Construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning model by Ting Chen, Xuefeng Zhang, Qunfeng Yu, Qin Yang, Lingmin Yuan, Fei Tong

“…A Self-Weighted Self-Evolutionary Learning Model (SWSELM) was developed to identify independent risk factors for sepsis mortality and to create a visualization system for clinical application.ResultsThe improved algorithm significantly outperformed other algorithms on 23 standard test functions. …”
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MST-YOLO: Small Object Detection Model for Autonomous Driving by Mingjing Li, Xinyang Liu, Shuang Chen, Le Yang, Qingyu Du, Ziqing Han, Junshuai Wang

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Intrusion detection model based on fuzzy theory and association rules by Jianwu ZHANG, Jiasen HUANG, Di ZHOU

“…An intrusion detection model based on fuzzy theory and improved Apriori algorithm was proposed.The BV-Apriori algorithm was used to generate the matching rule base,and the problem of excessive boundary in the continuous data partitioning process was solved by fuzzy set technology.The real-time analysis of the relationship between features and the update of the rule base were completed,and the intrusion detection model BVA-IDS (Boolean vector Apriori-intrusion detection system) was built.The results show that the mining efficiency of the BV-Apriori algorithm is significantly improved when compared with the existing Apriori-BR algorithm,in addition,the BVA-IDS model also performs well on intrusion detection indicators with high detection accuracy,and low false positive rate and false negative rate.…”
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Intrusion detection model based on fuzzy theory and association rules by Jianwu ZHANG, Jiasen HUANG, Di ZHOU

“…An intrusion detection model based on fuzzy theory and improved Apriori algorithm was proposed.The BV-Apriori algorithm was used to generate the matching rule base,and the problem of excessive boundary in the continuous data partitioning process was solved by fuzzy set technology.The real-time analysis of the relationship between features and the update of the rule base were completed,and the intrusion detection model BVA-IDS (Boolean vector Apriori-intrusion detection system) was built.The results show that the mining efficiency of the BV-Apriori algorithm is significantly improved when compared with the existing Apriori-BR algorithm,in addition,the BVA-IDS model also performs well on intrusion detection indicators with high detection accuracy,and low false positive rate and false negative rate.…”
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Channel-Hopping Sequence and Searching Algorithm for Rendezvous of Spectrum Sensing by Young-June Choi, Young-Sik Kim, Ji-Woong Jang

“…In this paper, we propose a method for applying the <i>p</i>-ary m-sequence as a channel-searching pattern for rendezvous in the asymmetric channel model of cognitive radio. We mathematically analyzed and calculated the ETTR when the m-sequence is applied to the conventional scheme, and our simulation results demonstrated that the ETTR performance is significantly better than that of the JS algorithm. …”
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A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models by George Raftopoulos, Nikos Fazakis, Gregory Davrazos, Sotiris Kotsiantis

“…We analyze these adaptations in terms of their methodological adjustments, impact on algorithmic bias and ability to maintain predictive performance comparable to the original models.…”
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Automatic body condition scoring system for dairy cows in group state based on improved YOLOv5 and video analysis by Jingwen Li, Pengbo Zeng, Shuai Yue, Zhiyang Zheng, Lifeng Qin, Huaibo Song

“…The precision, recall, and mean average precision of the model are 94.3 %, 92.5 %, and 91.8 %, respectively. …”
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Improving the Reliability of Biometric Authentication Processes Using a Model for Reducing Data Drift by Vladimir Zh. Kuklin, Naur Z. Ivanov, Alexander N. Muranov, Islam A. Alexandrov, Elena Yu. Linskaya

“…The test results have shown that the proposed solutions increase the level of security of personal data and improve the reliability of biometric authentication against fraudulent actions of intruders, including approaches based on adversarial algorithms. …”
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Optimization method improvement for nonlinear constrained single objective system without mathematical models by HOU Gong-yu, XU Zhe-dong, LIU Xin, NIU Xiao-tong, WANG Qing-le

“…Therefore, to improve the optimization accuracy of nonlinear constrained single objective systems that are without accurate mathematical models while considering the cost of obtaining samples, a new method based on a combination of support vector machine and immune particle swarm optimization algorithm (SVM-IPSO) is proposed. …”
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Improving Forecasts of the EGARCH Model Using Artificial Neural Network and Fuzzy Inference System by Geleta T. Mohammed, Jane A. Aduda, Ananda O. Kube

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A prediction model for soil heavy metal content based on improved tensor completion by Zhangang Wang, Wenjie Li, Tianhe Yun, Jiaxiang Qi

“…This paper presents a novel method for predicting soil heavy metal content using an advanced tensor completion algorithm. The proposed method estimates heavy metal concentrations at unsampled locations by constructing a prediction model within the Coarse-to-Fine (C2F) framework, leveraging data from sampled points. …”
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Load Estimation Algorithm Based on Extended Kalman Filter for Freight Train by DENG Hao

“…In the simulation experiment of 2,000-ton load, the estimation error ranged from 3.5% to 4%, demonstrating the proposed algorithm could effectively improve the accuracy of the ATO system, with lower requirements on multi-particle motion models.…”
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An Optimization Model for Production Scheduling in Parallel Machine Systems by Leting Zu, Wenzhu Liao, Xiaoxia Yang

“…The innovation lies in the introduction of a greedy algorithm that utilizes a water injection model to address this NP-hard integrated problem. …”
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Improvement of Assemble-to-Order Model Processes With Process Mining: Dynamic Analysis and Hybrid Approaches by Ufuk Celik, Yuksel Yurtay

“…The hybrid approach achieved a fitness value of 92.5% with 77% precision, significantly improving upon the initial life cycle miner algorithm results (fitness value: 79.5%). …”
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VOLTAGE PROFILE IMPROVEMENT WITH APPLICATION OF DIFFERENTLY OPTIMIZED FACTS CONTROLLERS by Joel R. SUTTER, John N. NDERU, Ariel M. MUTEGI

“…This research work presents a novel individual and Hybrid MGA and IGWO was utilized to develop FACTS-controlled optimization model for improvement of bus voltage profiles. The algorithm simultaneously solved the objective problem and augments device parameters as it searches for the best FACTS location and sizes. …”
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Parameter Identification in Triple-Diode Photovoltaic Modules Using Hybrid Optimization Algorithms by Dhiaa Halboot Muhsen, Haider Tarish Haider, Yaarob Al-Nidawi

“…Seven different experimental data sets are used to improve the performance of the proposed differential evolution with an integrated mutation per iteration algorithm (DEIMA). …”
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Multi-target detection of underground personnel based on an improved YOLOv8n model by WEN Yongzhong, JIA Pengtao, XIA Mingao, ZHANG Longgang, WANG Weifeng

“…An innovative multi-target detection algorithm, YOLOv8n-MSMLAS, was proposed based on the YOLOv8n network structure. …”
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Detection Model for Cotton Picker Fire Recognition Based on Lightweight Improved YOLOv11 by Zhai Shi, Fangwei Wu, Changjie Han, Dongdong Song, Yi Wu

“…The improved detection algorithm maintains high accuracy while achieving faster inference speed and fewer model parameters. …”
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SAMFA: A Flame Segmentation Algorithm for Infrared and Visible Aerial Images in the Same Scene by Jianye Yuan, Min Yang, Haofei Wang, Xinwang Ding, Song Li, Wei Gong

“…In response to the issues of poor flame segmentation performance in the current fire images and the large number of learnable parameters in large models, we propose an improved large model algorithm, SAMFA (Segmentation Anything Model, Fire, Adapter). …”
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Application of machine learning algorithm incorporating dietary intake in prediction of gestational diabetes mellitus by Tianze Ding, Peijie Liu, Jie Jia, Hui Wu, Jie Zhu, Kefeng Yang

“…We applied random forest mean decrease impurity for feature selection and the models are built using logistic regression, XGBoost, and LightGBM algorithms. …”
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