Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China by Zeqiang Chen, Lei Wu, Nengcheng Chen, Ke Wan

“…The model was also compared with the random forest, long short-term memory, deep neural network, and convolutional neural networks (1D) models to distinguish it from previous shallow machine learning models to estimate NEE, and the results show that deep learning models have great potential in NEE modeling. …”
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Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization by Guosheng Cui, Ye Li, Jianzhong Li, Jianping Fan

“…Nonnegative Matrix Factorization (NMF) is one of the most popular feature learning technologies in the field of machine learning and pattern recognition. It has been widely used and studied in the multi-view clustering tasks because of its effectiveness. …”
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The Short‐Time Prediction of the Energetic Electron Flux in the Planetary Radiation Belt Based on Stacking Ensemble‐Learning Algorithm by Rongxin Tang, Yuhao Tao, Jiahao Li, Zhou Chen, Xiaohua Deng, Haimeng Li

“…The deep neural network (DNN), the convolutional neural network (CNN), the combination of CNN and DNN (CNN&DNN), the linear regression (LR), and the light gradient boosting machine (LightGBM) are among the machine learning models chosen. We carefully compared the electron flux predictions for 20 energy levels and all five models can present valid short‐time flux forecasts. …”
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Nonlinearity of the post-spinel transition and its expression in slabs and plumes worldwide by Junjie Dong, Rebecca A. Fischer, Lars P. Stixrude, Matthew C. Brennan, Kierstin Daviau, Terry-Ann Suer, Katlyn M. Turner, Yue Meng, Vitali B. Prakapenka

“…Combining our data with results from the literature, and using a global analysis based on machine learning, we find a pronounced nonlinearity in the post-spinel boundary, with its slope ranging from –4 MPa/K at 2100 K, to –2 MPa/K at 1950 K, and to 0 MPa/K at 1600 K. …”
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Research on Spread Spectrum Codes Optimized by Sparrow Search Algorithm and Extreme Gradient Boosting by LIANG Zhiru, BIAN Dongming, ZHANG Gengxin

“…【Results】By comparing conventional peak detection and decision tree classification methods at different signal-to-noise ratios and comparing the classification accuracy of different sequence periods, the simulation results show that the spread code identification and classification method optimized by SSA with XGBOOST after preprocessing has a higher classification and identification success rate than conventional machine learning and peak detection methods. Its performance gradually improves at high sequence periods.…”
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Object Re-Identification Based on Federated Incremental Subgradient Proximal Optimization by Li Kang, Chuanghong Zhao, Jianjun Huang

“…Federated learning, as a distributed machine learning framework, can utilize dispersed data for model training without sharing raw data, thereby reducing communication costs and ensuring data privacy. …”
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Plant and marine-derived natural products: sustainable pathways for future drug discovery and therapeutic development by Muhammad Ahmad, Muhammad Ahmad, Maleha Tahir, Zibin Hong, Muhammad Anjum Zia, Hamza Rafeeq, Muhammad Shaheez Ahmad, Saif ur Rehman, Junming Sun

“…Future perspectives will highlight the role of responsible innovation, artificial intelligence, and machine learning in advancing drug discovery, underscoring the importance of continued research to meet global health needs.…”
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Integrating Modern Technologies into Traditional Anterior Cruciate Ligament Tissue Engineering by Aris Sopilidis, Vasileios Stamatopoulos, Vasileios Giannatos, Georgios Taraviras, Andreas Panagopoulos, Stavros Taraviras

“…Finally, we highlight the benefits of incorporating new technologies like artificial intelligence and machine learning that could revolutionize tissue engineering.…”
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Künstliche Intelligenz im Englischunterricht – Grundwissen und Praxisbeispiele by Inez De Florio-Hansen

“…What are the meanings of fundamental concepts such as algorithms, machine learning, and artificial neural networks? What are the best practices for inputting prompts (prompt engineering), and how can prompt quality be improved to achieve desired outcomes? …”
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A data-driven latent variable approach to validating the research domain criteria framework by S. K. L. Quah, B. Jo, C. Geniesse, L. Q. Uddin, J. A. Mumford, D. M. Barch, D. A. Fair, I. H. Gotlib, R. A. Poldrack, M. Saggar

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GroupFound: An effective approach to detect suspicious accounts in online social networks by Bo Feng, Qiang Li, Xiaowen Pan, Jiahao Zhang, Dong Guo

“…Most existing works mainly utilize machine learning based on features. However, once the spammers disguise the key features, the detection method will soon fail. …”
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Correlation-based feature selection of single cell transcriptomics data from multiple sources by Nenad S. Mitić, Saša N. Malkov, Mirjana M. Maljković Ružičić, Aleksandar N. Veljković, Ivan Lj. Čukić, Xin Lin, Minjie Lyu, Vladimir Brusić

“…Abstract When applying data mining or machine learning techniques to large and diverse datasets, it is often necessary to construct descriptive and predictive models. …”
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An Enhanced Deep Neural Network for Predicting Workplace Absenteeism by Syed Atif Ali Shah, Irfan Uddin, Furqan Aziz, Shafiq Ahmad, Mahmoud Ahmad Al-Khasawneh, Mohamed Sharaf

“…The efficacy of the proposed method is tested with traditional machine learning techniques, and the results indicate 90.6% performance in Deep Neural Network as compared to 73.3% performance in a single-layer Neural Network and 82% performance in Decision Tree, SVM, and Random Forest. …”
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A hybrid approach for intrusion detection in vehicular networks using feature selection and dimensionality reduction with optimized deep learning. by Fayaz Hassan, Zafi Sherhan Syed, Aftab Ahmed Memon, Saad Said Alqahtany, Nadeem Ahmed, Mana Saleh Al Reshan, Yousef Asiri, Asadullah Shaikh

“…The intended use of CFS and PCA in the machine learning pipeline serves two folds benefit, first is that the resultant feature matrix contains attributes that are most useful for recognizing malicious traffic, and second that after CFS and PCA, the feature matrix has a smaller dimensionality which in turn means that smaller number of weights need to be trained for the dense layers (connections are required for the dense layers) which resulting in smaller model size. …”
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TurkMedNLI: a Turkish medical natural language inference dataset through large language model based translation by İskender Ülgen Oğul, Fatih Soygazi, Belgin Ergenç Bostanoğlu

“…To ensure quality, we conducted comprehensive evaluations using both machine learning models and medical expert review. Our results show that BERTurk achieved 75.17% accuracy on TurkMedNLI test data and 76.30% on the normalized test set, while BioBERTurk demonstrated comparable performance with 75.59% accuracy on test data and 72.29% on the normalized dataset. …”
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Consensus Mechanism of IoT Based on Blockchain Technology by Yue Wu, Liangtu Song, Lei Liu, Jincheng Li, Xuefei Li, Linli Zhou

“…Offline fast election, which is the node that wins the election, becomes the block node. Machine learning methods are also introduced to identify or remove outliers in the sensor data before such data are uploaded to the chain. …”
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NATE: Non-pArameTric approach for Explainable credit scoring on imbalanced class. by Seongil Han, Haemin Jung

“…In contrast, tree-based machine learning models often provide enhanced predictive performance but struggle with interpretability. …”
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VisualSAF-A Novel Framework for Visual Semantic Analysis Tasks by Antonio V. A. Lundgren, Byron L. D. Bezerra, Carmelo J. A. Bastos-Filho

“…The framework leverages semantic variables extracted using machine learning algorithms to provide additional high-level information, augmenting the capabilities of the primary task model. …”
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Utilizing MRAMs With Low Resistance and Limited Dynamic Range for Efficient MAC Accelerator by Sateesh, Kaustubh Chakarwar, Shubham Sahay

“…The recent advancements in data mining, machine learning algorithms and cognitive systems have necessitated the development of neuromorphic processing engines which may enable resource and computationally intensive applications on the internet-of-Things (IoT) edge devices with unprecedented energy efficiency. …”
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Comparison of In Silico Tools for Splice-Altering Variant Prediction Using Established Spliceogenic Variants: An End-User’s Point of View by Woori Jang, Joonhong Park, Hyojin Chae, Myungshin Kim

“…This suggests that deep learning algorithms outperform traditional probabilistic approaches and classical machine learning tools in predicting the de novo and cryptic splice sites.…”
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