Non-Destructive Measurement of Rice Spikelet Size Based on Panicle Structure Using Deep Learning Method by Ruoling Deng, Weisen Liu, Haitao Liu, Qiang Liu, Jing Zhang, Mingxin Hou

“…Finally, the “SSM-Method” integrated with a spikelet detection model and calibration value was developed for the automatic measurement of spikelet sizes. …”
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Predicting cardiotoxicity in drug development: A deep learning approach by Kaifeng Liu, Huizi Cui, Xiangyu Yu, Wannan Li, Weiwei Han

“…The best machine learning model, XGBoost Morgan, achieved an accuracy (ACC) value of 0.84, and the deep learning model, Transformer_Morgan, achieved the best ACC value of 0.85, showing a high ability to distinguish between toxic and non-toxic compounds. …”
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A Reinforcement Learning Approach to Personalized Asthma Exacerbation Prediction Using Proximal Policy Optimization by Dahiru Adamu Aliyu, Emelia Akashah Patah Akhir, Maryam Omar Abdullah Sawad, Jameel Shehu Yalli, Yahaya Saidu

“…RL performance metrics confirmed the model’s stability and convergence, including cumulative rewards, policy, and value loss. These findings highlight PPO’s potential for real-time clinical integration, enabling continuous patient monitoring and proactive intervention strategies. …”
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Automated Anomaly Detection in Blast Furnace Shaft Static Pressure Using Adversarial Autoencoders and Mode Decomposition by Xiaodong Sun, Jie Zhu, Bing Tang, Zhaohui Jiang

“…Firstly, using VMD combined with sample entropy calculation and clustering algorithm, the trend, period, and other components of multidimensional signals are extracted, and then these components are integrated into an improved adversarial training autoencoder to detect global and local anomalies. …”
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Improving Cost Contingency Estimation in Infrastructure Projects with Artificial Neural Networks and a Complexity Index by Michael C. P. Sing, Qiuwen Ma, Qinhuan Gu

“…Machine learning (ML) algorithms have been developed for cost performance prediction in the form of single-point estimates where they provide only a definitive value. …”
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Emotional Factor Recognition and Teaching Strategy Adjustment in Cultural Teaching Supported by Artificial Intelligence by Yu Xiaochen

“…Emotion recognition technology collects students’ facial expressions, speech features, and physiological data, and uses deep learning algorithms to analyze students’ emotional states in real time, providing accurate feedback support for teaching. …”
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Evaluation of the Decision Tree Model for Air Condition Classification on the Global Air Pollution Dataset by Cindy Dinda Sabella, Yoga Pristyanto

“…Importance feature analysis identified PM2.5 and AQI as the dominant factors in influencing air quality, with PM2.5 having the highest importance value of 0.93. This research confirms that machine learning can be an effective tool for integrating and classifying air pollution. …”
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Implementation of MS Circle Map in Digital Image Encryption by Ichsani Mursidah, Suryadi MT, Sarifuddin Madenda, Suryadi Harmanto

“…This paper proposes a novel MS Circle Map-based image encryption algorithm, integrating chaotic dynamics for enhanced security. …”
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Bike-Sharing Static Rebalancing by Considering the Collection of Bicycles in Need of Repair by Sheng Zhang, Guanhua Xiang, Zhongxiang Huang

“…The sum of the initial bicycle number and the minimum net flow value was determined to be the demand for static rebalancing, and this led to the proposal of a bike-sharing demand prediction method based on autoregressive integrated moving average models. …”
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Optimal Histogram Filter by A. V. Ausiannikau, V. M. Kozel

“…The histogram filter is an easy-to-implement tool that can be easily integrated into any open distribution law identification (recognition) algorithm.…”
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Analysis of Comprehensive Artificial Neural Network Computer Media Aided Construction of Economic Forecasting Model by Tianfeng Li

“…In addition, ANN research work has continued to advance, has made great progress in many fields represented by economic forecasting, has successfully solved many practical problems, showing a more intelligent adaptation in the field of economics, and therefore has an important research value. Based on this, this paper first analyzes the economic system prediction, then studies the ANN medium model and learning algorithm, and finally verifies the effectiveness of the ANN economic prediction model integrating computer medium.…”
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Analysis of crop disease and pest occurrences: Insights from Japan's national surveys. by Jianqiang Sun, Sunao Ochi, Takehiko Yamanaka

“…Through comprehensive analysis and statistical modeling, we found that a simple algorithm-averaging data from the past five years without incorporating seasonal trends or meteorological variables-outperformed more complex models, underscoring the value of historical CDP survey data. …”
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Advanced Solar Irradiance Forecasting Using Hybrid Ensemble Deep Learning and Multisite Data Analytics for Optimal Solar-Hydro Hybrid Power Plants by Sudharshan Konduru, Naveen C., Jagabar Sathik M.

“…A novel hybrid decomposed residual ensembling model for deep learning (SBLTSRARW) using models such as autoregressive integrated moving average (ARIMA) and seasonal-trend decomposition using loess (STL) along with prediction and optimization models such as Bidirectional LSTM (Bi-LSTM), and Whale Optimization Algorithm (WOA) methods are used to predict the irradiances. …”
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COMPARISON OF SARIMA, SVR, AND GA-SVR METHODS FOR FORECASTING THE NUMBER OF RAINY DAYS IN BENGKULU CITY by Novi Puspita, Farit Mochamad Afendi, Bagus Sartono

“…This study aimed to forecast the data on the number of rainy days in Bengkulu City in the period January 2000 to December 2020 using the Seasonal Autoregressive Integrated Moving Average (SARIMA), Support Vector Regression (SVR), and Genetic Algorithm Support Vector Regression (GA-SVR) methods. …”
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Rolling Bearing Fault Diagnosis Based on SCNN and Optimized HKELM by Yulin Wang, Xianjun Du

“…Key parameters of the HKELM were dynamically adjusted using a novel optimization algorithm, significantly enhancing fault diagnosis accuracy and system stability. …”
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Lightweight Design of Drive Axle Housing based on OptiStruct by Xiaofei Song, Ronghui Lin, Shuaichao Li, Yunfa Qiao, Yulong Ji, Juan Deng, Zhitian Guo

“…In order to ensure the performance requirements， the traditional design concept generally adopts redundant design. Based on the integrated technology of CAD/CAE， the joint design between 3D mathematical model and finite element model is adopted， and the lightweight design of the driving axle housing is realized based on the size optimization algorithm of OptiStruct. …”
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Immune-related gene characterization and biological mechanisms in major depressive disorder revealed based on transcriptomics and network pharmacology by Shasha Wu, Shasha Wu, Qing Jiang, Jinhui Wang, Jinhui Wang, Daming Wu, Yan Ren

“…The Lasso algorithm confirmed 4 hub genes as potential biomarkers for MDD. …”
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Security decision method for the edge of multi-layer satellite network based on reinforcement learning by Peiliang ZUO, Shaolong HOU, Chao GUO, Hua JIANG, Wenbo WANG

“…This paper uses deep reinforcement learning algorithms to implement edge security decisions for satellite networks. …”
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Systematic elucidation of the effective constituents and potential mechanisms of Scrophulariae Radix against neoplasm based on LC-MS, network pharmacology, and molecular docking ap... by Shu-jie Yu, Xiao-bin Kong, Xin Jin, Meng-yi Shan, Gang Cheng, Pei-lu Wang, Wen-long Li, Pei-yuan Zhao, Yun-jie Sheng, Bing-qian He, Qi Shi, Hua-qiang Li, Qi-ming Zhao, Lu-ping Qin, Lu-ping Qin, Xiong-yu Meng, Xiong-yu Meng

“…As a result, the material–liquid ratio was significantly reduced from 100 g/mL to 32 g/mL, and the extraction efficiency was 1.332%, which was close to the predicted value of 1.346% in the response surface method, indicating that the algorithm model had a good fit. …”
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Diagnostic Utility of ¹⁸F-FDG PET/CT in Infective Endocarditis by Corina-Ioana Anton, Alice-Elena Munteanu, Mihaela Raluca Mititelu, Militaru Alexandru Ștefan, Cosmin-Alexandru Buzilă, Adrian Streinu-Cercel

“…¹⁸F-FDG PET/CT provides high diagnostic accuracy for suspected PVE, significantly aiding reclassification of ambiguous cases and detection of extracardiac complications and malignancies. Its integration into diagnostic algorithms may improve clinical management and outcomes in complex IE cases.…”
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