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A Core Ontology to Support Agricultural Data Interoperability

A Core Ontology to Support Agricultural Data Interoperabilit...

2023 Datenbanksysteme fur Business, Technologie und Web, BTW 2023 - 2023 Database Systems for Business, Technology and Web, BTW 2023

作者： Abdelmageed, Aly Hatem, Shahenda Wael, Tasneem Medhat, Walaa König-Ries, Birgitta Ellakwa, Susan F. Elkafrawy, Passent Algergawy, Alsayed Information Technology and Computer Science School Nile University Egypt Heinz Nixdorf Chair for Distributed Information Systems Friedrich-Schiller University of Jena Germany Climate Change Information Center Renewable Energy Expert Systems Agricultural Research Center Egypt Collage of Engineering Effat University Saudi Arabia

ISBN: (纸本)9783885797258

The amount and variety of raw data generated in the agriculture sector from numerous sources, including soil sensors and local weather stations, are proliferating. However, these raw data in themselves are meaningless and isolated and, therefore, may offer little value to the farmer. Data usefulness is determined by its context and meaning and by how it is interoperable with data from other sources. Semantic web technology can provide context and meaning to data and its aggregation by providing standard data interchange formats and description languages. In this paper, we introduce the design and overall description of a core ontology that facilitates the process of data interoperability in the agricultural domain. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Ontology

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Spatial filter by spike timing dependent plasticity

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BMC Neuroscience 2010年第1期11卷 1-2页

作者： Kazuhisa Fujita Department of Computer and Information Engineering Tsuyama National Collage of Technology 654-1 Numa Tsuyama Okayama 708-8506 Japan

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Investigating Neural Network-Based Deep Learning Strategies for Real-Time Data Analysis in Machine Learning

Investigating Neural Network-Based Deep Learning Strategies ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Walke, Suchita Nalluri, Mounika Lavanya, R. Rongali, Aruna Sri Vanjari, Hrishikesh Bhanudas Ritu Pillai Collage of Engg and Technology Mumbai University Computer Engineering India Murray State University Masters in Computer Information Systems United States Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Ece India Graduate Doctoral Information Technology Information Technology Emphasis/Information Technology University of the Cumberlands United States BVCOE Lavale Sppu Pune E &tc Engineering Dept Maharashtra India Ambala Haryana Mullana India

ISBN: (纸本)9798350357769

there are numerous distinct strategies and techniques that fall under the huge class of neural network-primarily based deep gaining knowledge of in device getting to know. those strategies revolve around developing and schooling synthetic neural networks, that are computational models that mimic the capabilities and structure of the human brain, to analyze huge datasets and make predictions or decisions based totally on the *** of the key challenges in actual-time information analysis is being capable of method incoming information fast and efficiently, as well as adapt to converting data styles or sudden occasions. traditional device learning algorithms are frequently restricted in their ability to deal with these demanding situations, that is in which deep gaining knowledge of techniques are available *** getting to know techniques contain education neural networks with a couple of layers, every of which strategies one-of-a-kind aspects of the data. This lets in for greater complex and accurate evaluation of the facts, in addition to the potential to handle larger and more numerous datasets. One famous method in deep studying is known as convolutional neural networks (CNNs). Those networks are specifically properly-suitable for studying visual information, including snap shots, by means of breaking down the data into smaller parts and studying them at one of a kind ranges of abstraction. This permits for the identification of patterns and features in the data which could then be used for category or prediction responsibilities. © 2023 IEEE.

关键词： actual-time convolutional neural networks numerous prediction synthetic neural networks

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The Development of Flying Type Moving Robot Using Image Processing

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通讯和计算机（中英文版） 2012年第10期9卷 1170-1178页

作者： Suriyon Tansuriyavon~ Yuuta Suzuki Boonmee Choompol Department of Media Information Engineering Okinawa National Collage of Technology Okinawa 9052192 Japan Faculty of Engineering Department of Electrical and Computer Engineering ThammasatUniversity PathumThani l 2121 Thailand

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Project Base Learning for IT Personnel Resources Development Using TVML

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通讯和计算机（中英文版） 2012年第10期9卷 1152-1159页

作者： Suriyon Tansuriyavong Endo Takanobu Boonmee Choompol Department of Media Information Engineering Okinawa National Collage of Technology Okinawa 9052192 Japan Faculty of Engineering Department of Electrical and Computer Engineering Thammasat University Patumtani 12121 Thailand

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Tracking CS graduates and non-retained students to the following year's academic programs

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ACM Inroads 2021年第4期12卷 45-57页

作者： Tang, Cara Tucker, Cindy S. Weiss, Mark Allen Zweben, Stuart H. Portland Community College 12000 SW 49th Ave. PortlandOR97219 United States Bluegrass Community and Technical Collage Computer and Information Technologies 500 Newtown Pike LexingtonKY40508 United States Florida International University College of Engineering and Computing 10555 West Flagler Street MiamiFL33174 United States Department of Computer Science and Engineering The Ohio State University 2015 Neil Avenue ColumbusOH43210 United States

What happens to students who depart from their computer science program but remain in academia? How many students go on to study at the next degree level? When they do, are they still studying CS? How many who don't get a degree go to other computing programs, or other STEM programs? Tis paper addresses these questions through a study of nearly 400,000 students who were enrolled in U.S. computer science bachelor's and associate's programs in 2017-2018. Te results will be of interest to computing educators and academic leaders, who can compare their local data with these national level data. © 2021 ACM.

关键词： Students

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Big data challenges and achievements: Applications on smart cities and energy sector 2

Big data challenges and achievements: Applications on smart ...

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2nd International Conference on Data Science, E-Learning and information Systems, DATA 2019

作者： Mohammed, Tareq Abed Ghareeb, Ahmed Al-Bayaty, Hussein Aljawarneh, Shadi Collage of Computer Science and Information Technology University of Kirkuk Kirkuk Iraq Program University of North Carolina at Charlotte Charlotte United States College of Engineering University of Kirkuk Kirkuk Iraq Jordan University of Science and Technology Irbid Jordan

ISBN: (纸本)9781450372848

In this paper, the Big Data challenges and the processing is analyzed, recently great attention has been paid to the challenges for great data, largely due to the wide spread of applications and systems used in real life, such as presentation, modeling, processing and large (often unlimited) data storage. Mass Data Survey, OLAP Mass Data, Mass Data Dissemination and Mass Data Protection. Consequently, we focus on further research trends and, as a default, we will explore a future research challenge research project in this area of research. © 2019 Association for Computing Machinery. ACM

关键词： Data mining

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Image Recognition of Photovoltaic Cell Occlusion Based on Subpixel Matching

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2024年 11卷 1-7页

作者： Jin, Yuexin Yu, Jinchi Yin, Xiaoju Wang, Yuxin Department of Renewable Energy Shenyang Institute of Engineering Shenyang110136 China Collage of Computer and Information Engineering Tianjin Agricultural University Tianjin300387 China

INTRODUCTION: During the operation of large photovoltaic power stations, they are often shielded by dust and bird droppings, which greatly reduce the power generation and even cause fires. Analysis of PV cell occlusion image recognition accuracy based on sub-pixel matching. OBJECTIVES: In order to find the location of the pv cells, we use the method of subpixel image matching. Improve recognition accuracy. METHODS: When the power plant is running normally, taken the original image for photovoltaic power station as the original sample, and then using the subpixel gradient matching algorithm, to match the original image and find out that the minimum matching values. RESULTS: If the calculation results is greater than a specified threshold, When the calculated result is greater than the specified threshold, the power station is considered abnormal. CONCLUSION: The experimental process shows that this method can better judge the operating status of photovoltaic power station, and can find out the location of mismatched photovoltaic cells more accurately, and the calculation accuracy reaches sub-pixel level. © 2024 Y. Jin et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

关键词： Image recognition

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Facial Expression Recognition Using Aggregated Handcrafted Descriptors based Appearance Method

Facial Expression Recognition Using Aggregated Handcrafted D...

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11th IEEE International Conference on System engineering and Technology, ICSET 2021

作者： Ibrahim, Dheyaa Ahmed Zebari, Dilovan Asaad Ahmed, Falah Y. H. Zeebaree, Diyar Qader Information Technology Collage Imam Ja'afar Al-Sadiq University Communications Engineering Techniques Department Baghdad Iraq Nawroz University Department of Computer Science Duhok42001 Iraq Management Science University Faculty of Information Sciences Engineering Selangor Shah Alam Malaysia Research Center of Duhok Polytechnic University Duhok Iraq

ISBN: (纸本)9781665437660

There have been quite a few studies on facial expression recognition over the years, and it is still a challenging subject due to the significant inter-class variability. Facial expression research in this field focuses on the development of techniques to identify, code, and extract facial expressions to improve prediction by computer. With great success of machine learning, the various texture descriptors are exploited to obtain a better performance. This paper proposes a method based on the aggregation between different descriptors Histogram of oriented Gradient (HOG) and Local Binary Pattern (LBP). First stage the input image has pre-processed to detect dace area which helps to extract most significant features. Then, Diagonal-HOG (D-HOG) also has extracted and aggregated all features. Finally, Support Vector Machine (SVM) has been used a classifier to classify each feature as well as aggregated features. We evaluate our method using Japanese Female Facial Expressions database (JAFFE), experimental results showed that the proposed method is accurate and efficient in recognizing facial expressions. © 2021 IEEE.

关键词： Support vector machines Histograms Codes Databases Face recognition Conferences Machine learning

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Analysis of Dense Descriptors in 3D Face Recognition

Analysis of Dense Descriptors in 3D Face Recognition

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11th IEEE International Conference on System engineering and Technology, ICSET 2021

作者： Zebari, Dilovan Asaad Abrahim, Araz Rajab Ibrahim, Dheyaa Ahmed Othman, Gheyath M. Ahmed, Falah Y. H. Nawroz University Department of Computer Science Kurdistan Region Duhok42001 Iraq Duhok Polytechnic University Technical College of Informatics Department of Information Technology Iraq Information Technology Collage Imam Ja 'Afar Al-Sadiq University Communications Engineering Techniques Department Baghdad Iraq Management Science University Faculty of Information Sciences Engineering Selangor Shah Alam Malaysia

ISBN: (纸本)9781665437660

In the past years, a revolution took place in the world of technology and developed rapidly in all areas, covering various aspects of life. One of the hottest topics that researchers work in is computer vision including artificial intelligence. As it has a great importance, it represents the basics for many applications that are currently used in various sectors. The technology of biometric recognition has progressively developed specially in security for identification purposes. Such technology is face recognition, which uses facial information of humans to recognize people. In this paper 3D (three-dimensional) face recognition approach is proposed by using dense descriptors Local Binary Pattern (LBP), Local Ternary Pattern (LTP), and Gabor with Support Vector Machine (SVM). LTP technique which is a variant and extension of LBP. LBP and LTP have been used for feature information extraction individuality and merging with Gabor from the 3D images, and then the SVM technique is employed to classify and recognize the faces according to extracted features. The database that depended in this work is Three-Dimensional Face Recognition Database (Texas 3DFRD). The accuracy obtained from the proposed model was about 94.9% after many attempts the better one is selected. © 2021 IEEE.

关键词： Support vector machines Three-dimensional displays Image resolution Databases Face recognition Merging Feature extraction

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