Quantitative method for network security situation based on attack prediction by Hao HU, Run-guo YE, Hong-qi ZHANG, Ying-jie YANG, Yu-ling LIU

“…To predict the attack behaviors accurately and comprehensively as well as to quantify the threat of attack,a quantitative method for network security situation based on attack prediction was proposed.By fusing the situation factors of attacker,defender and network environment,the capability of attacker and the exploitability rate of vulnerability were evaluated utilizing the real-time detected attack events,and the expected time-cost for attack-defense were further calculated.Then an attack prediction algorithm based on the dynamic Bayesian attack graph was designed to infer the follow-up attack actions.At last,the attack threat was quantified as the security risk situation from two levels of the hosts and the overall network.Experimental analysis indicates that the proposed method is suitable for the real adversarial network environment,and is able to predict the occurrence time of attack accurately and quantify the attack threat reasonably.…”
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An easy iris center detection method for eye gaze tracking system by Mingxin Yu, Yingzi Lin, Xiaoying Tang, Jing Xu, David Schmidt, Xiangzhou Wang, Yikang Guo

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Long short-term memory (LSTM) networks for precision prediction of Schottky barrier photodiode behavior at different illumination levels by Gökalp Tulum, Sajjad Nematzadeh, İlke Taşçıoğlu, Şemsettin Altındal, Fahrettin Yakuphanoğlu

“…Abstract This study has focused on modeling and predicting the electrical properties and parameters of CdZnO interlayered Al/p-Si Schottky Diodes (SDs) using the Long Short-Term Memory (LSTM) algorithm. …”
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Smart agriculture using IoT for automated irrigation, water and energy efficiency by Subir Gupta, Subrata Chowdhury, Ramya Govindaraj, Kassian T.T. Amesho, Sumarlin Shangdiar, Timoteus Kadhila, Sioni Iikela

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Paradigm predictive analysis of two-phase Eyring–Powell fluid flow over a vertical stretching sheet with temperature-dependent viscosity by multilayer neural networks by Zahoor Shah, Hamza Iqbal, Waqar Azeem Khan, Taseer Muhammad, Muhammad Shoaib

“…Its consistently low MSE (mean square error) values (at the scale of E-09 to E-10) make it a good approximation for most scenarios; there is very little deviation and strong predictability. The results obtained validate the robustness of the DLNNs-LMA approach to solve complex fluid dynamics problems. …”
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Alzheimer’s Prediction Methods with Harris Hawks Optimization (HHO) and Deep Learning-Based Approach Using an MLP-LSTM Hybrid Network by Raheleh Ghadami, Javad Rahebi

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Research on Temperature Control Method of Rice Noodles Extruder Based on APSO-MPC by Mengyao Zhang, Yunren Yang, Guohua Gao, Zhenlong Li, Yakai He, Peigang Li, Huangzhen Lv

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Modelling and Predicting the Breaking Strength and Mass Irregularity of Cotton Rotor-Spun Yarns Containing Cotton Fiber Recovered from Ginning Process by Using Artificial Neural Network Algorithm by Mohsen Shanbeh, Hossein Hasani, Somayeh Akhavan Tabatabaei

“…The purpose of this study was to develop predictive models of breaking strength and mass irregularity (CV𝑚%) of cotton waste rotor-spun yarns containing cotton waste collected from ginning process by using the artificial neural network trained with backpropagation algorithm. …”
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Accuracy of cross-sectional imaging in predicting tumor viability using the LI-RADS treatment response algorithm after image-guided percutaneous ablation with radiologic-pathologic explant correlation by Anuradha S. Shenoy-Bhangle, M. Saad Malik, Aamir Ali, Nan Nancy Jiang, Syed Yasir Andrabi, Amit Singal, Michael P. Curry, Maria-Andreea Catana, Devin E. Eckhoff, Salomao Faintuch, Muneeb Ahmed, Imad Ahmad Nasser, Ammar Sarwar

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Development of an algorithm to improve on the National Early Warning Score 2 (NEWS2) system's accuracy in predicting critical outcomes using additional patient data and amendments to the scoring process by Lynsey Threlfall, Chris Plummer, Edward Meinert, Cen Cong, Madison Milne-Ives

“…Using these datasets, we will train and test an algorithm that optimises the variables and their weightings to predict the risk of key clinical outcomes, including mortality, intensive care unit admission, sepsis and cardiac arrest, to demonstrate a proof of concept for a modified scoring system. …”
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Enhanced brain tumor diagnosis using combined deep learning models and weight selection technique by Karim Gasmi, Najib Ben Aoun, Najib Ben Aoun, Khalaf Alsalem, Ibtihel Ben Ltaifa, Ibrahim Alrashdi, Lassaad Ben Ammar, Manel Mrabet, Abdulaziz Shehab

Subjects: “…brain tumor prediction…”
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Multi-objective optimization and parameter sensitivity study on microreactor nuclear power systems by Ersheng You, Yiyi Li, Jianjun Xu, Dianchuan Xing, Haochun Zhang

Subjects: “…PSO algorithm…”
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CPDGA: foresee future DGA using proactive conformal propagation by LIU Shuangshuang, WANG Zhi, DONG Yimeng, LI Wanpeng

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Triage-HF Validation in Heart Failure Clinical Practice: Importance of Episode Duration by Daniel García Iglesias, David Ledesma Oloriz, Diego Pérez Diez, David Calvo Cuervo, Rut Álvarez Velasco, Alejandro Junco-Vicente, José Manuel Rubín López

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Prospects for predicting and preventing the heart failure deterioration: an analytical review by V. N. Larina, I. K. Skiba

“…An integrated approach using scales, algorithms and relevant therapy strategies can significantly improve treatment outcomes and quality of life in patients with HF.…”
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Prediction and Impact Analysis of Soil Nitrogen and Salinity Under Reclaimed Water Irrigation: A Case Study by Zeyu Liu, Kai Fang, Xiaoqin Sun, Yandong Wang, Zhuo Tian, Jing Liu, Liying Bai, Qilin He

“…The models achieved high predictive accuracy, with NSE values of 0.918, 0.946, 0.936, 0.967, and 0.887 for NO₃⁻-N, NH₄⁺-N, TN, EC, and Cl⁻, respectively, demonstrating their robustness. …”
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Predictive Study on the Cutting Energy Efficiency of Dredgers Based on Specific Cutting Energy by Junlang Yuan, Ke Yang, Taiwei Yang, Haoran Xu, Ting Xiong, Shidong Fan

“…First, eigenvalue screening is carried out based on the dredging knowledge and mechanism, then outliers are removed, and finally data processing is performed using Spearman correlation coefficient and PCA dimensionality reduction techniques. Subsequently, five machine learning algorithms, such as RF and XGBoost, are used in combination with a grid search to find the optimal hyperparameters, and Lasso is used as the meta-learner to integrate the prediction results. …”
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Data-Driven Digital Twin Framework for Predictive Maintenance of Smart Manufacturing Systems by Tarana Khan, Urfi Khan, Adnan Khan, Calahan Mollan, Inga Morkvenaite-Vilkonciene, Vijitashwa Pandey

“…Various machine learning (ML) algorithms exist for analysis and prediction that can be used in this scenario. …”
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Development and Validation of an Interpretable Machine Learning Model for Prediction of the Risk of Clinically Ineffective Reperfusion in Patients Following Thrombectomy for Ischem... by Hu X, Qi D, Li S, Ye S, Chen Y, Cao W, Du M, Zheng T, Li P, Fang Y

“…Our CIR risk prediction platform enables early intervention and personalized treatment. …”
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MODEL PREDICTIVE CONTROL FOR PHOTOVOLTAIC STATION MAXIMUM POWER POINT TRACKING SYSTEM by I. Elzein, Yu. N. Petrenko

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