检索结果-内蒙古大学图书馆

Alert and Response of Earthquake and Tsunami for Community Based Disaster Risk Reduction

Journal of Physics: Conference Series 2021年第1期1811卷

作者： Rr M I Retno Susilorini Andre Kurniawan Pamudji Ridwan Sanjaya Amrizarois Ismail Ardhito Hayyu Amasto Department of Infrastructure and Environmental Engineering Faculty of Environmental Science and Technology Soegijapranata Catholic University Semarang Master Program of Environmental and Urban Studies Faculty of Environmental Science and Technology Soegijapranata Catholic University Semarang Department of Information System Faculty of Computer Science Soegijapranata Catholic University Semarang

Indonesia had been suffered by earthquake and tsunami for many centuries. Since many lives have taken by tsunami strikes, alert and response system are very important as important part of Disaster Risk Reduction, especially in area with high population density. This research conducted in Kangkung fishing village in Bandar Lampung coastal area which is an earthquake and tsunami prone area. The aim of the research is to study alert and response of Community Based Disaster Risk Reduction. The research conducted by qualitative method and purposive-descriptive approach. There were 53 respondents which participated in the survey and interview. It is reported that 'Smart Resilient City' model suitable for community needs of alert and response of earthquake and tsunami disaster. This research meets conclusion that 'Smart Resilient City' model may become a great solution in Community Based Disaster Risk Reduction to cope with the hazard of earthquake and tsunami comprehensively and sustainably.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Effect of mono corpus quantity on statistical machine translation Indonesian – Lampung dialect of nyo

引用

Journal of Physics: Conference Series 2021年第1期1751卷

作者： Z Abidin Permata I Ahmad Rusliyawati Department of Information System Faculty of Engineering and Computer Science University of Teknokrat Indonesia Jl. ZA Pagar Alam no 9-10 Bandar Lampung Indonesia Department of Informatics Faculty of Engineering and Computer Science University of Teknokrat Indonesia Jl. ZA Pagar Alam no 9-10 Bandar Lampung Indonesia

Lampung Province is located on the island of Sumatera. For the immigrants in Lampung, they have difficulty in communicating with the indigenous people of Lampung. As an alternative, both immigrants and the indigenous people of Lampung speak Indonesian. This research aims to build a language model from Indonesian language and a translation model from the Lampung language dialect of nyo, both models will be combined in a Moses decoder. This research focuses on observing the effect of adding mono corpus to the experimental statistical machine translation of Indonesian - Lampung dialect of nyo. This research uses 3000 pair parallel corpus in Indonesia language and Lampung language dialect of nyo as source language and uses 3000 mono corpus sentences in Lampung language dialect of nyo as target language. The results showed that the accuracy value in bilingual evalution under-study score when using 1000 sentences, 2000 sentences, 3000 sentences mono corpus show the accuracy value of the bilingual evaluation under-study, respectively, namely 40.97 %, 41.80 % and 45.26 %.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Biometric authentication integrated with wireless communication malicious activity detection in a cyber physical system-based Fintech banking

引用

Optik 2023年 272卷

作者： Almuhannad Sulaiman Alorfi Saud Yonbawi Sultan Alahmari Abdullaev Abror Bozorboevich Mahendran Arumugam Pham Quang Huy Department of Information System Faculty of Computing and Information Technology King Abdulaziz University City Rabigh Saudi Arabia Department of Software Engineering College Of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia King Abdul Aziz City for Science and Technology Riyadh Saudi Arabia The Department of Acconting Economic Analysis and Audit Tashkent Institute of Finance Tashkent Uzbekistan Center for Transdisciplinary Research Saveetha Dental College Saveetha institute of Medical and Technical Science Chennai India University of Economics Ho Chi Minh City (UEH) Vietnam

Recently, biometric technology has been extensively embedded in mobile devices to enhance security of mobile devices. With rise of financial technology (FinTech) that employs mobile applications as well as devices as promotional platforms, biometrics has a significant role in strengthening the detection of this privacy application. This manuscript offers the design of salp swarm optimization with auto-encoder based biometric authentication (SSOAE-BMA) model for the recognition of abnormal activities in the Fintech banking applications based on wireless communication. The major aim of the SSOAE-BMA model lies in the proper authentication of persons via biometric matching process. Initially, the presented SSOAE-BMA model makes use of stacked ResNet-50 model for deriving feature vectors. Next, the SSOAE-BMA model utilizes AE for biometric verification and the performance of the AE model is adjusted using the Social Spider Optimization (SSO) Algorithm which in turn enhances the recognition outcomes. To demonstrate the improved performance of SSOAE-BMA model, a series of simulations were carried out. The experimental outcomes signified the enhancements of the SSOAE-BMA model over existing models.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Epileptic seizures detection using deep learning techniques: A review

arXiv

引用

arXiv 2020年

作者： Shoeibi, Afshin Khodatars, Marjane Ghassemi, Navid Jafari, Mahboobeh Moridian, Parisa Alizadehsani, Roohallah Panahiazar, Maryam Khozeimeh, Fahime Zare, Assef Hosseini-Nejad, Hossein Khosravi, Abbas Atiya, Amir F. Aminshahidi, Diba Hussain, Sadiq Rouhani, Modjtaba Nahavandi, Saeid Acharya, Udyavara Rajendra K. N. Toosi University of Technology Tehran*** Iran The Computer Engineering Department Ferdowsi University of Mashhad Mashhad*** Iran The Mashhad Branch Islamic Azad University Mashhad91735413 Iran Electrical and Computer Engineering Faculty Semnan University Semnan*** Iran The Faculty of Engineering Science and Research Branch Islamic Azad University Tehran*** Iran Deakin University Waurn PondsVIC3217 Australia The Institute for Computational Health Sciences School of Medicine University of California San FranciscoCA94143 United States Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad*** Iran The Faculty of Electrical and Computer Engineering K. N. Toosi University of Technology Tehran*** Iran The Department of Computer Engineering Faculty of Engineering Cairo University Cairo12613 Egypt System Administrator at Dibrugarh University Assam 786004 India The Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore599494 Singapore The Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore599489 Singapore The Department of Bioinformatics and Medical Engineering Taichung City41354 Taiwan

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated. © 2020, CC BY-NC-ND.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Retraction notice to “Biometric authentication integrated with wireless communication malicious activity detection in a cyber physical system-based Fintech banking” [Optik 272 (2023) 170294]

引用

Optik 2023年 288卷

作者： Almuhannad Sulaiman Alorfi Saud Yonbawi Sultan Alahmari Abdullaev Abror Bozorboevich Mahendran Arumugam Pham Quang Huy Department of Information System Faculty of Computing and Information Technology King Abdulaziz University City Rabigh Saudi Arabia Department of Software Engineering College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia King Abdul Aziz City for Science and Technology Riyadh Saudi Arabia The Department of Acconting Economic Analysis and Audit Tashkent Institute of Finance Tashkent Uzbekistan Center for Transdisciplinary Research Saveetha Dental College Saveetha institute of Medical and Technical Science Chennai India University of Economics Ho Chi Minh City (UEH) Viet Nam

来源：评论

学校读者我要写书评

暂无评论

Implementation of K-Means Algorithm for Clustering Corn Planting Feasibility Area in South Lampung Regency

引用

Journal of Physics: Conference Series 2021年第1期1751卷

作者： A A Aldino D Darwis A T Prastowo C Sujana Department of Informatics Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Jl. ZA. Pagar Alam No.9-11 Bandar Lampung Indonesia Department of Accounting Information System Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Jl. ZA. Pagar Alam No.9-11 Bandar Lampung Indonesia Department of Computer Engineering Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Jl. ZA. Pagar Alam No.9-11 Bandar Lampung Indonesia

South Lampung is a regency with the capital of Kalianda which has an area of 2,007.01 km2 that dominates the agricultural area. Based on the data of corn crops in the South Lampung Regency Agriculture Office through BPS (Central Bureau of Statistics), showing several areas with corn crops that vary in number. Therefore, a grouping of potential corn-producing regions is required to know which areas produce large or small amounts of corn. The distribution of crops is usually done based on the name of the corn-producing sub-district. The K-Means clustering method is one of the data mining methods that is non-hierarchical clustering that groups data in the form of one or more clusters. Data that have the same characteristics are grouped in one cluster and the remaining is grouped into another cluster so that the data that is in one cluster has a small degree of variation. So the authors tried to apply the K-Means clustering method from the corn crop data of the last 2 years to produce feasibility information from each sub-district.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Modeling of Prediction Bandwidth Density with Backpropagation Neural Network (BPNN) Methods

引用

IOP Conference Series: Materials science and engineering 2020年第1期852卷

作者： Cynthia Hayat Iwan Aang Soenandi Samuel Limong Johan Kurnia Information System Department Faculty of Engineering and Computer Science Krida Wacana Christian University Industrial Engineering Department Faculty of Engineering and Computer Science Krida Wacana Christian University

Using computer networks in campus area which is open access will cause some problems at the speed to access the information. The allocation of bandwidth that provided sometimes does not match the needs of the client, so it takes an accurate prediction of bandwidth usage. This research obtained that Neural Network backpropagation modeling can solve the problem. The stages of research conducted the stage of training and testing phase. Data training is traffic data weekly and conducted by feed-forward back method, with max error 0.001, max hidden layer neuron 5000, constant momentum 0.95 and increase ratio 0.1. Before the data train is conducted, the scaling of the input and target values in the range of 0.1-0.9, then resumes the denormalization after the data train to return the data into Kb form. The results obtained from the training process in the form of comparison data, training performance, and regression. Furthermore, data testing, conducted by using a network that has been developed from the previous results. The test results are shown in the form of real data and predictive data using 8 input layers. In the prediction process, the mean square error generated is 0.0031792 which indicates a low error rate, so it can be stated that the resulting modeling has a level of output accuracy in predicting the use of computer network bandwidth is very high.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Effect of ply thickness on impact damage modes of thin-ply CFRP laminates 22

Effect of ply thickness on impact damage modes of thin-ply C...

引用

22nd International Conference on Composite Materials, ICCM 2019

作者： Yamada, Kohei Yamamoto, Satoru Kanesaki, Manato Nishikawa, Masaaki Matsuda, Naoki Kawabe, Kazumasa Hojo, Masaki Industrial Technology Center of Fukui Prefecture 61-10 Kawaiwashizuka Fukui910-0102 Japan Department of System Engineering Faculty of Computer Science and Systems Engineering Okayama Prefectural University 111 Kuboki Soja Okayama719-1197 Japan Department of Mechanical Engineering and Science Kyoto University C3 Kyoto Daigaku-Katsura Nishikyo-ku Kyoto615-8540 Japan

In this study, impact tests and compressive after impact (CAI) tests of quasi-isotropic laminates, which had different ply thicknesses using thin-ply prepreg whose thickness was 0.02 mm, were conducted. The effects of ply thickness and impact energy on the damage modes in impact tests and CAI strength were investigated. Furthermore, a precise observation of failure by an impact load inside the laminates using X-ray computed tomography (X-ray CT) was carried out. Results confirmed that fiber breakages became a predominant damage mode by decreasing the ply thickness owing to constraining effect of neighbouring layers, while most of failures in thick-ply laminates were dominated by matrix damage such as matrix cracks and delamination. Moreover, when the ply thickness decreased, the decreasing rate of CAI strength became larger than that of thick-ply laminates owing to the accumulation of fiber breakage in thickness direction in the case of a high impact energy, while the CAI strength was improved when the impact energy was below 5 J/mm. © 2019 International Committee on Composite Materials. All rights reserved.

关键词： computerized tomography

来源：评论

学校读者我要写书评

暂无评论

Correction to: How Resiliency and Hope Can Predict Stress of Covid-19 by Mediating Role of Spiritual Well-being Based on Machine Learning

引用

Journal of religion and health 2021年第4期60卷 2322-2323页

作者： Roghieh Nooripour Simin Hosseinian Abir Jaafar Hussain Mohsen Annabestani Ameer Maadal Laurel Radwin Peyman Hassani-Abharian Nikzad Ghanbari Abolghasem Khoshkonesh Department of Counseling Faculty of Education and Psychology Alzahra University Tehran Iran. r.noori@alzahra.ac.ir. Department of Counseling Faculty of Education and Psychology Alzahra University Tehran Iran. Computer Science Department Liverpool John Moores University Liverpool L33AF UK. Faculty of Electrical Engineering Sharif University of Technology Azadi Ave PO Box 11365-11155 Tehran Iran. Department of Psychology Faculty of Humanities Islamic Azad University Shahr-E Rey Branch Tehran Iran. Center for Health Care Organizational and Implementation Research (CHOIR) VA Boston Healthcare System Boston MA USA. Institute for Cognitive Science Studies Brain and Cognition Clinic Tehran Iran. Clinical Psychology (Prevention and Treatment of Addiction) Faculty of Education and Psychology Shahid Beheshti University (SBU) Tehran Iran. Department of Counseling Faculty of Education and Psychology Shahid Beheshti University (SBU) Tehran Iran.

The original version of the article was inadvertently published with the ethical code in Ethical Considerations section.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Implementation of Facial Feature Extraction Using Viola-Jones Method for Mobile Robot system

引用

Journal of Physics: Conference Series 2020年第1期1500卷

作者： Ahmad Zarkasi Siti Nurmaini Deris Setiawan Ahmad Kuswandi Sri Desy Siswanti Intelligence System Research Group Faculty of Computer Science Faculty of Engineering Universitas Sriwijaya Palembang Indonesia

To be able to detect faces, the system must be able to identify the characteristics of the face, so that the camera can distinguish which parts of the face and non-faces. In identifying facial features performed on certain parameters, such as skin color, facial contours, lighting, facial pose. At this time face detection system uses an algorithm Viola-Jones is considered the most accurate in the detection and face tracking. In this research, we will discuss the application of interactions between humans and machines with image media in the form of faces. Interactions between humans and computers can be found in various places. One of the media interaction between humans and computers is with the image. This time the system will indicate to find how r o bot can act through the position of a person's face recorded on camera robot. This system uses the Viola-Jones algorithm method which is a fairly popular object detection method because the detection process is carried out very quickly. Besides, the bot will also follow the face based on the identity it stores in the database. In recognizing the human face this system uses the Eigenface method. Both of these algorithms will adjust the robot's motion as it should. The results of the experiment will show the robot can move quite well following the face stored in the database.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：