Triphasic CT Radiomics Model for Preoperative Prediction of Hepatocellular Carcinoma Pathological Grading by Huang H, Pan X, Zhang Y, Yang J, Chen L, Zhao Q, Huang L, Lu W, Deng Y, Huang Y, Ding K

“…Preoperative prediction of HCC pathological features (Ed, MVI, and SN grading) is clinically significant.A triphasic CT-based fusion model demonstrated strong predictive performance:Testing 1 dataset: AUCs of 0.890 (Ed), 0.895 (MVI), and 0.829 (SN) grading.Testing 2 (validation) dataset: AUCs of 0.836 (Ed), 0.871 (MVI), and 0.810 (SN) grading.The model aids in preoperative clinical decision-making and prognostic evaluation for HCC patients.Keywords: pathological grading, hepatocellular carcinoma, contrast-enhanced CT, radiomics…”
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Predicting suicidality in people living with HIV in Uganda: a machine learning approach by Anthony B. Mutema, Anthony B. Mutema, Anthony B. Mutema, Lillian Linda, Lillian Linda, Daudi Jjingo, Segun Fatumo, Segun Fatumo, Eugene Kinyanda, Allan Kalungi, Allan Kalungi, Allan Kalungi

“…The model’s performance was evaluated using the area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), sensitivity, specificity, and Mathew’s correlation coefficient (MCC).ResultsWe trained and evaluated eight different ML algorithms, including logistic regression, support vector machines, Naïve Bayes, k-nearest neighbors, decision trees, random forests, AdaBoost, and gradient-boosting classifiers. …”
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Short-Term Photovoltaic Power Combined Prediction Based on Feature Screening and Weight Optimization by Liqing Geng, Yadong Yang, Genghuang Yang, Yongfeng Zheng, Xiaocong Liu

“…Firstly, K-means is used to cluster the photovoltaic power; Secondly, CEEMDAN is used to decompose photovoltaic power and wavelet decomposition is used to decompose irradiance, and sample entropy and K-means are used to reconstruct each component of photovoltaic power into high, intermediate, and low frequency terms; Then, Spearman’s correlation coefficient is used to calculate the correlation between each meteorological factor and the decomposed irradiance component and the high, intermediate, and low frequency terms of photovoltaic power, and the feature selection is carried out; Then, CNN-BiLSTM-Attention is used to predict the high frequency term, LSTM is used to predict the intermediate frequency and low frequency terms, and the results are superimposed to obtain the preliminary prediction value; Finally, the dung beetle algorithm is used to optimize the weights of the initial prediction values of the training set of high, intermediate, and low frequency terms, and the optimal weight is substituted into the test set to obtain the final prediction result. …”
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End-to-End Collaborative Optimization Method for Microgrid Power Prediction and Optimal Scheduling by ZHANG Li, WANG Bao, JIA Jianxiong, SONG Zhumeng, YE Yutong, YU Yue, LIN Jiaqing, XU Xiaoyuan

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The effect of resampling techniques on the performances of machine learning clinical risk prediction models in the setting of severe class imbalance: development and internal valid... by Janny Xue Chen Ke, Arunachalam DhakshinaMurthy, Ronald B. George, Paula Branco

“…Conclusion Existing resampling techniques had a variable impact on models, depending on the algorithms and the evaluation metrics. Future research is needed to improve predictive performances in the setting of severe class imbalance.…”
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Predicting the Activity Level of the Great Gerbil (Rhombomys opimus) via Machine Learning by Fan Jiang, Peng Peng, Zhenting Xu, Yu Xu, Ding Yang, Shouquan Chai, Shuai Yuan, Limin Hua, Dawei Wang, Xuanye Wen

“…Because traditional assessment methods are difficult to monitor and cannot effectively predict the population growth trend of R. opimus, an R. opimus activity prediction model was constructed using the particle swarm optimization algorithm‐extreme learning machine (PSO‐ELM). …”
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Machine learning and discriminant analysis model for predicting benign and malignant pulmonary nodules by Zhi Li, Wenjing Zhang, Jinyi Huang, Ling Lu, Dongming Xie, Jinrong Zhang, Jiamin Liang, Yuepeng Sui, Linyuan Liu, Jianjun Zou, Ao Lin, Lei Yang, Fuman Qiu, Zhaoting Hu, Mei Wu, Yibin Deng, Xin Zhang, Jiachun Lu

“…Three widely applicable machine learning algorithms (Random Forests, Gradient Boosting Machine, and XGBoost) were used to screen the metrics, and then the corresponding predictive models were constructed using discriminative analysis, and the best performing model was selected as the target model. …”
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Gait stability prediction through synthetic time-series and vision-based data by Mauricio C. Cordeiro, Ciaran O. Cathain, Ciaran O. Cathain, Ciaran O. Cathain, Vitor B. Nascimento, Thiago B. Rodrigues

“…(2) how effectively do synthetic data-trained models predict the Margin of Stability (MoS) when tested on real-world data? …”
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Applied pharmacogenetics to predict response to treatment of first psychotic episode: study protocol by Sergi Mas, Sergi Mas, Sergi Mas, Laura Julià, Manuel J. Cuesta, Manuel J. Cuesta, Manuel J. Cuesta, Benedicto Crespo-Facorro, Benedicto Crespo-Facorro, Benedicto Crespo-Facorro, Benedicto Crespo-Facorro, Javier Vázquez-Bourgon, Javier Vázquez-Bourgon, Javier Vázquez-Bourgon, Carlos Spuch, Carlos Spuch, Ana Gonzalez-Pinto, Ana Gonzalez-Pinto, Angela Ibañez, Angela Ibañez, Judith Usall, Judith Usall, Cristina Romero-López-Alberca, Cristina Romero-López-Alberca, Ana Catalan, Ana Catalan, Ana Catalan, Ana Catalan, Ana Catalan, Anna Mané, Anna Mané, Anna Mané, Anna Mané, Miquel Bernardo, Miquel Bernardo, Miquel Bernardo, Miquel Bernardo

“…Here, we describe the rationale, aims and methodology of Applied Pharmacogenetics to Predict Response to Treatment of First Psychotic Episode (the FarmaPRED-PEP project), which aims to develop and validate predictive algorithms to classify FEP patients according to their response to antipsychotics, thereby allowing the most appropriate treatment strategy to be selected. …”
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Safety Prejudging Method for Power Transformer Based on Multi-Prediction Model Fusion by Dianyang LI, Yujie ZHANG, Shanyuan WANG, Jian FENG, Hongzhe WANG, Ling QIN

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Prediction of moisture content of hummus peach based on multi-burr hyperspectral data by GAO Aidi, QIAO Fengzhang, ZHU Wenxuan, ZHONG Xiaopin, DENG Yuanlong

“…For hyperspectral image data with spikes and noise, compared the effects of several data preprocessing methods, including polynomial smoothing algorithm (SG), multivariate scatter correction algorithm (MSC), standard normal variate algorithm (SNV), first-order derivative operator (D1), and second-order derivative operator (D2) on model prediction accuracy. …”
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Compressive-Sensing-Based Video Codec by Autoregressive Prediction and Adaptive Residual Recovery by Ran Li, Hongbing Liu, Rui Xue, Yanling Li

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PREDICTION STAGES OF THE DIFFICULT INERTIA SYSTEM BEHAVIOR WITH THE USE OF THE DEVELOPED SYSTEM MODEL by Vasyl V. Bychenkov

“…The model creation and use algorithm for prediction of the difficult inertia system behavior is offered in the article. …”
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Comprehensive Performance Assessment of Multi-Neural Ensemble Model for Mortality Prediction in ICU by M. Fathima Begum, Subhashini Narayan

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A Simple Computational Approach to Predict Long-Term Hourly Electric Consumption by Eugene Pinsky, Etienne Meunier, Pierre Moreau, Tanvi Sharma

“…By exploiting the patterns in past data points, we could forecast long-term consumption with a computationally simple algorithm. Our approach is simple to interpret. It incorporates the seasonality of past consumption and can predict power consumption for any time scale. …”
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Corrosion rate prediction for long-distance submarine pipelines based on MWIWOA-SVM by Zhengshan LUO, Haipeng LYU, Jihao LUO

“…MethodsTo address these issues, Multi-Way Improved Whale Optimization Algorithm (MWIWOA) was proposed to optimize the SVM-based prediction model for the internal corrosion rate of long-distance submarine pipelines. …”
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Combined Prediction of Dust Concentration in Opencast Mine Based on RF-GA-LSSVM by Shuangshuang Xiao, Jin Liu, Yajie Ma, Yonggui Zhang

“…The environment of opencast mines is intricate, with numerous factors influencing dust concentration, making accurate predictions challenging. To enhance the prediction accuracy of dust concentration in these mines, a combined prediction algorithm utilizing RF-GA-LSSVM is developed. …”
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Novel hybrid model to improve the monthly streamflow prediction: Integrating ANN and PSO by Baydaa Abdul Kareem, Salah L. Zubaidi

“…Precise streamflow forecasting is crucial when designing water resource planning and management, predicting flooding, and reducing flood threats. This study invented a novel approach for the monthly water streamflow of the Tigris River in Amarah City, Iraq, by integrating an artificial neural network (ANN) with the particle swarm optimisation algorithm (PSO), depending on data preprocessing. …”
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An optimal multi-disease prediction framework using hybrid machine learning techniques by Aditya Gupta, Amritpal Singh

“…The proposed work leverages genetic algorithm-based recursive feature elimination and AdaBoost to predict two prominent life-style diseases. …”
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A hybrid model for predicting response to risperidone after first-episode psychosis by Giovany Oliveira Costa, Vanessa K. Ota, Matheus Rodrigues Luiz, Joice Santos Rosa, Gabriela Xavier, Jessica Honorato-Mauer, Marcos L. Santoro, Carolina Muniz Carvalho, Daniel A. Cavalcante, Amanda V.G. Bugiga, Rodrigo A. Bressan, Gerome Breen, Ary Gadelha, Cristiano Noto, Diego R. Mazzotti, Sintia I. Belangero

“…The hybrid model, which included duration of untreated psychosis, Clinical Global Impression-Severity (CGI-S) scale scores, age, cannabis use, and 406 SNVs, showed the best performance (balanced accuracy: 72.9% [CI 0.62-0.84], RF algorithm). Conclusion: A hybrid model including clinical and genetic predictors can enhance prediction of response to antipsychotic treatment.…”
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