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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Integrating high dimensional quadratic regression with penalties based predictive modeling for hydro power plants accurate tariff prediction by Ritesh Dash, Anupa Sinha, Abinash Mahapatro, Bhabasis Mohapatra, Binod Kumar Sahu

“…The proposed model addresses the limitations of conventional method such as SVR, SARIMA and LSTM by integrating polynomial interaction terms with L2 regularization to balance model complexity and generalization. …”
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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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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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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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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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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

“…The new method of discovering vulnerabilities that this approach provides has many comparative advantages over the previous approaches. …”
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Road safety improvement in road traffic participant – vehicle – road – external environment system by E. V. Kurakina, V. A. Sklyarova

“…The use of an integrated approach instead of disparate single actions will allow achieving the tasks set for the state to reduce the accident rate on the country’s roads.Materials and methods. Analytical methods based on analysis of links, flows, temporary analysis of events, methods of road traffic safety assessment based on detection of safety and accident factors, detection of accident concentration places, methods of probability theory and processing of research results, software-computing methods of information technologies.Results. …”
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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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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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Dynamic risk prediction in financial-production systems using temporal self-attention and adaptive autoregressive models by Xuduo Lin, Ziang Qi 

“…In financial production systems, accurate risk prediction is crucial for decision- makers. Traditional forecasting methods face certain limitations when dealing with complex time-series data and nonlinear dependencies between systems, especially under extreme market fluctuations. …”
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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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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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Analysis of satellite big data requirements in numerical weather prediction by Hequn YANG, Xiaofeng WANG, Yanqing GAO, Yiwen LU, Bingxin MA, Xinyao WANG

“…Multi cooperative satellites can provide multi spectral, multi temporal, multi factor, multi scale and multi-level remote sensing data, which is rich in valuable information for numerical weather prediction (NWP).In order to support earth system seamless fine gridded forecasting service in the future, the application status of satellite observation big data was discussed for numerical weather prediction from the aspects of detection variables, time density, spatial coverage, horizontal and vertical resolution, as well as accuracy and timeliness.At the same time, in order to make satellite big data be highly tolerant with NWP, the challenges and prospects were summarized, such as multi-satellite integrated and consistent processing, all-weather, coupled data assimilation methods, deep integration with artificial intelligence, and interaction between satellite observation and prediction.…”
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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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MobileNetV3: an efficient deep learning-based feature selection and classification technique for cardiovascular disease by B. Dhanalaxmi, B. Naveen Kumar, Yeligeti Raju, Rama Seshagiri Rao Channapragada

“…Missing data handling, outlier detection, normalization using min–max normalization methods, categorical data encoding, and transformation are all done during the pre-processing phase. …”
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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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Comparison of the quality of logistic regression models and a classification tree in predicting hospital mortality in elderly patients with non-ST-elevation myocardial infarction by K. G. Pereverzeva, S. S. Yakushin, N. N. Peregudova, M. V. Mishutina

“…Using the CHAID (Chi Squared Automatic Interaction Detection) method to develop a classification tree for predicting hospital mortality in patients with non-ST-elevation myocardial infarction (non-STEMI) aged 75 years and older and compare the quality of the constructed model with the logistic regression model.Material and methods. …”
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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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Advancing smart communities with a deep learning framework for sustainable resource management. by Yongyan Zhao

“…The models outperformed baseline methods, with LSTMs achieving an MAE of 1.8 for water demand prediction and autoencoders detecting anomalies with an F1-score of 95.5%.…”
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