Detection of Pear Quality Using Hyperspectral Imaging Technology and Machine Learning Analysis by Zishen Zhang, Hong Cheng, Meiyu Chen, Lixin Zhang, Yudou Cheng, Wenjuan Geng, Junfeng Guan

“…In summary, the combination of HSI and machine learning models enabled an efficient, rapid, and non-destructive detection of pear quality and provided a practical value for quality control and the commercial processing of pears.…”
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Sensitivity Study on the Influence of Parameterization Schemes in WRF_ARW Model on Short- and Medium-Range Precipitation Forecasts in the Central Andes of Peru by Aldo S. Moya-Álvarez, Daniel Martínez-Castro, José L. Flores, Yamina Silva

“…A sensitivity study of the performance of the Weather Research and Forecasting regional model (WRF, version 3.7) to the use of different microphysics, cumulus, and boundary layer parameterizations for short- and medium-term precipitation forecast is conducted in the Central Andes of Peru. …”
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Hybrid deep learning CNN-LSTM model for forecasting direct normal irradiance: a study on solar potential in Ghardaia, Algeria by Boumediene Ladjal, Mohamed Nadour, Mohcene Bechouat, Nadji Hadroug, Moussa Sedraoui, Abdelaziz Rabehi, Mawloud Guermoui, Takele Ferede Agajie

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Enhancing the FFT-LSTM Time-Series Forecasting Model via a Novel FFT-Based Feature Extraction–Extension Scheme by Kyrylo Yemets, Ivan Izonin, Ivanna Dronyuk

“…Building upon this preprocessing method, the FFT-LSTM forecasting model, which combines the strengths of FFT and Long Short-Term Memory (LSTM) recurrent neural networks, was enhanced. …”
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Zenith Tropospheric Delay Forecasting in the European Region Using the Informer–Long Short-Term Memory Networks Hybrid Prediction Model by Zhengdao Yuan, Xu Lin, Yashi Xu, Jie Zhao, Nage Du, Xiaolong Cai, Mengkui Li

“…Developing a high-precision, long-term forecasting model for ZTD can provide valuable insights into the overall trends of predicted ZTD, which is essential for improving GNSS positioning and analyzing changes in regional climate and water vapor. …”
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Detection and simulation of Kermanshah dust storm using HYSPLIT and WRF-chem models by Tooba Alizadeh, Majid Rezaei Banafsheh, Hashem Rostamzadeh, Gholamreza Goodarzi, Hedar Maleki, Hamzeh Alizadeh

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The Role of Artificial Intelligence in Optometric Diagnostics and Research: Deep Learning and Time-Series Forecasting Applications by Luis F. F. M. Santos, Miguel Ángel Sánchez-Tena, Cristina Alvarez-Peregrina, José-María Sánchez-González, Clara Martinez-Perez

“…Utilizing the Artificial Intelligence model outputs, the Autoregressive Integrated Moving Average model provides forecasts from all classes through 2030, predicting decreases in research interest for Contactology, Low Vision, and Refractive Surgery but increases for Myopia and Dry Eye due to rising prevalence and lifestyle changes. …”
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Time-Series Large Language Models: A Systematic Review of State-of-the-Art by Shamsu Abdullahi, Kamaluddeen Usman Danyaro, Abubakar Zakari, Izzatdin Abdul Aziz, Noor Amila Wan Abdullah Zawawi, Shamsuddeen Adamu

“…Key findings reveal advancements in architectures and novel tokenization strategies tailored for temporal data. Forecasting dominates the identified tasks with 79.66% of the selected studies, while classification and anomaly detection remain underexplored. …”
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Advancements in seasonal rainfall forecasting: A seasonal auto-regressive integrated moving average model with outlier adjustments for Ghana's Western Region by Francis Ayiah-Mensah, Senyefia Bosson-Amedenu, Emmanuel Mensah Baah, John Awuah Addor

“…The methodology incorporates robust outlier detection and adjustment techniques, including the interquartile range (IQR) and winsorization, to increase the model's resilience against extreme weather events while preserving critical data characteristics. …”
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A Novel Optimized Hybrid VMD-PCA-XGBoost Model for Forecasting Precipitation: Exemplified by the Beijing-Tianjin-Hebei Study Region in China by Qiaoli Kong, Qian Li, Qi Bai, Xiaolong Mi, Joseph Awange, Shi Wang, Yi Yang, Guoli Bo

“…These findings underscore the model’s robustness and precision, offering a promising tool for improving precipitation forecasts. …”
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A Secure IIoT Environment That Integrates AI-Driven Real-Time Short-Term Active and Reactive Load Forecasting with Anomaly Detection: A Real-World Application by Md. Ibne Joha, Md Minhazur Rahman, Md Shahriar Nazim, Yeong Min Jang

“…Furthermore, we introduce an optimized Isolation Forest model for anomaly detection that considers the transient conditions of appliances when identifying irregular behavior. …”
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A Fusion of Deep Learning and Time Series Regression for Flood Forecasting: An Application to the Ratnapura Area Based on the Kalu River Basin in Sri Lanka by Shanthi Saubhagya, Chandima Tilakaratne, Pemantha Lakraj, Musa Mammadov

“…Thus, this study introduces a novel hybrid model that combines a deep leaning technique with a traditional Linear Regression model to first forecast water levels and then detect rare but destructive flood events (i.e., major and critical floods) with high accuracy, from 1 to 3 days ahead. …”
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Soft detection model of corrosion leakage risk based on KNN and random forest algorithms by Yang YANG, Chengzhi LI, Xuan DU, Xiao YU, Shaohua DONG

“…These identified indicators were then employed to develop an intelligent soft detection model that integrates pipeline and environmental data, based on the K-Nearest Neighbor (KNN) and Random Forest algorithms. …”
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Forecasting the incidence of acute lymphoid leukaemia in males and females in the Saudi population from 2020 to 2029: application of ARIMA models and public health implications by Saeed M. Kabrah, Budhi Handoko, Yasir Aljohani, Abdulrahman Mujalli, Mohammad Alobaidy, Arwa F. Flemban, Wesam F. Farrash, Abdulaziz H. Alharbi, M. S. J. Alzahrani

“…The models demonstrated high accuracy, with MAE of 1.19895 and 2.749188, MSE of 62.33 and 31.83, and MAPE of 0.6805807 and 1.443453 for males and females, respectively.Conclusion Forecasts indicate a substantial rise in ALL incidence among both sexes, highlighting the urgent need for improved surveillance, early detection, and healthcare capacity planning. …”
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Performance Evaluation of PM_2.5 Forecasting Using SARIMAX and LSTM in the Korean Peninsula by Chae-Yeon Lee, Ju-Yong Lee, Seung-Hee Han, Jin-Goo Kang, Jeong-Beom Lee, Dae-Ryun Choi

“…The National Institute of Environmental Research (NIER) currently relies on numerical models such as the Community Multiscale Air Quality (CMAQ) model for PM_2.5 forecasting. …”
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Detection of Economic Crises With Language Models and Comparative Analysis of Simple Time Series Analysis Models and Machine Learning Algorithms on the Stock Market by Kurban Kotan, Bayram Kotan, Serdar Kirisoglu

“…The aim is to develop an effective “smart, automatic crises detection and forecasting model selection application”. …”
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Workload Forecasting Methods in Cloud Environments: An Overview by Samah Aziz, Manar Kashmoola

“…We present an overview of approaches for workload forecasting in cloud systems in this study. We explore more sophisticated approaches like algorithms for deep learning (DL) and machine learning (ML) in addition to more conventional approaches like analysis of time series and models of regression. …”
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Financial risk forecasting with RGCT-prerisk: a relational graph and cross-temporal contrastive pretraining framework by Liyu Chen, Xiangwei Fan

“…Abstract Financial risk forecasting is critical for the early detection of corporate distress, yet traditional methods and recent deep learning models exhibit notable limitations. …”
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Introduction of neural network technologies to optimise the control of the operating modes of a sucker-rod pump installation by O. Turchyn

“…Analysis of data from the unit’s sensors using neural networks helped to identify optimal operating modes that ensure maximum production with minimal energy consumption. A forecasting model has been developed that can detect potential equipment failures in advance, which reduces the risks of emergencies and maintenance costs. …”
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Quantifying Temporal Dynamics in Global Cyber Threats: A GPT-Driven Framework for Risk Forecasting and Strategic Intelligence by Fahim Sufi, Musleh Alsulami

“…Despite the exponential rise in cybersecurity incidents worldwide, existing analytical approaches often fail to detect subtle temporal dynamics in cyber threats, particularly on a quarterly scale. …”
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