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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1863 条记录，以下是1621-1630 订阅

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Stuck Pipe Prediction in Geothermal Well Drilling at Darajat Using Statistical and machine learning application 20

Stuck Pipe Prediction in Geothermal Well Drilling at Darajat...

引用

3rd Asia Pacific conference on Research in Industrial and Systems Engineering, APCORISE 2020

作者： Bayan, Miftahul Zulkarnain Industrial Engineering University of Indonesia Depok Indonesia

ISBN: (纸本)9781450376006

Stuck pipe during Geothermal well drilling is one of the problems that affect non-productive time (NPT) which prolong the drilling schedule. This problem directly increases drilling cost due to increasing overall drilling time, consumables, mud, drill bit and as well as sacrifices its well when there is no more progress to reach the targeted reservoir depth. Also the stuck pipe problem will create a domino effect that might impact next well drilling and another site drilling schedule. Stuck pipe problem is made from many contributor factors and also related to other problem cases such as lost circulation, wellbore instability, temporary zone closure, and cementing job trouble. The current analytical method mostly uses one or two dominant variables to predict stuck pipe problems which limitedly represents drilling conditions, lithology and wellbore conditions. A combination of statistical method and machine learning approach is developed and comprehensively used to predict stuck pipe problem that represents different well lithology, wellbore and drilling condition. Historical data that represents the conditional situation and electronic data recorder is used to predict the stuck pipe problem which different for each of the well. Many factors contribute to successful drilling activities and no one single factor, which dominantly becomes the root cause of the problem. A statistical approach such as principal component analysis, logistic regression and discriminant analysis is very helpful to interpret data before the next step of the analysis. Those statistical analyses should be the baseline of data interpretation before creating an algorithm to use predictive analysis using machine learning and deep learning. © 2020 ACM.

关键词： computer aided instruction

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Prediction of Addiction to Social Media 3

Prediction of Addiction to Social Media

引用

3rd IEEE international conference on Electrical, computer and Communication Technologies: computer Science and Engineering, ICECCT 2019

作者： Valakunde, Nandakishor Ravikumar, Srinath Department of Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9781538681572

Social media addiction is a huge problem among the youth. Due to the outburst of various social media applications, people are getting addicted to social media. This application is designed to help people to realize their addictions and also to provide constructive results according to the inputs given. The application uses machine learning to predict the outcomes based on past data taken from surveys. © 2019 IEEE.

关键词： machine learning

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A Hybrid Distributed Frequent Itemset Mining Method with Its application in Medical Diagnosis 3rd

A Hybrid Distributed Frequent Itemset Mining Method with Its...

引用

3rd international conference on machine learning for Cyber Security, ML4CS 2020

作者： Zhang, Mingxue Xiao, Fuyuan School of Computer and Information Science Southwest University No. 2 Tiansheng Road Beibei District Chongqing400715 China

ISBN: (纸本)9783030624620

The world has entered an era of globalization, which represents the explosion of information. The application and development of big data technology make the scale of medical data geometric growth. People cannot intuitively see the correlation and the implicit relationship between complex medical data, which leads to the situation of more data and less knowledge. By using Spark and a variety of data pre-processing techniques and machine learning related algorithms, we implemented a platform which could help patients recommend more accurate treatment plans, help doctors analyze the relationship between diseases, and provide more natural results through a visual interface. Besides, we proposed a distributed frequent itemset mining algorithm (DSDFIM) based on the adjacency list and Spark. After evaluating, the proposed algorithm could reduce data transportation once between main memory and secondary storage, and improved the speed of data processing through distributed computing, compared with the classic algorithm. Meanwhile, it could solve the problem of merging frequent itemset of the same item under different independent paths. © 2020, Springer Nature Switzerland AG.

关键词： Data mining

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A Deep learning Approach to Radar Extrapolation 3rd

A Deep Learning Approach to Radar Extrapolation

引用

3rd international conference on machine learning for Cyber Security, ML4CS 2020

作者： Yi, Lei Tian, Wei Wang, Xiang Xu, Xiaolong Nanjing University of Information Science and Technology Nanjing210044 China Institute for Climate and Application Research Nanjing China

ISBN: (纸本)9783030624620

Nowcasting is really important for citizen and industry since it matters to everyone on social and economic activities and is significant for monitoring and forecasting disasters. As the foundation of the task, radar extrapolation is the most difficulty to be solved. However, the accuracy of the extrapolation is in a large bias. With the rapid development of computing power, recent researches show that artificial intelligence is a promising approach, especially deep learning approaches in learning accurate patterns and appear well suited for the task of extrapolation, given an ample account of radar echo maps. Since ConvLSTM has successfully introduced spatiotemporal sequence into the traditional LSTM, in this study, we modified a recurrent neural network (RNN) called bidirectional ConvLSTM (Bi-ConvLSTM) based on the ConvLSTM and used a reversed encoding-forecasting structure. The experiments show that the Bi-ConvLSTM achieved a better performance than the ConvLSTM. © 2020, Springer Nature Switzerland AG.

关键词： Radar

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application of Fuzzy Comprehensive Evaluation in Environmental Adaptability of Poultry House 3rd

Application of Fuzzy Comprehensive Evaluation in Environment...

引用

3rd international conference on machine learning for Cyber Security, ML4CS 2020

作者： Zhang, Fenghang Li, Linze Jiang, Yuxuan Zhou, Zhunan Wang, Xiuli College of Information Science and Engineering Shandong Agricultural University Tai’an271018 China

ISBN: (纸本)9783030624590

Aiming at the problems of low accuracy and heavy workload in the evaluation of poultry house environment based on a single environmental factor, this paper selected five factors that have a great impact on the environment of the poultry house and established a multi-factor environmental evaluation model of poultry house based on the main body of temperature, humidity, ammonia (NH3) concentration, carbon dioxide (CO2) concentration, and hydrogen sulfide (H2S) concentration. Through this system, the suitability of the poultry house environment can be judged more scientifically and accurately, and the evaluation process can be greatly simplified. © 2020, Springer Nature Switzerland AG.

关键词： Carbon dioxide

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Research on Automatic Target Detection and Recognition System Based on Deep learning Algorithm 3rd

Research on Automatic Target Detection and Recognition Syste...

引用

3rd international conference on machine learning for Cyber Security, ML4CS 2020

作者： Zhang, Qinghui Xu, Hongbin Li, Zhengyu Liu, Xiaobin Li, Yuxi Jiao, Yingjie Xi’an Modern Control Technology Research Institute Xi’an710065 China

ISBN: (纸本)9783030624620

Automatic target detection and recognition is the cornerstone of the intelligent unmanned systems to realize higher-level tasks. In this paper, the deep learning algorithm of Faster R-CNN was studied in depth, and the target detection model is designed combining the RPN network and the fast R-CNN. The target detection and recognition device with the ability of image acquisition and intelligent processing was also designed. Combining the device with the Faster R-CNN model, the automatic target detection and recognition system was developed. At last, the VGG-16 model was adopted for training the detection model, and the system was used for target detection experiments. The results show that the recognition accuracies of the system for the visible light images of trucks and tanks are 89.7% and 90.3%, respectively, and that for infrared images of tanks is 63.7%. Therefore, a good recognition effect has been achieved. This work provides a reference for the application of deep learning algorithms in the field of automatic target detection and recognition. © 2020, Springer Nature Switzerland AG.

关键词： learning algorithms

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3rd international conference on Computational Intelligence, Security and Internet of Things, ICCISIoT 2020

3rd International Conference on Computational Intelligence, ...

引用

3rd international conference on Computational Intelligence, Security and Internet of Things, ICCISIoT 2020

ISBN: (纸本)9783030667627

The proceedings contain 27 papers. The special focus in this conference is on Computational Intelligence, Security and Internet of Things. The topics include: Computing Exact Solutions of Evolutionary Rescue Equations of Spatial Population Dynamics;variants of Generative Adversarial Networks for Credit Card Fraud Detection;a Compact Network Intrusion Classifier Using Fuzzy Logic;Tamper Detection and Correction of ELF Binaries by Remote Parties via Merkle Trees;device Independent Key Generation Using Decoy State;a Novel Cryptographic Approach to Implement Confidentiality Using Point Reflection Property;security and Anonymity Aspects in Tails and Windows 10 Operating Systems;exploiting Aspect-Classified Sentiments for Cyber-Crime Analysis and Hack Prediction;application of IoT and Weather Prediction for Enhancement of Agricultural Productivity;intangible learning Experience Using Leap Motion Controller for Distance Mapping;enhancing Data Privacy in the Internet of Things (IoT) Using Edge Computing;Priority Based GTS Allocation in Zigbee Wireless Sensor Network;MITM Intrusion Analysis for Advanced Metering Infrastructure Communication in a Smart Grid Environment;smart Border Security System Using Internet of Things;cluster Based Redundancy Elimination Algorithm for Data Aggregation Scheduling Scheme in Wireless Sensor Networks;integrated Internet of Things and Analysis Framework for Industrial applications Using a Multi Tiered Analysis Architecture;ioT Sensor Network Based Implementation for Rainfall Induced Landslide Monitoring;multi Agent Based Resource Provisioning in Fog Computing;a Novel Salp Swarm Algorithm for Controller Placement Problem;earthquake Magnitude Prediction Using machine learning Technique;real-Time Vision-Based Vehicle-to-Vehicle Distance Estimation on Two-Lane Single Carriageway Using a Low-Cost 2D Camera at Night;analysing Social Media Responses in Natural Disaster.

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AI/ML applications for Aerospace and Defense 3rd

AI/ML Applications for Aerospace and Defense

引用

3rd international conference on Dynamic Data Driven application Systems, DDDAS 2020

作者： Reddy, Kishore K. Surana, Amit Kodzwa, Paul Zable, Shane LaRowe, Richard Brewer, Eric Burd, Steven Raytheon Technologies Research Center East HartfordCT06108 United States Raytheon Intelligence and Space ArlingtonVA22209 United States Raytheon Missile and Defense TucsonAZ85756 United States Collins Aerospace Cedar RapidsIA52498 United States Pratt and Whitney East HartfordCT06108 United States

ISBN: (纸本)9783030617240

The vast majority of leading executives with aerospace and defense industries expect artificial intelligence (AI) technologies to influence every part of their operations over the next decade. Security leaders have recognized that the human cognitive band-width is emerging as the most severe constraint for data utilization. For this reason, governments need big data, AI and machine learning (ML) to give its analysts the edge in real-time response. Similarly, commercial aerospace is actively seeking AI "killer apps" to revolutionize operations through reducing fuel consumption, accelerating pilot training, innovative product designs and better customer service. For ex-ample, as a leading aerospace and defense corporation, Raytheon Technologies (RTX) is actively exploring the transformative capabilities of AI and ML. This panel discussion will present examples and experiences across all RTX business units. © 2020, Springer Nature Switzerland AG.

关键词： machine learning

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application of computer Vision and Deep learning in Breast Cancer Assisted Diagnosis 2019

Application of Computer Vision and Deep Learning in Breast C...

引用

3rd international conference on machine learning and Soft Computing (ICMLSC)

作者： Gu Yunchao Yang Jiayao Beihang Univ Sch Comp Sci & Engineer 37 Xueyuan Rd Beijing Peoples R China Xianyang Rainbow Middle Sch 1 Rainbow Rd Xianyang Shaanxi Peoples R China

ISBN: (纸本)9781450366120

In the general process of breast cancer diagnosis, doctors mainly analyze and judge B-mode ultrasound images through vision, which depends heavily on doctors' operational experience and technical level. Artificial intelligence methods represented by machine learning algorithms have made rapid progress in recent years, especially natural image classification, target detection, semantics segmentation based on computer vision technology have been relatively mature, and have been widely used successfully in various fields. So as to improve the automation ability and reduce human errors, etc. By using artificial intelligence technology such as computer vision and in-depth learning, an automated method is established to diagnose breast cancer B-mode ultrasound images. This method can quickly strengthen the correct diagnostic rate of front-line medical staff and reduce the difference of operation level between urban and rural doctors. It has obvious medical needs and wide social significance.

关键词： Ultrasound imaging Deep learning Convolution neural network Target detection

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Effects of AI Scaffolding on ZPD in MOOC Instructional RPGs 3rd

Effects of AI Scaffolding on ZPD in MOOC Instructional RPGs

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3rd international conference on Innovative Technologies and learning, ICITL 2020

作者： Warden, Clyde A. Chen, Judy F. Marketing Department National Chung Hsing University Taichung Taiwan Business Administration Department Overseas Chinese University Taichung Taiwan

ISBN: (纸本)9783030638849

This study conducts development, application, and testing of artificial intelligence (AI) tools to both monitor learner ZPD and choose appropriate scaffolding for students in a large class, while students learn- by-doing through a Role Playing Game (RPG). machine learning algorithms are developed and integrated into the cloud-based activity at both the individual and group level. Decision trees are developed that decide a range of scaffolding to supply individual learners and groups. Finally, data are tested across control and test groups. Research results show that learners in both the blended and fully online modalities accurately recall mere-exposure scaffolding (MES). Not only do learners recall seeing the MES, over the eight RPG rounds, they also accurately recall the main pedagogical message contained in the MES. Learners receiving MES in an online mode demonstrate more behaviors associating with the core pedagogical MES message content comparing to those in a blended mode. Fully online learners more frequently check their group’s online RPG statistics and status information while also taking more time to prepare group attributes for a new RPG round. © 2020, Springer Nature Switzerland AG.

关键词： machine learning

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