scholarly journals Crop Pests Prediction Method Using Regression and Machine Learning Technology: Survey

IERI Procedia ◽  
2014 ◽  
Vol 6 ◽  
pp. 52-56 ◽  
Author(s):  
Yun Hwan Kim ◽  
Seong Joon Yoo ◽  
Yeong Hyeon Gu ◽  
Jin Hee Lim ◽  
Dongil Han ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Method ◽  
Learning Technology ◽  
Crop Pests

Prediction of Football Match Results Based on Edge Computing and Machine Learning Technology

2022 ◽  
Vol 13 (2) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Rapid Development ◽  
Prediction Method ◽  
Edge Computing ◽  
Machine Learning Algorithms ◽  
Learning Technology ◽  
Cloud Server ◽  
Football Match ◽  
The Common ◽  
Characteristic Features

With the rapid development of artificial intelligence, various machine learning algorithms have been widely used in the task of football match result prediction and have achieved certain results. However, traditional machine learning methods usually upload the results of previous competitions to the cloud server in a centralized manner, which brings problems such as network congestion, server computing pressure and computing delay. This paper proposes a football match result prediction method based on edge computing and machine learning technology. Specifically, we first extract some game data from the results of the previous games to construct the common features and characteristic features, respectively. Then, the feature extraction and classification task are deployed to multiple edge nodes.Finally, the results in all the edge nodes are uploaded to the cloud server and fused to make a decision. Experimental results have demonstrated the effectiveness of the proposed method.

Identifying the underlying factors associated with antidepressant drug discontinuation: Content analysis of patients’ drug reviews (Preprint)

2020 ◽  
Author(s):  
Mohammad Alarifi ◽  
Somaieh Goudarzvand3 ◽  
Abdulrahman Jabour ◽  
Doreen Foy ◽  
Maryam Zolnoori
Keyword(s):  
Machine Learning ◽  
Antidepressant Drug ◽  
Prediction Method ◽  
Analytical Framework ◽  
Structured Data ◽  
Withdrawal Symptoms ◽  
Machine Learning Techniques ◽  
Drug Discontinuation ◽  
Factors Associated ◽  
Learning Techniques

BACKGROUND The rate of antidepressant prescriptions is globally increasing. A large portion of patients stop their medications which could lead to many side effects including relapse, and anxiety. OBJECTIVE The aim of this was to develop a drug-continuity prediction model and identify the factors associated with drug-continuity using online patient forums. METHODS We retrieved 982 antidepressant drug reviews from the online patient’s forum AskaPatient.com. We followed the Analytical Framework Method to extract structured data from unstructured data. Using the structured data, we examined the factors associated with antidepressant discontinuity and developed a predictive model using multiple machine learning techniques. RESULTS We tested multiple machine learning techniques which resulted in different performances ranging from accuracy of 65% to 82%. We found that Radom Forest algorithm provides the highest prediction method with 82% Accuracy, 78% Precision, 88.03% Recall, and 84.2% F1-Score. The factors associated with drug discontinuity the most were; withdrawal symptoms, effectiveness-ineffectiveness, perceived-distress-adverse drug reaction, rating, and perceived-distress related to withdrawal symptoms. CONCLUSIONS Although the nature of data available at online forums differ from data collected through surveys, we found that online patients forum can be a valuable source of data for drug-continuity prediction and understanding patients experience. The factors identified through our techniques were consistent with the findings of prior studies that used surveys.

EFFICIENCY IMPROVEMENT OF ROAD SURFACE INSPECTION USING MACHINE LEARNING TECHNOLOGY

2017 ◽  
Vol 73 (2) ◽  
pp. I_409-I_415
Author(s):  
Naoko FUKUSHI ◽  
Daishiro KOBAYASHI ◽  
Seiji IWAO ◽  
Ryosuke KASAHARA ◽  
Nobuyoshi YABUKI
Keyword(s):  
Machine Learning ◽  
Road Surface ◽  
Surface Inspection ◽  
Learning Technology ◽  
Efficiency Improvement

scholarly journals Automobile tire life prediction based on image processing and machine learning technology

2021 ◽  
Vol 13 (3) ◽  
pp. 168781402110027
Author(s):  
Jianchen Zhu ◽  
Kaixin Han ◽  
Shenlong Wang
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Life Prediction ◽  
Traffic Accidents ◽  
Confusion Matrix ◽  
Small Sample ◽  
Image Feature ◽  
Engineering Practice ◽  
Learning Technology ◽  
K Nearest Neighbor

With economic growth, automobiles have become an irreplaceable means of transportation and travel. Tires are important parts of automobiles, and their wear causes a large number of traffic accidents. Therefore, predicting tire life has become one of the key factors determining vehicle safety. This paper presents a tire life prediction method based on image processing and machine learning. We first build an original image database as the initial sample. Since there are usually only a few sample image libraries in engineering practice, we propose a new image feature extraction and expression method that shows excellent performance for a small sample database. We extract the texture features of the tire image by using the gray-gradient co-occurrence matrix (GGCM) and the Gauss-Markov random field (GMRF), and classify the extracted features by using the K-nearest neighbor (KNN) classifier. We then conduct experiments and predict the wear life of automobile tires. The experimental results are estimated by using the mean average precision (MAP) and confusion matrix as evaluation criteria. Finally, we verify the effectiveness and accuracy of the proposed method for predicting tire life. The obtained results are expected to be used for real-time prediction of tire life, thereby reducing tire-related traffic accidents.

scholarly journals Implementation Framework for a Blockchain-Based Federated Learning Model for Classification Problems

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1116
Author(s):  
Zeba Mahmood ◽  
Vacius Jusas
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Global Perspective ◽  
Learning Technology ◽  
Classification Problems ◽  
Zero Knowledge ◽  
Privacy And Security ◽  
Implementation Framework ◽  
The Past

This paper introduces a blockchain-based federated learning (FL) framework with incentives for participating nodes to enhance the accuracy of classification problems. Machine learning technology has been rapidly developed and changed from a global perspective for the past few years. The FL framework is based on the Ethereum blockchain and creates an autonomous ecosystem, where nodes compete to improve the accuracy of classification problems. With privacy being one of the biggest concerns, FL makes use of the blockchain-based approach to ensure privacy and security. Another important technology that underlies the FL framework is zero-knowledge proofs (ZKPs), which ensure that data uploaded to the network are accurate and private. Basically, ZKPs allow nodes to compete fairly by only submitting accurate models to the parameter server and get rewarded for that. We have conducted an analysis and found that ZKPs can help improve the accuracy of models submitted to the parameter server and facilitate the honest participation of all nodes in FL.

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