Artificial potential field path length reduction using Kenneth-Nnanna-Saleh algorithm by Kenneth Christopher Ugwoke, Nnanna Nwojo Agwu, Saleh El-Yakub Abdullahi

“…Simulation environments, each with varying complexity in obstacle arrangement, were designed for various simulations of the proposed algorithm. A Python-based computer simulation program was implemented and used to simulate the KNS, APF, and a similar waypoint reduction algorithm -Ramer-Douglas-Peucker (RDP) and the results were analyzed. …”
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Research on PAPR reduction algorithm based on CWGAN-SLM for multi-wavelet OFDM system by Guang YANG, Zhaoyang WU, Min NIE, Xiaohong YAN, Fan JIANG

“…In order to meet the demand for low peak to average ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) technology in the future 6G satellite-ground integrated system, an algorithm combining selective mapping (SLM) algorithm and multi-wavelet OFDM technology was proposed firstly.However, the PAPR reduction was limited and the computational complexity was high.To solve this problem, a multi-wavelet OFDM PAPR reduction algorithm based on conditional Wasserstein generative adversarial network (CWGAN) and SLM was proposed, which was called CWGAN-SLM algorithm.CWGAN was introduced to generate more time-domain alternative signals to reduce the PAPR in the CWGAN-SLM algorithm.Simulation results indicate that the CWGAN-SLM algorithm greatly reduces the PAPR of the system and the computational complexity, and has a lower bit error rate.Compared with the GAN and WGAN, the CWGAN has the advantages of easy training, strong stability and good PAPR performance.…”
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A Heuristic Attribute-Reduction Algorithm Based on Conditional Entropy for Incomplete Information Systems by Yanling Bao, Shumin Cheng

Subjects: “…attribute reduction…”
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Design of the Equivalent Stiffness in the 3-PCR Vibration Reduction Platform based on Genetic Algorithm by Zheng Liangchen, Niu Junchuan, Zhang Fuliang, Liu Zhihui

“…The equivalent stiffness of vibration reduction platform in engineering application can be realized by using this design method. …”
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Research on PAPR reduction algorithm based on CWGAN-SLM for multi-wavelet OFDM system by Guang YANG, Zhaoyang WU, Min NIE, Xiaohong YAN, Fan JIANG

“…In order to meet the demand for low peak to average ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) technology in the future 6G satellite-ground integrated system, an algorithm combining selective mapping (SLM) algorithm and multi-wavelet OFDM technology was proposed firstly.However, the PAPR reduction was limited and the computational complexity was high.To solve this problem, a multi-wavelet OFDM PAPR reduction algorithm based on conditional Wasserstein generative adversarial network (CWGAN) and SLM was proposed, which was called CWGAN-SLM algorithm.CWGAN was introduced to generate more time-domain alternative signals to reduce the PAPR in the CWGAN-SLM algorithm.Simulation results indicate that the CWGAN-SLM algorithm greatly reduces the PAPR of the system and the computational complexity, and has a lower bit error rate.Compared with the GAN and WGAN, the CWGAN has the advantages of easy training, strong stability and good PAPR performance.…”
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Habit Predicting Higher Education EFL Students’ Intention and Use of AI: A Nexus of UTAUT-2 Model and Metacognition Theory by Shaista Rashid

“…With the emergence of AI technology, its adoption in higher education has become an interesting field for researchers. …”
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LASSO-derived nomogram prediction model for lymph node metastasis in colorectal cancer: a retrospective analysis by Xiyun Quan, Yi Deng, Zhimin Liu, Zhenqin Gao, Huimei Yi, Ming Li

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Based on the improved SCGM(1,1)c and WIV rainfall landslide susceptible area prediction model by Qian Zhang, Shujie Cao, Yanliang Du, MingYuan Du, Yixuan Zhao, Yaoqi Nie

“…On the basis of the single factor system cloud grey model (SCGM (1,1)c), an improved SCGM (1,1)c model is proposed based on Markov prediction theory and CS algorithm optimization to predict rainfall. …”
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A predictive model for functional cure in chronic HBV patients treated with pegylated interferon alpha: a comparative study of multiple algorithms based on clinical data by Ya-mei Ye, Yong Lin, Fang Sun, Wen-yan Yang, Lina Zhou, Chun Lin, Chen Pan

“…Predictor variables were identified (LASSO), followed by multivariate analysis and logistic regression analysis. Subsequently, predictive models were developed via logistic regression, random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and support vector machine (SVM) algorithms. …”
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A real-time AI tool for hybrid learning recommendation in education: Preliminary results by Chaman Verma

“…In recent years, owing to previous pandemics, online and offline education have collaboratively influenced students' academic experiences globally. …”
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Assessment of Information predictability of stochastic processes by A. V. Ausiannikau

“…The necessary theoretical information for parameter estimation algorithms informational predictability of stochastic processes. …”
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Machine learning models for predicting interaction affinity energy between human serum proteins and hemodialysis membrane materials by Simin Nazari, Amira Abdelrasoul

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Prediction of maximum forming depth in single point incremental forming of 6061 aluminum alloy based on Adaboost regression by LIANG Zhikai, ZHANG Zhichao, HU Lan, PANG Qiu

“…Based on the experimental results， a regression model using the Adaboost algorithm is developed to predict the forming depth of 6061 aluminum alloy thin sheets at the forming diameter of 100 mm. …”
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Game simulators as educational tools for developing algorithmic thinking skills in computer science education by Maksym S. Kovtaniuk, Svitlana V. Shokaliuk, Alexander N. Stepanyuk

“… This paper presents an analysis of game simulators as educational tools for developing algorithmic thinking skills in computer science education. …”
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Predicting dementia in people with Parkinson’s disease by Mohamed Aborageh, Tom Hähnel, Patricia Martins Conde, Jochen Klucken, Holger Fröhlich

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Review of pedestrian trajectory prediction methods by Linhui LI, Bin ZHOU, Weiwei REN, Jing LIAN

“…With the breakthrough of deep learning technology and the proposal of large data sets, the accuracy of pedestrian trajectory prediction has become one of the research hotspots in the field of artificial intelligence.The technical classification and research status of pedestrian trajectory prediction were mainly reviewed.According to the different modeling methods, the existing methods were divided into shallow learning and deep learning based trajectory prediction algorithms, the advantages and disadvantages of representative algorithms in each type of method were analyzed and introduced.Then, the current mainstream public data sets were summarized, and the performance of mainstream trajectory prediction methods based on the data sets was compared.Finally, the challenges faced by the trajectory prediction technology and the development direction of future work were prospected.…”
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TIRDH: A Novel Three-Shadow-Image Reversible Data Hiding Algorithm Using Weight and Modulo by Li-Chian Chin, Yu-Chen Li, Hung-Mo Hsieh, Chung-Ming Wang

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Prediction of the 180 day functional outcomes in aneurysmal subarachnoid hemorrhage using an optimized XGBoost model by Weichong Zhou, Xingfu Liao, Hui Shi, Mingfeng Wang, Yunchong Xiao, Xilu Yu, Yilong Wu, YanYi Liu, Yin Peng, Hai Su

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One-point Method for Speed Strategy Optimization Based on Slope Simplification and Speed Limit Prediction by XING Jian, HAN Bing

Subjects: “…speed limit prediction…”
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Vessel Traffic Flow Prediction in Port Waterways Based on POA-CNN-BiGRU Model by Yumiao Chang, Jianwen Ma, Long Sun, Zeqiu Ma, Yue Zhou

“…Aiming at the stage characteristics of vessel traffic in port waterways in time sequence, which leads to complexity of data in the prediction process and difficulty in adjusting the model parameters, a convolutional neural network (CNN) based on the optimization of the pelican algorithm (POA) and the combination of bi-directional gated recurrent units (BiGRUs) is proposed as a prediction model, and the POA algorithm is used to search for optimized hyper-parameters, and then the iterative optimization of the optimal parameter combinations is input into the best combination of iteratively found parameters, which is input into the CNN-BiGRU model structure for training and prediction. …”
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