Chess Position Evaluation Using Radial Basis Function Neural Networks by Dimitrios Kagkas, Despina Karamichailidou, Alex Alexandridis

“…The proposed approach introduces models based on the radial basis function (RBF) neural network architecture trained with the fuzzy means algorithm, in conjunction with a novel set of input features; different methods of network training are also examined and compared, involving the multilayer perceptron (MLP) and convolutional neural network (CNN) architectures and a different set of input features. …”
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Experimental Evaluation of ZigBee-Based Wireless Networks in Indoor Environments by Jin-Shyan Lee, Yuan-Ming Wang

“…It attempts to provide a low-data rate, low-power, and low-cost wireless networking on the device-level communication. In this paper, we have established a realistic indoor environment for the performance evaluation of a 51-node ZigBee wireless network. …”
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Optimizing Artificial Neural Networks For The Evaluation Of Asphalt Pavement Structural Performance by Gaetano Bosurgi, Orazio Pellegrino, Giuseppe Sollazzo

“…In this paper, the influence on the final quality of different features conditioning the network architecture has been examined, for maximising the resulting quality and, consequently, the final benefits of the methodology. …”
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Data Flow Forecasting for Smart Grid Based on Multi-Verse Expansion Evolution Physical–Social Fusion Network by Kun Wang, Bentao Hu, Jiahao Zhang, Ruqi Zhang, Hongshuo Zhang, Sunxuan Zhang, Xiaomei Chen

“…Secondly, establish a financial flow data forecasting framework using MVE²-STFN. Then, a feature extraction model is developed by integrating convolutional neural networks (CNN) for spatial feature extraction and bidirectional long short-term memory networks (BiLSTM) for temporal feature extraction. …”
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YLOMF: You Look Once for Multi-Feature—A Multi-Feature Multi-Task Network for Intelligent Real-Time Perception in Autonomous Driving by B. N. Anoop, S. K. Abhilash, Venu Madhav Nookala, S. Prita, S. Raghavendra

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UGC-Net: Uncertainty-Guided Cost Volume Optimization with Contextual Features for Satellite Stereo Matching by Wonje Jeong, Soon-Yong Park

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Feature Extraction Model of SE-CMT Semantic Information Supplement by DU Ruishan, ZHOU Changkun, XIE Hongtao, LI Hongjie

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Critical evaluation of feature importance assessment in FFNN-based models for predicting Kamlet-Taft parameters by Yoshiyasu Takefuji

“…Mohan et al. developed a feed-forward neural network (FFNN) model to predict Kamlet-Taft parameters using quantum chemically derived features, achieving notable predictive accuracy. …”
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Deep Time Series Intelligent Framework for Power Data Asset Evaluation by Lihong Ge, Xin Li, Li Wang, Jian Wei, Bo Huang

“…In the evaluation of the complex and rich Solar-Power dataset and Electricity dataset, TSENet achieved significant performance improvements over other state-of-the-art baseline methods.Through the synergistic design of deep convolutional structures and an efficient memory mechanism, it effectively addresses issues such as inadequate modeling of long-term dependencies, insufficient extraction of short-term features, and high prediction volatility, thereby significantly enhancing both the accuracy and robustness of forecasting in power asset evaluation tasks.…”
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RFID Privacy Risk Evaluation Based on Synthetic Method of Extended Attack Tree and Information Feature Entropy by Peng Li, Chao Xu, Long Chen, Ruchuan Wang

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Aerial–Terrestrial Image Feature Matching: An Evaluation of Recent Deep Learning Methods by Hui Wang, Jiangxue Yu, San Jiang, Dejin Zhang, Qingquan Li

“…However, their performance in handling challenging large-angle aerial–terrestrial datasets still needs to be evaluated. To assess their performance for aerial–terrestrial images, this study has reviewed and evaluated four types of recent deep-learning-based feature matching networks and selected four sets of aerial–terrestrial datasets for experimental tests. …”
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Evaluation of Shelf Life Prediction for Broccoli Based on Multispectral Imaging and Multi-Feature Data Fusion by Xiaoshuo Cui, Xiaoxue Sun, Shuxin Xuan, Jinyu Liu, Dongfang Zhang, Jun Zhang, Xiaofei Fan, Xuesong Suo

“…However, few studies have used spectral image information to predict and evaluate the shelf life of broccoli. In this study, multispectral imaging combined with multi-feature data fusion was used to predict and evaluate the shelf life of broccoli. …”
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Evolution of Bluetooth Technology: BLE in the IoT Ecosystem by Grigorios Koulouras, Stylianos Katsoulis, Fotios Zantalis

“…It examines the current state of BLE, including its applications, challenges, limitations, and recent advancements in areas such as security, power management, and mesh networking. The recent release of Bluetooth Low Energy version 6.0 by the Bluetooth Special Interest Group (SIG) highlights the technology’s ongoing evolution and growing importance within the IoT. …”
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Extraction of Concealed Features From RF-EMF Monitoring at Kindergartens and Schools by Nikola Djuric, Dragan Kljajic, Nicola Pasquino, Vidak Otasevic, Snezana Djuric

“…The analysis is performed on a case study of EMF-sensitive areas in the Serbian city of Novi Sad, i.e., two kindergartens and an elementary school, revealing some of the concealed features in the behavior of the EMFs exposure in those areas, through a comparative evaluation.…”
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INTEVAL as a Positively Charged Social Network by Per Bastøe, Anita Haslie

“… Background: In this article Bastøe and Haslie seeks to employ theoretical insights from two connected bodies of literature to understand the unique characteristics of The International Evaluation Research Group (Inteval). One perspective draws on insights from social network theories about how some networks are supportive and innovative while others are not. …”
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Time Series Forecasting Model Based on the Adapted Transformer Neural Network and FFT-Based Features Extraction by Kyrylo Yemets, Ivan Izonin, Ivanna Dronyuk

“…In today’s data-driven world, where information is one of the most valuable resources, forecasting the behavior of time series, collected by modern sensor networks and IoT systems, is crucial across various fields, including finance, climatology, and engineering. …”
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PolSAR image classification using shallow to deep feature fusion network with complex valued attention by Mohammed Q. Alkhatib, M. Sami Zitouni, Mina Al-Saad, Nour Aburaed, Hussain Al-Ahmad

“…Deep Learning (DL) methods offer effective solutions for overcoming these challenges in PolSAR feature extraction. Convolutional Neural Networks (CNNs) play a crucial role in capturing PolSAR image characteristics by exploiting kernel capabilities to consider local information and the complex-valued nature of PolSAR data. …”
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FEPA-Net: A Building Extraction Network Based on Fusing the Feature Extraction and Position Attention Module by Yuexin Liu, Yulin Duan, Xuya Zhang, Wen Zhang, Chang Wang

“…In this paper, we propose the FEPA-Net network model, which integrates the feature extraction and position attention module for the extraction of buildings in remote sensing images. …”
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Geometric Detail-Preserved Point Cloud Upsampling via a Feature Enhanced Self-Supervised Network by Shengwei Qin, Yao Jin, Hailong Hu

“…The first module, called the feature enhancement module (FEM), aims to prevent feature blurring. …”
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Coalmine image super-resolution reconstruction via fusing multi-dimensional feature and residual attention network by Jian CHENG, Lifei MI, Hao LI, Heping LI, Guangfu WANG, Yongzhuang MA

“…To address issues such as the loss of edge texture information and blurring of details in coalmine images, a coalmine image super-resolution reconstruction method integrating multi-dimensional features and residual attention networks is proposed. …”
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