Solar radiation prediction: A multi-model machine learning and deep learning approach by C Vanlalchhuanawmi, Subhasish Deb, Md. Minarul Islam, Taha Selim Ustun

“…Focusing on five input variables—solar irradiance, dew point, temperature, relative humidity, and wind speed—this study evaluates the predictive performance of 13 data-driven models, comprising ten machine learning (ML) and three deep learning (DL) algorithms. …”
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Ensemble learning methods with single and multi-model deep learning approaches for cephalometric landmark annotation by S. Rashmi, S. Srinath, R. Rakshitha, B. V. Poornima

“…Abstract The study explores end-to-end deep learning frameworks and ensemble methods to enhance the accuracy of anatomical landmark identification in cephalometric radiographs, crucial for precise cephalometric analysis and effective orthodontic treatment planning. …”
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Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook by Amna Naseeb, Muhammad Zain, Nisar Hussain, Amna Qasim, Fiaz Ahmad, Grigori Sidorov, Alexander Gelbukh

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Multi-model deep learning approach for the classification of kidney diseases using medical images by Waleed Obaid, Abir Hussain, Tamer Rabie, Dhafar Hamed Abd, Wathiq Mansoor

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Deep Learning Integration of Multi-Model Forecast Precipitation Considering Long Lead Times by Wei Fang, Hui Qin, Qian Lin, Benjun Jia, Yuqi Yang, Keyan Shen

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Enhancing Agricultural Disease Detection: A Multi‐Model Deep Learning Novel Approach by Muhammad Khalid Hamid, Said Khalid Shah, Ghassan Husnain, Yazeed Yasin Ghadi, Shahab Ahmad Al Maaytah, Ayman Qahmash

“…This research focusses on employing multi‐model deep‐learning techniques to identify diseases in the leaves of economically significant crops that are potatoes, tomatoes, grapes, apples, and peaches. …”
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Automated multi-model framework for malaria detection using deep learning and feature fusion by Osama R. Shahin, Hamoud H. Alshammari, Raed N. Alabdali, Ahmed M. Salaheldin, Neven Saleh

“…This study proposes an advanced, automated diagnostic framework for malaria detection using a multi-model architecture integrating deep learning and machine learning techniques. …”
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MULTI-MODEL STACK ENSEMBLE DEEP LEARNING APPROACH FOR MULTI-DISEASE PREDICTION IN HEALTHCARE APPLICATION by Bhaskar Adepu, T. Archana

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Using social media for environmental insight: a multi-model deep learning framework approach by Minglu Che, Chengyao Wang, Yanyun Nian, Pinqi Rao

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Multi‐Model Assessment of PCA‐Informer Hybrid Model Against Empirical and Deep Learning Methods in TEC Forecasting by Yang Lin, Hanxian Fang, Die Duan, Ding Yang, Hongtao Huang, Chao Xiao, Ganming Ren

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Enhanced Multi-Model Deep Learning for Rapid and Precise Diagnosis of Pulmonary Diseases Using Chest X-Ray Imaging by Rahul Kumar, Cheng-Tang Pan, Yi-Min Lin, Shiue Yow-Ling, Ting-Sheng Chung, Uyanahewa Gamage Shashini Janesha

“…<b>Methods:</b> This study introduces an Enhanced Multi-Model Deep Learning (EMDL) approach to address these challenges. …”
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Multi-Model Attentional Fusion Ensemble for Accurate Skin Cancer Classification by Iftekhar Ahmed, Biggo Bushon Routh, Md. Saidur Rahman Kohinoor, Shadman Sakib, Md Mahfuzur Rahman, Farag Azzedin

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Global Ionospheric TEC Forecasting for Geomagnetic Storm Time Using a Deep Learning‐Based Multi‐Model Ensemble Method by Xiaodong Ren, Pengxin Yang, Dengkui Mei, Hang Liu, Guozhen Xu, Yue Dong

“…In this study, we developed a new deep learning‐based multi‐model ensemble method (DLMEM) to forecast geomagnetic storm‐time ionospheric TEC that combines the Random Forest (RF) model, the Extreme Gradient Boosting (XGBoost) algorithm, and the Gated Recurrent Unit (GRU) network with the attention mechanism. …”
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Confidence-Aware Multi-Model Image Classification for Early Disease Detection in Plants by Zhong Tianyi, Iqbal Muhammad Azhar, Zhang Xu

“…Digital agriculture is essential for enhancing crop yields by integrating modern digital methods to prevent and manage crop diseases. To address this, a deep learning-based Confidence-Aware Multi-Model Image Classification (CAMIC) framework has been developed. …”
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Comparative analysis of deep learning and traditional methods for IoT botnet detection using a multi-model framework across diverse datasets by Saeed Ullah, Junsheng Wu, Zhijun Lin, Mian Muhammad Kamal, Hala Mostafa, Muhammad Sheraz, Teong Chee Chuah

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Advancing breast cancer prediction: Comparative analysis of ML models and deep learning-based multi-model ensembles on original and synthetic datasets by Kazi Arman Ahmed, Israt Humaira, Ashiqur Rahman Khan, Md Shamim Hasan, Mukitul Islam, Anik Roy, Mehrab Karim, Mezbah Uddin, Ashique Mohammad, Md Doulotuzzaman Xames

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Advancing breast cancer prediction: Comparative analysis of ML models and deep learning-based multi-model ensembles on original and synthetic datasets. by Kazi Arman Ahmed, Israt Humaira, Ashiqur Rahman Khan, Md Shamim Hasan, Mukitul Islam, Anik Roy, Mehrab Karim, Mezbah Uddin, Ashique Mohammad, Md Doulotuzzaman Xames

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Smart Grain Storage Solution: Integrated Deep Learning Framework for Grain Storage Monitoring and Risk Alert by Xinze Li, Wenfu Wu, Hongpeng Guo, Yunshandan Wu, Shuyao Li, Wenyue Wang, Yanhui Lu

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Multi-Model Segmentation Algorithm for Rotator Cuff Injury Based on MRI Images by Mengqi Li, Jingchao Fang, Haonan Hou, Li Yuan, Jin Guo, Zhenlong Liu

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A Multi-Model Machine Learning Framework for Identifying Raloxifene as a Novel RNA Polymerase Inhibitor from FDA-Approved Drugs by Nhung Thi Hong Van, Minh Tuan Nguyen

“…RNA-dependent RNA polymerase (RdRP) represents a critical target for antiviral drug development. We developed a multi-model machine learning framework combining five traditional algorithms (ExtraTreesClassifier, RandomForestClassifier, LGBMClassifier, BernoulliNB, and BaggingClassifier) with a CNN deep learning model to identify potential RdRP inhibitors among FDA-approved drugs. …”
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