double JPEG detection and localization by Ahmed M. Fouad, Hala H. Zayed, Ahmed Taha

“…This paper proposes a multi-branch convolutional neural network and compares it with single-branch models to demonstrate its effectiveness in detecting double JPEG compression. …”
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FUSCANet: Enhancing Skin Disease Classification Through Feature Fusion and Spatial-Channel Attention Mechanisms by Qinyang Liu, Xuan Wang, Hongjiu Liu, Xiangzhen Zang, Lei Li, Zhanlin Ji, Ivan Ganchev

“…In response to this need, a novel lightweight neural network model, called Feature fUsion and Spatial-Channel Attention Network (FUSCANet) model, is proposed in this paper, based on the MobileViT framework, aiming at classifying multi-class skin disease images on mobile or embedded devices. …”
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UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism by Shaodan Lin, Deyao Huang, Libin Wu, Zuxin Cheng, Dapeng Ye, Haiyong Weng

“…Results The ConvGAM model, leveraging the ConvNeXt-Large backbone network and the Global Attention Mechanism (GAM), achieves outstanding performance in feature extraction, crucial for detecting small and complex disease patterns. …”
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Genome-wide characterization of the GmLUX binding preferences and its epigenic features in the soybean genome by Lv Tianxiao, Hong Xiao, Jingxing Wang, Ling Zhang, Tian Fan, Mingkun Huang, Chang-En Tian, Hua Yang, Yufang Hu

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Models, systems, networks in economics, engineering, nature and society by A.D. Stolyarov, V.V. Gordeev, V.I. Abramov

Grinding wheel wear evaluation with the PMSCNN model by Sumei Si, Zekai Si, Deqiang Mu, Hailiang Tang

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Trust-Based Anomaly Detection in Emerging Sensor Networks by Renyong Wu, Xue Deng, Rongxing Lu, Xuemin (Sherman) Shen

“…However, due to the openness of wireless media and the inborn self-organization feature of WSNs, that is, frequent interoperations among neighbouring nodes, network security has been tightly related to data credibility and/or transmission reliability, thus trust evaluation of network nodes is becoming another interesting issue. …”
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Generative adversarial networks (GANS) for generating face images by Dolly Indra, Muh Wahyu Hidayat, Fitriyani Umar

“…Generative Adversarial Networks (GANs), a type of deep learning model, have demonstrated remarkable capabilities in generating high-quality synthetic images through a competitive training process between a generator, which creates new data, and a discriminator, which evaluates its authenticity. …”
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Sustainable Transmitters for High-Capacity Metro-Access Networks by Stefano Gaiani, Alberto Gatto, Paola Parolari, Pierpaolo Boffi

“…The simulations evaluate the performance of the various architectures in terms of capacity as a function of the propagation distance (up to 50 km), comparing also the associated signal to noise ratio curves. …”
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Development of Robot Feature for Stunting Analysis Using Long-Short Term Memory (LSTM) Algorithm by Muhammad Rahadian Abdurrahman, Halim Al Aziz, Farras Adhani Zayn, Muhammad Agus Purnomo, Heru Agus Santoso

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Attention residual network for medical ultrasound image segmentation by Honghua Liu, Peiqin Zhang, Jiamin Hu, Yini Huang, Shanshan Zuo, Lu Li, Mailan Liu, Chang She

“…Additionally, a spatial hybrid convolution module is integrated to augment the model’s ability to extract global information and deepen the vertical architecture of the network. During the feature fusion stage of the skip connections, a channel attention mechanism and a multi-convolutional self-attention mechanism are respectively introduced to suppress noisy points within the fused feature maps, enabling the model to acquire more information regarding the target region. …”
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A Self-Supervised Monocular Depth Estimation Framework Based on Detail Recovery and Feature Fusion by Shun Li, Chongzheng Huang, Xiangzhe Li, Zhengyou Liang

“…Specifically, ASAM selectively emphasizes critical features and spatial locations in images to enhance the model’s ability to capture details. …”
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Data‐Driven Feature Decomposition Integrated Prediction Model for Dust Concentration in Open‐Pit Mines by Shuangshuang Xiao, Jin Liu, Qing Yang, Zhiguo Chang, Yonggui Zhang

“…Combining the characteristics of dust concentration data and the concept of multimodal information integration modeling, a support vector machine (SVM)‐long short‐term memory (LSTM) network was chosen to build a data feature‐driven dust concentration combination prediction model. …”
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Fault diagnosis of marine electric thruster gearbox based on MPDCNN under strong noisy environments by Qianming SHANG, Wanying JIANG, Yi ZHOU, Zhengqiang WANG, Yubo SUN

“…Meanwhile, a novel parallel dual-channel convolutional neural network structure is designed to explore both global features and deeper, finer details of the data, thereby enhancing the diagnostic performance of the method in strong noise environments.ResultsExperimental evaluation results under different noise conditions show that the proposed method achieves a fault diagnosis accuracy of over 98% in environments with strong noise. …”
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Multimodal Contrastive Learning for Remote Sensing Image Feature Extraction Based on Relaxed Positive Samples by Zhenshi Zhang, Qiujun Li, Wenxuan Jing, Guangjun He, Lili Zhu, Shijuan Gao

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CSSDet: small object detection via cross-scale feature enhancement on drone-view images by Gui Cheng, Qing Ding, Bowen Cai, Chaoya Dang, Yu Wang, Xiaolong Zuo, Zhenfeng Shao

“…CSSDet integrates an additional detection head for small objects and employs a bidirectional weighted feature pyramid network for effective cross-scale feature fusion, enhancing global perceptual capability. …”
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Advanced cloud intrusion detection framework using graph based features transformers and contrastive learning by Vijay Govindarajan, Junaid Hussain Muzamal

“…Network flows are modeled as graphs to capture relational patterns among IP addresses and services, and a Graph Neural Network (GNN) is used to extract structured embeddings. …”
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Malware Detection and Classification in Android Application Using Simhash-Based Feature Extraction and Machine Learning by Wafaa Al-Kahla, Eyad Taqieddin, Ahmed S. Shatnawi, Rami Al-Ouran

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Revolutionizing classification: A novel gray level co-occurrence matrix and statistical feature-based segmentation approach by Abdelwahed Motwakel

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EEG-based schizophrenia diagnosis using deep learning with multi-scale and adaptive feature selection by Alanoud Al Mazroa, Majdy M. Eltahir, Shouki A. Ebad, Faiz Abdullah Alotaibi, Venkatachalam K, Jaehyuk Cho

“…For classification purposes, AWFM enables our model to modify the importance of features dynamically. We evaluated our technique using a publicly available dataset of EEG recordings acquired from patients who have schizophrenia and everyday individuals. …”
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