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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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automated Detection of Lunar Craters Using Deep learning

Automated Detection of Lunar Craters Using Deep Learning

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IEEE Joint international Information Technology and Artificial Intelligence conference (ITAIC)

作者： Yutong Jia Gang Wan Lei Liu Yitian Wu Chenyang Zhang School of Aerospace Information Space Engineering University of PLA Beijing China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

In view of the problems of traditional lunar crater detection algorithm (CDA) needs artificial building crater, morphological characteristics, and the extraction precision is not high and the retrieval speed slower. In this paper, based on the Image segmentation convolutional network U-Net model, an automatic crater extraction method based on the improved U-Net model is proposed. the features of craters on the lunar surface were parameterized by deep convolutional neural network, and the residual block and multiple types of dense skip connection were introduced into the convolutional network in multi-scale sampling to further accelerate the convergence speed of the model and improve the detection accuracy, thus realizing the intelligent detection of craters on the lunar surface. Finally, the accuracy of the algorithm is verified by selecting DEM on the lunar surface and craters marking data set. the experimental results show that the Improved U-Net model can quickly and accurately detected craters on the lunar surface.

关键词： Deep learning Image segmentation Moon Surface morphology Feature extraction Surface treatment Synthetic aperture radar

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Experiments with Clifford Geometric Algebra Applied to Cryptography

Experiments with Clifford Geometric Algebra Applied to Crypt...

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international Symposium on Soft Computing and intelligent Systems (SCIS)

作者： David W. H. A. da Silva Marcelo A. Xavier C. Edward Chow Carlos Paz de Araujo Computer Science University of Colorado C. Springs Colorado Springs USA Research and Advanced Engineering Ford Motor Company Dearborn USA Electrical Engineering University of Colorado C. Springs Colorado Springs USA

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

the combination of flexibility, simplicity, elegance, and power that is found in Clifford Geometric Algebra (GA) is probably one of the main reasons for growing interest from those willing to explore new algebraic structures for producing many applications in physics, engineering, and computer science. As a result, to the surprise of many, existing applications, when modified to run as implementations of Clifford GA algorithms, demonstrate higher performance, better maintenance, less complexity, and a friendlier learning curve for those new to GA. Nevertheless, Clifford GA is virtually unexplored in cryptography, an area of science that can be greatly benefited by a mathematical tool equipped with the aforementioned potential. In this work, we introduce preliminary experiments of cryptographic solutions based on Clifford geometric algebra, including a key exchange protocol, a hash algorithm, and a private-key encryption scheme, with the hope of providing appealing pieces of evidence that this powerful mathematical resource is worth investigating as a strong candidate for broad adoption in cryptography. As proof of concept, we provide access to a library written in Ruby language in which we implement the ideas discussed in this work and where the reader will be able to find some predefined numerical examples.

关键词： Cryptography Syntactics Springs Algebra Transforms Computer science Tools

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Pneumonia Detection: An Efficient Approach Using Deep learning

Pneumonia Detection: An Efficient Approach Using Deep Learni...

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international conference on Computing and Networking Technology (ICCNT)

作者： Ayush Pant Akshat Jain Kiran C Nayak Daksh Gandhi B. G. Prasad B.M.S. College of Engineering Bengaluru Karnataka

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

Pneumonia is one of the largest infectious diseases that cause death in children and elderly people across the globe. Pneumonia impacts all the elderly and young people's families and children everywhere but is most prevalent in Sub-Saharan Africa and South Asia. In December 2019 Wuhan, a city of China was affected by deadly, gruesome Pneumonia which was declared a pandemic by World Health Organisation. But the reason for the outbreak was not clear to everyone. Later, the doctors identified the disease as a new species of coronavirus, also currently known as COVID-19. the main motivation behind this research was to identify Pneumonia just by using the X-Ray images of the patients. As doctors must do a lot of certain tests to identify if the patient has Pneumonia or not. To solve the cumbersome problem, an ensemble of two deep learning models is developed, to make the work of the doctors simpler. In this paper, a comparison between previously written relevant research papers is done and concluded with an ensembled deep learning model to achieve a remarkable test data accuracy or unseen data accuracy.

关键词： Diseases Lung X-ray imaging data models Medical services Deep learning Feature extraction

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Prediction of chronic Insomnia using Machine learning Techniques

Prediction of chronic Insomnia using Machine Learning Techni...

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international conference on Computing and Networking Technology (ICCNT)

作者： Md. Muhaiminul Islam Abu Kaisar Mohammad Masum Sheikh Abujar Syed Akhter Hossain Daffodil International University Dhaka Bangladesh

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

the world is changed extremely over the last decade by the power of technology. Consequently, human lives are undergoing multiple changes that have both positive and negative effects on human health. A lot of virtual involvements, lack of physical activity and extreme use of radio-wave devices are leading people into various health-related issues and Insomnia is one of them. the disorder is also known as sleeplessness. this can occur independently or can occur as a result of another problem. this may turn into permanent disease and chronic(long-time) insomnia can seriously damage a human brain. However, the presence of insomnia can be detected by different medical tests according to various internal factors of sleep. But this kind of approach is not only expensive but also time-consuming. Expensive tests and equipment are also not available in many developing countries. To bridge this gap we have decided to build an intelligent model based on a machine learning approach that is able to predict chronic insomnia. For acquiring best results 7 different machine learning classifiers were used where our Logistic regression model outperformed all of them. With an accuracy of 98%, our model can easily classify insomniac and normal people.

关键词： Classification algorithms Machine learning algorithms Prediction algorithms data models Sensitivity Depression Logistics

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11th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2018

11th International Joint Conference on Biomedical Engineerin...

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11th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2018

ISBN: (纸本)9783030291952

the proceedings contain 25 papers. the special focus in this conference is on Biomedical engineering Systems and Technologies. the topics include: Formal Neuron Models: Delays Offer a Simplified Dendritic Integration for Free;discovering Trends in Environmental Time-Series with Supervised Classification of Metatranscriptomic Reads and Empirical Mode Decomposition;how to Realize Device Interoperability and Information Security in mHealth Applications;evaluation of Power-Based Stair Climb Performance via Inertial Measurement Units;Considerations on the Usability of SClínico;interoperability in Pervasive Health: A Systematic Review;Coping with "Exceptional" Patients in META-GLARE;A Multiagent-Based Model for Epidemic Disease Monitoring in DR Congo;predictive Modeling of Emerging Antibiotic Resistance Trends;exploring the Value of Electronic Health Records from Multiple datasets;a New Compact Optical System Proposal and Image Quality Comparison Against Other Affordable Non-mydriatic Fundus Cameras;application of a Rule-Based Classifier to data Regarding Radiation Toxicity in Prostate Cancer Treatment;Detecting MRSA Infections by Fusing Structured and Unstructured Electronic Health Record data;multi-layered learning for Information Extraction from Adverse Drug Event Narratives;application of Sample Entropy of Pulse Waves in Identifying Characteristic Physiological Patterns of Parkinson’s Disease Sufferers;META-GLARE’s Supports to Agent Coordination;Realistic Synthetic data Generation: the ATEN Framework;design and Optimization of an Open Configuration Microfluidic Device for Clinical Diagnostics;transferability of Deep learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of the Upper Gastrointestinal Tract;Min-Cut Segmentation of Retinal OCT Images;an Optimized Method for 3D Body Scanning Applications Based on KinectFusion.

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Effect of Inter-organizational learning on Construction SMEs Performance 11

Effect of Inter-organizational Learning on Construction SMEs...

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11th Curtin University Technology, Science and engineering international conference, CUTSE 2018

作者： Alashwal, A.M. Low, W.W. Kamis, N.A.M. School of Computing Engineering and Mathematics Western Sydney University NSW Australia Department of Civil and Construction Engineering Curtin University Malaysia Miri Sarawak Malaysia Department of Quantity Surveying University of Malaya Kuala Lumpur Malaysia

Construction small and medium-sized enterprises (SMEs) face different challenges pertaining to their competitiveness and capabilities. Inter-organizational learning (IOL) is a key activity that contributes to organizational development and long-term survival. the purpose of this paper is to investigate whether IOL has an influence on SMEs performance. A questionnaire survey was used to collect data from 81 construction SMEs in Malaysia. Partial least square-structural equation modeling (PLS-SEM) was used to analyze the data. the results showed a strong and positive relationship between IOL and SME performance. Based on the findings, a new model has been proposed to explain how performance can be enhanced through three activities of IOL, namely internalization, externalization, and intra-organization learning. Enhancing the performance of SMEs is essential for the performance of the whole construction industry and this paper has contributed toward this main goal. © Published under licence by IOP Publishing Ltd.

关键词： Construction industry

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IoT Based Street Lighting Using Dual Axis Solar Tracker and Effective Traffic Management System Using Deep learning: Bangladesh Context

IoT Based Street Lighting Using Dual Axis Solar Tracker and ...

引用

international conference on Computing and Networking Technology (ICCNT)

作者： Mohd. Saifuzzaman Syeda Farjana Shetu Nazmun Nessa Moon Fernaz Narin Nur Mohammad Hanif Ali Jahangirnagar University Savar Bangladesh Daffodil International University Dhaka Bangladesh Notre Dame University Dhaka Bangladesh

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

With the rapid increase of population, nonrenewable energy loss has become one of the prior concerns worldwide in recent years. Researchers are working on the proper utilization of renewable energy to save the rest of the non-renewable energy sources for future generations. One of the purposes of this research is to develop an intelligent automation system that can decide luminous control operation based on light intensity. Here, LDR sensor is installed in such a way that it can be able to identify Night and Day mode using a certain intensity value, and as well as IR sensors take over the control of street light. the solar cell will be used for power supply here and DC current will work as a secondary backup. the article also involves automatic monitoring of traffic controls through a video system. the camera incorporates automatic text retrieval from video data utilizing lip reading by decoding facial expression utilizing deep learning methods. the proposed solution includes a test data collection, an interpretation of the picture frame, and a text production of the specified words. the test data collection will be structured in the proposed methodology by integrating all potential facial expressions of different words.

关键词： Training data Lighting Internet of things Roads data collection Surveillance Speech recognition

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Integration of multispectral and radar satellite images for flood range determination of historical flood in Slovakia 2010 11

Integration of multispectral and radar satellite images for ...

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11th international Scientific and Technical conference on Geodesy, Cartography and Geoinformatics, GCG 2019

作者： Pukanská, Katarína Bartoš, Karol Hurčíková, Viera Kseňak, Ľubomír Gil, Marcin Bieda, Agnieszka Institute of Geodesy Cartography and Geographical Information Systems Faculty of Mining Ecology Process Control and Geotechnologies Technical University of Košice Košice Slovakia Faculty of Environmental Geomatic and Energy Engineering Kielce University of Technology Kielce Poland Department of Geomatics Faculty of Mining Surveying and Environmental Engineering AGH University of Science and Technology Kraków Poland

ISBN: (纸本)9780367346515

the objective of this work is to evaluate the extent of historic floods on rivers within Košice region in June 2010. Nowadays, massive floods are an annual reality, as the increasing number of natural disasters is a consequence of global climate changes. their increasing frequency and severity cause immense damage to property, but also loss of human lives. For the purpose of mapping these floods, historical multispectral satellite images from Landsat 5 were used as the input data, together with radar image from Envisat, from the time of floods in 2010. the results of the floods were compared with the corresponding satellite data from the data with a normal situation. All used image and radar data are freely available to download and process. Given data sets were opened and processed in open-source software ESA-SNAP. the result of the study is the determination of the extent and quantification of historical floods in Eastern Slovakia in 2010. © 2020 Taylor & Francis Group, London.

关键词： Floods

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Ridge Regression Based on Gradient Descent Method with Memory Dependent Derivative

Ridge Regression Based on Gradient Descent Method with Memor...

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IEEE international conference on Software engineering and Service Sciences (ICSESS)

作者： Fanming Huang Dan Li Jiachen Xu Yutao Wu Yidan Xing Zan Yang Tongji Zhejiang College Jiaxing Zhejiang China Faculty of Science Tongji Zhejiang College Jiaxing Zhejiang China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

Ridge regression is a supervised biased estimation regression method in machine learning. In fact, its principle is similar to that of ordinary least squares(OLS) estimation, except that OLS is improved by giving up the unbiasedness of OLS plus a penalty term, which solves the problem of multicollinearity (approximate linear correlation of independent variables), between data. Ridge regression algorithms are the same in optimizing the loss function of linear regression, and usually use gradient descent or stochastic gradient descent. However, when dealing with complex nonlinear problems, these methods can easily fall into the trap of local minima, and global consciousness and convergence are not strong. In order to retain the gradient information well and enhance the global convergence, the gradient descent method of memory-dependent derivative is introduced into the optimized loss function part of the ridge regression algorithm.

关键词： Machine learning algorithms Search methods Memory management Estimation Optimization Convergence Software engineering

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A Monitoring Device for Urine Volume and Turbidity using Continuity Flow and Beer-Lambert's Law for Post-Operative Patients 11

A Monitoring Device for Urine Volume and Turbidity using Con...

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11th IEEE international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM )

作者： Padilla, Maria Victoria C. Cruz, Febus Reidj G. Mapua Univ Sch Grad Studies Manila Philippines Technol Univ Philippines Coll Engn Manila Philippines Mapua Univ Sch Elect Elect & Comp Engn Manila Philippines

ISBN: (纸本)9781728130446

the amount and quality of urine are two of the most closely monitored signs to establish the condition of patients who undergo surgical operations. However, with the conventional way of measuring the volume and quality of the urine, loss of time and human error cannot be prevented. the study aimed to automatically measure the volume based on flow rate and the turbidity of the urine of the post-operative patients. this study focused on measuring the volume and turbidity of the urine through Continuity Flow principle using an infrared sensor and Beer-Lambert's law using photodetector sensors, respectively. the urinary bladder system model was designed and constructed in order to demonstrate a urine discharge and test the functionality of the measuring devices. through a mobile application, the medical staff can monitor the results of these measurements. the gathered data for volume using the device was compared to the actual measurements using a graduated cylinder. the tests revealed that actual volume is always more than the measured volume with an average flowrate of 18.1575 mL/s and an accuracy of 95.5%. Measurements on turbidity compared to the conventional way of testing the physical parameters of urine in urinalysis yielded a satisfactory testing result based on the voltage ranges resulted from Decision Tree Classifier. Furthermore, through the series of experiments conducted in the assessment of the prototype developed, the expected value compared to the actual values showed that the device output has no significant difference with the expected output.

关键词： Biomedical optoelectronics bladder model machine learning urinalysis

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