Intelligent model for forecasting fluctuations in the gold price by Mahdieh Tavassoli, Mahnaz Rabeei, Kiamars Fathi Hafshejani

“…The study also employed Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP) neural network models in deep learning mode to predict gold price fluctuations. …”
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Urdu Handwritten Characters Data Visualization and Recognition Using Distributed Stochastic Neighborhood Embedding and Deep Network by Mujtaba Husnain, Malik Muhammad Saad Missen, Shahzad Mumtaz, Dost Muhammad Khan, Mickäel Coustaty, Muhammad Muzzamil Luqman, Jean-Marc Ogier, Hizbullah Khattak, Sikandar Ali, Ali Samad

“…We performed three tasks in a disciplined order; namely, (i) we generated a state-of-the-art dataset of both the Urdu handwritten characters and numerals by inviting a number of native Urdu participants from different social and academic groups, since there is no publicly available dataset of such type till date, then (ii) applied classical approaches of dimensionality reduction and data visualization like Principal Component Analysis (PCA), Autoencoders (AE) in comparison with t-Stochastic Neighborhood Embedding (t-SNE), and (iii) used the reduced dimensions obtained through PCA, AE, and t-SNE for recognition of Urdu handwritten characters and numerals using a deep network like Convolution Neural Network (CNN). The accuracy achieved in recognition of Urdu characters and numerals among the approaches for the same task is found to be much better. …”
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Real-time detection and monitoring of public littering behavior using deep learning for a sustainable environment by Eaman Alharbi, Ghadah Alsulami, Sarah Aljohani, Waad Alharbi, Somayah Albaradei

“…This dataset was then used to train different models, including LRCN, CNN-RNN, and MoViNets. After extensive testing, MoViNets demonstrated the most promising results. …”
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MaDis-Stereo: Enhanced Stereo Matching via Distilled Masked Image Modeling by Jihye Ahn, Hyesong Choi, Soomin Kim, Dongbo Min

“…Although Transformer-based stereo models have been studied recently, their performance still lags behind CNN-based stereo models due to the inherent data scarcity issue in the stereo matching task. …”
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An LJDRNN-based efficient energy intensity prediction in carbon fiber composite material manufacturing process by Rangaswamy Nikhil, Karthikeyan A G, Prabhu Loganathan, Tabrej Khan, Tamer A Sebaey

“…The proposed LJDRNN achieved an accuracy of 98.32%, outperforming the JRNN (92.10%), RNN (87%), ANN (78%), and CNN (86%), thus demonstrating its superiority in energy intensity prediction. …”
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Land use and land cover classification for change detection studies using convolutional neural network by V. Pushpalatha, P.B. Mallikarjuna, H.N. Mahendra, S. Rama Subramoniam, S. Mallikarjunaswamy

“…Therefore, this paper proposed the Convolutional Neural Network (CNN)-based deep learning method for LULC classification. …”
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MFCEN: A lightweight multi-scale feature cooperative enhancement network for single-image super-resolution by Jiange Liu, Yu Chen, Xin Dai, Li Cao, Qingwu Li

“…In the deep feature extraction part, a novel integrated multi-level feature module was introduced. Compared to existing CNN and transformer hybrid super-resolution networks, MFCEN significantly reduced the number of parameters while maintaining performance. …”
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GS-DTA: integrating graph and sequence models for predicting drug-target binding affinity by Junwei Luo, Ziguang Zhu, Zhenhan Xu, Chuanle Xiao, Jingjing Wei, Jiquan Shen

“…Meanwhile, for each protein, a framework combining CNN, Bi-LSTM, and Transformer is used to extract the contextual and structural information of the protein amino acid sequences, and this combination can help to understand a comprehensive and detailed features of the protein. …”
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CGV-Net: Tunnel Lining Crack Segmentation Method Based on Graph Convolution Guided Transformer by Kai Liu, Tao Ren, Zhangli Lan, Yang Yang, Rong Liu, Yuantong Xu

“…To address this issue, this study proposes CGV-Net (CNN, GNN, and ViT networks), a novel tunnel crack segmentation network model that integrates convolutional neural networks (CNNs), graph neural networks (GNNs), and Vision Transformers (ViTs). …”
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Mood Detection from Physical and Neurophysical Data Using Deep Learning Models by Zeynep Hilal Kilimci, Aykut Güven, Mitat Uysal, Selim Akyokus

“…For this purpose, Feedforward Neural Network (FFNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) neural network are employed as deep learning methodologies. …”
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Deep Learning-Based Speech Emotion Recognition Using Multi-Level Fusion of Concurrent Features by Samuel, Kakuba, Alwin, Poulose, Dong, Seog Han, Senior Member, Ieee

“…Spatial and temporal features have been extracted sequentially in deep learning-based models using convolutional neural networks (CNN) followed by recurrent neural networks (RNN) which may not only be weak at the detection of the separate spatial-temporal feature representations but also the semantic tendencies in speech. …”
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Exploring Effects of Mental Stress with Data Augmentation and Classification Using fNIRS by M. N. Afzal Khan, Nada Zahour, Usman Tariq, Ghinwa Masri, Ismat F. Almadani, Hasan Al-Nashah

“…Linear discriminant analysis (LDA) showed a maximum accuracy of 60%, whereas non-augmented data classified by a convolutional neural network (CNN) provided the highest classification accuracy of 73%. …”
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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

“…Four deep learning models were designed, including Multi-Layer Perceptron model, Encoder-Decoder model, CNN-LSTM Attention model and Deep FM model. For the implementation of deep learning, 70% of the data was adopted for training and 30% for evaluation. …”
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A Framework for Early Detection of Acute Lymphoblastic Leukemia and Its Subtypes From Peripheral Blood Smear Images Using Deep Ensemble Learning Technique by Sajida Perveen, Abdullah Alourani, Muhammad Shahbaz, M. Usman Ashraf, Isma Hamid

“…Experimental results are obtained and comparative analysis among 7 well-known CNN Network architectures (AlexNet, VGGNet, Inception, ResNet-50, ResNet-18, Inception and DenseNet-121) is also performed that demonstrated that the proposed platform achieved comparatively high accuracy (99.95%), precision (99.92%), recall (99.92%), F1-Score (99.90%), sensitivity (99.92%) and specificity (99.97%). …”
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A Parallel Image Denoising Network Based on Nonparametric Attention and Multiscale Feature Fusion by Jing Mao, Lianming Sun, Jie Chen, Shunyuan Yu

“…It not only solved the problem of insufficient edge feature extraction but also solved the problem of the saturation of deep CNN performance. In this paper, a nonparametric attention mechanism is introduced in the two-branch feature extraction module, which enabled the network to pay attention to and learn the key information in the feature map, and improved the learning performance of the network. …”
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Progressive Self-Prompting Segment Anything Model for Salient Object Detection in Optical Remote Sensing Images by Xiaoning Zhang, Yi Yu, Daqun Li, Yuqing Wang

“…Most existing ORSI-SOD methods rely on pre-trained CNN- or Transformer-based backbones to extract features from ORSIs, followed by multi-level feature aggregation. …”
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PortNet: Achieving lightweight architecture and high accuracy in lung cancer cell classification by Kaikai Zhao, Youjiao Si, Liangchao Sun, Xiangjiao Meng

“…Result: Our tests demonstrated that PortNet significantly reduces the total parameter count to 2,621,827, which is over a fifth smaller compared to some mainstream CNN models, marking a substantial advancement for deployment in portable devices. …”
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G-UNETR++: A Gradient-Enhanced Network for Accurate and Robust Liver Segmentation from Computed Tomography Images by Seungyoo Lee, Kyujin Han, Hangyeul Shin, Harin Park, Seunghyon Kim, Jeonghun Kim, Xiaopeng Yang, Jae Do Yang, Hee Chul Yu, Heecheon You

“…Convolutional neural network (CNN)-based models have limited segmentation performance due to their localized receptive fields. …”
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Satellite-Based Forest Stand Detection Using Artificial Intelligence by Patrik Kovacovic, Rastislav Pirnik, Julia Kafkova, Mario Michalik, Alzbeta Kanalikova, Pavol Kuchar

“…Several models, including YOLOv8, YOLOv5 and Mask R-CNN, were tested and compared. An optimal model was selected based on parameters such as detection accuracy, total training time, and the precision of labeling detected image elements. …”
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QoE-Driven Big Data Management in Pervasive Edge Computing Environment by Qianyu Meng, Kun Wang, Xiaoming He, Minyi Guo

“…Then, with respect to accuracy, we propose a Tensor-Fast Convolutional Neural Network (TF-CNN) algorithm based on deep learning, which is suitable for high-dimensional big data analysis in the pervasive edge computing environment. …”
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