RBMDC-Net: Effective Jaw Cyst Segmentation Network Using Residual Bottleneck and Multiscale Dilated Convolution by Huixia Zheng, Xiaoliang Jiang, Xu Xu, Zhenfei Yuan

“…In our approach, the standard convolutional module in the U-Net encoding path is replaced with residual bottleneck module (RBM) that enables the network to extract features at various scales using multiple kernel sizes and residual connection. …”
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A comprehensive construction of deep neural network‐based encoder–decoder framework for automatic image captioning systems by Md Mijanur Rahman, Ashik Uzzaman, Sadia Islam Sami, Fatema Khatun, Md Al‐Amin Bhuiyan

“…The long short‐term memory network functions as a sequence processor, generating a fixed‐length output vector for final predictions, while the VGG‐19 model is utilized as an image feature extractor. …”
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LiFi for Industry 4.0: Main Features, Implementation and Initial Testing of IEEE Std 802.15.13 by Kai Lennert Bober, Anselm Ebmeyer, Falko Dressler, Ronald Freund, Volker Jungnickel

“…As industrial communication continues to evolve to increase flexibility through wireless communication, networked optical wireless communication (OWC), also known as LiFi, has emerged as a promising candidate technology due to its unlicensed spectrum and relatively deterministic propagation. …”
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Modélisation du changement d’échelles en télédétection par une méthode neuronale : application a l’étude de l’évolution de l’occupation hivernale des sols en Bretagne... by Thomas Houet, Laurence Hubert-Moy, Grégoire Mercier

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Multi-sensor data fusion method for water quality evaluation based on interval evidence theory by Jian ZHOU, Chen-hao MA, Lin-feng LIU, Li-juan SUN, Fu XIAO

“…For the inevitable uncertainty and random uncertainty in the process of measuring water quality data with the sensor network,a multi-sensor data fusion method for water quality evaluation based on interval evidence theory was proposed.Considering the precision error of sensor and the abnormalities of measured data,every water quality data measured by sensor was represented by interval number.By calculating the distance between the water quality data and the features of each water quality class,the interval evidence of water quality class was acquired.According to the interval evidence combining rule,a comprehensive interval evidence was obtained by combining the interval evidence of each sensor.Finally,the water quality class was determined based on the comprehensive interval evidence by the decision rule.Experiments show that the proposed method can evaluate water quality class more accurately from the uncertain water quality data.…”
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Multi-sensor data fusion method for water quality evaluation based on interval evidence theory by Jian ZHOU, Chen-hao MA, Lin-feng LIU, Li-juan SUN, Fu XIAO

“…For the inevitable uncertainty and random uncertainty in the process of measuring water quality data with the sensor network,a multi-sensor data fusion method for water quality evaluation based on interval evidence theory was proposed.Considering the precision error of sensor and the abnormalities of measured data,every water quality data measured by sensor was represented by interval number.By calculating the distance between the water quality data and the features of each water quality class,the interval evidence of water quality class was acquired.According to the interval evidence combining rule,a comprehensive interval evidence was obtained by combining the interval evidence of each sensor.Finally,the water quality class was determined based on the comprehensive interval evidence by the decision rule.Experiments show that the proposed method can evaluate water quality class more accurately from the uncertain water quality data.…”
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TFTformer: A novel transformer based model for short-term load forecasting by Ahmad Ahmad, Xun Xiao, Huadong Mo, Daoyi Dong

“…A linear transformation layer post embedding improves feature representation, aligning and standardising features across sequences for improved pattern recognition. …”
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Spatial Heterogeneity in Soil Microbial Communities Impacts Their Suitability as Bioindicators for Evaluating Productivity in Agricultural Practices by Guoqiang Li, Xuanjing Li, Ting Jin, Muyilan Jiang, Peng Shi, Gehong Wei

“…Agricultural practice treatments exerted significant impacts on bacterial community structures and network topological features in both intra-row and inter-row soils. …”
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DFN-YOLO: Detecting Narrowband Signals in Broadband Spectrum by Kun Jiang, Kexiao Peng, Yuan Feng, Xia Guo, Zuping Tang

“…The DFN-YOLO model incorporates a deformable channel feature fusion network (DCFFN), replacing the concatenate-to-fusion (C2f) module to enhance the extraction and integration of channel features. …”
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A river network model using a weight-based merged LSTM for multi-source monitoring integration by Jonggyu Jung, Taeseung Park, Jaegwan Park, Dogeon Lee, YoonKyung Cha

“…Rivers typically exhibit spatial connectivity from upstream to downstream, with various heterogeneous monitoring systems operating concurrently across basins. While graph neural networks (GNNs) have shown promise in modeling spatial connectivity, they remain limited by reliance on features common to all nodes. …”
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Unsupervised Restoration of Underwater Structural Crack Images via Physics-Constrained Image Translation and Multi-Scale Feature Retention by Xianfeng Zeng, Wenji Ai, Zongchao Liu, Xianling Wang

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Temporal-Spatial Feature Extraction in IoT-Based SCADA System Security: Hybrid CNN-LSTM and Attention-Based Architectures for Malware Classification and Attack Detection by Onur Polat, Ali Ayid Ahmad, Saadin Oyucu, Enes Algul, Ferdi Dogan, Ahmet Aksoz

“…The developed model identifies complex attacks in the network by taking advantage of the strengths of CNNs that reveal spatial features and LSTMs that detect temporal dependency. …”
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SDA-Net: A Spatially Optimized Dual-Stream Network with Adaptive Global Attention for Building Extraction in Multi-Modal Remote Sensing Images by Xuran Pan, Kexing Xu, Shuhao Yang, Yukun Liu, Rui Zhang, Ping He

“…To address these challenges, a novel building extraction network based on multi-modal remote sensing data called SDA-les (AGAFMs) was designed in the decoding stage to fuse multi-modal features at various scales, which dynamically adjust the importance of features from a global perspective to better balance the semantic information. …”
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ENHANCING NETWORK INTRUSION DETECTION USING MACHINE LEARNING AND META-MODELLING FOR IMPROVED CYBER SECURITY PERFORMANCE by Sunita, Pankaj Verma, Nitika, Jaspreet Kaur, Vijay Rana

“…This study is based on the analysis of network intrusion detection and the improvement of various machine learning methods that produce high accuracy and guarantee secure network traffic from malicious activities. …”
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Multi-scale feature fusion and feature calibration with edge information enhancement for remote sensing object detection by Lihua Yang, Yi Gu, Hao Feng

“…EMF-DETR introduces a multi-scale edge-aware feature extraction network named MEFE-Net. The network improves object recognition and localization capabilities by extracting multi-scale features and enhancing edge information for targets at each scale, demonstrating exceptional performance in small object detection. …”
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Explainable brain age prediction: a comparative evaluation of morphometric and deep learning pipelines by Maria Luigia Natalia De Bonis, Giuseppe Fasano, Angela Lombardi, Carmelo Ardito, Antonio Ferrara, Eugenio Di Sciascio, Tommaso Di Noia

“…In this study, we present a comparative evaluation of two pipelines: one using morphometric features from FreeSurfer and the other employing 3D convolutional neural networks (CNNs). …”
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ANALYSIS AND EVALUATION OF ROMANIAN VOICE COMMANDS FOR THE CONTROL OF MECHATRONIC SYSTEMS by BOROZAN, Olesea

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Hybrid Optimized Feature Selection and Deep Learning Method for Emotion Recognition That Uses EEG Data by asmaa Bashar Hmaza, Rajaa K. Hasoun

“…First, particle swarm optimization (PSO) identifies and optimizes critical functions and reduces feature dimensionality. Thereafter, long short-term memory (LSTM), gated recurrent unit (GRU), and simple recurrent neural network (RNN) architectures are used in emotion identification. …”
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Multi-Scenario Simulation Evaluation and Strategic Zoning of Habitat Services Based on Habitat Quality and Ecological Network: A Case Study of Lanzhou City by Jin Shi, Xianglong Tang

“…The findings indicate that under four development scenarios, the ecological network generally shows a three-segment distribution. …”
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BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification by Anderson P. Avila Santos, Breno L. S. de Almeida, Robson P. Bonidia, Peter F. Stadler, Polonca Stefanic, Ines Mandic-Mulec, Ulisses Rocha, Danilo S. Sanches, André C.P.L.F. de Carvalho

“…Extracted features, when embedded into the deep network, enable optimal utilization of spatial and sequential nuances of ncRNA sequences. …”
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