Very Short-Term Blackout Prediction for Grid-Tied PV Systems Operating in Low Reliability Weak Electric Grids of Developing Countries by Benson H. Mbuya, Aleksandar Dimovski, Marco Merlo, Thomas Kivevele

“…A very short-term power outage prediction model framework based on a hybrid random forest (RF) algorithm was developed using open-source Python machine learning libraries and using a dataset generated from the pilot project’s experimental microgrid. …”
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LASSO–MOGAT: a multi-omics graph attention framework for cancer classification by Fadi Alharbi, Aleksandar Vakanski, Murtada K. Elbashir, Mohanad Mohammed

“… The application of machine learning (ML) methods to analyze changes in gene expression patterns has recently emerged as a powerful approach in cancer research, enhancing our understanding of the molecular mechanisms underpinning cancer development and progression. …”
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Allosteric Fluorescent Detection of Saccharides and Biomolecules in Water from a Boronic Acid Functionalized Arene Ruthenium Assembly Hosting Fluorescent Dyes by Alaa Maatouk, Thibaud Rossel, Bruno Therrien

“…All data were analyzed by unsupervised machine learning technologies (PCA and cluster analysis), suggesting that such systems with multiple analyte–response behaviors are offering new perspectives for the development of highly sensitive, easily tunable, water-soluble, fluorescent-based sensing arrays for biomolecules and other analytes.…”
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A Real-Time Data Monitoring Framework for Predictive Maintenance Based on the Internet of Things by Mudita Uppal, Deepali Gupta, Nitin Goyal, Agbotiname Lucky Imoize, Arun Kumar, Stephen Ojo, Subhendu Kumar Pani, Yongsung Kim, Jaeun Choi

“…A sensor fault prediction model based on a machine learning algorithm is proposed in this paper, where the k-nearest neighbors model achieved better performance with 99.63% accuracy, 99.59% F1-score, and 99.67% recall. …”
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Optimizing Kernel Extreme Learning Machine based on a Enhanced Adaptive Whale Optimization Algorithm for classification task. by ZeSheng Lin

“…Data classification is an important research direction in machine learning. In order to effectively handle extensive datasets, researchers have introduced diverse classification algorithms. …”
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An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain by Zeynep Hilal Kilimci, A. Okay Akyuz, Mitat Uysal, Selim Akyokus, M. Ozan Uysal, Berna Atak Bulbul, Mehmet Ali Ekmis

“…For this purpose, historical data can be analyzed to improve demand forecasting by using various methods like machine learning techniques, time series analysis, and deep learning models. …”
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An Adversarial Attack via Penalty Method by Jiyuan Sun, Haibo Yu, Jianjun Zhao

“…Deep learning systems have achieved significant success across various machine learning tasks. However, they are highly vulnerable to attacks. …”
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Integrated bioinformatics analysis identifies ALDH18A1 as a prognostic hub gene in glutamine metabolism in lung adenocarcinoma by Hao Ren, Deng-Feng Ge, Zi-Chen Yang, Zhen-Ting Cheng, Shou-Xiang Zhao, Bin Zhang

“…In this study on lung adenocarcinoma, we employed consensus clustering and applied 101 types of machine learning methods to systematically identify key genes associated with glutamine metabolism and develop a risk model. …”
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Strength prediction and failure mode classification for SRC shear beams using GA-BP ANN method by Gangfeng Yao, Bingyi Li

“…Considering the advantages of machine-learning (ML) approaches, the back-propagation (BP) artificial neural network (ANN) method combined with genetic algorithm (GA) was employed to the prediction of strength and failure mode of SRC shear beams. …”
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Battery Health Monitoring and Remaining Useful Life Prediction Techniques: A Review of Technologies by Mohamed Ahwiadi, Wilson Wang

“…Data-driven techniques leverage historical data, AI, and machine learning algorithms to identify degradation trends and predict RUL, which can provide flexible and adaptive solutions. …”
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Evaluation of 3D seed structure and cellular traits in-situ using X-ray microscopy by Marcus Griffiths, Barsanti Gautam, Clara Lebow, Keith Duncan, Xinxin Ding, Pubudu Handakumbura, John C. Sedbrook, Christopher N. Topp

“…Seeds of pennycress (Thlaspi arvense L.) an oilseed cover crop, were scanned and segmented using a machine learning model. Seed morphological analysis and a coat thickness map was applied to compare seed volumes of four genotypes. …”
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Targeted maximum likelihood based estimation for longitudinal mediation analysis by Wang Zeyi, Laan Lars van der, Petersen Maya, Gerds Thomas, Kvist Kajsa, Laan Mark van der

“…To tackle causal and statistical challenges due to the complex longitudinal data structure with time-varying confounders, competing risks, and informative censoring, there exists a general desire to combine machine learning techniques and semiparametric theory. …”
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Plant Leaf Identification Using Feature Fusion of Wavelet Scattering Network and CNN With PCA Classifier by S. Gowthaman, Abhishek Das

“…Unlike traditional machine learning methods that often struggle to capture the intricate features of leaves, CNNs are well-suited for this task. …”
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Parameter Acquisition Study of Mining-Induced Surface Subsidence Probability Integral Method Based on RF-AGA-ENN Model by Jinman Zhang, Liangji Xu, Jiewei Li, Yueguan Yan, Ruirui Xu

“…To obtain more accurate PIM parameters in the absence of observational data, we propose a combined machine learning model (RF-AGA-ENN)—random forest (RF) extracts the best combination of features as the input layer of Elman neural network (ENN); ant colony algorithm (ACO) and genetic algorithm (GA) are combined (called AGA) for the weights and thresholds of ENN optimization. …”
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Design and Evaluation of a Leader–Follower Isomorphic Vascular Interventional Surgical Robot by Pengfei Chen, Yutang Wang, Dapeng Tian

“…We classified operators with different operational experience using machine learning methods. The classification process includes time-frequency domain feature extraction, feature selection based on the Relief method and random forest (RF) method, and a BP neural network (NN) classifier. …”
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Diagnosing and Predicting the Earth’s Health via Ecological Network Analysis by Zi-Ke Zhang, Ye Sun, Chu-Xu Zhang, Kuan Fang, Xiang Xu, Chuang Liu, Xueqi Wang, Kui Zhang

“…Secondly, we identify the importance of each element by a machine learning approach. Thirdly, we use a spreading model to predict the Earth’s health. …”
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Online medical privacy protection strategy under information value-added mechanism by Shengzhi MING, Jianming ZHU, Zhiyuan SUI, Xian ZHANG

“…China’s economic level and people’s living standards have developed rapidly in recent years, and the medical level and medical technology have made breakthroughs continuously.With the promotion and deepening of“Internet Plus” to business model innovation in various fields, the development of “Internet Plus” medical has been rapidly developed.Due to the continuous development of data processing technologies such as machine learning and data mining, the risk of users’ personal medical data disclosure in the process of online medical treatment has also attracted the attention of researchers.Considering the deductibility of information, the discount mechanism was adopted to describe the change of user’s private information value in different stages of the game.Combined with the current research status in the field of online medical privacy protection motivation, how to mobilize the enthusiasm of both players from the level of privacy protection motivation was explored with game analysis.In view of the game characteristics of users’ strong willingness to continually use the online medical platform and intermittently provide privacy, the repeated game method was adopted to better describe the game process between users and the online medical platform.The tendency change law of the players on both sides of the game was obtained.Moreover, the Nash equilibrium of the game model was analyzed under different model parameters and the change trend of the game strategy of both sides with the progress of the game stage.When the parameters were met 2(c_p-c_n)≥l_p(p_n-p_p), the user started to choose from “agree to share private data” to “refuse to share private data”.The above conclusion was verified by simulation experiments.Based on the above conclusions, from the perspective of online medical platform and users, policy suggestions on how to realize privacy protection from the level of privacy protection motivation in the process of online medical treatment were given.…”
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The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review. by Quan Duy Vo, Yukihiro Saito, Toshihiro Ida, Kazufumi Nakamura, Shinsuke Yuasa

“…The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has played a pivotal role in refining iPSC classification, monitoring cell functionality, and conducting genetic analysis. …”
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An Ecolevel Estimation Method of Individual Driver Performance Based on Driving Simulator Experiment by Yiping Wu, Xiaohua Zhao, Ying Yao, Jian Rong

“…Because of obvious advantage in mining hidden relationship, machine learning was adopted to explore the complicated relationship between driver performance and vehicle fuel consumption and thus to predict the ecolevel of individual driver performance in this study. …”
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The Short‐Time Prediction of Thermospheric Mass Density Based on Ensemble‐Transfer Learning by Peian Wang, Zhou Chen, Xiaohua Deng, Jing‐Song Wang, Rongxing Tang, Haimeng Li, Sheng Hong, Zhiping Wu

“…In this paper, three machine‐learning prediction algorithms are investigated, including the Bidirectional Long Short‐Term Memory, the Transformer, and the Light Gradient Boosting Machine (LightGBM) ensemble model of the above models. …”
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