Enhancing seizure detection with hybrid XGBoost and recurrent neural networks by Santushti Santosh Betgeri, Madhu Shukla, Dinesh Kumar, Surbhi B. Khan, Muhammad Attique Khan, Nora A. Alkhaldi

“…This study investigates machine learning and deep learning algorithms for seizure prediction, comparing their effectiveness on a large EEG dataset of epileptic patients. …”
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Hybrid Machine Learning-Based Fault-Tolerant Sensor Data Fusion and Anomaly Detection for Fire Risk Mitigation in IIoT Environment by Jayameena Desikan, Sushil Kumar Singh, A. Jayanthiladevi, Shashi Bhushan, Vinay Rishiwal, Manish Kumar

“…The proposed approach also deploys machine learning algorithms to dynamically adjust probabilistic models based on real-time sensor reliability, thereby improving prediction accuracy even in the presence of unreliable sensor data. …”
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Artificial intelligence survival models for identifying relevant risk factors for incident diabetes in Azar cohort population by Neda Gilani, Mohammadhossein Somi, Farzaneh Hamidi, Pasqualina Santaguida, Elnaz Faramarzi, Reza Arabi Belaghi

“…In contrast, the RF analysis identified 21 important variables predicting a higher probability of having diabetes. …”
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Apple Watercore Grade Classification Method Based on ConvNeXt and Visible/Near-Infrared Spectroscopy by Chunlin Zhao, Zhipeng Yin, Yushuo Tan, Wenbin Zhang, Panpan Guo, Yaxing Ma, Haijian Wu, Ding Hu, Quan Lu

“…These images served as input for training and prediction using the ConvNeXt deep convolutional neural network. …”
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Modeling forest canopy structure and developing a stand health index using satellite remote sensing by Pulakesh Das, Parinaz Rahimzadeh-Bajgiran, William Livingston, Cameron D. McIntire, Aaron Bergdahl

“…The plot-level data were used to develop regression models for LAI and LCR estimation using microwave (Sentinel-1) and optical (Sentinel-2) remote sensing data and applying the Random Forest (RF) and Support Vector Machine (SVM) machine learning algorithms. The RF model showed higher prediction accuracy than the SVM model at the site level. …”
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CBID: A Scalable Method for Distributed Data Aggregation in WSNs by Aristides Mpitziopoulos, Damianos Gavalas, Charalampos Konstantopoulos, Grammati Pantziou

“…This results in a significant reduction of the overall energy expenditure and response time. …”
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An Efficient Transmission Power Design for SWIPT Multi-antenna Network Integrated by an Intelligent Reflecting Surface by Pham Viet Tuan, Dinh Long Hoang, Tu Ha Nguyen, Trong Duc Hoang

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DETERMINING TEACHING SCHEDULE AT STATE SENIOR HIGH SCHOOL 1 DEPOK USING ASSIGNMENT THEORY WITH HUNGARIAN METHOD AND NEW IMPROVED ONES ASSIGNMENT METHOD ASSISTED BY PYTHON by Catherine Richelle Hindarto, Febi Sanjaya

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Evaluation and Application of Urban Traffic Signal Optimizing Control Strategy Based on Reinforcement Learning by Yizhe Wang, Xiaoguang Yang, Yangdong Liu, Hailun Liang

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Analysis of the impact of mobile robots on the efficiency of warehousing and transport processes in modern textile manufacturing by Karabegović Isak

“…AMRs leverage advanced technologies such as LiDAR sensors, SLAM algorithms, artificial intelligence, and IoT systems to navigate complex industrial environments autonomously, optimize routes, and execute tasks without direct human intervention. …”
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Design Methodology of Power-Level Modulation for PV Power Loss Minimization and DC Series Arc Fault Detection and Extinguishing by Wan Kim, Hwa-Pyeong Park

“…Operating the conventional arc fault detection algorithm under normal conditions, such as irradiance variations, results in a reduction in PV power generation. …”
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Advanced lightweight deep learning vision framework for efficient pavement damage identification by Shuai Dong, Yunlong Wang, Jin Cao, Jia Ma, Yang Chen, Xin Kang

“…Moreover, LPDD-YOLO can obtain a 47.3% reduction in parameters and a 54.4% decrease in GFLOPs. …”
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Distribution Generation Network Arrangement by Capacitor Placement and Sizing in Renewable Energy Sources with Uncertainties Based on Self-adaption Kho-Kho Optimizer by Lu Xingxing

“…Post-optimization results indicated a reduction in power loss costs from 4.11 × 10^5 to 1.05 × 10^5 units, representing a 25.54% decrease. …”
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Evaluating Spatial Patterns of Ecosystem Services based on a Comparative Approach on Spatial Statistics in the Central Part of Isfahan Province by Sedighe Abdollahi, Alireza Ildoromi, Abdolrassoul Salmanmahini, Sima Fakheran

“…Then, spatial accuracy of the investigated algorithms was evaluated and compared using the Receiving Operator Characteristic method. …”
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Structural optimization of stiffened panel structures with continuous and discrete design variables using deep reinforcement learning by Ryota NONAMI, Mitsuru KITAMURA

“…Consequently, heuristic approaches such as Genetic Algorithms (GA) are often employed; however, GA typically imposes a high computational burden in large-scale optimization problems. …”
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Probabilistic daily runoff forecasting in high-altitude cold regions using a hybrid model combining DBO and transformer variants by Qiying Yu, Wenzhong Li, Yungang Bai, Zhenlin Lu, Yingying Xu, Chengshuai Liu, Lu Tian, Chen Shi, Biao Cao, Tianning Xie, Jianghui Zhang, Caihong Hu

“…Across various forecast periods, the model’s NSE values are 6.9–26.9 % higher than those of the TCN and Transformer models, offering more reliable short-term and long-term predictions. Furthermore, the Bootstrap algorithm’s probabilistic approach provides valuable insights into forecast uncertainty, a crucial feature for managing water resources and mitigating flood risks in high-altitude cold regions with complex hydrological dynamics.…”
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A Novel Forest Dynamic Growth Visualization Method by Incorporating Spatial Structural Parameters Based on Convolutional Neural Network by Linlong Wang, Huaiqing Zhang, Kexin Lei, Tingdong Yang, Jing Zhang, Zeyu Cui, Rurao Fu, Hongyan Yu, Baowei Zhao, Xianyin Wang

“…The results show that: first, spatial structural parameters C and U have a certain contribution to the forest growth, and C and U can explain 21.5&#x0025;, 15.2&#x0025;, and 9.3&#x0025; of the variance in DBH, H, and CW growth models, respectively; second, CNN model outperformed machine learning algorithms SVR, MARS, Cubist, RF, and XGBoost in terms of prediction performance; third, based on FDGVM-CNN-SSP, we simulated Chinese fir plantations at individual tree level and stand level from 2018 to 2022 and found that DBH and H&#x0027;s fitting performance in measured and predicted data was highly consistent with <italic>R</italic>² and root-mean-square error (RMSE) of 86.8&#x0025;, 2.06 cm in DBH and 79.2&#x0025;, 1.11 m in H, but CW&#x0027;s <italic>R</italic>² and RMSE of 72.2&#x0025;, 0.65 m caused crowding (C) inconsistency.…”
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Functional Monitoring of Patients With Knee Osteoarthritis Based on Multidimensional Wearable Plantar Pressure Features: Cross-Sectional Study by Junan Xie, Shilin Li, Zhen Song, Lin Shu, Qing Zeng, Guozhi Huang, Yihuan Lin

“…The multidimensional gait features extracted from the data and physical characteristics were used to establish the KOA functional feature database for the plantar pressure measurement system. 40mFPWT and TUGT regression prediction models were trained using a series of mature machine learning algorithms. …”
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Exploration of ductility for refractory high entropy alloys via interpretive machine learning by Shaolong Zheng, Lingwei Yang, Liyang Fang, Chenran Xu, Guanglong Xu, Yifang Ouyang, Xiaoma Tao

“…This study constructs an ML model for accurate ductility prediction from sparse compositional data, accelerating the design of ductile RHEAs within infinite compositional space. …”
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ҚАЗАҚСТАН РЕСПУБЛИКАСЫНЫҢ НАН-ТОҚАШ КӘСІПОРЫНДАРЫН ТАРАТУДЫН ЛОГИСТИКАЛЫҚ ЖҮЙЕЛЕРІН ҚҰРУ МӘСЕЛЕЛЕРІ... by D. K. Tursynbaeva, S. .. Bolatkizy, A. E. Toylybayev

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