FOX-TSA hybrid algorithm: Advancing for superior predictive accuracy in tourism-driven multi-layer perceptron models by Sirwan A. Aula, Tarik A. Rashid

“…Results indicate that the hybrid FOX-TSA achieves superior predictive accuracy (94.64 %), faster convergence speed (reducing iterations by 25 %), and improved F1-score (94.63 %) on the test dataset. These findings underline the potential of the hybrid FOX-TSA algorithm to advance predictive modelling in the tourism sector and other domains requiring complex data handling.…”
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Application of machine learning and genetic optimization algorithms for modeling and optimizing soybean yield using its component traits. by Mohsen Yoosefzadeh-Najafabadi, Dan Tulpan, Milad Eskandari

“…Using the E-B algorithm, we were able to increase the prediction accuracy by improving the values of R2, MAE, and RMSE by 0.1, 0.24 kg.ha-1, and 0.96 kg.ha-1, respectively. …”
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Development of weighted residual RNN model with hybrid heuristic algorithm for movement recognition framework in ambient assisted living by Mustufa Haider Abidi, Hisham Alkhalefah, Zeyad Almutairi

“…The weighted deep learning model and a hybrid heuristic algorithm are proposed to achieve this goal. …”
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A Short-Term Solar Photovoltaic Power Optimized Prediction Interval Model Based on FOS-ELM Algorithm by G. Ramkumar, Satyajeet Sahoo, T. M. Amirthalakshmi, S. Ramesh, R. Thandaiah Prabu, Kasipandian Kasirajan, Antony V. Samrot, A. Ranjith

“…This approach can replace existing knowledge with new information on a continuous basis. The variance of model uncertainty is computed in the first stage by using a learning algorithm to provide predictable PV power estimations. …”
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A sampling-based winner determination model and algorithm for logistics service procurement auctions under double uncertainty by Mingqiang Yin, Hao Wang, Qiang Liu, Xiaohu Qian, He Zhang, Xianming Lang

“…With the objective of minimizing total cost, a new two-stage stochastic winner determination model under double uncertainty is constructed, which is further transformed into mixed-integer linear programming model by using an improved sample average approximation algorithm based on the chi-square test and the Latin hypercube sampling method. …”
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Development of an optimization model for a monitoring point in tunnel stress deduction using a machine learning algorithm by Xuyan Tan, Weizhong Chen, Luyu Wang, Wei Ye

“…Compared with common methods, the proposed model shows significantly improved characterization abilities, establishing its reliability for optimizing the monitoring scheme.…”
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Short-Term Traffic Flow Forecasting Method Based on LSSVM Model Optimized by GA-PSO Hybrid Algorithm by Qichun Bing, Dayi Qu, Xiufeng Chen, Fuquan Pan, Jinli Wei

“…Because of the uncertainty and nonlinearity, short-term traffic flow forecasting remains a challenging task. In order to improve the accuracy of short-term traffic flow forecasting, a short-term traffic flow forecasting method based on LSSVM model optimized by GA-PSO hybrid algorithm is put forward. …”
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Dynamical analysis of fractional hepatitis B model with Gaussian uncertainties using extended residual power series algorithm by Qursam Fatima, Mubashir Qayyum, Murad Khan Hassani, Ali Akgül

“…This manuscript extends existing mathematical models for HBV by introducing a treatment compartment to improve understanding, diagnosis, and treatment strategies. …”
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A Hybrid Internet of Behavior Algorithm for Predicting IoT Data of Plant Growing using LSTM and NB Models by Khansaa Yaseen Ahmad, Omar Muayad Abdullah

“…To improve the prediction accuracy, the outputs of the LSTM system were used as inputs to the Naïve Bayes algorithm. …”
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A Hybrid Deep Learning and Improved SVM Framework for Real-Time Railroad Construction Personnel Detection with Multi-Scale Feature Optimization by Jianqiu Chen, Huan Xiong, Shixuan Zhou, Xiang Wang, Benxiao Lou, Longtang Ning, Qingwei Hu, Yang Tang, Guobin Gu

“…The experimental results demonstrate that the ISVM method significantly improves accuracy and real-time performance compared to traditional detection methods and single deep learning models. …”
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Pitaya detection using an improved lightweight Faster R-CNN based on MobileNetV3 in densely planted pitaya orchards by Yulong Nan, Huichun Zhang, Jiaqiang Zheng, Kunqi Yang

“… Accurate and rapid fruit detection was very important for robot picking precisely, so the large model size and slow detection speed of the detection algorithm are problems that need to be solved urgently. …”
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LPBSA: Pre-clinical data analysis using advanced machine learning models for disease prediction by Dana R. Hamad, Tarik A. Rashid

“…The current study introduces an optimization algorithm, Learner Performance-Based Behavior with Simulated Annealing (LPBSA), integrated with Multilayer Perceptron (MLP) as a neural network technique to improve disease prediction accuracy. …”
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Optimizing Personalized Recommender Systems for Teachers’ Digital Learning Models Using Deep Learning Algorithms by Jun Zhong, Wenjuan Zhang

“…To address this issue, this paper proposes a personalized recommendation algorithm based on Graph Neural Networks (PRAGNN) for teachers’ digital learning models. …”
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Spatial modeling of brine level and salinity in the Qarhan Salt Lake using GIS and automated machine learning algorithms by Dongmei Yu, Zitao Wang, Chao Yue, Jianping Wang

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A recurrent neural network and parallel hidden Markov model algorithm to segment and detect heart murmurs in phonocardiograms. by Andrew McDonald, Mark J F Gales, Anurag Agarwal

“…We propose a novel recurrent neural network and hidden semi-Markov model (HSMM) algorithm that can both segment the signal and detect a heart murmur, removing the need for a two-stage algorithm. …”
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Forecasting Models and Genetic Algorithms for Researching and Designing Photovoltaic Systems to Deliver Autonomous Power Supply for Residential Consumers by Ekaterina Gospodinova, Dimitar Nenov

“…It is suggested that two minimized criteria be used to create a model for forecasting FOU. This model can be used with a genetic algorithm to make a prediction that fits a specific case, such as a time series representation based on discrete fuzzy sets of the second type. …”
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Estimation and Modeling of Fluctuating Wind Amplitude and Phase Spectrum Using APES Algorithm Based on Field Monitored Data by Dan-hui Dan, Xiang-jie Wang, Xing-fei Yan, Wei Cheng

“…Compared with the measured FWAS, the stochastic Davenport eFWAS model proposed in this paper can accurately describe the statistical properties of the local wind field and improve the modeling accuracy of the FWAS, which is important in antiwind structural design and safety assessment.…”
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An Optimization Model for Shell Plate Seam Landing Using Minimum Manufacturing Cost and a Solution by Genetic Algorithm by Ho Rim Pae, Min Hyok Jon, Chol Jun Pak

“…In this paper, a new optimization model and its solution method for the landing of seams and butts (for convenience, seam and butt are simply called seam) on the ship hull surface are proposed in order to improve the shipbuilding efficiency. …”
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A non-intrusive load monitoring algorithm based on real-time feature extraction and deep learning model by Behrooz Taheri, Mostafa Sedighizadeh, Mohammad Reza Nasiri, Alireza Sheikhi Fini

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Prediction of Coiled Tubing Erosion Rate Based on Sparrow Search Algorithm Back-Propagation Neural Network Model by Yinping Cao, Fengying Fang, Guowei Wang, Wenyu Zhu, Yijie Hu

“…To accurately predict the erosion rate of coiled tubing, this study studied the influence law of erosion rate through experiments, screened the main influencing factors of erosion rate by grey relational analysis (GRA), and established a back-propagation neural network (BPNN) model optimized by the sparrow search algorithm (SSA) to predict the erosion rate. …”
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