Machine learning approaches for forecasting compressive strength of high-strength concrete by Mohammed Shaaban, Mohamed Amin, S. Selim, Islam M. Riad

“…Artificial intelligence (AI) methods reduce time and money. This research proposes a machine learning (ML) model using the Python programming language to predict the compressive strength of HSC. …”
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A high-precision displacement prediction model for landslide geological hazards based on APSO-SVR-LSTM combination by Nianhong Wang, Jun Zhang, Yunfei Xiang, Saipeng Huang

“…The APSO is employed to optimize the hyperparameters of the SVR model, ensuring an optimal parameter combination. …”
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Economic Structure Analysis Based on Neural Network and Bionic Algorithm by Yanjun Dai, Lin Su

“…In deep neuroevolutionary method, the structure space of convolutional neural network is proposed to solve the search space design of neural structure search (NAS), and the GA-based deep neuroevolutionary method under the structure space of convolutional neural network is proposed to solve the problem that numerous hyperparameters and network structure parameters can produce explosive combinations when designing deep learning models. …”
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Forecasting Renewable energy and electricity consumption using evolutionary hyperheuristic algorithm by Yang Cao, Jun Yu, Rui Zhong, Masaharu Munetomo

“…Abstract This research utilizes time series models to forecast electricity generation from renewable energy sources and electricity consumption. …”
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VGG-16 Accuracy Optimization for Fingerprint Pattern Imager Classification by Agus Andreansyah, Julian Supardi

“…A novel aspect of this research is the optimization of the VGG-16 model by making specific adjustments to the hyperparameters, including setting the learning rate to 0.0001, using 50 epochs, and selecting a training-to- validation data split of 80%:10%. …”
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A Multi-Mode Dynamic Fusion Mach Number Prediction Framework by Luping Zhao, Weihao Li, Wentao Xu

“…The precise control of Mach numbers in supersonic and hypersonic compressor wind tunnel systems is a critical challenge in aerodynamic research. Although existing studies have improved prediction accuracy to some extent through machine learning methods, they generally neglect the multi-mode characteristics of complex wind tunnel systems, limiting the generalizability of the models. …”
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Automated Rooftop Solar Panel Detection Through Convolutional Neural Networks by Simon Pena Pereira, Azarakhsh Rafiee, Stef Lhermitte

“…To address this issue, deep-learning techniques, can support collecting data about PV systems from aerial and satellite imagery. Previous research, however, lacks the consideration for ground truth data-specific characteristics of PV panels. …”
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Authorship Classification in a Resource Constraint Language Using Convolutional Neural Networks by Md. Rajib Hossain, Mohammed Moshiul Hoque, M. Ali Akber Dewan, Nazmul Siddique, Md. Nazmul Islam, Iqbal H. Sarker

“…Authorship classification is a method of automatically determining the appropriate author of an unknown linguistic text. Although research on authorship classification has significantly progressed in high-resource languages, it is at a primitive stage in the realm of resource-constraint languages like Bengali. …”
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Solar Energy Datasets of Deep Learning Models Incorporating with GK-2A and ASOS Ground Measurements by Jong-Sung Ha, Seungtaek Jeong, Seyun Min, Yejin Lee, Suhwan Kim, Doehee Han, Jong-Min Yeom

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A case study on the application of a data-driven (XGBoost) approach on the environmental and socio-economic perspectives of agricultural groundwater management by Sheng-Wei Wang, Yen-Yu Chen, Shu-Han Hsu, Yu-Hsuan Kao, Masaomi Kimura, Li-chiu Chang, Tzi-Wen Pan, Chuen-Fa Ni

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Lithium-ion battery RUL prediction based on optimized VMD-SSA-PatchTST algorithm by Pei Tang, Zetao Qiu, Zhongran Yao, Jiahao Pan, Dashuai Cheng, Xiaoyong Gu, Changcheng Sun

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Data Quality Improvement Method for Power Equipment Condition Based on Stacked Denoising Autoencoders Improved by Particle Swarm Optimization by JI Rong, HOU Huijuan, SHENG Gehao, ZHANG Lijing, SHU Bo, JIANG Xiuchen

“…Therefore, data cleaning is of great significance. Most existing research focuses on direct identification and elimination of abnormal data, which compromises the integrity of the data. …”
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Satellite Image Price Prediction Based on Machine Learning by Linhan Yang, Zugang Chen, Guoqing Li

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Determination of disintegration time using formulation data for solid dosage oral formulations via advanced machine learning integrated optimizer models by Mohammed Ghazwani, Umme Hani

“…These findings highlight NODE’s efficacy in modeling complex data relationships, offering significant potential for optimizing tablet formulations in pharmaceutical research to design proper fast-disintegrating tablets.…”
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Human Action Recognition Based on The Skeletal Pairwise Dissimilarity by E.E. Surkov, O.S. Seredin, A.V. Kopylov

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Prediction of the packaging chemical migration into food and water by cutting-edge machine learning techniques by Behzad Vaferi, Mohsen Dehbashi, Reza Yousefzadeh, Ali Hosin Alibak

“…Due to the costly and time-intensive nature of experimental measurements, employing artificial intelligence (AI) methodologies is beneficial. This research uses five renowned AI-based techniques (namely, long short-term memory, gradient boosting regressor, multi-layer perceptron, Random Forest, and convolutional neural networks) to anticipate chemical migration from packaging materials to the food/water structure, considering variables such as temperature, chemical characteristics, and packaging/food types. …”
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GA-Attention-Fuzzy-Stock-Net: An optimized neuro-fuzzy system for stock market price prediction with genetic algorithm and attention mechanism by Burak Gülmez

“…Genetic algorithms optimize the hyperparameters, including learning rates and network architectures, while the attention mechanism enhances the model's ability to focus on relevant temporal patterns. …”
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Migrative armadillo optimization enabled a one-dimensional quantum convolutional neural network for supply chain demand forecasting. by Mohamed Irhuma, Ahmad Alzubi, Tolga Öz, Kolawole Iyiola

“…The Migrative Armadillo Optimization (MAO) algorithm effectively optimizes the hyperparameters of the model. Specifically, the 1D-QNN approach offers exponential speed in the forecasting tasks as well as provides accurate prediction. …”
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EGNAS: Efficient Graph Neural Architecture Search Through Evolutionary Algorithm by Younkyung Jwa, Chang Wook Ahn, Man-Je Kim

“…The primary objective of our research is to enhance the efficiency and effectiveness of Neural Architecture Search (NAS) with regard to Graph Neural Networks (GNNs). …”
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An improved beluga whale optimizer-Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images. by Anilkumar P, Venugopal P

“…Semantic segmentation process over Remote Sensing images has been regarded as hot research work. Even though the Remote Sensing images provide many essential features, the sampled images are inconsistent in size. …”
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