Adversarial attacks dataset for low light image enhancementMendeley Data by Axel Martinez, Matthieu Olague, Gustavo Olague, Emilio Hernandez, Julio Cesar Lopez-Arredondo

“…Enhancing images in low-light conditions is a field of research where deep convolutional neural networks have shown considerable effectiveness. …”
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Predictive Modeling of Dairy Sales Using Multi-Perspective Fusion Bi-LSTM Integrated with Universal Scale CNN: Insights from the Dairy Supply Chain by Naveen D. Chandavarkar, Dr. Soumya S

“…The performance of proposed Multi-Perspective Fusion Bi-LSTM with Universal Scale CNN is evaluated by different performance metrics which includes RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), MSE (Mean Square Error) and R2 (R Square).   …”
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Ocular Disease Detection Using Fundus Images: A Hybrid Approach of Grad-CAM and Multiscale Retinex Preprocessing With VGG16 Deep Features and Fine KNN Classification by Shreemat Kumar Dash, Kante Satyanarayana, Santi Kumari Behera, Sudarson Jena, Ashoka Kumar Ratha, Prabira Kumar Sethy, Aziz Nanthaamornphong

“…This research investigates the application of deep feature extraction for classifying eight different ocular diseases. The VGG16, a pretrained convolutional neural network (CNN) model, was employed for feature extraction, while the fine k-nearest neighbor (KNN) classifier was utilized for classification. …”
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Diagnosis Retinal Disease by using Deep Learning Models by attallh salih, Manar Kashmoola

“…The proposed model consists of three different convolutional neural network (CNN) models to be used in this approach and compare the results of each one with others. …”
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Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models by Zheng Han

“…FedAVG-DWA provides the best performance in different clients’ systems. However, system heterogeneity, communication costs, and data imbalance remain critical. …”
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EEG Depression Recognition Based on Multi-domain Features Combined with CBAM Model by CHEN Yu, HU Xiuxiu, WANG Sheng

“…Firstly, the continuous wavelet transform (CWT) is used to extract time-frequency domain features, and combined with the spatial information of EEG electrodes to form a 2D feature image, which jointly retains the spatial, time and frequency information of EEG; then the convolutional neural network (CNN) is used) to extract spatial and frequency domain features, and then input bidirectional long and short-term memory ( BiLSTM) to capture time information; finally combined with attention mechanism (AM) , different weights are assigned to the multi-domain features extracted from the network, enabling the selection of more representative depressive features, thereby improving the accuracy of identifying depression. …”
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Single-level Discrete Two Dimensional Wavelet Transform Based Multiscale Deep Learning Framework for Two-Wheeler Helmet Detection by Amrutha Annadurai, Manas Ranjan Prusty, Trilok Nath Pandey, Subhra Rani Patra

“…In particular, four different modes are used for segmenting a single image namely approximation, horizontal detail, vertical detail and diagonal detail. …”
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Deep learning-developed multi-light source discrimination capability of stretchable capacitive photodetector by Su Bin Choi, Jun Sang Choi, Hyun Sik Shin, Jeong-Won Yoon, Youngmin Kim, Jong-Woong Kim

“…It shows high sensitivity at both 448 and 505 nm wavelengths, detecting light sources under mechanical deformations, different wavelengths and frequencies. By integrating a one-dimensional convolutional neural network (1D-CNN) model, we classified the light source power level with 96.52% accuracy even the light of two wavelengths is mixed. …”
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Multimodal fusion based few-shot network intrusion detection system by Congyuan Xu, Yong Zhan, Zhiqiang Wang, Jun Yang

“…Existing few-shot learning methods, while reducing reliance on large datasets, mostly handle single-modality data and fail to fully exploit complementary information across different modalities, limiting detection performance. …”
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Artificial Intelligence for Earthquake Prediction: A Preliminary System Based on Periodically Trained Neural Networks Using Ionospheric Anomalies by Sergio Baselga

“…The use of three-dimensional data matrices, having spatiotemporal information to feed a convolutional neural network, is proposed in this contribution. …”
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THE EMPIRICAL COMPARISON OF DEEP NEURAL NETWORK OPTIMIZERS FOR BINARY CLASSIFICATION OF OCT IMAGES by R. Loganathan, S. Latha

“…The Adam optimizer could train all binary convolutional neural networks based on these results.…”
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RETRACTED PAPER: Enhancing 3D human pose estimation through multi-feature fusion by Xianlei GE, Vladimir MARIANO

“…The proposed model utilizes convolutional kernels of different sizes to extract feature maps with diverse resolutions and dimensions. …”
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Prescription Recommendation Algorithm Based on Herbal Property-Driven Compatibility Mechanism Semantic Modeling by Geng Xueru, Zhang Jiantong, Luo Tao, Hou Jianchen, Tao Xiaohua

“…This is followed by aggregating higher-order heterogeneous path information of nodes through a graph convolutional network model. Finally, an attention mechanism is employed to fuse information from symptom interaction graphs, symptom-herb interaction graphs, and herb interaction graphs, distinguishing the influence of different dimensions of TCM semantic information. …”
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Brain Representation in Conscious and Unconscious Vision by Ning Mei, David Soto

“…Moreover, this pattern of results generalised when the models were trained and tested with different participants. Remarkably, these observations results held even when the analysis was restricted to observers that showed null perceptual sensitivity. …”
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RoBERTa-Based Multi-Feature Integrated BiLSTM and CNN Model for Ceramic Review Analysis by LiHua Yang, Jun Wang, WangRen Qiu

“…To address the limitation that the Robustly Optimized BERT Pretraining Approach (RoBERTa) may not effectively capture local dependencies and salient features within the text, we propose a feature fusion framework based on RoBERTa’s multi-output architecture. By feeding different outputs of RoBERTa into Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks, the model effectively captures both local static patterns and global contextual dependencies, thereby enhancing its capability to handle complex textual inputs. …”
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UMEDNet: a multimodal approach for emotion detection in the Urdu language by Adil Majeed, Hasan Mujtaba

“…In the end, we analyzed the impact of UMEDNet and found that our model integrates data on different modalities and leads to better performance.…”
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Intelligent Model for Brain Tumor Identification Using Deep Learning by Abdul Hannan Khan, Sagheer Abbas, Muhammad Adnan Khan, Umer Farooq, Wasim Ahmad Khan, Shahan Yamin Siddiqui, Aiesha Ahmad

“…The processing of medical images plays a crucial role in assisting humans in identifying different diseases. The classification of brain tumors is a significant part that depends on the expertise and knowledge of the physician. …”
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Multiple sclerosis diagnosis with brain MRI retrieval: A deep learning approach by R.M. Haggag, Eman M. Ali, M.E. Khalifa, Mohamed Taha

“…We experiment with Nine different distance metrics to measure query and database image similarity. …”
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Ambiguous facial expression detection for Autism Screening using enhanced YOLOv7-tiny model by Akhil Kumar, Ambrish Kumar, Dushantha Nalin K. Jayakody

“…Children with autism spectrum disorder show ambiguous facial expressions which are different from the facial attributes of normal children. …”
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Presentation Attack Detection using iris periocular visual spectrum images by Andrés Valenzuela, Juan E. Tapia, Violeta Chang, Christoph Busch

“…The analysis was carried out by evaluating the performance of five Convolutional Neural Networks (CNN) using both facial and periocular iris images for PAD with two different attack instruments. …”
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