检索结果-内蒙古大学图书馆

Comparison of Feature Selection Methods to Classify Inhibitors in DUD-E database

Procedia Computer science 2018年 144卷 194-202页

作者： Heri Kuswanto Renny Yunia Nurhidayah Hayato Ohwada Department of Statistics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember (ITS) Kampus ITS Sukolilo Surabaya 60111 Indonesia Department of Industrial Administration Faculty of Science and Technology Tokyo Universty of Science Chiba-Japan

In designing a new drug, inhibitor compound is usually used to control the enzyme work to recover a particular disease. In the drug design technique, the classification of inhibitor is carry out by docking software to simulate the bounding of mixing (new inhibitor candidate) with the targeted enzyme. DUD-E is a database to simulate docking with high dimensional data characteristic, which lead to the feasibility of machine learning approach as the analytical tool. A compound with specific characterictics can be classified into ligand or decoy by using many characterictics leading to a problem in the machine learning algorithm. This paper discusses feature selection analysis to obtain the compound characteristics which are effectively determine ligand or decoy. This paper examined Mutual Information-based Feature Selection (MIFS), Correlation-based Feature Selection (CFS) as well as Fast Correlation-Based Filter (FCBF), and the results show that the FCBF always selects less number of features with fastest runtime of classification. The highest classification accuracy is obtained when all features are used in the classification by k-NN. However, the accuracy is slightly different with classification using selected features. The CFS method performs well for data-A with accuracy of 89,55%, while the MIFS outperforms the others for data-B and data-C with the classification accuracy of 92,34% and 95,20% consecutively.

关键词： DUD-E k-NN feature accuracy runtime

来源：评论

学校读者我要写书评

暂无评论

Closed Shop Scheduling Optimisation using Max-Plus Automata

引用

Journal of Physics: Conference Series 2019年第4期1341卷

作者： Dian Ayu Merdekawati Subiono Faculty of Mathematics Computing and Data Science Sepuluh Nopember Institute of Technology Surabaya Indonesia

An appropriate scheduling system is needed in manufacturing activities to minimize production time and costs. Flow shop is one type of production flow that widely used in manufacturing. Based on the demand criteria, it has been known as a scheduling problem called closed shop. This scheduling emphasizes the make-to-stock (MTS) which produces a stock. We take the closed shop scheduling case studies from an existing literature and optimize scheduling using max-plus automata optimum. We compare the result with regular scheduling and the previous result in the literature. We show that the result of max-plus automata optimum scheduling are optimum compared than the existing regular scheduling and the existing previous result of the literature.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Inventory models for short life cycle clothing products use a logistic growth model

引用

Journal of Physics: Conference Series 2020年第1期1490卷

作者： V Lukitosari A P Subriadi Mathematics Department Faculty of Mathematics Computing and Data Science Sepuluh Nopember Institute of Technology (ITS) Surabaya 60111 Indonesia Department of Information System Sepuluh Nopember Institute of Technology (ITS) Surabaya 60111 Indonesia

Clothes are products that follow short-life fashion and market demand. Products with a short lifetime occur due to technological developments and/or changes in market tastes. The clothing industry is one of the industries that has a short and obsolete sales period in stages. The excess number of products that accumulate in the warehouse due to obsolescence can cause the company to get a profit that is not optimal. One strategy to increase demand for the product is to apply discounts. There are two types of discounts used, namely a single discount and multiple discounts. The demand function of inventory model is considered analogous to the logistic growth model. The results show profit is maximized to get optimal order quantity and optimal discount price.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Machine learning in forensic applications

引用

Significance 2019年第2期16卷 29-35页

作者： Alicia Carriquiry Heike Hofmann Xiao Hui Tai Susan VanderPlas Distinguished professor of liberal arts and sciences and a professor of statistics at Iowa State University and director of CSAFE. She researches applications of statistics in human nutrition bioinformatics forensic sciences and traffic safety. CSAFE researcher and professor in the Department of Statistics at Iowa State University with research interests in data visualisation multivariate categorical data analysis statistical computing exploratory data analysis and interactive statistical graphics. PhD student in the Department of Statistics and Data Science at Carnegie Mellon University and is currently working on matching problems in forensic science. Previously she was a statistician with the Singapore government. Research assistant professor in the Department of Statistics at Iowa State University working as part of the research faculty at CSAFE.

The 2009 National Academy of sciences report found pattern-evidence disciplines to be rife with subjectivity. In the decade since, machine learning methods have been developed to try to address that issue. By Alicia C...

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Algorithms-based Object Detection and Localization Revisited

引用

Journal of Physics: Conference Series 2021年第1期1892卷

作者： Safa Riyadh Waheed Norhaida Mohd Suaib Mohd Shafry Mohd Rahim Myasar Mundher Adnan A. A. Salim Big Data Center & Faculty of Engineering School of Computing Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia Computer Techniques Engineering Department Faculty of Information Technology Imam Jaafar Al-Sadiq University Baghdad Iraq UTM-IRDA Digital Media Institute of Human-Centred Engineering Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia Islamic University Najaf Iraq Laser Center & Physics Department Faculty of Science Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia

The computer vision (CV) is an emerging area with sundry promises. This communication encompasses the past development, recent trends and future directions of the CV in the context of deep learning (DL) algorithms-based object detections and localizations techniques. To identify the object location inside an image and recognize it by a computer program as fast as the human brain the machine learning and DL techniques have been evolved. However, the main limitations of the machine are related to the prolonged time consumption to handle vast amount of data to perform the same task as the human brain. To overcome these shortcomings, the convolution neural networks (NNs)-based deep NN has been developed, which detects and classifies the object with high precision. To train the deep NNs, massive amount of data (in the form of images and videos) and time is needed, making the computational cost of the CV very high. Thus, transfer learning techniques have been proposed wherein a model trained on one task can be reused on another linked task, thereby producing excellent outcomes. In this spirit, diverse DL-based algorithms have been introduced to detect and classify the object. These algorithms include the region-based convolutional NN (R-CNN), fast R-CNN, Faster R-CNN, mask E-CNN and You Only Look Once. A comparative evaluation among these techniques has been made to reveal their merits and demerits in the CV.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Optimal control for extraction lipid model of microalgae Chlorella Vulgaris using PMP method

引用

Journal of Physics: Conference Series 2019年第1期1218卷

作者： Mardlijah Nur Ilmayasinta Elmir Arif Irhami Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Surabaya Indonesia

Microalgae is one of the energy to be used as biodiesel. Chlorella Vulgaris is one of the most economical algae to produce biodiesel, because these green algae are rich in carbohydrates, require no special care, and easy to grow. Algae oil obtained for biodiesel production is obtained through a fairly long process, one of which is the process of lipid extraction. In the study of lipid extraction process using solid-liquid or leaching extraction is the transfer of dissolved dissolved components from solids into the solvent. In the study used CO2-expended methanol (CXM). Soxhlet Extraction Method is a method for lipid extraction using hexane, in the process of extraction with Soxhlet tool there is a process of lipid mass transfer contained in Chlorella Vulgaris to flow CXM. This process of displacement has created a diferential equation which can be viewed as a system by Sriati Monalisa Siahaan(2016) to be used as a mathematical model to predict extractable lipid results for subsequent use as the main ingredient of biodiesel production. In her research, Sriati refers to a research conducted by Yi-Hung Yang(2015). In this research aims to perform optimal control with Pontryagin Maximum Principle (PMP) method to produce maximum lipid extraction result. This method is used to obtain the best control of the dynamic system from the initial state to the last by maximizing the objective function and more modern than other methods such as Linear Quadratic Regulator (LQR). Using the PMP method to optimal control in this systems, can increase the value of lipid concentration.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Association of structural MRI with Braak staging using neural network-based risk scoring

引用

Alzheimer's & Dementia 2023年第S3期19卷

作者： Matthew I. Miller Shangran Qiu Prajakta S. Joshi Joyce Lee Rhoda Au Vijaya B. Kolachalama Boston University School of Medicine Boston MA USA College of Arts & Sciences Boston University Boston MA USA Framingham Heart Study Boston University Chobanian & Avedisian School of Medicine Boston MA USA Boston University Alzheimer’s Disease Center Boston University Boston MA USA Boston University School of Public Health Boston MA USA Department of Anatomy and Neurobiology Boston University Chobanian & Avedisian School of Medicine Boston MA USA Hariri Institute for Computing and Computational Science & Engineering Boston University Boston MA USA Faculty of Computing & Data Sciences Boston University Boston MA USA Boston University Alzheimer’s Disease Center Boston University Chobanian & Avedisian School of Medicine Boston MA USA Whitaker Cardiovascular Institute Boston University Chobanian & Avedisian School of Medicine Boston MA USA

Background The gold standard of Alzheimer’s disease (AD) diagnosis remains histopathologic examination of neuropathologic changes. However, the dependence on post-mortem microscopic analysis limits the role of pathology in clinical care. Magnetic resonance imaging (MRI) is routinely used to diagnose suspected dementia (DE) due to AD, but correlating neuroimaging to underlying pathologic changes remains challenging. Herein, we report the development of two risk scores derived from convolutional neural network (CNN) processing of MRI scans, which we then validate against Braak staging of neurofibrillary tangles from autopsy reports. Method Using T1-weighted MRIs from the National Alzheimer’s Coordinating Center (NACC), we trained two CNNs (Figure 1) to predict continuous scores for overall cognitive status (“COG” score) and AD probability (“ALZ” score). The COG score, ranging from 0-2, graded a subject’s cognitive status on a spectrum from normal cognition (NC, 0), to mild cognitive impairment (MCI, 1), and DE due to any cause (2). The ALZ score then graded each DE individual’s probability of AD from 0 to 1. These CNNs were then applied to a subset of participants with autopsy reports available within 6 months of MRI. CNN performance was evaluated by area under receiver operating characteristic curve (AUC). Differences in risk scores were assessed by Kruskal-Wallis H testing with post hoc Dunn testing. Result The CNN models for COG and ALZ score estimation were trained on 4,822 NACC participants. Thresholding of COG scores distinguished individuals with DE from those with NC and MCI (AUC = 0.87). ALZ scores distinguished individuals with AD from those with non-AD causes of DE (AUC = 0.77). Among 74 persons with available neuropathology data, COG scores differed significantly among Braak staging groups (p<0.001), increasing from a mean of 0.99±0.36 in Stage 1 to 1.47±0.41 in Stage 6 (p = 0.010). Significant differences in ALZ scores were also observed (p = 0.039) betwe

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Quantum Secret Sharing Scheme Using Orbital Angular Momentum onto Multiple Spin States Based on Fibonacci Compression Encoding

引用

Communications in Theoretical Physics 2018年第10期70卷 384-390页

作者： Hong Lai Ming-Xing Luo Yong-Jian Xu Josef Pieprzyk Jun Zhang Lei Pan Mehmet A.Orgun School of Computer and Information Science and Centre for Research and Innovation in Software Engineering（RISE） Southwest UniversityChongqing 400715China Information Security and National Computing Grid Laboratory School of Information Science and TechnologySouthwest Jiaotong UniversityChengdu 610031China Data61 Commonwealth Scientific and Industrial Research OrganisationSydneyAustralia Institute of Computer Science Polish Academy of SciencesWarsaw01-248Poland School of Software and Electrical Engineering Swinburne University of TechnologyHawthornVIC3122Australia School of Information Technology Deakin UniversityGeelongVIC3220Australia Department of Computing Macquarie UniversitySydneyNSW 2109Australia Faculty of Information Technology Macao University of Science and TechnologyAvenida Wai LongTaipa999078Macao

Since the use of a quantum channel is very expensive for transmitting large messages, it is vital to develop an effective quantum compression encoding scheme that is easy to implement. Given that, with the single-photon spin-orbit entanglement, we propose a quantum secret sharing scheme using orbital angular momentum onto multiple spin states based on Fibonacci compression encoding. In our proposed scheme, we can represent the frequency of any secret message which is typically collection of bits encodings of text or integers as a bitstring using the base Fibonacci sequence, which is encoded multiple spin states for secret shares transmitted to participants. We demonstrate that Fibonacci compression encoding carries excellent properties that enable us to achieve more robust quantum secret sharing schemes with fewer number of photons.

关键词： spin-orbit entanglement multiple spin states Fibonacci compression encoding quantum secretsharing

来源：评论

学校读者我要写书评

暂无评论

Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review

arXiv

引用

arXiv 2020年

作者： Shoeibi, Afshin Khodatars, Marjane Jafari, Mahboobeh Ghassemi, Navid Sadeghi, Delaram Moridian, Parisa Khadem, Ali Alizadehsani, Roohallah Hussain, Sadiq Zare, Assef Sani, Zahra Alizadeh Khozeimeh, Fahime Nahavandi, Saeid Rajendra Acharya, U. Gorriz, Juan M. The Data Science and Computational Intelligence Institute University of Granada Spain The Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Deakin University VIC3217 Australia The Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran System Administrator at Dibrugarh University Assam786004 India Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran The School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Dept. of Psychiatry University of Cambridge United Kingdom

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA, and also has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methods, deep learning (DL) networks have gained popularity recently compared to conventional machine learning (ML). Unlike ML, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL techniques for COVID-19 diagnostic and segmentation of lungs is discussed, concentrating on works that used X-Ray and CT images. Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL is presented. Lastly, the challenges faced in the detection of COVID-19 using DL techniques and directions for future research are discussed. © 2020, CC BY-NC-ND.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Lattice Blind Signatures with Forward Security

arXiv

引用

arXiv 2020年

作者： Le, Huy Quoc Duong, Dung Hoang Susilo, Willy Tran, Ha Thanh Nguyen Trinh, Viet Cuong Pieprzyk, Josef Plantard, Thomas Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong Northfields Avenue WollongongNSW2522 Australia Department of Mathematical and Physical Sciences Concordia University of Edmonton 7128 Ada Blvd NW EdmontonABT5B 4E4 Canada Faculty of Information and Communication Technology Hong Duc University 565 Quang Trung Thanh Hoa Viet Nam CSIRO Data61 SydneyNSW Australia Institute of Computer Science Polish Academy of Sciences Warsaw Poland

Blind signatures play an important role in both electronic cash and electronic voting systems. Blind signatures should be secure against various attacks (such as signature forgeries). The work puts a special attention to secret key exposure attacks, which totally break digital signatures. Signatures that resist secret key exposure attacks are called forward secure in the sense that disclosure of a current secret key does not compromise past secret keys. This means that forward-secure signatures must include a mechanism for secret-key evolution over time periods. This paper gives a construction of the first blind signature that is forward secure. The construction is based on the SIS assumption in the lattice setting. The core techniques applied are the binary tree data structure for the time periods and the trapdoor delegation for the key-evolution mechanism. Copyright © 2020, The Authors. All rights reserved.

关键词： Network security

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：