Harnessing Novel Data‐Driven Techniques for Precise Rainfall–Runoff Modeling by Saad Sh. Sammen, Reza Mohammadpour, Karam AlSafadi, Ali Mokhtar, Shamsuddin Shahid

“…In this study, data‐driven techniques, such as the group method of data handling (GMDH), extreme learning machine (ELM), and two hybrids of artificial neural network (ANN) with Cuckoo search algorithm (ANN + Cuckoo) and genetic algorithm (ANN + GA) were used to model the rainfall–runoff relationship. …”
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Impacts of Spatial Expansion of Urban and Rural Construction on Typhoon-Directed Economic Losses: Should Land Use Data Be Included in the Assessment? by Siyi Zhou, Zikai Zhao, Jiayue Hu, Fengbao Liu, Kunyuan Zheng

“…Results demonstrate three key findings: (1) By introducing prototype learning, a meta-learning approach, to guide model updates, we achieved precise assessments with small training samples, attaining an MAE of 1.02, representing 58.5–76.1% error reduction compared to conventional machine learning algorithms. …”
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Collaborative governance model for spoil disposal and gully infill land creation near open-pit coal mines by Shaogang LEI, Jianying ZHANG, Chang LIU, Liang WANG, Zhenwang JIA

“…The results show that the total area of natural gullies around the open-pit mine treated by this model is 2.588 × 105 m2, reducing the amount of earthwork discharge in the mining area by about 1.536 × 107 m3; By combining the CLiDE geomorphic evolution and soil erosion assessment model, we simulated and evaluated a 66.53% reduction in regional soil erosion after collaborative treatment of surrounding gullies over a 30-year period. …”
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Investigating Spatial Effects through Machine Learning and Leveraging Explainable AI for Child Malnutrition in Pakistan by Xiaoyi Zhang, Muhammad Usman, Ateeq ur Rehman Irshad, Mudassar Rashid, Amira Khattak

“…The use of machine learning algorithms, specifically XGBoost and Random Forest, highlighted the significant role played by the average value in the neighbourhood in predicting childhood stunting in nearby districts, confirming that the spatial spillover effect is not bounded by geographical boundaries. …”
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Study on privacy preserving encrypted traffic detection by Xinyu ZHANG, Bingsheng ZHANG, Quanrun MENG, Kui REN

“…Existing encrypted traffic detection technologies lack privacy protection for data and models, which will violate the privacy preserving regulations and increase the security risk of privacy leakage.A privacy-preserving encrypted traffic detection system was proposed.It promoted the privacy of the encrypted traffic detection model by combining the gradient boosting decision tree (GBDT) algorithm with differential privacy.The privacy-protected encrypted traffic detection system was designed and implemented.The performance and the efficiency of proposed system using the CICIDS2017 dataset were evaluated, which contained the malicious traffic of the DDoS attack and the port scan.The results show that when the privacy budget value is set to 1, the system accuracy rates are 91.7% and 92.4% respectively.The training and the prediction of our model is efficient.The training time of proposed model is 5.16 s and 5.59 s, that is only 2-3 times of GBDT algorithm.The prediction time is close to the GBDT algorithm.…”
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An Efficient Method for Diagnosing Brain Tumors Based on MRI Images Using Deep Convolutional Neural Networks by Thanh Han-Trong, Hinh Nguyen Van, Huong Nguyen Thi Thanh, Vu Tran Anh, Dung Nguyen Tuan, Luu Vu Dang

“…Those results of the evaluated algorithms through the coefficient F1-score are greater than 94% and the highest value is 97.65%.…”
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Gender Disparities in Artificial Intelligence–Generated Images of Hospital Leadership in the United States by Mia Gisselbaek, MD, Joana Berger-Estilita, MD, PhD, Laurens Minsart, MD, Ekin Köselerli, MD, Arnout Devos, PhD, Francisco Maio Matos, PhD, Odmara L. Barreto Chang, MD, PhD, Peter Dieckmann, PhD, Melanie Suppan, MD, Sarah Saxena, MD, PhD

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Improving Performance of KNN and C4.5 using Particle Swarm Optimization in Classification of Heart Diseases by Pareza Alam Jusia, Abdul Rahim, Herti Yani, Jasmir Jasmir

“…In this research, a comparison was made of the performance of the PSO-based K-NN and C4.5 algorithms. Following experiments employing PSO optimization to improve the K-NN and C4.5 algorithms, the findings indicated that the K-NN algorithm performed exceptionally well with PSO, achieving an accuracy of 89.09%, precision of 89.61%, recall of 90.79%, and an AUC value of 0.935.…”
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Gauge-fixing quantum density operators at scale by Amit Jamadagni, Eugene Dumitrescu

“…We provide a theory, algorithms, and simulations of nonequilibrium quantum systems using a one-dimensional (1D) completely positive (CP), matrix-product (MP) density-operator (ρ) representation. …”
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Environmental performance driven optimization of urban modular housing layout in Singapore by Xiaoyu Shen, Xue Ye

“…It integrates three-dimensional cellular automata, parametric performance simulation, genetic optimization algorithms, and hierarchical clustering algorithms. …”
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Facial expression recognition based on adaptation of the classifier to videos of the user by E.N. Churaev, A.V. Savchenko

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Achieving Closed-Loop Control Simulation of Human-Artefact Interaction: A Comparative Review by Wilhelm Frederik van der Vegte, Imre Horváth

“…For human decision making, however, validated algorithms exist only to predict the timing but not the reasoning behind it. …”
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Discovery of Meaningful Rules by using DTW based on Cubic Spline Interpolation by Luis Alexander Calvo-Valverde, David Elías Alfaro-Barboza

“…Short-term predictions based on “the shape” of meaningful rules lead to a vast number of applications. …”
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Day-Ahead Scheduling of IES Containing Solar Thermal Power Generation Based on CNN-MI-BILSTM Considering Source-Load Uncertainty by Kun Ding, Yalu Sun, Boyang Chen, Jing Chen, Lixia Sun, Yingjun Wu, Yusheng Xia

“…The validity of the proposed model is verified by algorithm prediction simulation and day-ahead scheduling experiments under different configurations.…”
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Machine learning assisted estimation of total solids content of drilling fluids by B.T. Gunel, Y.D. Pak, A.Ö. Herekeli, S. Gül, B. Kulga, E. Artun

“…In the final stage, different evaluation metrics were employed to evaluate and compare the performance of different classes of machine learning models. Among all algorithms tested, random forests algorithm was found to be the best predictive model resulting in consistently high accuracy. …”
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In-situ real-time monitoring of flavonoids extraction from propolis extracts based on near infrared spectroscopy by WANG Wuli, MA Haile, LI Han, ZHANG Yong, XIA Hong

“…The Rp (prediction correlation coefficient) and RMSEP (the root mean square prediction error) were 0.929 1 and 0.242 7 respectively, which showed that the in-situ monitoring of the model had good regression and prediction ability.ConclusionNear-infrared spectroscopy can be used to quickly and accurately monitor the flavonoid content in the extraction process of propolis residue, and realize real-time monitoring in situ.…”
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Short-term forecasting method for net load considering the impact of high proportion of distributed renewable energy by Yuhang Song, Haibo Zhang, Hanru Wu, Joseph Ndonji

“…Example validation shows that the proposed combination algorithm not only enhances the prediction accuracy, but also exhibits excellent generalization capabilities.…”
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Research on low-Impact development layouts in old urban residential communities based on multi-objective optimization. by Fuqin Liu, Yijun Fan, Xinran Liu, Yanjuan Li

“…This research focuses on an old residential community in Beijing utilizing the SWMM in combination with the NSGA-II algorithm. A Pareto optimal curve was identified iteratively, with construction costs, runoff reduction effects, and node overload time as the optimization objectives and the maximum area for the low impact development (LID) layout as a constraint. …”
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Application of Big Data Technology in the EMU Fault Early Warning by WU Zhenyi

“…It uses a method of combining equipment fault mechanism and artificial intelligence algorithm to construct a fault early warning and prediction model of key components of EMU, which reflects the working status of a component with its key physical properties and identify potential faults in advance. …”
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Evaluasi Kinerja MLLIB APACHE SPARK pada Klasifikasi Berita Palsu dalam Bahasa Indonesia by Antonius Angga Kurniawan, Metty Mustikasari

“…The four algorithms were chosen because of their proven ability to classify and several algorithms that are rarely used but have good abilities in terms of classification. …”
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