Application of Data Mining Technology on Surveillance Report Data of HIV/AIDS High-Risk Group in Urumqi from 2009 to 2015 by Dandan Tang, Man Zhang, Jiabo Xu, Xueliang Zhang, Fang Yang, Huling Li, Li Feng, Kai Wang, Yujian Zheng

“…The decision tree algorithm was the poorest among the four algorithms, with 79.1761% diagnostic accuracy on MSM dataset, 87.0283% diagnostic accuracy on FSW dataset, and 74.3879% accuracy on IDU. …”
Get full text

Deep learning-based target spraying control of weeds in wheat fields at tillering stage by Haiying Wang, Yu Chen, Shuo Zhang, Peijie Guo, Yuxiang Chen, Guangrui Hu, Yuxuan Ma

“…In this study, a target spraying decision and hysteresis algorithm is designed in conjunction with deep learning, which is deployed on a testbed for validation. …”
Get full text

An explainable machine learning model for predicting the risk of distant metastasis in intrahepatic cholangiocarcinoma: a population-based cohort study by Jinzhe Bi, Yaqun Yu

“…Feature selection was performed using three methods, including least absolute shrinkage and selection operator (LASSO) regression, the Boruta algorithm, and recursive feature elimination (RFE). …”
Get full text

Spatial Multi-objective Optimization of Land Use of Kashgar Area Based on NSGA-Ⅱ and GeoSOS-FLUS by Wang Jiaojiao, Yin Xiaojun, Gou Zhenzhen, Liu Shannan, Wang Dimeng

“…[Methods] Food security, economic benefits, and ecological benefits (carbon sequestration, water production, and soil erosion) were set as the multi-objective functions of the algorithm, and the NSGA-Ⅱ algorithm was used to obtain a reasonable optimization of the land use quantity structure. …”
Get full text

Penentuan Jalur Diagnostik Penyakit Berbasis Konsep Pembelajaran Mesin: Studi kasus Penyakit Hepatitis C by Jimmy Tjen, Valentino Pratama

“…Based on the experiment, the distance correlation-based classification tree algorithm outperforms the classical classification tree algorithm by around 3% while using only 7 features instead of 12 as in the classical algorithm. …”
Get full text

Improving Model-Based Deep Reinforcement Learning with Learning Degree Networks and Its Application in Robot Control by Guoqing Ma, Zhifu Wang, Xianfeng Yuan, Fengyu Zhou

“…Deep reinforcement learning is the technology of artificial neural networks in the field of decision-making and control. The traditional model-free reinforcement learning algorithm requires a large amount of environment interactive data to iterate the algorithm. …”
Get full text

A band-limited receiver technology based on PAM4 modulation for the short distance optical transmission system by Qun LIU, Huiqin DU, Xianglin WU

“…In intensity modulation and direct detection of the short distance optical transmission system,there is some serious ISI (inter symbol interference) for the detected PAM4 signal due to the band-limited performance for the optical devices.A band-limited compensation algorithm based on DFE (decision feedback equalizer) combined FTN (fast than Nyquist) transmission was proposed.Then the algorithm performance gain was simulated in different band-limited scenarios,and the simulation results show that this algorithm can be useful to demodulate PAM4 receiver signal.What is more,there is about 1 dB performance gain at the bit error rate (BER) of 10^-3for the serious conditions.…”
Get full text

A band-limited receiver technology based on PAM4 modulation for the short distance optical transmission system by Qun LIU, Huiqin DU, Xianglin WU

“…In intensity modulation and direct detection of the short distance optical transmission system,there is some serious ISI (inter symbol interference) for the detected PAM4 signal due to the band-limited performance for the optical devices.A band-limited compensation algorithm based on DFE (decision feedback equalizer) combined FTN (fast than Nyquist) transmission was proposed.Then the algorithm performance gain was simulated in different band-limited scenarios,and the simulation results show that this algorithm can be useful to demodulate PAM4 receiver signal.What is more,there is about 1 dB performance gain at the bit error rate (BER) of 10^-3for the serious conditions.…”
Get full text

Research on fault diagnosis of CTCS on-board equipment based on knowledge graph by CHEN Huiyuan

“…In the crucial knowledge reasoning stage, a deduction lattice algorithm was introduced to create a rule decision tree that supported quick inference of equipment failures. …”
Get full text

Dual-Resource Optimization Configuration and Collaborative Scheduling for Flexible Job Shop Under Worker Flexibility Constraints by Zijie Ren, Hongyi Qu, Mejdl Safran, Jiafu Wan

Get full text

Machine Learning-Based Prediction Performance Comparison of Marshall Stability and Flow in Asphalt Mixtures by Muhammad Farhan Zahoor, Arshad Hussain, Afaq Khattak

“…The potential of various machine learning (ML) algorithms to predict Marshall Stability (MS) and Marshall Flow (MF) was investigated in this work. …”
Get full text

Short‐term electric power and energy balance optimization scheduling based on low‐carbon bilateral demand response mechanism from multiple perspectives by Juan Li, Yonggang Li, Huazhi Liu

“…An optimal scheduling model of LCBDR is established. The enhanced decision tree classifier (EDTC) algorithm is used to predict the electricity consumption behavior of transferable load (TL) users, and an improved particle swarm optimization (PSO) algorithm with “ε‐greedy” strategy is proposed to solve this model. …”
Get full text

An Approach to Truck Driving Risk Identification: A Machine Learning Method Based on Optuna Optimization by Zhaofei Wang, Hao Li, Qiuping Wang

“…Second, the truck driving risk was quantified into three categories of low level, medium level, and high level risk, and the unbalanced data were processed using a hybrid sampling algorithm. …”
Get full text

A Task Offloading and Resource Allocation Strategy Based on Multi-Agent Reinforcement Learning in Mobile Edge Computing by Guiwen Jiang, Rongxi Huang, Zhiming Bao, Gaocai Wang

Get full text

A Sensory Glove With a Limited Number of Sensors for Recognition of the Finger Alphabet of Polish Sign Language by Jakub Piskozub, Pawel Strumillo

“…The influence hierarchy of individual piezoelectric sensors was determined using a decision tree algorithm during previous stage of research, which achieved 94% accuracy with data from only three sensors. …”
Get full text

Control strategy of robotic manipulator based on multi-task reinforcement learning by Tao Wang, Ziming Ruan, Yuyan Wang, Chong Chen

Get full text

Design and Implementation of OLAP System for Distributed Data Warehouse by Murtadha Hamad, Abdullah Mahdi

Get full text

Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model. by Li Wang, Yu Zhang, Feng Li, Caiyun Li, Hongzeng Xu

“…Finally, a unique double-layer stacking model is designed to improve the performance of the algorithm. Seven classical artificial intelligence methods of Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (ADB), Extra Tree (ET), and Gradient Boosting Decision Tree (GBDT) were selected as candidate models for the base model of the first layer of the model, and extreme gradient enhancement (XGBOOST) was selected as the meta-model for the second layer.…”
Get full text

Feasibility of Licensed Vocational Nurses Using a CDS App to Communicate Signs and Symptoms of a UTI by Alyce S. Ashcraft, Donna C. Owen, Kyle Johnson, Huaxin Song

Get full text

Deep Reinforcement Learning for Uplink Scheduling in NOMA-URLLC Networks by Benoit-Marie Robaglia, Marceau Coupechoux, Dimitrios Tsilimantos

“…Our approach involves 1) formulating the NOMA-URLLC problem as a Partially Observable Markov Decision Process (POMDP) and the introduction of an agent state, serving as a sufficient statistic of past observations and actions, enabling a transformation of the POMDP into a Markov Decision Process (MDP); 2) adapting the Proximal Policy Optimization (PPO) algorithm to handle the combinatorial action space; 3) incorporating prior knowledge into the learning agent with the introduction of a Bayesian policy. …”
Get full text