Reconstructing Evapotranspiration in British Columbia Since 1850 Using Publicly Available Tree-Ring Plots and Climate Data by Hang Li, John Rex

“…This can be accomplished using satellite imagery since the 1980s, but prior to that, a different approach is required. Using a global ET dataset (1982 to 2010) with 1 km resolution, climate station information from 1850 to 2010, and 54 tree-ring plots from the International Tree-Ring Data Bank (ITRDB) database, ET reconstructions were developed for each vegetated pixel with point-by-point regressions in British Columbia. …”
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Classification of English Words into Grammatical Notations Using Deep Learning Technique by Muhammad Imran, Sajjad Hussain Qureshi, Abrar Hussain Qureshi, Norah Almusharraf

“…The classification of parts of speech into different grammatical notations is the major problem that non-native English learners face. …”
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Channel computation based on multi-scale attention residual network by Wengang Li, Deli Zhou, Qiong Ye

“…The extracted features are then integrated and exploited through a residual convolutional architecture to derive an estimation of the channel matrix. …”
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Fault Diagnosis under Variable Working Conditions Based on STFT and Transfer Deep Residual Network by Yan Du, Aiming Wang, Shuai Wang, Baomei He, Guoying Meng

“…Unlike traditional deep convolutional neural network (DCNN) methods, by combining with transfer learning, the TDRN can make a bridge between two different working conditions, thereby using the knowledge learned from a working condition to achieve a high classification accuracy in another working condition. …”
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Intelligent SDN to enhance security in IoT networks by Safi Ibrahim, Aya M. Youssef, Mahmoud Shoman, Sanaa Taha

“…This strategy improves the efficacy of multiclassification techniques, ultimately resulting in greater accuracy in the identification and classification of different security threats in SDN environments. The initial DNN model exhibited satisfactory performance, with an accuracy of 87%. …”
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A real-time predicting online tool for detection of people’s emotions from Arabic tweets based on big data platforms by Naglaa Abdelhady, Ibrahim E. Elsemman, Taysir Hassan A. Soliman

“…For the first stage, two different approaches: The deep Learning (DL) approach and the Transfer Learning-based (TL) approach to find the optimal classifier for identifying and predicting emotion. …”
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Multi-Dimensional Feature Fusion and Enhanced Attention Streaming Movie Prediction Algorithm by Hanqing Hu, Tianmu Tian, Chengjing Liu, Xueyuan Bai

“…Second, an attention mechanism was applied to dynamically assign importance to different time steps and features, enabling the model to focus on the most critical information based on time periods and movie types. …”
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DDoS attack detection in intelligent transport systems using adaptive neuro-fuzzy inference system by G. Usha, H. Karthikeyan, Kumar Gautam, Nikhil Pachauri

“…Moreover, Intelligent Transportation Systems is different from the standard vehicular ad hoc network design since it functions in a highly dynamic environment brought on by the quick mobility between the nodes in short connection times. …”
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Diagnostics of retinal pathologies by optical coherence tomography images using artificial intelligence tools by V. V. Neroev, A. A. Bragin, O. V. Zaytseva

“…The study used a dataset (20,000 eyes), publicly available on the Internet, which contains OCT images of healthy retina (5,000 eyes) and retina affected by three different pathologies (choroid neovascularization, macular edema, multiple drusen, 15,000 eyes). …”
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Fingerprint Classification Based on Multilayer Extreme Learning Machines by Axel Quinteros, David Zabala-Blanco

“…A brute-force heuristic optimization approach is applied to determine the hyperparameters that maximize classification accuracy across different M-ELM configurations while avoiding excessive training times. …”
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A model for tobacco growing area classification based on time series features of thermogravimetric analysis by Jiaxu Xia, Yunong Tian, Xianwei Hao, Yuhan Peng, Guanqun Luo, Zhihua Gan

“…By analyzing 375 tobacco samples from ten different provinces, CNN is employed to extract local features, while LSTM captures long-term dependencies in the DTG data. …”
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A novel hybrid model by integrating TCN with TVFEMD and permutation entropy for monthly non-stationary runoff prediction by Huifang Wang, Xuehua Zhao, Qiucen Guo, Xixi Wu

“…In this study, the proposed model is employed to predict the monthly runoff datasets of the Fenhe River, and different comparative models are established. The results show that the Nash-Sutcliffe efficiency coefficient (NSE) value of this model is 0.99, and all the indicators are better than other models. …”
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Reservoir parameter prediction technology based on deep learning and its application in the Panyu 4 Sag, Pearl river mouth Bain by Yingwei Li, Yanhui Zhu, Zhenshen Li, Xiaozhao Zhang, Guofu Cai

“…It then constructs training samples by simulating different lithologies and extracting the corresponding elastic parameters from well-log data. …”
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Multi-kernel inception-enhanced vision transformer for plant leaf disease recognition by Sk Mahmudul Hassan, Kumar Sekhar Roy, Ruhul Amin Hazarika, Mehbub Alam, Mithun Mukherjee

“…The use of multiple filters with different kernel sizes efficiently uses computing resources to extract relevant features without the need for deeper networks. …”
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Wearable Sensor-Based Behavioral User Authentication Using a Hybrid Deep Learning Approach with Squeeze-and-Excitation Mechanism by Sakorn Mekruksavanich, Anuchit Jitpattanakul

“…Experiments performed on two datasets, HMOG and USC-HAD, indicate the efficacy of our technique across different tasks. In the HMOG dataset, SE-DeepConvNet attains a minimal equal error rate (EER) of 0.38% and a maximum accuracy of 99.78% for the Read_Walk activity. …”
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On Explainability of Reinforcement Learning-Based Machine Learning Agents Trained with Proximal Policy Optimization That Utilizes Visual Sensor Data by Tomasz Hachaj, Marcin Piekarczyk

“…This estimation makes it possible to determine whether the network makes decisions as expected (decision-making is related to the model’s observation of objects belonging to different semantic classes in the environment) and to detect unexpected, seemingly chaotic behavior that might be, for example, the result of data bias, bad design of the reward function or insufficient generalization abilities of the model. …”
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Modeling Equatorial to Mid‐Latitudinal Global Night Time Ionospheric Plasma Irregularities Using Machine Learning by Ephrem Beshir Seba, Giovanni Lapenta

“…Furthermore, north‐south hemispherical asymmetry in PI observed across different seasons. Through the RF parameter importance analysis method, we determine that temporal, geographical, and magnetic disturbance‐related factors play a crucial role in predicting global PI variabilities. …”
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Accurate Detection and Tracking of Small-Scale Vehicles in High-Altitude Unmanned Aerial Vehicle Bird-View Imagery by Heshan Zhang, Xin Tan, Mengwei Fan, Cunshu Pan, Zhanji Zheng, Shuang Luo, Jin Xu

“…Significantly, the algorithm proposed in this paper has sufficient robustness for small-scale tracking tasks of aerial videos captured at different altitudes.…”
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Deep-learning model for embryo selection using time-lapse imaging of matched high-quality embryos by Lisa Boucret, Floris Chabrun, Magalie Boguenet, Pascal Reynier, Pierre-Emmanuel Bouet, Pascale May-Panloup

“…The model was developed with a new approach based on matched KID (Known Implantation Data) embryos derived from the same cohort of a stimulation cycle, both judged to be of good quality according to classical morphological criteria and morphokinetics, transferred fresh or frozen, but with a different implantation fate (clinical pregnancy vs. failure of implantation). …”
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KediNet: A Hybrid Deep Learning Architecture for Thai Dessert Recognition by Nawanol Theera-Ampornpunt, Panisa Treepong, Naphadon Phokabut, Sarawut Phlaichana

“…Our main contributions include: 1) the proposal of KediNet, which outperforms nine state-of-the-art deep learning models; 2) a comprehensive comparison of classification performance across different model architectures, image resolutions, and data augmentation techniques; and 3) the creation of THDESS-20, a new publicly available dataset containing 5,242 images across 20 classes of Thai desserts. …”
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