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11th IEEE international conference on intelligent Computing and Information Systems, ICICIS 2023

作者： Alsherif, Mohamed Daowd, Mohamed Bassiuny, A.M. Metered, Hassan Ahmed Helwan University Faculty of Engineering Mechatronics Department Cairo Egypt Helwan University and Heliopolis University Faculty of Engineering Electrical Department Cairo Egypt Faculty of Engineering Mechatronics Department Cairo Egypt Helwan University Faculty of Engineering Automotive Engineering Department Cairo Egypt

ISBN: (纸本)9798350322101

Recently, the study of self-driving autonomous vehicles has gained great popularity. the development of automated driving will change humans' lives which can provide a fully safe transportation system for them. Such a system requires huge computing power with a high cost, So the simulator is a good choice to train and test self-driving cars as an initial step before implementing them in real. this paper provides a study on how a self-driving car is trained for steering angle prediction and following the road lane in the Udacity simulator via the input data from the car's front-center camera. It also explores the utilization of pre-trained Keras transfer learning models in self-driving cars. the methodology involves collecting the necessary driving data and preprocessing it. then the transfer learning models are used as base models. the models are trained on a part of the lake track road of the Udacity simulator and tested on the rest part of the road. Also, the trained models have been evaluated by mean square error, the coefficient of determination R square, and Root mean square error. the car on the simulator acts autonomously very well and follows the road's lane using the center camera which shows promising performance. the findings contribute to the advancement of autonomous driving technology and provide insights for further research in this field. © 2023 IEEE.

关键词： Autonomous vehicles

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Modelling Survival by Machine learning Methods in Liver Transplantation: Application to the UNOS dataset 20th

Modelling Survival by Machine Learning Methods in Liver Tran...

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20th international conference on intelligent data engineering and automated learning (ideal)

作者： Guijo-Rubio, David Villalon-Vaquero, Pedro J. Gutierrez, Pedro A. Dolores Ayllon, Maria Briceno, Javier Hervas-Martinez, Cesar Univ Cordoba Dept Comp Sci Cordoba Spain Unit Hepatobiliary Surg & Liver Transplantat Cordoba Spain

ISBN: (纸本)9783030336172;9783030336165

the aim of this study is to develop and validate a machine learning (ML) model for predicting survival after liver transplantation based on pre-transplant donor and recipient characteristics. For this purpose, we consider a database from the United Network for Organ Sharing (UNOS), containing 29 variables and 39,095 donor-recipient pairs, describing liver transplantations performed in the United States of America from November 2004 until June 2015. the dataset contains more than a 74% of censoring, being a challenging and difficult problem. Several methods including proportional-hazards regression models and ML methods such as Gradient Boosting were applied, using 10 donor characteristics, 15 recipient characteristics and 4 shared variables associated with the donor-recipient pair. In order to measure the performance of the seven state-of-the-art methodologies, three different evaluation metrics are used, being the concordance index (ipcw) the most suitable for this problem. the results achieved show that, for each measure, a different technique obtains the highest value, performing almost the same, but, if we focus on ipcw, Gradient Boosting outperforms the rest of the methods.

关键词： United Network for Organ Sharing Liver transplant Survival analysis Machine learning

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List representation applied to sparse datacubes for data warehousing and data mining

List representation applied to sparse datacubes for data war...

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4th international conference on intelligent data engineering and automated learning (ideal 2003)

作者： Wang, F Marir, F Gordon, J Helian, N London Metropolitan Univ Dept Comp London N7 8DB England Rutherford Appleton Lab CCLRC E Sci Ctr Didcot OX11 0QX Oxon England

ISBN: (纸本)354040550X

Typically 80% of the data in the logical OLAP datacube, the core engine of data warehouses, are zero. When it comes to sparse, the performance quickly degrades due to the heavy I/O overheads in sorting and merging intermediate results. In this work, we first introduce a list representation in main memory for storing and computing datasets. the sparse transaction dataset is compressed as the empty cells are removed Accordingly we propose a new algorithm for association rule mining on the platform of list representation, which just needs to scan the transaction database once to generate all the possible rules. In contrast, the well-known apriori algorithm requires repeated scans of the databases, thereby resulting in heavy I/O accesses particularly when considering large candidate datasets. In our opinion, this new algorithm using list representation economizes storage space and accesses.

关键词： data warehouses

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intelligent Emergency Notification Mobile Service via Multi-Task BERT Models 11

Intelligent Emergency Notification Mobile Service via Multi-...

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11th IEEE international conference on Mobile Cloud Computing, Services, and engineering, MobileCloud 2023

作者： Tan, Wenhao Zhang, Yanzhou Liu, Kaikai Computer Engineering Department San JoséCA United States

ISBN: (纸本)9798350322798

Mobile social networks have the potential to become the most effective platform for delivering urgent information during natural disaster events. Social media applications like Twitter are ideal for real-Time news delivery but lack many important features to become an effective emergency notification system. the most significant challenge is determining which news is an emergency and whether it is relevant to the user. In this project, we propose an intelligent emergency notification social network application that can automatically perform data mining from online social media platforms, filter out non-emergency and unrelated news, and deliver only classified emergency notifications to users. Rather than using multiple separate systems or machine learning models to achieve our goals, we propose a joint Multi-class Text Classification model based on BERT to filter out non-emergency tweets and classify the type of emergencies in one end-To-end neural network model. Additionally, we train a Named Entity Recognition (NER) model to extract locations from the classified emergency news. We evaluated our system in multiple historical emergency events and demonstrated the effectiveness of emergency news delivery using various performance metrics. © 2023 IEEE.

关键词： Neural network models

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An intelligent Diagnosis Method of Bearing Fault Based on Acoustic Signal Processing 11

An Intelligent Diagnosis Method of Bearing Fault Based on Ac...

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11th international conference on Prognostics and System Health Management, PHM-Jinan 2020

作者： Zhang, Guowei Ji, Shanshan Han, Baokun Jia, Sixiang Shandong University of Science and Technology College of Mechanical and Electronic Engineering Qingdao266590 China

ISBN: (纸本)9781728151816

Acoustic signals are widespread applied in fault diagnosis. However, it is still a difficult problem to extract feature of acoustic signals under large rotation speed fluctuation. Sparse filtering (SF) as an effective unsupervised algorithm, has been skillfully applied in many fields. However, the effect of SF on acoustic signal processing under large rotation speed fluctuation is not ideal. Hence, we proposed an effective feature learning algorithm called deep sparse filtering (DSF) to overcome this teaser. Firstly, frequency domain signals are used as the basis of DSF for feature leaning, then weight adjustment is performed by back propagation (BP). the efficiency of the DSF model is verified by the collected bearing data set. © 2020 IEEE.

关键词： Failure analysis

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Classifying the Complexity of Competency in Elementary School based on Supervised Learners

Classifying the Complexity of Competency in Elementary Schoo...

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Joint international conference on Computer engineering, Network and intelligent Multimedia (CENIM) / 11th AUN/SEED-Net Regional conference on Computer and Information engineering (RCCIE)

作者： Yuhana, Umi Laili Koczy, Laszlo T. Sardjono, Tri Arief Purnama, I. Ketut Eddy Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Inst Teknol Sepuluh Nopember Dept Informat Dept Elect Engn Surabaya Indonesia Szechenyi Istvan Univ Dept Informat Gyor Hungary Tech Univ Budapest Dept Telecommun & Media Informat Budapest Hungary Inst Teknol Sepuluh Nopember Dept Biomed Engn Dept Elect Engn Surabaya Indonesia Inst Teknol Sepuluh Nopember Dept Comp Engn Dept Elect Engn Surabaya Indonesia

ISBN: (纸本)9781538675090

Complexity of competency (CoC) expresses the difficulty level of a competency. the CoC is one of the important parameters for determining the minimum passing level of competency in an assessment system. In Indonesia, the value of CoC is defined by experts based on conditions of subject, students and teachers in each school. the definition process is determined subjectively, where different experts may evaluate CoC in different ways. this is a problem of data classification that requires an automated tool that copes with the amount of data and produces uniform results. To apply an intelligent classifier is essential to solve the issue. this study aims to find the best method for classifying the complexity of competency in Elementary School. Four supervised learning techniques, namely, Naive Bayes, Multilayer Perceptron, Sequential Minimal Optimization, and RIPPER, were implemented to analyze the dataset. Based on an experiment with 203 data, we found that the Multilayer Perceptron achieved the best performance in the sense of Mean Absolute Error, Root Mean Squared Error, and Receiver Operating Characteristic value. At the same time SMO is better than all other methods in precision, recall, and F-Measure.

关键词： intelligent classifier the complexity of competency supervised learners

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Nursing-task recognition using accelerometers and RFID

Nursing-task recognition using accelerometers and RFID

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11th international conference on intelligent Autonomous Systems, IAS 2010

作者： Takebe, Yoshihiro Kanai-Pak, Masako Kuwahara, Noriaki Maeda, Jukai Hirata, Miwa Kitajima, Yasuko Gueta, Lounell Bahoy Ota, Jun Center for Engineering Univ. of Tokyo 5-1-5 Kashiwanoha Kashiwa City Chiba 277-8568 Japan Tokyo Ariake University of Medical and Health Sciences Japan Kyoto Institute of Technology Japan

ISBN: (纸本)9781607506126

A nurse activity efficiency is controlled greatly by the order of activities, and the system for nurses to learn the ideal order of activities is expected. For the construction of this system, it is required to recognize nurse activities from the sensor data, to calculate the order of activities and to compare the activity order that is recognized with other nurses' or optimized activity order. In this paper, we used accelerometers and RFID tags to recognize these nursing activities. We verified the accuracy of activity recognition and determined the activities that could not be easily recognized with experience. © 2010 IOS Press.

关键词： Accelerometers

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automated piecewise power-law modeling of biological systems

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JOURNAL OF BIOTECHNOLOGY 2010年第3期149卷 154-165页

作者： Machina, Anna Ponosov, Arkady Voit, Eberhard O. Georgia Inst Technol Integrat BioSyst Inst Atlanta GA 30332 USA Georgia Inst Technol Wallace H Coulter Dept Biomed Engn Atlanta GA 30332 USA CIGENE N-1432 As Norway

Recent trends suggest that future biotechnology will increasingly rely on mathematical models of the biological systems under investigation. In particular, metabolic engineering will make wider use of metabolic pathway models in stoichiometric or fully kinetic format. A significant obstacle to the use of pathway models is the identification of suitable process descriptions and their parameters. We recently showed that, at least under favorable conditions, Dynamic Flux Estimation (DFE) permits the numerical characterization of fluxes from sets of metabolic time series data. However, DFE does not prescribe how to convert these numerical results into functional representations. In some cases, Michaelis-Menten rate laws or canonical formats are well suited, in which case the estimation of parameter values is easy. However, in other cases, appropriate functional forms are not evident, and exhaustive searches among all possible candidate models are not feasible. We show here how piecewise power-law functions of one or more variables offer an effective default solution for the almost unbiased representation of uni- and multivariate time series data. the results of an automated algorithm for their determination are piecewise power-law fits, whose accuracy is only limited by the available data. the individual power-law pieces may lead to discontinuities at break points or boundaries between sub-domains. In many practical applications, these boundary gaps do not cause problems. Potential smoothing techniques, based on differential inclusions and Filippov's theory, are discussed in Appendix A. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Biochemical Systems theory Canonical model Differential inclusion Inverse problem Machine learning Parameter estimation Piecewise approximation Power-law

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Grapevine Winter Pruning Automation: On Potential Pruning Points Detection through 2D Plant Modeling using Grapevine Segmentation 11

Grapevine Winter Pruning Automation: On Potential Pruning Po...

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11th IEEE Annual international conference on CYBER Technology in Automation, Control, and intelligent Systems, CYBER 2021

作者： Fernandes, Miguel Scaldaferri, Antonello Fiameni, Giuseppe Teng, Tao Gatti, Matteo Poni, Stefano Semini, Claudio Caldwell, Darwin Chen, Fei Active Perception and Robot Interactive Learning Laboratory Istituto Italiano di Tecnologia Department of Advanced Robotics Genova16163 Italy Italy Università Cattolica Del Sacro Cuore Department of Sustainable Crop Production Piacenza29122 Italy Lab Istituto Italiano di Tecnologia Genova16163 Italy T-Stone Robotics Institute The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Hong Kong

ISBN: (纸本)9781665425278

Grapevine winter pruning is a complex task, that requires skilled workers to execute it correctly. the complexity of this task is also the reason why it is time consuming. Considering that this operation takes about 80-120 hours/ha to be completed, and therefore is even more crucial in large-size vineyards, an automated system can help to speed up the process. To this end, this paper presents a novel multidisciplinary approach that tackles this challenging task by performing object segmentation on grapevine images, used to create a representative model of the grapevine plants. Second, a set of potential pruning points is generated from this plant representation. We will describe (a) a methodology for data acquisition and annotation, (b) a neural network fine-tuning for grapevine segmentation, (c) an image processing based method for creating the representative model of grapevines, starting from the inferred segmentation and (d) potential pruning points detection and localization, based on the plant model which is a simplification of the grapevine structure. With this approach, we are able to identify a significant set of potential pruning points on the canes, that can be used, with further selection, to derive the final set of the real pruning points. © 2021 IEEE.

关键词： data acquisition

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An NTV-Light GBM-Based Method for Identifying Rice Pests with Small Samples in Complex Backgrounds 11

An NTV-Light GBM-Based Method for Identifying Rice Pests wit...

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11th international conference on Information Systems and Computing Technology, ISCTech 2023

作者： Liao, Xuanying Yang, Hongyun Sun, Aizhen Jiangxi Agricultural University School of Computer and Information Engineering Nanchang China School of Software Jiangxi Agricultural University Nanchang China

ISBN: (纸本)9798350342406

To address the problems of complex background of rice pest images in natural scenes, few pest image data samples and low accuracy of pest image recognition. A Global-Local Feature Fusion of Integrated learning-Model is proposed for the accurate recognition of six common rice pests, including rice green stink bug, rice black stink bug, East Asian flying locust, Chinese rice locust, borer and giant stem borer. data enhancement and data expansion of the collected pest images were carried out by random rotation, left-right flip, changing brightness, contrast, chromaticity, saturation and adding noise;the global features of the image are extracted using the NetVLAD algorithm;the local features of the image are extracted using the migration learning model VGG16 network;the extracted global-local features are fused to reduce the interference of complex backgrounds on important features and enhance the specificity and generalisation of the features;the integrated learning model LightGBM is introduced for classification to improve the accuracy and generalisation of the whole model. the experimental results show that the model has a recognition accuracy of 100% in the training set;99.77% in the validation set;90% in the test set, with a Macro Precision value of 91.90%, a Macro Recall value of 90% and a Macro F1-score value of 89.72%. the model provides a reliable and universal method for fully automated and intelligent diagnosis of small samples of rice pests in real environments. © 2023 IEEE.

关键词： Image recognition

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