Impact and optimization of vehicle charging scheduling on regional clean energy power supply network management by Penghui Xu, Xiaobo Wang, Zhichao Li

“…In particular, the demand forecasting model based on machine learning effectively solves the dilemma of matching the charging load with a clean energy supply. …”
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Cell Differentiation Trajectory Predicts Prognosis and Immunotherapeutic Response in Clear Cell Renal Cell Carcinoma by Jin Xu, Xi Chen, Yinyu Chen, Qiushuang Wang, Yingliang Jin, Huashuo Zhao

“…Combined with bulk RNA-seq data, we classified patients into two clusters and found that this classification was closely correlated with patient prognosis and immunotherapeutic responses. Based on machine learning, we identified a prognostic risk model composed of 14 DRGs, including BTG2, CDKN1A, COL6A1, CPM, CYB5D2, FOSB, ID2, ISG15, PLCG2, SECISBP2, SOCS3, TES, ZBTB16, and ZNF704, to predict the survival rate of patients and then constructed a nomogram model integrating clinicopathological characteristics and risk score for clinical practice. …”
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SurvBeNIM: The Beran-Based Neural Importance Model for Explaining Survival Models by Lev V. Utkin, Danila Y. Eremenko, Andrei V. Konstantinov, Vladimir A. Muliukha

“…It aims to explain predictions of machine learning survival models, which are in the form of survival or cumulative hazard functions. …”
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Modelling the flowing bottom hole pressure of oil and gas wells using multivariate adaptive regression splines by Okorie Ekwe Agwu, Saad Alatefi, Ahmad Alkouh, Raja Rajeswary Suppiah

“…Traditional methods, such as using downhole gauges or relying on empirical and mechanistic models, have limitations, prompting the exploration of alternative approaches such as machine learning (ML). However, most ML models operate as black box models, lacking transparency and interpretability. …”
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Advancing Sustainable Development Goals through Earth Observation Satellite Data: Current Insights and Future Directions by Qiang Zhao, Le Yu

“…Despite existing challenges in data standardization, accessibility, and cross-platform compatibility, advancements in artificial intelligence, machine learning, and collaborative frameworks are anticipated to optimize EO data use. …”
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Interval Prediction of Photovoltaic Power Using Improved NARX Network and Density Peak Clustering Based on Kernel Mahalanobis Distance by Wen-He Chen, Long-Sheng Cheng, Zhi-Peng Chang, Han-Ting Zhou, Qi-Feng Yao, Zhai-Ming Peng, Li-Qun Fu, Zong-Xiang Chen

“…Comparative research of point forecasting is implemented to evaluate the machine learning and deep learning methods, with the proposed SAE-BRNARX under four different periods. …”
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Indoor Positioning System in Learning Approach Experiments by Dodo Zaenal Abidin, Siti Nurmaini, Erwin, Errissya Rasywir, Yovi Pratama

“…The DNN method can estimate the actual space and get better position results, whereas machine learning methods such as the DNN algorithm can handle more effectively large data and produce more accurate data. …”
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Predicting metabolite response to dietary intervention using deep learning by Tong Wang, Hannah D. Holscher, Sergei Maslov, Frank B. Hu, Scott T. Weiss, Yang-Yu Liu

“…Existing prediction methods are typically limited to traditional machine learning models. Deep learning methods dedicated to such tasks are still lacking. …”
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Random Forests in Count Data Modelling: An Analysis of the Influence of Data Features and Overdispersion on Regression Performance by Ciza Arsène Mushagalusa, Adandé Belarmain Fandohan, Romain Glèlè Kakaï

“…Machine learning algorithms, especially random forests (RFs), have become an integrated part of the modern scientific methodology and represent an efficient alternative to conventional parametric algorithms. …”
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Refined matrix completion for spectrum estimation of heart rate variability by Lei Lu, Tingting Zhu, Ying Tan, Jiandong Zhou, Jenny Yang, Lei Clifton, Yuan-Ting Zhang, David A. Clifton

“…The results underscore the potential of our developed matrix completion-based statistical machine learning model in providing reliable HRV spectrum uncertainty estimation.…”
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LSTM+MA: A Time-Series Model for Predicting Pavement IRI by Tianjie Zhang, Alex Smith, Huachun Zhai, Yang Lu

“…However, the existing research on IRI prediction mainly focuses on using linear regression or traditional machine learning, which cannot take into account the historical effects of IRI caused by climate, traffic, pavement construction and intermittent maintenance. …”
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Testing convolutional neural network based deep learning systems: a statistical metamorphic approach by Faqeer ur Rehman, Clemente Izurieta

“…Machine learning technology spans many areas and today plays a significant role in addressing a wide range of problems in critical domains, i.e., healthcare, autonomous driving, finance, manufacturing, cybersecurity, etc. …”
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Neural circuits underlying context-dependent competition between defensive actions in Drosophila larvae by Maxime Lehman, Chloé Barré, Md Amit Hasan, Benjamin Flament, Sandra Autran, Neena Dhiman, Peter Soba, Jean-Baptiste Masson, Tihana Jovanic

“…Using neuronal manipulations, machine learning-based behavioral detection, electron microscopy (EM) connectomics and calcium imaging in Drosophila larvae, we map second-order interneurons that are differentially involved in the competition between defensive actions in response to competing aversive cues. …”
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Adaptive Ensemble Learning Model-Based Binary White Shark Optimizer for Software Defect Classification by Jameel Saraireh, Mary Agoyi, Sofian Kassaymeh

“…In recent decades, Data-mining techniques and machine learning for classifying problematic software projects have emerged to improve software quality. …”
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Identification of Buffalo Breeds Using Self-Activated-Based Improved Convolutional Neural Networks by Yuanzhi Pan, Hua Jin, Jiechao Gao, Hafiz Tayyab Rauf

“…Moreover, feature maps extracted from CNN are further transferred to obtain rich feature vectors. Different machine learning (Ml) classifiers are adopted to classify the feature vectors. …”
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Gut–brain axis and neuropsychiatric health: recent advances by Ceymi Doenyas, Gerard Clarke, Renáta Cserjési

“…There is also evidence that the gut–brain axis plays a role in obesity and machine learning algorithms may assist in differentiating between obese and overweight individuals based on their microbiota data. …”
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Predicting the Spatial-Temporal Distribution of Human Brucellosis in Europe Based on Convolutional Long Short-Term Memory Network by Li Shen, Chenghao Jiang, Minghao Sun, Xuan Qiu, Jiaqi Qian, Shuxuan Song, Qingwu Hu, Heilili Yelixiati, Kun Liu

“…Taking major European countries as an example, this study established the temporal and spatial distribution sequence of brucellosis, analyzed the temporal and spatial distribution characteristics of brucellosis, and quantitatively predicted its epidemic law by using different traditional or machine learning models. This paper indicates that the epidemic of brucellosis in major European countries has statistical periodic characteristics, and in the same cycle, brucellosis has the characteristics of piecewise trend. …”
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Data-Driven Methodology for the Prediction of Fluid Flow in Ultrasonic Production Logging Data Processing by Hongwei Song, Ming Li, Chaoquan Wu, Qingchuan Wang, Shunke Wei, Mingxing Wang, Wenhui Ma

“…This method consists of an application of machine learning techniques on the probability density function (PDF) and the power spectral density (PSD) of the power spectrum output of an ultrasonic Doppler sensor in the pipe. …”
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A Review of AI Applications in Unconventional Oil and Gas Exploration and Development by Feiyu Chen, Linghui Sun, Boyu Jiang, Xu Huo, Xiuxiu Pan, Chun Feng, Zhirong Zhang

“…It also explains how machine learning improves reservoir simulation and history matching. …”
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A Computational Approach to Understanding Agglutinative Structures in Urdu by Muhammad Shoaib Tahir, Mahnoor Amjad

“…Furthermore, the research explores the limitations of traditional rule-based models in handling Urdu’s morphological complexity and advocates for the adoption of machine learning and deep learning techniques. These modern approaches, particularly Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), show promise in accurately modeling Urdu's agglutinative morphology, though they require extensive linguistic data and computational resources. …”
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