Mechanical Design of McKibben Muscles Predicting Developed Force by Artificial Neural Networks by Michele Gabrio Antonelli, Pierluigi Beomonte Zobel, Muhammad Aziz Sarwar, Nicola Stampone

“…The latter was used to train 27 artificial neural networks (ANNs) to identify the best algorithm for predicting the developed forces. The best ANN was tested on three numerical models and a prototype with a combination of parameters not included in the dataset, comparing predicted and numerical responses. …”
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Enhancing Crop Yield Prediction Using IoT-Based Soil Moisture and Nutrient Sensors by Alsalami Zaid, Mohammed G., Srinivas Tummala

“…Crop yield prediction is crucial for ensuring food security by enabling farmers to optimize resource use, manage risks, and plan for market demands, ultimately leading to increased agricultural productivity and sustainability..The IoT-based crop yield prediction system integrates advanced sensing technologies, communication protocols, machine learning algorithms, and real-time monitoring to optimize crop production. …”
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Predicting fertilizer treating of maize using digital image processing and deep learning approaches by Eshete Derb Emiru, Kassie Bishaw

“…VGG16 performed better than VGG19 in predicting fertilizer treatment for maize due to its lower complexity, which minimizes the risk of overfitting and enhances generalization, especially with smaller datasets.…”
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FLORA: a novel method to predict protein function from structure in diverse superfamilies. by Oliver C Redfern, Benoît H Dessailly, Timothy J Dallman, Ian Sillitoe, Christine A Orengo

“…Although global structure comparison methods can be used to transfer functional annotations, the relationship between fold and function is complex, particularly in functionally diverse superfamilies that have evolved through different secondary structure embellishments to a common structural core. The majority of prediction algorithms employ local templates built on known or predicted functional residues. …”
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Porosity prediction of tight reservoir rock using well logging data and machine learning by Yawen He, Hongjun Zhang, Zhiyu Wu, Hongbo Zhang, Xin Zhang, Xiaojing Zhuo, Xiaoli Song, Sha Dai, Wei Dang

“…These models are further optimized with the particle swarm optimization (PSO) algorithm to enhance their predictive accuracy. Comparative analysis reveals that the PSO-GBDT model outperforms other models, achieving an R2 exceeding 0.99. …”
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Prediction Model for Compaction Quality of Earth-Rock Dams Based on IFA-RF Model by Weiwei Lin, Yuling Yan, Pu Xu, Xiao Zhang, Yichuan Zhong

“…The method utilizes a dynamic inertia weight, an adaptive factor, and a differential evolution strategy to enhance the search capability of the firefly algorithm. Furthermore, the random forest (RF) algorithm’s <i>Ntree</i> and <i>Mtry</i> parameters are adaptively optimized through the improved firefly algorithm (IFA) to develop a dam compaction quality prediction model. …”
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On the Prediction and Forecasting of PMs and Air Pollution: An Application of Deep Hybrid AI-Based Models by Youness El Mghouchi, Mihaela Tinca Udristioiu

“…This study aims to develop robust predictive and forecasting models for hourly PM concentrations in Craiova, Romania, using advanced hybrid Artificial Intelligence (AI) approaches. …”
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PREDIKSI PRODUKTIVITAS JAGUNG DI INDONESIA SEBAGAI UPAYA ANTISIPASI IMPOR MENGGUNAKAN JARINGAN SARAF TIRUAN BACKPROPAGATION by Anjar Wanto

“…This algorithm is able to predict data well, especially data that is maintained for a certain period of time. …”
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A novel deep learning model for predicting marine pollution for sustainable ocean management by Michael Onyema Edeh, Surjeet Dalal, Musaed Alhussein, Khursheed Aurangzeb, Bijeta Seth, Kuldeep Kumar

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Software Defect Prediction Using Deep Q-Learning Network-Based Feature Extraction by Qinhe Zhang, Jiachen Zhang, Tie Feng, Jialang Xue, Xinxin Zhu, Ningyang Zhu, Zhiheng Li

“…Machine learning-based software defect prediction (SDP) approaches have been commonly proposed to help to deliver high-quality software. …”
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Predicting climate change impacts on the distribution of endemic fish Cyprinion muscatense in the Arabian Peninsula by Ali Gholamhosseini, Masoud Yousefi, Hamid Reza Esmaeili

“…We used an ensemble approach by considering two regressions‐based species distribution modeling (SDM) algorithms: generalized linear models (GLM), and generalized additive models (GAM) to model the species habitat suitability and predict the impacts of climate change on the species habitat suitability. …”
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Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach by Iancu-Bogdan Teodoru, Zakaria Owusu-Yeboah, Mircea Aniculăesi, Andreea Vasilica Dascălu, Florian Hörtkorn, Alessia Amelio, Irina Lungu

“…Random Forest emerged as the optimal algorithm, providing robust and accurate UCS predictions. …”
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MMPred: a tool to predict peptide mimicry events in MHC class II recognition by Filippo Guerri, Filippo Guerri, Valentin Junet, Valentin Junet, Judith Farrés, Xavier Daura, Xavier Daura, Xavier Daura

“…We present MMPred, a software tool that integrates epitope prediction and sequence alignment algorithms to streamline the computational analysis of molecular mimicry events in autoimmune diseases. …”
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A Crash Severity Prediction Method Based on Improved Neural Network and Factor Analysis by Chen Zhang, Jie He, Yinhai Wang, Xintong Yan, Changjian Zhang, Yikai Chen, Ziyang Liu, Bojian Zhou

“…The results showed that although the algorithms produced almost the same accuracy in their predictions, a backpropagation method combined with a nonlinear inertial weight setting in PSO produced fast global and accurate local optimal searching, thereby demonstrating a better understanding of the entire model explanation, which could best fit the model, and at last, the factor analysis showed that non-road-related factors, particularly vehicle-related factors, are more important than road-related variables. …”
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Enhancing Stroke Prediction with Logistic Regression and Support Vector Machine Using Oversampling Techniques by Syamsul Risal, Fajar Apriyadi, A. Sumardin, Andini Dani Achmad, Annisa Nurul Puteri

“…This study compares the performance of Logistic Regression (LR) and Support Vector Machine (SVM) algorithms combined with different oversampling methods—SMOTE, Borderline-SMOTE, ADASYN, Random Over Sampling (ROS), and Random Under Sampling (RUS)—on a stroke prediction dataset. …”
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Machine learning models integrating intracranial artery calcification to predict outcomes of mechanical thrombectomy by Guangzong Li, Yuesen Zhang, Di Li, Manhong Zhao, Lin Yin

“…The Extra Trees model demonstrated the highest predictive accuracy. The top three predictors were a history of hypertension, serum albumin level, and total calcified volume.ConclusionThe total volume of IAC is a critical imaging biomarker for predicting MT outcomes in patients with anterior circulation AIS. …”
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Energy consumption prediction using modified deep CNN-Bi LSTM with attention mechanism by Adel Binbusayyis, Mohemmed Sha

“…Traditional techniques have limitations in accuracy and error rates, necessitating advancements in prediction techniques. To enhance prediction accuracy, a proposed smart city system utilizes the Household Energy Consumption dataset, employing deep learning algorithms. …”
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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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Machine learning-based prediction of diabetic peripheral neuropathy: model development and clinical validation by Meng Sun, Xingling Sun, Fei Wang, Li Liu

“…Nine machine learning models were developed and compared for DPN risk prediction.ResultsStochastic Gradient Boosting (SGBT) demonstrated the best performance (training AUC: 0.933, 95% CI: 0.921–0.946; testing AUC: 0.811, 95% CI: 0.776–0.843). …”
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Machine learning-driven SLC prognostic signature for glioma: predicting survival and immunotherapy response by Jianghua Lin, Xiao Yang, Kaijun Zhao, Yu’e Liu, Yu’e Liu

“…The model demonstrated superior predictive performance compared to existing glioma prognostic models. …”
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