Optimizing Solar Radiation Prediction Based on The Internet of Things Platform in Photovoltaic Power Plant by Neda Ashrafi Khozani, Maryam Mahmoudi, Shabnam Nasr Esfahani

“…Utilizing an innovative fitness function in model optimization enhances prediction accuracy and adaptability to real photovoltaic power plant environments. …”
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Class Imbalance in the Automatic Interpretation of Remote Sensing Images: A Review by Pengdi Chen, Yuanrui Ren, Baoan Zhang, Yuan Zhao

“…The solution to the proposed problem involves three aspects: model innovation and optimization, loss function improvement, and data augmentation. Experiments on benchmark datasets have demonstrated the effectiveness of these methods. …”
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Adapting language generation to dialogue environments and users for task-oriented dialogue systems by Atsumoto Ohashi, Ryuichiro Higashinaka

“…Inspired by recent advances in reinforcement learning (RL) for language generation tasks, we propose ANTOR, a method for Adaptive Natural language generation for Task-Oriented dialogue via Reinforcement learning. …”
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A Multi-Scale attention network for building extraction from high-resolution remote sensing images by Jing Chang, Xiaohui He, Dingjun Song, Panle Li, Mengjia Qiao, Xijie Cheng

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Game Strategies for Data Transfer Infrastructures Against ML-Profile Exploits by Nageswara S. V. Rao, Chris Y. T. Ma, Fei He

“…The infrastructure’s throughput profile, estimated as a function of the connection round trip time using Machine Learning (ML) methods, is an indicator of its operational state, and has been utilized for monitoring, diagnosis and optimization purposes. …”
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Video Analysis and Frame Prediction Based on Improved Object Detection and ConvGRU by Xijuan Wang, Ru Chen

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Real-Time Scheduling with Independent Evaluators: Explainable Multi-Agent Approach by Artem Isakov, Danil Peregorodiev, Ivan Tomilov, Chuyang Ye, Natalia Gusarova, Aleksandra Vatian, Alexander Boukhanovsky

“…This study introduces a multi-agent reinforcement learning approach to address the challenges of real-time scheduling in dynamic environments, with a specific focus on healthcare operations. …”
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DIGITAL TRAINING GAMES AS COMPONENTS OF MULTIMEDIA EDUCATIONAL RESOURCE by Iryna Khoroshevska, Oleksii Khoroshevskyi

“…It is mentioned that training games support activating the process of learning the educational materials by the users with their being represented in a game form. …”
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Simulation and Empirical Studies of Long Short-Term Memory Performance to Deal with Limited Data by Khusnia Nurul Khikmah, Kusman Sadik, Khairil Anwar Notodiputro

“…This research is proposed to determine the performance of time series machine learning in the presence of noise, where this approach is intended to forecast time series data. …”
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PLFF-SLAM: A Point and Line Feature Fused Visual SLAM Algorithm for Dynamic Illumination Environments by Shucheng Huang, Wenhan Ren, Mingxing Li

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A Dual-Perspective Self-Supervised IoT Intrusion Detection Method Based on Topology Reconstruction and Feature Perturbation by Ruisheng Li, Huimin Shen, Qilong Zhang

“…This paper presents a graph-based framework leveraging self-supervised learning to address these challenges. This framework employs a graph encoder to capture topological information and generate graph embeddings, utilizing topology reconstruction and feature perturbation to create diverse loss graphs that enhance feature learning. …”
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The Relationship between Translanguaging Perceptions and Language Teacher Immunity by Mohammad Aliakbari, Fateme Fadaeian

“…Acknowledging the difference in the lexical backgrounds of students and teachers aids in having a realistic picture of how learning happens. Translanguaging promotes learning by exploring the multilingual capabilities of students. …”
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GeoTemporal clustering for aquifer delineation: a big data approach to synchronizing and analyzing variable-length groundwater time series by Khalid ElHaj, Dalal Alshamsi

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Accurate Extraction of Rural Residential Buildings in Alpine Mountainous Areas by Combining Shadow Processing with FF-SwinT by Guize Luan, Jinxuan Luo, Zuyu Gao, Fei Zhao

“…Based on the comparative experiments with existing deep learning models, the Swin Transformer was selected as the baseline model. …”
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Use of AI-methods over MD simulations in the sampling of conformational ensembles in IDPs by Souradeep Sil, Ishita Datta, Sankar Basu

“…Intrinsically Disordered Proteins (IDPs) challenge traditional structure-function paradigms by existing as dynamic ensembles rather than stable tertiary structures. …”
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RDM-YOLO: A Lightweight Multi-Scale Model for Real-Time Behavior Recognition of Fourth Instar Silkworms in Sericulture by Jinye Gao, Jun Sun, Xiaohong Wu, Chunxia Dai

“…Additionally, the minimum point distance intersection over union (MPDIoU) loss function is proposed to enhance bounding box regression efficiency, mitigating challenges posed by overlapping targets and positional deviations. …”
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Self-supervised denoising method for single neutron image based on the S2S-NR network by Wangtao Yu, Peng Xu, Xinghui Cai, Man Zhou, Jie Bao

“…In recent years, self-supervised learning has become a powerful tool for single image denoising. …”
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A Novel Hybrid Approach Using an Attention-Based Transformer + GRU Model for Predicting Cryptocurrency Prices by Esam Mahdi, Carlos Martin-Barreiro, Xavier Cabezas

“…We evaluate the performance of our proposed model by comparing it with four other machine learning models, two are non-sequential feedforward models: radial basis function network (RBFN) and general regression neural network (GRNN), and two are bidirectional sequential memory-based models: bidirectional long short-term memory (BiLSTM) and bidirectional gated recurrent unit (BiGRU). …”
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Three-Stage Bidding Strategy of Generation Company Based on Double Deep Q-Network under Incomplete Information Condition by Pengpeng YANG, Beibei WANG, Peng XU, Gaoqin WANG, Yaxian ZHENG

“…Finally, a PJM 5-bus test case is used to compare the rewards obtained by DDQN and traditional Q-learning algorithm. The results shows that the DDQN algorithm can make appropriate decisions according to the complex state while the Q-learning algorithm has poor performance. …”
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LittleFaceNet: A Small-Sized Face Recognition Method Based on RetinaFace and AdaFace by Zhengwei Ren, Xinyu Liu, Jing Xu, Yongsheng Zhang, Ming Fang

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