The Effective Evaluation of Emotions in the Visual Emotion Images Using Convolutional Neural Networks by Modestas Motiejauskas, Gintautas Dzemyda

“…This effectively improves the recognition of emotions when training a convolutional neural network against the baseline. The proposed contrastive-center loss function optimizes deep neural networks by enhancing feature discriminability. …”
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Optimizing Solar Radiation Prediction with ANN and Explainable AI-Based Feature Selection by Ibrahim Al-Shourbaji, Abdalla Alameen

“…This paper presents an Artificial Neural Network (ANN) model optimized using feature selection techniques based on Explainable AI (XAI) methods to enhance SR prediction performance. …”
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Evaluation of the Features of Geographical and Biogeotourism Heritage of Landscapes in Order to Develop Wetland Ecotourism in the International Wetlands of Hormozgan by Monireah Ashrafi, Abbas Moradi, Mohammad Akbarian, Marziyeh Rezaei

“…In examining the suitability of areas for tourism in Indonesia, the factors of water clarity and clarity, ocean currents, beach type, layer and beach typology were evaluated (1). Other researches have been conducted on topics such as Nebkazar Sirik and other mandabi ecosystems of Hormozgan and its relationship with tourism, but so far no research has been conducted on the biogeotourism features of landscapes in the international wetlands of Hormozgan. …”
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RaNet: a residual attention network for accurate prostate segmentation in T2-weighted MRI by Muhammad Arshad, Chengliang Wang, Muhammad Wajeeh Us Sima, Jamshed Ali Shaikh, Salem Alkhalaf, Fahad Alturise

“…To address these challenges, we propose RaNet (Residual Attention Network), a novel framework based on ResNet50, incorporating three key modules: the DilatedContextNet (DCNet) encoder, the Multi-Scale Attention Fusion (MSAF), and the Feature Fusion Module (FFM). …”
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Evaluation of Feature Selection and Regression Models to Predict Biomass of Sweet Basil by Using Drone and Satellite Imagery by Luana Centorame, Nicolò La Porta, Michela Papandrea, Adriano Mancini, Ester Foppa Pedretti

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Landslide susceptibility evaluation and determination of critical influencing factors in eastern Sichuan mountainous area, China by Lin Zhang, Zhengxi Guo, Shi Qi, Tianheng Zhao, Bingchen Wu, Peng Li

“…Landslide susceptibility evaluation and determination of critical influencing factors is a prerequisite for preventing hazardous risks, especially in landslide-prone mountainous areas. …”
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A Hierarchical Feature-Based Time Series Clustering Approach for Data-Driven Capacity Planning of Cellular Networks by Vineeta Jain, Anna Richter, Vladimir Fokow, Mathias Schweigel, Ulf Wetzker, Andreas Frotzscher

“…To evaluate the effectiveness of HFTSC, we conduct a comprehensive case study using real-world data from thousands of network elements. …”
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Leveraging Multi-Modality and Enhanced Temporal Networks for Robust Violence Detection by Gwangho Na, Jaepil Ko, Kyungjoo Cheoi

“…Additionally, we refine the multi-scale temporal network (MTN) to improve feature extraction across multiple temporal scales between video snippets. …”
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Enhancing convergence speed with feature enforcing physics-informed neural networks using boundary conditions as prior knowledge by Mahyar Jahani-nasab, Mohamad Ali Bijarchi

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Automated sleep staging from single-channel electroencephalogram using hybrid neural network with manual features and attention by Qingyun Wan, Yuyang Bo, Ying Zhang, Mufeng Li, Xiaoqiu Wang, Chuang Chen, Lanying Liu, Wenzhong Wu

“…However, prior studies often overlook expert-derived manual features, relying solely on deep neural networks for automatic feature extraction. …”
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Combination of gray level features with deep transfer learning for copra classification using machine learning and neural networks by A. Stephen Sagayaraj, T. Kalavathi Devi

“…These concatenated features were evaluated using various machine learning classifiers and neural networks. …”
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Multi-Level Feature Fusion Attention Generative Adversarial Network for Retinal Optical Coherence Tomography Image Denoising by Yiming Qian, Yichao Meng

“…<b>Methods</b>: We propose MFFA-GAN, a generative adversarial network integrating multilevel feature fusion and an efficient local attention (ELA) mechanism. …”
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CD-CTFM: A Lightweight CNN-Transformer Network for Remote Sensing Cloud Detection Fusing Multiscale Features by Wenxuan Ge, Xubing Yang, Rui Jiang, Wei Shao, Li Zhang

“…In the encoder part, we utilize a lightweight network combing CNN and Transformer as backbone, which is conducive to extracting local and global features simultaneously. …”
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X-FASNet: cross-scale feature-aware with self-attention network for cognitive decline assessment in Alzheimer's disease by Wenhui Chen, Shunwu Xu, Yiran Peng, Yiran Peng, Hong Zhang, Jian Zhang, Huaihao Zheng, Hao Yan, Zhaowen Chen, Zhaowen Chen

“…Current multi-scale neural networks have limited cross-scale feature integration capabilities, which constrain their effectiveness in identifying early neurodegenerative markers. …”
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Extraction of Agricultural Parcels Using Vector Contour Segmentation Network with Hybrid Backbone and Multiscale Edge Feature Extraction by Feiyu Teng, Ling Wu, Shukuan Liu

“…Simultaneously, this paper proposes a hybrid backbone for feature extraction. A hybrid backbone combines the respective advantages of the Resnet and Transformer backbone networks to balance local features and global features in feature extraction. …”
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A comparative study of convolutional neural networks and traditional feature extraction techniques for adulteration detection in ground beef by Leila Bahmani, Saied Minaei, Alireza Mahdavian, Ahmad Banakar, Mahmoud Soltani Firouz

“…Convolutional Neural Network (CNN) was also used to extract features and classify images. …”
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Highly Accurate Brain Tumor Segmentation and Classification Using Multiple Feature Sets by Megha Sunil Borse, Murali Prasad R, Tummala Ranga Babu

“…The Deep Convolutional Network (DCNN) is used to segment the image. The Pulse Coupled Neural Networks (PCNN) categorize the input images as normal and tumor. …”
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Multilayer neural network model for unbalanced data by Xue ZHANG, Zhiguo SHI, Xuan LIU

“…Classification of unbalanced data often has low performance of the classifier because of the unbalance of data between classes.Using AUC (the area under the ROC curve) as evaluation index,combined with one class F-score feature selection and genetic algorithm,a multilayer neural network model was established,and a more favorable feature set for unbalanced data classification was selected,so as to establish a deeper model suitable for classification of unbalanced data.Based on Tensor Flow,a multilayer neural network model was established.Using four different UCI datasets for testing,and comparing with the traditional machine learning algorithms such as Naive Bayesian,KNN,neural networks,etc,the performance of the proposed model built on the unbalanced data classification is more excellent.…”
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Multilayer neural network model for unbalanced data by Xue ZHANG, Zhiguo SHI, Xuan LIU

“…Classification of unbalanced data often has low performance of the classifier because of the unbalance of data between classes.Using AUC (the area under the ROC curve) as evaluation index,combined with one class F-score feature selection and genetic algorithm,a multilayer neural network model was established,and a more favorable feature set for unbalanced data classification was selected,so as to establish a deeper model suitable for classification of unbalanced data.Based on Tensor Flow,a multilayer neural network model was established.Using four different UCI datasets for testing,and comparing with the traditional machine learning algorithms such as Naive Bayesian,KNN,neural networks,etc,the performance of the proposed model built on the unbalanced data classification is more excellent.…”
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DualCMNet: a lightweight dual-branch network for maize variety identification based on multi-modal feature fusion by Xinhua Bi, Hao Xie, Ziyi Song, Jinge Li, Chang Liu, Xiaozhu Zhou, Helong Yu, Chunguang Bi, Ming Zhao

“…Additionally, existing multimodal methods face high computational complexity, making it difficult to balance accuracy and efficiency.MethodsBased on multi-modal data from 11 maize varieties, this paper presents DualCMNet, a novel dual-branch deep learning framework that utilizes a one-dimensional convolutional neural network (1D-CNN) for hyperspectral data processing and a MobileNetV3 network for spatial feature extraction from images. …”
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