Learning to Learn Sequential Network Attacks Using Hidden Markov Models by Timothy Chadza, Konstantinos G. Kyriakopoulos, Sangarapillai Lambotharan

“…Baum-Welch (BW), Viterbi training, gradient descent, differential evolution (DE) and simulated annealing, are deployed for the detection of attack stages in the network traffic, as well as, forecasting both the next most probable attack stage and its method of manifestation. …”
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China’s efforts on the CSEP and a retrospective case by Huaizhong Yu, Jingxue Zhang, Yue Liu, Yongxian Zhang, Chieh-Hung Chen, Rui Yan, Gang Li, Zeping Li, Yuchuan Ma, Zhengyi Yuan, Wen Yang, Donghui Jia, Binbin Zhao

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A Systematic Literature Review of Concept Drift Mitigation in Time-Series Applications by Mujaheed Abdullahi, Hitham Alhussian, Norshakirah Aziz, Said Jadid Abdulkadir, Yahia Baashar, Abdussalam Ahmed Alashhab, Afroza Afrin

“…This is possible because of their high detection accuracy and effective memory. Moreover, this SLR presents a roadmap for detecting CDs using Artificial Intelligence (AI)-based learners, along with a comparative analysis of well-known baseline methods. …”
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Object Ontologies as a Priori Models for Logical-Probabilistic Machine Learning by D. N. Gavrilin, A.V. Mantsivoda

“…The combination of LPML and object ontologies is capable of solving the forecasting problems, the tasks of automated control, problem detection, decision making, and business process synthesis. …”
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Species distribution modelling using MaxEnt: overview and prospects by Yuliia Novoseltseva

“…This allows for the reconstruction of historical species ranges, the detection of changes in their distribution, and the forecasting of future trends, namely the prediction of potential ranges, the assessment of the impact of climate change and anthropogenic pressure, and the development of effective biodiversity conservation strategies. …”
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Landsat Time Series Reconstruction Using a Closed-Form Continuous Neural Network in the Canadian Prairies Region by Masoud Babadi Ataabadi, Darren Pouliot, Dongmei Chen, Temitope Seun Oluwadare

“…To address these challenges, this research explores the application of a closed-form continuous-depth neural network (CFC) integrated within a recurrent neural network (RNN) called CFC-mmRNN for reconstructing historical Landsat time series in the Canadian Prairies region from 1985 to present. The CFC method was evaluated against the continuous change detection (CCD) method, widely used for Landsat time series reconstruction and change detection. …”
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Molecular epidemiological characterization of human bocavirus (HBoV) in acute respiratory infection (ARI) patients in Yucheng, China by Qi Wen, Qi Wen, Ronghua Yang, Qin Luo, Qiangqiang Shi, Ze Chen, Chen Gao, Haijun Du, Guoyong Mei, Shuying Li, QinQin Song, Jun Han

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Cyber security Enhancements with reinforcement learning: A zero-day vulnerabilityu identification perspective. by Muhammad Rehan Naeem, Rashid Amin, Muhammad Farhan, Faisal S Alsubaei, Eesa Alsolami, Muhammad D Zakaria

“…Beta was at 0.00, meaning no bias within the forecast. Gamma was also at 0.00, resulting in a very high level of precision within the forecast. …”
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Pattern-Based Feature Extraction for Improved Deep Learning in Financial Time Series Classification by Seyed Ali Hosseini, Francesco Grimaccia, Alessandro Niccolai, Silvia Trimarchi

“…In this paper, the authors introduce a novel feature extraction method based on pattern detection in financial data to enhance the performance of deep learning models for financial time series classification. …”
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Effectiveness of machine learning models in diagnosis of heart disease: a comparative study by Waleed Alsabhan, Abdullah Alfadhly

“…The results of this research not only illuminate the optimal scaling methods and ML models for forecasting heart disease, but also offer valuable perspectives on the pragmatic ramifications of implementing these models within a healthcare environment. …”
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Pre-seismic anomalies and co-seismic disturbance induced by the 2024 Mw 7.1 Wushi earthquake: multi-GNSS observations and modeling by Jianghe Chen, Pan Xiong, Haochen Wu, Xiaoran Zhang, Xuemin Zhang, Ting Zhang, Qingshan Ruan

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Landslide susceptibility assessment based on an interpretable coupled FR-RF model: A case study of Longyan City, Fujian Province, Southeast China by Zong-yue Lu, Gen-yuan Liu, Xi-dong Zhao, Kang Sun, Yan-si Chen, Zhi-hong Song, Kai Xue, Ming-shan Yang

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Metabolism and immune responses of striped hamsters to ectoparasite challenges: insights from transcriptomic analysis by Xinchang Lun, Yiguan Wang, Ning Zhao, Yujuan Yue, Fengxia Meng, Qiyong Liu, Xiuping Song, Ying Liang, Liang Lu

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Innovation ARIMA models application to predict pressure variations in water supply networks with open-loop control. Case study in Noja (Cantabria, Spain) by David Muñoz-Rodríguez, Manuel J. González-Ortega, María-Jesús Aguilera-Ureña, Andrés Ortega-Ballesteros, Alberto-Jesus Perea-Moreno

“…—which is subsequently validated using real operational data from Noja, a coastal town in northern Spain characterized by significant seasonal population fluctuations. By accurately forecasting CPC pressure, this system enhances the detection of anomalous patterns, enabling more efficient network pressure management. …”
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Epidemiological, molecular, and evolutionary characteristics of G1P[8] rotavirus in China on the eve of RotaTeq application by Rui Peng, Rui Peng, Mengxuan Wang, Saleha Shahar, Guangping Xiong, Qing Zhang, Lili Pang, Hong Wang, Xiangyu Kong, Dandi Li, Zhaojun Duan

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An AI-Driven Particle Filter Technology for Battery System State Estimation and RUL Prediction by Mohamed Ahwiadi, Wilson Wang

“…The main contributions of the AI-PF technique are as follows: (1) A novel dynamic sample degeneracy detection method is proposed to provide real-time assessment of particle weights so as to promptly identify degeneracy and improve computational efficiency. (2) An adaptive crossover and mutation strategy is proposed to reallocate low-weight particles and maintain particle diversity to improve modeling and RUL forecasting accuracy. …”
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Explainable Multi-Scale CAM Attention for Interpretable Cloud Segmentation in Astro-Meteorological Applications by Qing Xu, Zichen Zhang, Guanfang Wang, Yunjie Chen

“…Accurate cloud segmentation is critical for astronomical observations and solar forecasting. However, traditional threshold- and texture-based methods suffer from limited accuracy (65–80%) under complex conditions such as thin cirrus or twilight transitions. …”
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An advanced approach to tomato apex head thickness measurement using lightweight YOLO variants, faster RCNN, and RGB-depth sensor by Amora Amir, Suraj Giri, Surya Giri, Marya Butt

“…This study presents an automated method for detecting and measuring the apex head thickness of tomato plants, a critical phenotypic trait associated with plant health, fruit development, and yield forecasting. …”
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Refining Rainfall Derived from Satellite Radar for Estimating Inflows at Lam Pao Dam, Thailand by Nathaporn Areerachakul, Jaya Kandasamy, Saravanamuthu Vigneswaran, Kittitanapat Bandhonopparat

“…To improve accuracy, satellite-derived rainfall estimates were adjusted using ground-based rainfall measurements from stations located near and within the catchment area, applying the 1-DVAR method. The Kriging method was employed to estimate the spatial distribution of rainfall over the catchment area. …”
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An Exploration of the Viral Coverage of Mosquito Viromes Using Meta-Viromic Sequencing: A Systematic Review and Meta-Analysis by Shenglin Chen, Yuan Fang, Ryosuke Fujita, Emad I. M. Khater, Yuanyuan Li, Wenya Wang, Peijun Qian, Lulu Huang, Zhaoyu Guo, Yi Zhang, Shizhu Li

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