Feature fusion ensemble classification approach for epileptic seizure prediction using electroencephalographic bio-signals by Yazeed Alkhrijah, Yazeed Alkhrijah, Shehzad Khalid, Shehzad Khalid, Syed Muhammad Usman, Amina Jameel, Muhammad Zubair, Haya Aldossary, Aamir Anwar, Saad Arif

“…It is then fed into three classifiers, including support vector machines, random forest, and long short-term memory (LSTM) units. …”
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Sensor‐Guided Smart Irrigation for Tomato Production: Comparing Low and Optimum Soil Moisture in Greenhouse Environments by Ibrahim Dirlik, Ferhat Uğurlar, Cengiz Kaya

“…Future research should explore the integration of advanced sensors, machine learning algorithms, and predictive models to further optimize irrigation strategies, with an emphasis on scalability and environmental impact. …”
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A Deep Learning Model for Identifying the Risk of Mesenteric Malperfusion in Acute Aortic Dissection Using Initial Diagnostic Data: Algorithm Development and Validation by Zhechuan Jin, Jiale Dong, Chengxiang Li, Yi Jiang, Jian Yang, Lei Xu, Ping Li, Zhun Xie, Yulin Li, Dongjin Wang, Zhili Ji

“…The training and internal validation cohort consisted of 450 patients from Beijing Anzhen Hospital, whereas the external validation cohort comprised 75 patients from Nanjing Drum Tower Hospital. Three machine learning models were developed: the benchmark model using laboratory parameters, the multiorgan feature–based AAD complicating MMP (MAM) model based on computed tomography angiography images, and the integrated model combining both data modalities. …”
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Multi-layer obstacle-avoiding rectilinear steiner minimal tree algorithm based on deep reinforcement learning by Xing Huang, Yuhan Zhu, Yanbo Xu, Yajun Xie, Genggeng Liu

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Analytical Inertia Identification of Doubly Fed Wind Farm with Limited Control Information Based on Symbolic Regression by Mengxuan Shi, Yang Li, Xingyu Shi, Dejun Shao, Mujie Zhang, Duange Guo, Yijia Cao

“…First, a dynamic model for the inertia response of DFIG wind farms is established, and a mathematical expression for the equivalent virtual inertia time constant under different control strategies is derived. …”
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A federated learning-based privacy-preserving image processing framework for brain tumor detection from CT scans by Abdullah Al-Saleh, Ghanshyam G. Tejani, Shailendra Mishra, Sunil Kumar Sharma, Seyed Jalaleddin Mousavirad

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Constrained Heat Kernel Graph Diffusion Convolution: A High-Dimensional Statistical Approximation via Information Theory by Zhuo-Chen He

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Artificial Intelligence in the Sustainable Design and Manufacturing of Products in Civil Engineering in the Context of Industry 4.0 by Tomáš Mandičák, Annamária Behúnová, Peter Mésároš

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Proximal remote sensing of dissolved organic matter in aqua-culture ponds via multi-temporal spectral correction by Wenxu Lv, Yancang Wang, Huiqiong Cao, Peng Cheng, Xiaohe Gu, Zhuoran Ma, Mengjie Li, Ruiyin Tang, Qichao Zhao, Xuqing Li, Lan Zhang, Shuaifei Liu

“…After transfer learning optimization, the model using the corrected spectrum still exhibited superior performance in 2024. …”
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Ecological and Ergonomic Aspects of Operating Hybrid Sunflower Cultivation Technologies by M. A. Kerimov, V. V. Vetushko

“…The convergence of pre-injury conditions and hazardous operator actions in time and space leads to injury, the occurrence of which can be mathematically modeled as a function. A comprehensive indicator – the injury risk criterion – is proposed and modeled as a function of harmful factors, including workplace dust concentration, ambient temperature, and the technical condition of the machine-tractor unit. …”
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Connotation and application paradigm of intelligent mining data intelligence enabling technology by FU Xiang, WANG Kai, WANG Ranfeng

“…The algorithm layer combined industrial mechanism models and swarm intelligence algorithms to construct a dynamic decision-making system based on multi-objective optimization, achieving collaborative optimization of mining processes and safety-weighted priority control. …”
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Enhancing prediction accuracy of key biomass partitioning traits in wheat using multi‐kernel genomic prediction models integrating secondary traits and environmental covariates by Sudip Kunwar, Md Ali Babar, Diego Jarquin, Yiannis Ampatzidis, Naeem Khan, Janam Prabhat Acharya, Jordan McBreen, Samuel Adewale, Gina Brown‐Guedira

“…Genomic best linear unbiased predictor (GBLUP) and Bayesian methods performed similarly or better than machine learning models for SPI, harvest index (HI), and GY. …”
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Artificial intelligence in geoenergy: bridging petroleum engineering and future-oriented applications by Sungil Kim, Tea-Woo Kim, Suryeom Jo

“…Results highlight the extensive use of machine learning (ML) and deep learning (DL) for tasks such as proxy modeling, dimensionality reduction, data generation, and optimization. …”
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Autonomous regenerative agriculture: Swarm robotics to change farm economics by A.K.M. Abdullah Al-Amin, James Lowenberg‑DeBoer, Kit Franklin, Edward Dickin, James M Monaghan, Karl Behrendt

“…The profit maximizing optimization model of a 500 hectare (ha) British West Midlands farm found that per annum profitability of regenerative strip intercropping system was £56.88 ha−1 higher for farm operated using smaller swarm robotics than farm operated using smaller conventional mechanization with human drivers. …”
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Signal-to-event encoding parameter selection for multiple event classification with spiking neural networks by Mateusz Pabian, Dominik Rzepka, Mirosław Pawlak, Mirosław Pawlak, Marek Miśkowicz, Ryszard Sroka

“…This paper studies the problem of optimal event-based signal encoding if data are to be processed by a machine learning model, such as the spiking neural network (SNN). …”
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Evaluation of Ecosystem Health Based on AWDO-SVR Algorithm in Shiyang River Basin by WANG Wenchuan, LI Lei, ZHENG Ye, XU Dongmei, XU Lei

“…This paper evaluates the ecological health of Shiyang River Basin by the adaptive wind-driven optimization (AWDO) algorithm and support vector regression (SVR) coupled algorithm for problems in health assessment of watershed ecosystem,finds the optimal parameters of support vector machine (SVM) by AWDO algorithm for uncertainty of parameters from SVM,proposes an evaluation model based on AWDO-SVR algorithm,and evaluates nine indexes such as water resource endowment,water resource development and utilization,and social and economic function of Shiyang River Basin by the model with advantages of fast and simple operation and no need of weight.The results show that the ecological health is sub-health for the upper reaches of Shiyang River,and morbid for the middle and lower reaches respectively.The evaluation result is the same as that of the variable set model,indicating that AWDO-SVR algorithm can be effectively applied to the ecosystem health evaluation of the river basin.…”
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IoT-Based Traffic Prediction for Smart Cities by Zhinong Miao, Qilong Liao

“…The primary objective was to develop a predictive model that improves traffic forecasting accuracy, reduces congestion, and optimizes real-time traffic management. …”
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An IoT-enabled AI system for real-time crop prediction using soil and weather data in precision agriculture by MD Shaifullah Sharafat, Nilavro Das Kabya, Rahimul Islam Emu, Mehrab Uddin Ahmed, Jakaria Chowdhury Onik, Mohammad Aminul Islam, Riasat Khan

“…Results and conclusions: The highest accuracy among machine learning models was achieved using Random Forest (95.8%) and Gradient Boosting (95.5%). …”
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MS-YOLOv8: multi-scale adaptive recognition and counting model for peanut seedlings under salt-alkali stress from remote sensing by Fan Zhang, Longgang Zhao, Longgang Zhao, Dongwei Wang, Jiasheng Wang, Igor Smirnov, Juan Li

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Research on the Inversion Method of Dust Retention in Grassland Plant Canopies Based on UAV-Borne Hyperspectral Data by Yibo Zhao, Shaogang Lei

“…The spectral index with the highest absolute value of the correlation coefficient with the canopy dust retention is selected as the feature variable for each spectral index. In addition, machine learning methods such as the partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF) methods are used to develop models for the inversion of canopy dust retention. …”
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