A comprehensive review of deep learning approaches for rice disease detection: Datasets, methodologies, and future directions by Usman Idris Ismail, Hui Na Chua, Rosdiadee Nordin, Muhammed Kabir Ahmed

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Early prediction of proton therapy dose distributions and DVHs for hepatocellular carcinoma using contour-based CNN models from diagnostic CT and MRI by Toshiya Rachi, Taku Tochinai

“…PSNR ranged from 24 to 28 dB, and SSIM exceeded 0.94 in most conditions. Gamma passing rates averaged 82–83% for IMPT and 92–93% for passive techniques. …”
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A review on amino acid based protein classification using supervised artificial intelligence (AI) models by Rida Zulfiqar, Talha Ahmed Khan, Abdullah Ayub Khan, Rubaika Akhtar, Sajid Ullah

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How Resilient Are Kolmogorov–Arnold Networks in Classification Tasks? A Robustness Investigation by Ahmed Dawod Mohammed Ibrahum, Zhengyu Shang, Jang-Eui Hong

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Characterizing Storm-Induced Coastal Flooding Using SAR Imagery and Deep Learning by Deanna Edwing, Lingsheng Meng, Suna Lv, Xiao-Hai Yan

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Experimental Study on Long Short-term Memory Networks for Identifying P-wave Primary Phase by Tianzhe WANG, Wanji ZHANG, Shanbo QI, Guoming JIANG

“…Additionally, while the new convolutional recurrent neural network has only seven network layers, it achieves an accurate phase identification of complex network models, showcasing the strengths of convolutional neural networks. …”
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A Comparative Study of a Deep Reinforcement Learning Solution and Alternative Deep Learning Models for Wildfire Prediction by Cristian Vidal-Silva, Roberto Pizarro, Miguel Castillo-Soto, Ben Ingram, Claudia de la Fuente, Vannessa Duarte, Claudia Sangüesa, Alfredo Ibañez

“…This study compared three deep learning models for wildfire prediction: Deep Reinforcement Learning (DRL) with Actor–Critic architecture, Convolutional Neural Network (CNN), and Transformer-based models. …”
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Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism by Wangyong Guo, Shijin Liu, Liguo Weng, Xingyu Liang

“…Subsequently, the Global Attention mechanism helps the model focus more on the most relevant parts of the input sequence, improving the model’s performance and generalization ability. …”
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Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework by Lingkang Zeng, Wei Yao, Ze Hu, Hang Shuai, Zhouping Li, Jinyu Wen, Shijie Cheng

“…Generator tripping scheme (GTS) is the most commonly used scheme to prevent power systems from losing safety and stability. …”
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Improving CNN Fish Detection and Classification with Tracking by Boubker Zouin, Jihad Zahir, Florian Baletaud, Laurent Vigliola, Sébastien Villon

“…As the size of the data collected outgrew the ability to process it, new means of automatic processing have been explored. Convolutional neural networks (CNNs) have been the most popular method for automatic underwater video analysis for the last few years. …”
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Multi-S3P: Protein Secondary Structure Prediction With Specialized Multi-Network and Self-Attention-Based Deep Learning Model by M. M. Mohamed Mufassirin, M. A. Hakim Newton, Julia Rahman, Abdul Sattar

“…In addition, using a self-attention mechanism allows the model to focus on the most important features for improving performance. …”
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Enhancing basal cell carcinoma classification in preoperative biopsies via transfer learning with weakly supervised graph transformers by Johan Björkman, Sigrid Lagerroth, Jan Siarov, Filmon Yacob, Noora Neittaanmäki

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MMAgentRec, a personalized multi-modal recommendation agent with large language model by Xiaochen Xiao

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Fault diagnosis method for rigid guides in vertical shaft hoisting systems by WANG Jianfeng, JIN Yuanzhi, ZHANG Yong, WANG Yongzhen, HE Jiacong

“…At present, vibration detection methods are mostly used for rigid guide fault diagnosis, but the diagnostic accuracy is easily affected by operating conditions such as cage load and running speed. …”
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Forecasting Groundwater Level by Characterizing Multiple Spatial Dependencies of Environmental Factors Using Graph‐Based Deep Learning by Yinghan Wu, Gang Mei, Kaixuan Shao, Nengxiong Xu, Jianbing Peng

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Relation extraction based on CNN and Bi-LSTM by Xiaobin ZHANG, Fucai CHEN, Ruiyang HUANG

“…Relation extraction aims to identify the entities in the Web text and extract the implicit relationships between entities in the text.Studies have shown that deep neural networks are feasible for relation extraction tasks and are superior to traditional methods.Most of the current relation extraction methods apply convolutional neural network (CNN) and long short-term memory neural network (LSTM) methods.However,CNN just considers the correlation between consecutive words and ignores the correlation between discontinuous words.On the other side,although LSTM takes correlation between long-distance words into account,the extraction features are not sufficiently extracted.In order to solve these problems,a relation extraction method that combining CNN and LSTM was proposed.three methods were used to carry out the experiments,and confirmed the effectiveness of these methods,which had some improvement in F1 score.…”
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Breast Cancer Detection Using Mammography: Image Processing to Deep Learning by Shahzad Ahmad Qureshi, Aziz-Ul-Rehman, Lal Hussain, Touseef Sadiq, Syed Taimoor Hussain Shah, Adil Aslam Mir, Muhammad Amin Nadim, Darnell K. Adrian Williams, Tim Q. Duong, Qurat-Ul-Ain Chaudhary, Natasha Habib, Asrar Ahmad, Syed Adil Hussain Shah

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Novel Gabor-Type Transform and Weighted Uncertainty Principles by Saifallah Ghobber, Hatem Mejjaoli

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Wavelet-Enhanced Desnowing: A Novel Single Image Restoration Approach for Traffic Surveillance Under Adverse Weather Conditions by Zihan Shen, Yu Xuan, Qingyu Yang

“…Image restoration under adverse weather conditions refers to the process of removing degradation caused by weather particles while improving visual quality. Most existing deweathering methods rely on increasing network scale and data volume to achieve better performance, which requires more expensive computing power. …”
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Enhancing the Accuracy of Image Classification for Degenerative Brain Diseases with CNN Ensemble Models Using Mel-Spectrograms by Sang-Ha Sung, Michael Pokojovy, Do-Young Kang, Woo-Yong Bae, Yeon-Jae Hong, Sangjin Kim

“…As the population ages, the prevalence of these neurodegenerative disorders is increasing, providing motivation for active research in this area. However, most studies are conducted using brain imaging, with relatively few studies utilizing voice data. …”
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