Multi-Agent Deep Reinforcement Learning for Integrated Demand Forecasting and Inventory Optimization in Sensor-Enabled Retail Supply Chains by Yongbin Yang, Mengdie Wang, Jiyuan Wang, Pan Li, Mengjie Zhou

“…While existing approaches employ statistical and machine learning methods for demand forecasting, they often fail to capture complex temporal dependencies and lack the ability to simultaneously optimize inventory decisions. …”
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Forecasting monthly runoff in a glacierized catchment: A comparison of extreme gradient boosting (XGBoost) and deep learning models. by Mohammed Majeed Hameed, Adil Masood, Aadil Hamid, Ahmed Elbeltagi, Siti Fatin Mohd Razali, Ali Salem

“…Given the significant autocorrelation in runoff time series data, which may hinder the evaluation of prediction models, a novel statistical method is employed to assess the effectiveness of forecasting models in detecting turning points in the runoff data. …”
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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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Wastewater SARS-CoV-2 monitoring in a university hospital forecasts multilevel epidemic curves in Taipei City, Taiwan by Chung-Yen Chen, Yu-Hsiang Chang, Chi-Hsin Sally Chen, Sui-Yuan Chang, Chang-Chuan Chan, Pau-Chung Chen, Ta-Chen Su

“…Wastewater monitoring at targeted institutions offers a promising approach for early detection; however, its utility in forecasting broader epidemic trends remains underexplored. …”
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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

“…These approaches have already shown considerable advantages over traditional methods, especially due to their capacity to efficiently process large datasets and detect complex patterns. …”
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Comparing Machine Learning-Based Crime Hotspots Versus Police Districts: What’s the Best Approach for Crime Forecasting? by Eugenio Cesario, Paolo Lindia, Andrea Vinci

“…Compared to traditional police district partitioning, these data-driven methods offer significant advantages in improving crime forecasting accuracy across urban environments.…”
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FORECASTING THE CONSUMER PRICE INDEX WITH GENERALIZED SPACE-TIME AUTOREGRESSIVE SEEMINGLY UNRELATED REGRESSION (GSTAR-SUR): COMPROMISE REGION AND TIME by Prizka Rismawati Arum, Anita Retno Indriani, M Al Haris

“…The GeneralizedSpace-Time Autoregressive (GSTAR) method is a suitable method to be applied to CPI data because it involves elements of time and location (spatiotemporal). …”
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BLACKMAIL AS A METHOD OF EXTORTION by KHOMUTOVA RUSLANA, DAVTYAN DAVID

“…An important section is the analysis of the difficulties of detecting and investigating extortion, the features of latency, the use of new methods of criminology and interdepartmental interaction, as well as forecasting new threats. …”
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Linguistic Summarization and Outlier Detection of Blended Learning Data by Pham Dinh Phong, Pham Thi Lan, Tran Xuan Thanh

“…Those extracted linguistic summaries in the form of sentences in natural language are easy to understand for humans. Furthermore, a method of detecting the exceptional cases or outliers of the learning courses based on linguistic summaries expressing common rules in different scenarios is also proposed. …”
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The global, regional, and national alcohol-related colorectal cancer burden and forecasted trends: results from the global burden of disease study 2021 by Jinfeng Yao, Guo Chen

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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

“…Extreme precipitation events pose significant challenges to societal infrastructure and environmental stability, particularly in vulnerable regions like the Beijing-Tianjin-Hebei area of China. Traditional forecasting methods, such as numerical weather prediction, radar nowcasting, downscaling techniques, etc., frequently fail to capture the complex nonlinear dynamics of such events. …”
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Overview of anomaly detection techniques for industrial Internet of things by Haili SUN, Xiang LONG, Lansheng HAN, Yan HUANG, Qingbo LI

“…In view of the differences of existing anomaly detection methods and the applicability when applied to security protection of the industrial Internet of things (IIoT), based on technical principles, the network anomaly detection papers published from 2000 to 2021 were investigated and the security threats faced by IIoT were summarized.Then, network anomaly detection methods were classified into 9 classes and the characteristics of each class was studied.Through longitudinal comparison, the merits and shortcomings of different methods and their applicability to IIoT scenarios were sorted out.In addition, statistical analysis and comparison of common data sets were made, and the development trend in the future was forecasted from 4 directions.The analysis results can guide the selection of adaptive methods according to application scenarios, identify key problems to be solved, and point out the direction for subsequent research.…”
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Research Progress on Rapid Detection of Pyrethroid Pesticide Residues by Xiaoyao WANG, Siyao ZHONG, Xuping SHENTU, Zihong YE, Haizhi HUANG, Xiaoping YU

“…In this review, the technologies for rapid detection of PYRs, including enzyme linked immunosorbent assay (ELISA), lateral flow immunoassay (LFIA), fluorescence sensing and electrochemical sensing, are summarized, the prospects and advantages of the combination of these methods with novel nanomaterials and technologies are reviewed, and the development trend of becoming high sensitive, intelligent and portable is forecasted, aiming to provide scientific references for the further development of rapid detection technology for PYRs.…”
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H i Intensity Mapping Cross-correlation with Thermal Sunyaev–Zel’dovich Fluctuations: Forecasted Cosmological Parameter Estimation for FAST and Planck by Ayodeji Ibitoye, Furen Deng, Yichao Li, Yin-Zhe Ma, Yan Gong, Xuelei Chen

“…The 21 cm emission from neutral hydrogen surveys holds great potential as a valuable method for exploring the large-scale structure (LSS) of the Universe. …”
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Detecting outbreaks using a spatial latent field. by Cosmin Safta, Jaideep Ray, Wyatt Bridgman

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Research on Monitoring Oceanic Precipitable Water Vapor and Short-Term Rainfall Forecasting Using Low-Cost Global Navigation Satellite System Buoy by Maosheng Zhou, Pengcheng Wang, Zelu Ji, Yunzhou Li, Dingfeng Yu, Zengzhou Hao, Min Li, Delu Pan

“…To enhance rainfall forecasting accuracy, a balanced threshold selection (BTS) method is proposed, significantly improving the balance between the probability of detection (POD) and false alarm rate (FAR). …”
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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

“…Despite advances in treatment, there is a lack of localized, sex-specific forecasts to guide public health interventions.Objective This study aims to forecast the future incidence of acute lymphoid leukaemia in males and females using ARIMA models.Methods Saudi national cancer registries data from 1990 to 2019 were used. …”
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Role of lymphocyte-to-C-reactive protein ratio in forecasting microvascular obstruction in ST-segment elevation myocardial infarction post-percutaneous coronary intervention by Jiahua Liu, Xinjia Du, Yanfei Ren, Yan Mei, Lei Chen, Yuan Lu

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Detection of conveyor belt defects using unified diagnostic criteria. by Gerike P.B., Gericke B.L.

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Accident Detection and Flow Prediction for Connected and Automated Transport Systems by Yi Zhang, Fang Liu, Sheng Yue, Yuxuan Li, Qianwei Dong

“…Effective accident detection and traffic flow forecasting are of great importance for quick respond, impact elimination and intelligent control of the traffic flow consisting of autonomous vehicles. …”
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