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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Prediction of Radiation Belts Electron Fluxes at a Low Earth Orbit Using Neural Networks With PROBA‐V/EPT Data by Edith Botek, Viviane Pierrard, Alexandre Winant

“…Abstract We introduce for the first time the PROBA‐V/EPT electron flux data to train a deep learning data‐driven model with the purpose of investigating the Earth’s radiation belts dynamics. …”
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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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The study on multi-defect detection for leather using object detection techniques by Hasan Onur Ataç, Ahmet Kayabaşı, M. Fatih Aslan

“…AI-based vision techniques were used to detect flaws on the leather on photographs taken with a high-resolution camera. Deep learning algorithms Mask R-CNN, YOLOv8 and within the framework of Detectron2, RetinaNet R101 3x, Faster R-CNN R101-FPN 3x models were performed and a comparison was made between these algorithms. …”
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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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Classification Performance Comparison of BERT and IndoBERT on SelfReport of COVID-19 Status on Social Media by Irwan Budiman, Mohammad Reza Faisal, Astina Faridhah, Andi Farmadi, Muhammad Itqan Mazdadi, Triando Hamonangan Saragih, Friska Abadi

“…The classification of social media messages can be achieved through the application of classification algorithms. Many deep learning-based algorithms, such as Convolutional Neural Networks (CNN) or Long Short-Term Memory (LSTM), have been used for text classification. …”
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Sentiment analysis of movie reviews: A flask application using CNN with RoBERTa embeddings by Biplov Paneru, Bipul Thapa, Bishwash Paneru

“…In this work, we analyze sentiment in IMDB movie reviews using a hybrid deep learning model combining RoBERTa embeddings with a convolutional neural network (R-CNN). …”
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Deep attributes and decisions fusion for no-reference video quality analysis by Adil Baig

“…This study describes a novel, deep learning-based strategy for NR-VQA that uses several pre-trained deep neural networks to characterize probable image and video distortions across parallel. …”
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Open-world disaster information identification from multimodal social media by Chen Yu, Bin Hu, Zhiguo Wang

“…Abstract The application of multimodal deep learning for emergency response and recovery, specifically in disaster social media analysis, is of utmost importance. …”
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Gradient pooling distillation network for lightweight single image super-resolution reconstruction by Zhiyong Hong, GuanJie Liang, Liping Xiong

“…In recent years, significant progress about SISR has been achieved through the utilization of deep learning technology. However, these deep methods often exhibit large-scale networks architectures, which are computationally intensive and hardware-demanding, and this limits their practical application in some scenarios (e.g., autonomous driving, streaming media) requiring stable and efficient image transmission with high-definition picture quality. …”
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CRE-YOLO: Efficient Jaboticaba Tree Detection on UAV Platforms by Junyu Huang, Renbo Luo, Yuna Tan, Zhuowen Wu

“…The primary objective is to compress the parameters of deep learning models while improving accuracy to enable their deployment on UAV platforms for rapid Jaboticaba tree identification. …”
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