Bus Arrival Time Prediction Based on the Optimized Long Short-Term Memory Neural Network Model With the Improved Whale Algorithm by Bing Zhang, Lingfeng Tang, Dandan Zhou, Kexin Liu, Yunqiang Xue

“…This article proposes using the improved whale optimization algorithm–long short-term memory (IWOA–LSTM) model to predict bus arrival times and improving the whale algorithm by optimizing the hyperparameters of the LSTM model, so that the advantages and disadvantages of the whale algorithm and the LSTM model can complement each other, thus enhancing the robustness of the model. …”
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An online clustering algorithm predicting model for prostate cancer based on PHI-related variables and PI-RADS in different PSA populations by Jiyuan Hu, Qi Miao, Jiayi Ren, Hongbo Su, Xianlu Zhang, Jianbin Bi, Gejun Zhang

“…Some researches have created nomograms for predicting risk, but these are not easily visualized. …”
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DGCA3QM: DESIGN OF A DUAL GENETIC ALGORITHM BASED AUTOREGRESSION MODEL FOR CORRELATIVE PREDICTION OF AIR QUALITY METRICS by Harna M. Bodele, G. M. Asutkar, Kiran G. Asutkar

“…To overcome these issues, this text proposes design of a Dual Genetic Algorithm (DGA) based Auto regression model for Correlative prediction (AC) of Air Quality Metrics. …”
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A diagnostic prediction model for anti-neutrophil cytoplasmic antibody associated vasculitis combined with glomerulonephritis based on machine learning algorithm by Wang Jian-mei, Zhu Ge-li, Cao Chen-lin, Peng Qing-quan

“…The model had significant predictive power， with an area under the curve （AUC） of 1.000. …”
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A model adapted to predict blast vibration velocity at complex sites: An artificial neural network improved by the grasshopper optimization algorithm by Yong Fan, Guangdong Yang, Yong Pei, Xianze Cui, Bin Tian

“…Through a comprehensive evaluation of the running time results, the root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R2), a new algorithm, the grasshopper optimization algorithm (GOA), which is suitable for optimizing an ANN to predict PPV, is obtained. …”
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Predicting Treatment Outcomes in Patients with Drug-Resistant Tuberculosis and Human Immunodeficiency Virus Coinfection, Using Supervised Machine Learning Algorithm by Mojisola Clara Hosu, Lindiwe Modest Faye, Teke Apalata

“…Our findings showed that machine learning can be used to predict TB patients’ treatment outcomes.…”
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Predicting CO₂ Emissions with Advanced Deep Learning Models and a Hybrid Greylag Goose Optimization Algorithm by Amel Ali Alhussan, Marwa Metwally, S. K. Towfek

“…Global carbon dioxide (CO₂) emissions are increasing and present substantial environmental sustainability challenges, requiring the development of accurate predictive models. Due to the non-linear and temporal nature of emissions data, traditional machine learning methods—which work well when data are structured—struggle to provide effective predictions. …”
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A diagnostic prediction model for anti-neutrophil cytoplasmic antibody associated vasculitis combined with glomerulonephritis based on machine learning algorithm by Wang Jian-mei, Zhu Ge-li, Cao Chen-lin, Peng Qing-quan

“…The model had significant predictive power， with an area under the curve （AUC） of 1.000. …”
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Blood Glucose Concentration Prediction Based on Double Decomposition and Deep Extreme Learning Machine Optimized by Nonlinear Marine Predator Algorithm by Yang Shen, Deyi Li, Wenbo Wang, Xu Dong

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Extreme gradient boosting algorithm based urban daily traffic index prediction model: a case study of Beijing, China by Jiancheng Weng, Kai Feng, Yu Fu, Jingjing Wang, Lizeng Mao

“…Based on long-term historical TPI data, this research proposed a daily dimensional road network TPI prediction model by using an extreme gradient boosting algorithm (XGBoost). …”
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Ecological and Statistical Evaluation of Genetic Algorithm (GARP), Maximum Entropy Method, and Logistic Regression in Predicting Spatial Distribution of Astragalus sp. by Amir Ghahremanian, Abbas Ahmadi, Hamid Toranjzar, Javad Varvani, Nourollah Abdi

“…MaxEnt, which is a presence-only model, outperformed both the GARP and logistic regression models in predicting suitable habitats for Astragalus sp. Results revealed that soil salinity, elevation, and soil acidity significantly influenced species distribution. …”
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A Study on the Establishment of a Variable Stiffness Physical Model of Abdominal Soft Tissue and an Interactive Massage Force Prediction Algorithm by Xinyi Tang, Ping Shi, Zhenjie Luo, Sujiao Li, Hongliu Yu

“…Furthermore, a transformer-based machine learning algorithm was developed. This algorithm predicts interaction forces using anthropometric and physiological characteristics. …”
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A Prediction Method for Floor Water Inrush Based on Chaotic Fruit Fly Optimization Algorithm–Generalized Regression Neural Network by Zhijie Zhu, Chen Sun, Xicai Gao, Zhuang Liang

“…To this end, a prediction method for floor water inrush combining the chaotic fruit fly optimization algorithm (CFOA) and the generalized regression neural network (GRNN) is proposed. …”
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Long-term prediction of wind speed in La Serena City (Chile) using hybrid neural network-particle swarm algorithm by Juan A Lazzús, Ignacio Salfate

“…In order to obtain a more effective correlation and prediction, a particle swarm algorithm was implemented to update the weights of the network. 43800 data points of wind speed were used (years 2003- 2007), and the past values of wind speed, relative humidity, and air temperature were used as input parameters, considering that these meteorogical parameters are more readily available around the globe. …”
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Development and validation of a risk prediction model for kinesiophobia in postoperative lung cancer patients: an interpretable machine learning algorithm study by Chuang Li, Youbei Lin, Xuyang Xiao, Xinru Guo, Jinrui Fei, Yanyan Lu, Junling Zhao, Lan Zhang

“…This study demonstrates that machine learning models—particularly the RF algorithm—hold substantial promise for predicting kinesiophobia in postoperative lung cancer patients. …”
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A Risk Prediction Model (CMC-AKIX) for Postoperative Acute Kidney Injury Using Machine Learning: Algorithm Development and Validation by Ji Won Min, Jae-Hong Min, Se-Hyun Chang, Byung Ha Chung, Eun Sil Koh, Young Soo Kim, Hyung Wook Kim, Tae Hyun Ban, Seok Joon Shin, In Young Choi, Hye Eun Yoon

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Poster #19 - Influence of meteorological factors on influenza in a warm temperate to subtropical transitional zone and predictions based on an LSTM algorithm by Nadia Nisar, Nazish Badar, Inayah Safdar, Muhammad Salman

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Using a seasonal and trend decomposition algorithm to improve machine learning prediction of inflow from the Yellow River, China, into the sea by Shuo Wang, Shuo Wang, Ke Yang, Ke Yang, Hui Peng, Hui Peng

“…Time decomposition algorithms, combined with machine learning, are effective tools to enhance the capabilities of inflow prediction models. …”
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PREDICTION MODEL FOR STUDENTS' ON-TIME GRADUATION USING ALGORITHM SUPPORT VECTOR MACHINE (SVM) BASED  PARTICLE SWARM OPTIMIZATION (PSO) by Syarif Hidayatulloh, Gandung Triyono, Kiki Ari Suwandi kosasih

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Using Sono-Electro-Persulfate Process for Atenolol Removal from Aqueous Solutions: Prediction and Optimization with the ANFIS Model and Genetic Algorithm by Nasrin Zahedi, Bahare Dehdashti, Farzaneh Mohammadi, Maryam Razaghi, Zeynab Moradmand, Mohammad Mehdi Amin

“…Finally, an adaptive neuro-fuzzy inference system (ANFIS) with 99.63% accuracy and a genetic algorithm (GA) were used to analyze and interpret data and predict optimal conditions. …”
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