XCF-LSTMSATNet: A Classification Approach for EEG Signals Evoked by Dynamic Random Dot Stereograms by Tingting Zhang, Xu Yan, Xin Chen, Yi Mao

“…Stereovision is the visual perception of depth derived from the integration of two slightly different images from each eye, enabling understanding of the three-dimensional space. …”
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A New Hybrid Wavelet Transform-Deep Learning for Smart Resilient Inverters in Microgrids Against Cyberattacks by Chou-Mo Yang, Pei-Min Huang, Chun-Lien Su, Mahmoud Elsisi

“…While the model shows high performance, further research is needed to validate its generalizability across different inverter hardware and against novel, zero-day attack variants.…”
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LMSOE-Net: lightweight multi-scale small object enhancement network for UAV aerial images by Zhixing Ma, Peidong Luo, Xiaole Shen

“…We further optimize the model by incorporating shared convolution with detail-enhancement capabilities in the detection head, which improves the detection of small objects across different scales. …”
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Deep Learning-Based Brain Tumor Segmentation in MRI Images: A MobileNetV2-DeepLabV3+ Approach by Farhad Abedinzadeh Torghabeh, Seyyed Abed Hosseini

“…We aim to achieve accurate localization and delineation of tumor regions across different axial views.Material and Methods: The dataset used in this study consists of 3064 T1-weighted contrast-enhanced MRI images obtained from patients diagnosed with glioma, meningioma, and pituitary tumors. …”
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Personalizing Seizure Detection for Individual Patients by Optimal Selection of EEG Signals by Rosanna Ferrara, Martino Giaquinto, Gennaro Percannella, Leonardo Rundo, Alessia Saggese

“…The tailored channel selection boosts detection accuracy and ensures robust performance across different seizure types while reducing the computational burden typical of multi-electrode systems. …”
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PlantView: Integrating deep learning with 3D modeling for indoor plant augmentation by Sitara Afzal, Haseeb Ali Khan, Jong Weon Lee

“…We developed a custom dataset comprising over 28,000 images of 48 different plant species at various growth stages, captured under diverse lighting conditions and camera settings. …”
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Detection of Violent Scenes in Cartoon Movies Using a Deep Learning Approach by Noreen Fayyaz Khan, Sareer Ul Amin, Zahoor Jan, Changhui Yan

“…A significant contribution of this study is the meticulous categorization of violent scenes into four distinct types, allowing for further investigation into the diverse effects of different violence categories. Furthermore, the study introduces an innovative approach by integrating a dense layer into the sequential model to enhance final classification. …”
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Automatic Detection of Tiny Drainage Outlets and Ventilations on Flat Rooftops from Aerial Imagery by L. Arzoumanidis, W. Li, W. Li, J. Knechtel, Y. Kosmayadi, Y. Dehbi

“…This paper presents an automated approach to detecting drainage outlets and ventilation systems on flat rooftops, using a custom-labeled dataset of highresolution aerial imagery. We evaluated two different object detection methods, with FCOS (Fully Convolutional One-Stage Object Detection) outperforming Faster R-CNN in identifying these small utilities. …”
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Underwater Time Delay Estimation Based on Meta-DnCNN with Frequency-Sliding Generalized Cross-Correlation by Meiqi Ji, Xuerong Cui, Juan Li, Lei Li, Bin Jiang

“…Firstly, a multi-sub-window reconstruction is performed on the frequency-sliding generalized colorboxpinkcross-correlation (FS-GCC) matrix between signals to capture the time delay characteristics from different frequency bands and conduct the enhancement and extraction of features. …”
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Integration of Nuclear, Clinical, and Genetic Features for Lung Cancer Subtype Classification and Survival Prediction Based on Machine- and Deep-Learning Models by Bin Xie, Mingda Mo, Haidong Cui, Yijie Dong, Hongping Yin, Zhe Lu

“…<b>Conclusions:</b> Our study was the first to incorporate the characteristics of nuclei and the genetic information of patients to predict the subtypes and OS of patients with lung cancer. The combination of different factors and the usage of artificial intelligence methods achieved a small breakthrough in the results of previous studies regarding AUC values.…”
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ECG Signal Analysis for Detection and Diagnosis of Post-Traumatic Stress Disorder: Leveraging Deep Learning and Machine Learning Techniques by Parisa Ebrahimpour Moghaddam Tasouj, Gökhan Soysal, Osman Eroğul, Sinan Yetkin

“…In parallel, statistical features were extracted directly from the ECG signals and used in traditional machine learning (ML) classifiers for performance comparison. Four different segment lengths (5 s, 10 s, 15 s, and 20 s) were tested to assess their effect on classification accuracy. …”
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Investigation of a transformer-based hybrid artificial neural networks for climate data prediction and analysis by Shangke Liu, Ke Liu, Zheng Wang, Yuanyuan Liu, Bin Bai, Rui Zhao

“…Lastly, LSTM is adept at handling long-term dependencies, ensuring the model can remember and utilize information over extended time spans.Results and DiscussionExperiments conducted on temperature data from Guangdong Province in China validate the performance of the proposed model. Compared to four different climate prediction decomposition methods, the proposed hybrid model with the Transformer method performs the best. …”
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Severity classification and disposition prediction using ensemble learning for home-based patient management with adequate decision making by Amjad El Khatib, Chaima Ben Abdallah, Ahmed Nait Sidi Moh

“…Comparative analysis of different models provided valuable insights into performance tradeoffs, reinforcing the potential of ensemble learning for home-based patient assessment.…”
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Quantitative Assessment of Bolt Looseness in Beam–Column Joints Using SH-Typed Guided Waves and Deep Neural Network by Ru Zhang, Xiaodong Sui, Yuanfeng Duan, Yaozhi Luo, Yi Fang, Rui Miao

“…The dispersion properties of the I-shaped steel beam were analyzed using the semi-analytical finite element method, and a mode weight coefficient was presented to clarify the mode distribution under different types of external loads. Two pairs of transducers arranged on the same side of the bolt-connected region were utilized to obtain the directly incoming and end-reflected wave packets from four wave propagation paths. …”
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Early stroke behavior detection based on improved video masked autoencoders for potential patients by Meng Wang, Guanci Yang, Kexin Luo, Yang Li, Ling He

“…After that, the optimal parameters of EPBR-PS were determined through the experiment of learning rate and fusion weights of different features. On the NTU-ST dataset, comparative analysis with eight models demonstrated the superiority of EPBR-PS, evidenced by the average recognition accuracy of 89.61%, surpassing that 1.67% over the benchmark VideoMAE. …”
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A Deep Learning Framework for Chronic Kidney Disease stage classification by Gayathri Hegde M, P Deepa Shenoy, Venugopal KR, Arvind Canchi

“…To evaluate the proposed method, eight DL models — Feedforward Neural Network, Recurrent Neural Network, Deep Neural Network, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU) and Bidirectional GRU were trained on selected features using different FS methods, as well as complete dataset. …”
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EnGCI: enhancing GPCR-compound interaction prediction via large molecular models and KAN network by Weihao Liu, Xiaoli Li, Bo Hang, Pu Wang

“…Each module leverages different sources of multimodal information, and their fusion enhances the overall accuracy of GPCR-compound interaction (GCI) prediction. …”
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DSGRec: dual-path selection graph for multimodal recommendation by Zihao Liu, Wen Qu

“…We introduce independent contrastive learning tasks for the auxiliary signals, enabling DSGRec to explore the mechanisms behind feature embeddings from different perspectives. This approach ensures that each auxiliary module aligns with the user-item interaction view independently, calibrating its contribution based on historical interactions. …”
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Real-Time Common Rust Maize Leaf Disease Severity Identification and Pesticide Dose Recommendation Using Deep Neural Network by Zemzem Mohammed Megersa, Abebe Belay Adege, Faizur Rashid

“…The agricultural experts verified the accuracy of the recommendation system across different stages of the disease, and the system demonstrated 100% accuracy. …”
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Carbon Emission Reduction in Traffic Control: A Signal Timing Optimization Method Based on Rainbow DQN by Juan Lv, Zhaowei Wang, Jianxiao Ma

“…The model’s applicability is validated under various scenarios, including different proportions of electric vehicles and traffic volumes. …”
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