Analysis and Classification of Fake News Using Sequential Pattern Mining by M. Zohaib Nawaz, M. Saqib Nawaz, Philippe Fournier-Viger, Yulin He

“…Current machine and deep learning based methodologies for classification/detection of fake news are content-based, network (propagation) based, or multimodal methods that combine both textual and visual information. …”
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A cross‐project defect prediction method based on multi‐adaptation and nuclear norm by Qingan Huang, Le Ma, Siyu Jiang, Guobin Wu, Hengjie Song, Libiao Jiang, Chunyun Zheng

“…Existing CPDP methods based on the deep learning model may not fully consider the differences among projects. …”
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Solving Spatial Optimization Problems via Lagrangian Relaxation and Automatic Gradient Computation by Zhen Lei, Ting L. Lei

“…This paper aims to ease the development of Lagrangian relaxation algorithms for GIS practitioners by employing the automatic (sub)gradient (autograd) computation capabilities originally developed in modern Deep Learning. Using the classic <i>p</i>-median problem as an example, we demonstrate how Lagrangian relaxation can be developed with paper and pencil, and how the (sub)gradient computation derivation can be automated using autograd. …”
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Remaining Useful Life Prediction Techniques of Electric Valves for Nuclear Power Plants with Convolution Kernel and LSTM by Hang Wang, Min-jun Peng, Yong-kuo Liu, Shi-wen Liu, Ren-yi Xu, Hanan Saeed

“…Experiments show that the proposed method could predict RUL more accurately compared to other typical machine learning and deep learning methods. This will further enhance maintenance efficiency of any plant.…”
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Avances en el aprovechamiento de biopolímeros y productos peruanos by Erika Del Milagro Lozano-Flores

“…Asimismo, el análisis de palabras clave destaca la relevancia de técnicas como "machine learning", "deep learning" y "neural networks". Los mapas de colaboración reflejan que Estados Unidos y China son líderes en producción y coautoría. …”
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Forecasting Travel Speed in the Rainfall Days to Develop Suitable Variable Speed Limits Control Strategy for Less Driving Risk by Ping Wang, Yajie Zhang, Saisai Wang, Li Li, Xiaohui Li

“…The experimental results show that a significant decrease happens in the travel speed in the rainfall day during peak hours. Furthermore, the deep learning algorithm that considers more factors such as the rainfall intensity and traffic flow could improve the prediction accuracy. …”
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Secured Wireless Network Based on a Novel Dual Integrated Neural Network Architecture by H. V. Ramachandra, Pundalik Chavan, S. Supreeth, H. C. Ramaprasad, K. Chatrapathy, G. Balaraju, S. Rohith, H. S. Mohan

“…DINN is designed for any presence of deep learning-based attack in a physical security layer. …”
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MultiChem: predicting chemical properties using multi-view graph attention network by Heesang Moon, Mina Rho

“…Recent advances in deep learning approaches have offered deeper insights into molecular structures. …”
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Digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop AI and YOLO foundation models by Juan Jose Mora, Guy Blomme, Nancy Safari, Sivalingam Elayabalan, Ramasamy Selvarajan, Michael Gomez Selvaraj

“…We developed and evaluated several deep learning foundation models, including YOLO-NAS, YOLOv8, YOLOv9, and Faster-RCNN to perform accurate disease detection on both platforms. …”
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Application of improved and efficient image repair algorithm in rock damage experimental research by Mingzhe Xu, Xianyin Qi, Diandong Geng

“…To address this issue, this paper focuses on the restoration of image data acquired through digital image technology, leveraging deep learning techniques, and using soft and hard rocks made of similar materials as research subjects, an improved Incremental Transformer image algorithm is employed to repair distorted or missing strain nephograms during uniaxial compression experiments. …”
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Variational graph autoencoder for reconstructed transcriptomic data associated with NLRP3 mediated pyroptosis in periodontitis by Pradeep K. Yadalam, Prabhu Manickam Natarajan, Carlos M. Ardila

“…This method identifies natural groupings within biological data without prior labels. VGAE, a deep learning model, captures complex graph relationships for tasks like link prediction and edge detection. …”
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AI Methods for Antimicrobial Peptides: Progress and Challenges by Carlos A. Brizuela, Gary Liu, Jonathan M. Stokes, Cesar de laFuente‐Nunez

“…Initially, classical ML approaches dominated the field, but recently there has been a shift towards deep learning (DL) models. Despite significant contributions, existing reviews have not thoroughly explored the potential of large language models (LLMs), graph neural networks (GNNs) and structure‐guided AMP discovery and design. …”
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In-Depth Learning Layout and Path Optimization of Energy Service Urban Distribution Sites under e-Commerce Environment by Kun Wang, Ki-Hyung Bae

“…This paper fully considers the characteristics of the network operation mode of the energy service city distribution site and establishes an optimization model for the location selection and vehicle routing of the distribution center with the lowest total system cost under the simultaneous delivery service mode; based on the hierarchical solution strategy, a combination of deep learning is designed. Algorithms mainly include two-stage hybrid heuristic algorithm of cluster analysis, maximum coverage and genetic algorithm; simulation analysis is conducted to verify the effectiveness of the model and algorithm by data simulation, finally get the integrated optimization plan of distribution center location and routing, and put forward the operation strategy through the result expansion analysis. …”
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Prediction and Evaluation of Coal Mine Coal Bump Based on Improved Deep Neural Network by Shuang Gong, Yi Tan, Wen Wang

“…Based on the research results of rock burst, 305 groups of rock burst engineering case data are collected as the sample data of coal bump prediction, and then, the prediction model based on a dropout and improved Adam-based deep neural network (DA-DNN) is established by using deep learning technology. The DA-DNN model avoids the problem of determining the index weight, is completely data-driven, reduces the influence of human factors, and can realize the learning of complex and subtle deep relationships in incomplete, imprecise, and noisy limited data sets. …”
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MFEMDroid: A Novel Malware Detection Framework Using Combined Multitype Features and Ensemble Modeling by Wei Gu, Hongyan Xing, Tianhao Hou

“…Combining static analysis methods with deep Learning is a promising approach to defend against that. …”
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Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm by Raji Krishna, Hemamalini S

“…In the first phase of this paper, uncertainty parameters like day‐ahead power from renewable energy sources (RES) and load demand (LD) are forecasted using the long short‐term memory (LSTM) deep learning algorithm. The LSTM outperforms the artificial neural network (ANN) model in terms of mean square error (MSE) and prediction accuracy (R2) for both training and testing datasets. …”
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Optimization of an Intelligent Sorting and Recycling System for Solid Waste Based on Image Recognition Technology by Haitao Chen

“…Image recognition is a technique to recognize images by capturing real-life images through devices and performing feature extraction, and this technique has been widely used since its inception. The deep learning-based classification algorithm for recyclable solid waste studied in this paper can classify solid waste efficiently and accurately, solving the problem that people do not know how to classify solid waste in daily life. …”
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Cross-Camera External Validation for Artificial Intelligence Software in Diagnosis of Diabetic Retinopathy by Meng-Ju Tsai, Yi-Ting Hsieh, Chin-Han Tsai, Mingke Chen, An-Tsz Hsieh, Chung-Wen Tsai, Min-Ling Chen

“…To investigate the applicability of deep learning image assessment software VeriSee DR to different color fundus cameras for the screening of diabetic retinopathy (DR). …”
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Efficient multiplayer battle game optimizer for numerical optimization and adversarial robust neural architecture search by Rui Zhong, Yuefeng Xu, Chao Zhang, Jun Yu

“…As a potential optimization technique, EMBGO holds promise for diverse applications in real-world problems and deep learning scenarios. The source code of EMBGO is made available in https://github.com/RuiZhong961230/EMBGO.…”
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Predicting wind power using LSTM, Transformer, and other techniques by Arun Kumar M, Rithick Joshua K, Sahana Rajesh, Caroline Dorathy Esther J, Kavitha Devi MK

“…In this study, we bridge the gap by exploring various machine learning (ML) and deep learning (DL) methodologies to enhance wind power forecasts. …”
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