An integrated deep convolutional neural networks framework for the automatic segmentation and grading of glioma tumors using multimodal MRI scans by Otung John Peter Odong, Mohammed Abo-Zahhad, Moataz Abdelwahab

“…Consequently, automated diagnosis of brain tumors is essential for optimal clinical management and glioma surgical interventions. …”
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Wind Power Short-Term Prediction Method Based on Time-Domain Dual-Channel Adaptive Learning Model by Haotian Guo, Keng-Weng Lao, Junkun Hao, Xiaorui Hu

“…After TCN-based feature compression, an adaptive weighted early fusion mechanism dynamically optimizes channel weights. The ACON adaptive activation function autonomously learns optimal activation patterns, with fused features visualized through visualization techniques. …”
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Intelligent Agricultural Greenhouse Extraction Method Based on Multifeature Modeling: Fusion of Geometric, Spatial, and Spectral Characteristics by Yujie Wu, Jiguang Dai, Yue Zhu, Tengda Zhang

“…It consists of three main components: a strip-like feature enhancement encoder, a frequency-guided pyramid decoder, and a lightweight foreground enhancement segmentation head. …”
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Fault Diagnosis in a Four-Arm Delta Robot Based on Wavelet Scattering Networks and Artificial Intelligence Techniques by Claudio Urrea, Carlos Domínguez

“…Two case studies investigate faults through control effort and vibration signals, with control effort detecting motor and encoder faults, while vibration signals identify bearing faults. …”
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AFENet: An Attention-Focused Feature Enhancement Network for the Efficient Semantic Segmentation of Remote Sensing Images by Jiarui Li, Shuli Cheng

“…To address these limitations, we propose an attention-focused feature enhancement network (AFENet) with a novel encoder–decoder architecture. The encoder architecture combines ResNet50 with a parallel multistage feature enhancement group (PMFEG), enabling robust feature extraction through optimized channel reduction, scale expansion, and channel reassignment operations. …”
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Efficient Region-Wise Packing of Stereoscopic ERP Videos Based on Information Loss Minimization by Hossein Pejman, Stephane Coulombe, Carlos Vazquez, Ahmad Vakili

“…Experimental results, using the VVenC Versatile Video Coding (VVC) encoder, show that compared with the standard side-by-side (SbS) format, with uniform horizontal half-downsampling (UHHDS), the proposed CA packing method provides an average 13.84% and 12.02% Bjøntegaard-Delta bitrate (BD-BR) reduction for Random Access (RA) and Low Delay B (LDB) configurations, respectively, with an average encoding time comparable to SbS. …”
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Deep learning based decision-making and outcome prediction for adolescent idiopathic scoliosis patients with posterior surgery by Kai Chen, Xiao Zhai, Ziqiang Chen, Haojue Wang, Mingyuan Yang, Changwei Yang, Yushu Bai, Ming Li

“…However, surgical decision-making and optimal strategies still lack standardization and personalized customization. …”
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Information extraction from green channel textual records on expressways using hybrid deep learning by Jiaona Chen, Jing Zhang, Weijun Tao, Yinli Jin, Heng Fan

“…We proposed a hybrid approach based on BIO labeling, pre-trained model, deep learning and CRF to build a named entity recognition (NER) model with the optimal prediction performance. Eight entities are designed and proposed in the NER processing for the expressway green channel. three typical pre-trained natural language processing models are utilized and compared to recognize entities and obtain feature vectors, including bidirectional encoder representations from transformer (BERT), ALBERT, and RoBERTa. …”
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Fast Multimodal Trajectory Prediction for Vehicles Based on Multimodal Information Fusion by Likun Ge, Shuting Wang, Guangqi Wang

“…Finally, we propose a multi-stage decoder that generates more accurate and reasonable predicted trajectories by predicting trajectory reference points and performing spatial and posture optimization on the predicted trajectories. Comparative experiments with existing advanced algorithms demonstrate that our method improves the minimum Average Displacement Error (minADE), minimum Final Displacement Error (minFDE), and Miss Rate (MR) by 10.3%, 10.3%, and 14.5%, respectively, compared to the average performance. …”
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Assessing the transferability of BERT to patient safety: classifying multiple types of incident reports by Ying Wang, Farah Magrabi

“…The default parameters of BERT were found to be the most optimal configuration. For incident type, fine-tuned BERT achieved high F-scores above 89% across all test datasets (CNNs=81%). …”
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A Remaining Useful Life Prediction Method for Rolling Bearings Based on Hierarchical Clustering and Transformer–GRU by Wenping Lei, Xing Dong, Fuyuan Cui, Guangzhong Huang

“…Specifically, the initially filtered feature set is further clustered using hierarchical clustering, and the optimal number of clusters is determined by the elbow method to construct a compact and representative feature set. …”
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Granger Causality-Based Forecasting Model for Rainfall at Ratnapura Area, Sri Lanka: A Deep Learning Approach by Shanthi Saubhagya, Chandima Tilakaratne, Pemantha Lakraj, Musa Mammadov

“…The identification of the optimal set of features for the LSTM model was conducted using Random Forest and Granger Causality tests. …”
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Handling Sensor Faults in Hydroponics: A Deep Learning Imputation Technique for Accurate Tomato Yield Prediction by Viji Venugopal, Paresh Tanna, Ramesh Karnati

“…DLPIM introduces a feature encoder-based generator with a 4-headed self-attention mechanism and a Crop Growth Rate (CGR) guided temporal processor to capture complex time-series patterns while maintaining physiological consistency. …”
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A flexible framework for identifying urban villages using Sentinel-2 observations and deep learning by Yizhen Wu, Xi Li, Yu Gong, Dennis Mwaniki

“…The proposed framework exhibited optimal performance (mIoU: 83.33 %) with robust spatial generalization, surpassing traditional methods (mIoU improvements: 0.46–12.49 %). …”
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Enhanced CLIP-GPT Framework for Cross-Lingual Remote Sensing Image Captioning by Rui Song, Beigeng Zhao, Lizhi Yu

“…Furthermore, our method achieves comparable or superior performance to traditional “encoder-decoder” baselines in terms of BLEU, CIDEr, and METEOR metrics, while requiring only one-fifth of the training time. …”
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Power Line Segmentation Algorithm Based on Lightweight Network and Residue-like Cross-Layer Feature Fusion by Wenqiang Zhu, Huarong Ding, Gujing Han, Wei Wang, Minlong Li, Liang Qin

“…Experimental results demonstrate that compared with classical UNet, the optimized network achieves 2.05% and 2.58% improvements in F1-Score and IoU, respectively, while reducing the parameter count to 57.25% of the original model. …”
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A Hybrid Deep Learning Approach for Cotton Plant Disease Detection Using BERT-ResNet-PSO by Chetanpal Singh, Santoso Wibowo, Srimannarayana Grandhi

“…Then, the output of the encoded feature is passed to ResNet-based architecture for feature extraction and further optimized by PSO to increase the classification accuracy. …”
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Predictive Models for Environmental Perception in Multi-Type Parks and Their Generalization Ability: Integrating Pre-Training and Reinforcement Learning by Kangen Chen, Tao Xia, Zhoutong Cao, Yiwen Li, Xiuhong Lin, Rushan Bai

“…By combining image data and user review texts, a unified architecture is constructed, including a text encoder, image visual encoder, and multimodal fusion module. …”
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Diagnosis Method for High-Speed Train Axle Box Bearing Slight Faults Based on Improved SAE and Temperature-Vibration Fusion by XU Xiao, SONG Dongli, WANG Zifan

“…Then, by optimizing the dimensionality reduction algorithm based on SAE (stacked auto encoder), an effective fusion method of temperature-vibration features is proposed to achieve nonlinear fusion and dimensionality reduction of temperature and vibration features. …”
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AfaMamba: Adaptive Feature Aggregation With Visual State Space Model for Remote Sensing Images Semantic Segmentation by Hongkun Chen, Huilan Luo, Chanjuan Wang

“…It employs a lightweight ResNet18 as the encoder, and during the decoding phase, it first utilizes a multiscale feature adaptive aggregation module to ensure that the output features from each stage of the encoder contain rich multiscale semantic information. …”
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