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International Conference on Computing and Networking Technology (ICCNT)

作者： N. Muthuvairavan Pillai S Manimala Aruna Sri Rongali Anchit Gugnani A Senthil Kumar G. V. Sriramakrishnan Computer Science and Business Systems R.M.D Engineering College Kavarapettai Thiruvallur district Computer science and engineering University SJCE JSS Science and technology University Mysore Karnataka India Department Information Technology Emphasis/Information Technology Ph.D University and city with the country University of The Cumberlands US Physiotherapy Amity University Jaipur Rajasthan Artificial Intelligent department Shri Vishnu Engineering College for Women Bhimavaram AP Department Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamilnadu

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Medical imagery has been increasingly used in diagnosing and treating various diseases, leading to advances in healthcare. While humans usually lead the qualitative interpretation of visual features, Automated Classification of Medical Images Using Convolution Neural Networks (CNNs) has gained traction for accurate image classification. CNNs are a type of deep learning algorithm that attempts to mimic the structure of the human brain and are ideal for image classification due to their convolution layers that can detect patterns within the image. These convolution layers enable the networks to learn the characteristics of the data through a series of convolution and pooling operations. Additionally, they can rapidly identify relationships and patterns within the dataset and extract features from the input image, thus allowing a more accurate classification. With the increasing availability of medical imaging data, advancements in the field of ML/AI has enabled the use of CNNs for automated medical image classification, helping clinicians to diagnose diseases accurately with less time and effort.

关键词： Training Image segmentation Accuracy Convolution Medical services Feature extraction Convolutional neural networks Medical diagnostic imaging X-ray imaging Image classification

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Prediction of the Impact of Covid-19 Vaccine on Public Health Using Twitter

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2022年第1期49卷 19-29页

作者： Syed, Ayesha Ayub Gaol, Ford Lumban Suparta, Wayan Abdurachman, Edi Trisetyarso, Agung Matsuo, Tukoro Ph.D. student at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Associate Professor at Informatics Engineering and Information System at Bina Nusantara University Jakarta Indonesia Malaysia Associate Professor at the Department of Doctor of Research in Management at Bina Nusantara University Jakarta Indonesia Assistant Professor at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Professor at the Graduate School of Industrial Technology Advanced Institute of Industrial Technology Japan and Department of M-Commerce and Multimedia Applications Asia University Taiwan

Social media is a source of big data. Media like Twitter and Facebook has been used for collecting and analyzing user data for different purposes. The data can be used to analyze people opinions towards certain topics and incidents by applying sentiment analysis and then certain useful insights can be drawn from the analyzed data. during the current time of Covid-19, people have been sharing information regarding Covid-19 statistics, vaccines, and discussing the effects of the vaccine concerning public health. The purpose of this study is to analyze tweet data regarding the Covid-19 vaccine by applying sentiment analysis and predicting the impact of the vaccine on public health. Also, the tweets are analyzed for hidden topics by applying Topic Modelling using Latent dirichlet Allocation (LdA) and Latent Semantic Analysis (LSA). The source of data for this study is Twitter API. The coding and data analysis is done using Python programming language in the Spyder (Scientific Python development Environment) that is an integrated development environment for scientific programming, testing, and data analysis. The results of the study indicate a greater positive sentiment reflecting a healthy public discussion about the Covid-19 vaccine, information, awareness, and public acceptance. With these results, a positive impact of the Covid-19 vaccine on public health is predicted. The results of topic modeling discovered 10 hidden topics from the tweet dataset. © 2022. All Rights Reserved.

关键词： Vaccines

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The role of cloud computing technology: A savior to fight the lockdown in COVId 19 crisis, the benefits, characteristics and applications

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International Journal of Intelligent Networks 2021年 2卷 166-174页

作者： Alhomdy, Sharaf Thabit, Fursan Abdulrazzak, Fua'ad Hasan Haldorai, Anandakumar Jagtap, Sudhir Professor. Emirates International University-Yemen Ph.D Researcher. School of Computational Sciences S.R.T.M. University Nanded India Associated Professor. Department of Communication Technology and Networks Thamar University Yemen Associated Professor. Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Professor and Principle of Swami Vivekanand Campus India

The contagion of the Coronavirus (COVId-19) led to a global lockdown that put governments in emergency mode. With the total number of positive cases worldwide exceeding the 97.46 million mark, social distancing appears to be the only effective strategy to contain the virus at the moment. As a result, companies face obstacles and find it difficult to respond to this current challenge of remote working. The impact of the novel COVId-19 has created many new challenges, and many of us have had to adopt new ways of working. With the need for accessing to critical applications and the scalability of the infrastructure, cloud computing is emerging as an underlying technology. The cloud technology had a major role in fighting the epidemic;it becomes a salvation for governments and organizations in numerous fields of life, education, health, industry, communication, remote surveillance, and more information. Therefore, this study presents the benefits, characteristics and applications of cloud computing and explains how the cloud contributes to improving life in all regions of the world during COVId-19. It shows that the cloud computing helps countries in combating COVId 19, in education and health sectors, also in the economic and commercial aspects. It investigates the current state by distributing an online questionnaire to various people of academic and non-academic backgrounds in different places over the world in the ICT and education sectors. The results showed that there is an effective role for cloud computing during the epidemic. © 2021 The Author(s)

关键词： COVId-19

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The Mid-Infrared Region Absorption of Graphene-based photodetector with Au-grating Structure

The Mid-Infrared Region Absorption of Graphene-based Photode...

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IEEE International Conference on Nano/Molecular Medicine and engineering (NANOMEd)

作者： Wei-Yu Long Kuan-Chou Lin Yu-Xuan Lu Chih-Ting Lin B.S. degree from the Department of Electrical Engineering from National Taiwan University. He is currently a Ph.D candidate in the Graduate School of Advanced Technology National Taiwan University B.S. degree in civil engineering from National Taiwan University and M.S. degree in Institute of Electronics Engineering National Taiwan University B.S. degree from the Department of Materials Science and Engineering from Central South University China B.S. degree in civil engineering from National Taiwan University the M.S. degree in Institute of Applied Mechanics from National Taiwan University and the Ph.D. degree in electrical engineering and computer science University of Michigan Ann Arbor MI USA

Graphene, a single layer of carbon atoms arranged in a two-dimensional honeycomb lattice, has garnered immense attention for its exceptional electrical conductivity, transparency, and mechanical strength. These properties make graphene an ideal candidate for photodetectors, as it can efficiently convert incident photons into electrical signals. The integration of graphene into surface plasmonic devices has opened up new avenues for photodetection technology. In our research, we combine graphene and surface plasmonic devices to realize a high responsivity mid-infrared graphene photodetector with a grating metal/graphene/SiO 2 structure. The Bruker Vertex70 Fourier-transform infrared spectroscopy (FTIR) was used for the mid-infrared spectrum measurement. With different linewidth of Au grating structure, the peak of absorption redshifts as the grating linewidth increases and the intensity is also different. Further, by adding graphene, the total absorption increases. due to these advantages, the Au-grating graphene device can be used as infrared photodetectors.

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UFTR: A Unified Framework for Ticket Routing

arXiv

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arXiv 2020年

作者： Han, Jianglei Li, Jing Sun, Aixin Ph.D student at School of Computer Science and Engineering Nanyang Technological University Singapore Inception Institute of Artificial Intelligence United Arab Emirates Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

—Corporations today face increasing demands for the timely and effective delivery of customer service. This creates the need for a robust and accurate automated solution to what is formally known as the ticket routing problem. This task is to match each unresolved service incident, or âAIJticketâĂİ, to the right group of service experts. Existing studies divide the task into two independent subproblems âĂŞ initial group assignment and inter-group transfer. However, our study addresses both subproblems jointly using an end-to-end modeling approach. We first performed a preliminary analysis of half a million archived tickets to uncover relevant features. Then, we devised the UFTR, a Unified Framework for Ticket Routing using four types of features (derived from tickets, groups, and their interactions). In our experiments, we implemented two ranking models with the UFTR. Our models outperform baselines on three routing metrics. Furthermore, a post-hoc analysis reveals that this superior performance can largely be attributed to the features that capture the associations between ticket assignment and group assignment. In short, our results demonstrate that the UFTR is a superior solution to the ticket routing problem because it takes into account previously unexploited interrelationships between the group assignment and group transfer problems. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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determinant of importance-performance and willingness to participate in Komodo adaptive conservation programs

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Journal for Nature Conservation 2024年 81卷

作者： Sianipar, Imelda Masni Juniaty Lee, Chun-Hung Wang, Hurng-Jyuhn Kim, doo-Chul Suryawan, I Wayan Koko PhD Student in Asia-Pacific Regional Studies College of Humanities and Social Sciences National Dong Hwa University Department of International Relations Faculty of Social Science and Political Science Universitas Kristen Indonesia Department of Natural Resources and Environmental Studies College of Environmental Studies and Oceanography National Dong Hwa University Program in Asia-Pacific Regional Studies College of Humanities and Social Sciences National Dong Hwa University Department of Rural Environment Management Graduate School of Environmental and Life Science Okayama University Ph.D Program in Asia-Pacific Regional Studies College of Humanities and Social Sciences National Dong Hwa University Department of Environmental Engineering Faculty of Infrastructure Planning Universitas Pertamina Jalan Sinabung II Terusan Simprug

This study delves into the evaluation of the Komodo dragons Adaptive Conservation Program (KACP) at Komodo National Park in Indonesia through Importance-Performance Analysis (IPA). It aims to understand how the programs components align with community priorities and assess its effectiveness. Utilizing a mixed-methods approach, this research analyzes community perceptions of KACP's critical elements, spanning eco-tourism initiatives to environmental protection strategies. The community is categorized into local advocates, eco-tourism enthusiasts, and eco-community harmonizers, identifying how each group perceives and interacts with various facets of KACP. The findings reveal significant differences in perceptions of importance and performance among these groups, providing insights into areas of strength and those needing improvement. Additionally, the study includes an analysis of demographic influences on these perceptions through Logit and Probit models, aiming to understand the impacts across different community segments. The results highlight the necessity for an adaptive and responsive design in conservation efforts, considering community expectations and ecological impacts. despite offering comprehensive insights, the study acknowledges limitations in the demographic scope and suggests future research directions, such as longitudinal studies and an expanded analysis of variables. These findings offer critical guidance for policymakers and conservation practitioners to enhance conservation programs’ effectiveness and community alignment, emphasizing the need to adapt to the challenges and changes introduced by KACP. © 2024 Elsevier GmbH

关键词： Adaptive capacity Community perception Conservation strategy evaluation Importance-performance analysis Protected areas

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different RNA Secondary Structure Prediction Methods

Different RNA Secondary Structure Prediction Methods

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International Conference on Electronic Systems, Signal Processing and Computing

作者： Pradnya S. Borkar Anjali R. Mahajan Ph.D Student Department of Computer Science and Engg. Priydarshini Institute of Engineering and Technology Nagpur Maharashtra Professor and Head Department of Computer Science and Engg. Priydarshini Institute of Engineering and Technology NagpurMaharashtra

ISBN: (纸本)9781479921041

Bioinformatics is the combination of two fields Biology and computer science. Now a days this application plays vital role for each molecular biological study. It uses various implementations and algorithms for analyzing and integration of biological data. RNA (ribo-nucleic acid) provides a mechanism to copy the genetic information of the dNA. It is very important to study the folding of RNA molecules to understand their role and functionalities. Researchers can find different important role of RNA molecules depending upon their RNA secondary structure which will be useful to discover antiviral drugs for diseases. In this paper we have given a review of various implementations and algorithms for RNA Secondary structure prediction.

关键词： RNA secondary structure RNA folding Prediction methods

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TRACKING CONTROL IN THE PRESENCE OF NONLINEAR dYNAMIC FRICTIONAL EFFECTS: ROBOT EXTENSION

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Asian Journal of Control 2008年第3期1卷

作者： M. Feemster d.M. dawson A. Behal W. dixon Matthew Feemster received the B.S degree in Electrical Engineering from Clemson University Clemson South Carolina in December 1994. Upon graduation he remained at Clemson University and received the M.S. degree in Electrical Engineering in 1997. During this time he also served as a research/teaching assistant. His research work focused on the design and implementation of various nonlinear control algorithms with emphasis on the induction motor and mechanical systems with friction present. He is currently working toward his Ph.D. degree in Electrical Engineering at Clemson University. Darren M. Dawson was born in 1962 in Macon Georgia. He received an Associate Degree in Mathematics from Macon Junior College in 1982 and a B.S. Degree in Electrical Engineering from the Georgia Institute of Technology in 1984. He then worked for Westinghouse as a control engineer from 1985 to 1987. In 1987 he returned to the Georgia Institute of Technology where he received the Ph.D. Degree in Electrical Engineering in March 1990. During this time he also served as a research/teaching assistant. In July 1990 he joined the Electrical and Computer Engineering Department and the Center for Advanced Manufacturing (CAM) at Clemson University where he currently holds the position of Professor. Under the CAM director's supervision he currently leads the Robotics and Manufacturing Automation Laboratory which is jointly operated by the Electrical and Mechanical Engineering departments. His main research interests are in the fields of nonlinear based robust adaptive and learning control with application to electro-mechanical systems including robot manipulators motor drives magnetic bearings flexible cables flexible beams and high-speed transport systems. Aman Behal was born in India in 1973. He received his Masters Degree in Electrical Engineering from Indian Institute of Technology Bombay in 1996. He is currently working towards a Ph.D in Controls and Robotics at Clemson University. His research focuses on the control of no

In this paper, we extend the observer/control strategies previously published in [25] to an n -link, serially connected, direct drive, rigid link, revolute robot operating in the presence of nonlinear friction effects modeled by the Lu-Gre model. In addition, we also present a new adaptive control technique for compensating for the nonlinear parameterizable Stribeck effects. Specifically, an adaptive observer/controller scheme is developed which contains a feedforward approximation of the Stribeck effects. This feedforward approximation is used in a composite controller/observer strategy which forces the average square integral of the position tracking error to an arbitrarily small value. Experimental results are included to illustrate the performance of the proposed controllers.

关键词： Friction compensation nonlinear control

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KINGPINS OR MULES: AN ANALYSIS OF dRUG OFFENdERS INCARCERATEd IN FEdERAL ANd STATE PRISONS

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Criminology & Public Policy 2006年第3期3卷

作者： ERIC L. SEVIGNY JONATHAN P. CAULKINS Eric L. Sevigny is a Ph.D. student in the Graduate School of Public and International Affairs at the University of Pittsburgh. Mr. Sevigny's research interests lie in the area of drug policy with an emphasis on enforcement sanctioning international control and treatment/harm reduction. His previous experience includes substance abuse counseling and substance abuse treatment needs research of special populations including prisoners the homeless and adolescents. Mr. Sevigny received a B.A. in Psychology from Middlebury College. Jonathan P. Caulkins Ph.D. is Professor of Operations Research and Public Policy at Carnegie Mellon University's Heinz School of Public Policy. Dr. Caulkins specializes in mathematical modeling and systems analysis of social policy problems with a particular focus on issues pertaining to drugs crime violence and prevention. Dr. Caulkins has also published on airline operations sulfur dioxide pollution trading markets Internet-based advertising flexible manufacturing systems and personnel performance evaluation among other topics. At RAND he has been a consultant visiting scientist codirector of RAND's Drug Policy Research Center (1994–1996) and founding director of RAND's Pittsburgh office (1999–2001). Dr. Caulkins received a B.S. and M.S. in Systems Science from Washington University and an S.M. in Electrical Engineering and Computer Science and Ph.D. in Operations Research both from M.I.T.

Research Summary: drug policy reformers and defenders contest the extent to which low-level drug offenders are being sent to prison and for how long. Using data from the Survey of Inmates in Federal and State Correctional Facilities, 1997 (BJS, 2000), we assess the seriousness of incarcerated drug offenders along dimensions of dangerousness, culpability, and harm—specifically, functional role and drug group participation, type and amount of drugs, firearms involvement, and criminal conviction and arrest history. We find that only about 1.6% of federal and 5.7% of state inmates can be described as “unambiguously low-level.” Alternatively, not many are “kingpins.” Rather, most fall into a middle spectrum representing different degrees of seriousness that depend on what factors are emphasized. Policy Implications: Our findings dampen hopes of dramatically reducing prison populations by getting out of prison those who are unambiguously low-level drug offenders. They simply do not represent the majority of incarcerated drug offenders. In particular, most played some role in distribution, so eliminating prison terms for users (decriminalization) would not have affected many now in prison. Indeed, if decriminalization increased demand, it could plausibly increase prison populations by increasing the number of suppliers still subject to imprisonment. On the other hand, “drug courier exceptions” to sentencing laws that apply to minor role offenders possessing large quantities could have a greater prison reduction impact.

关键词： Incarceration, drug Policy, Inmate Surveys, Low-Level drug Offenders, Culpability

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An implementation of parallel production systems with two load balancing mechanisms

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SYSTEMS ANd computerS IN JAPAN 1995年第14期26卷 1-14页

作者： Miyazaki, J Yokota, H Member School of Information Science Japan Advanced Institute of Science and Technology Ishikawa-ken Japan 923 Received his B.E. degree from Tokyo Institute of Technology Tokyo Japan in 1992 and his M.S. degree from Japan Advanced Institute of Science and Technology (JAIST) Ishikawa Japan in 1994. He is a Ph.D. student at School of Information Science JAIST. His research interests include parallel rule base systems active database systems and high performance I/O systems. He is a member of the Institute of Electronics Information and Communication Engineers of Japan and Information Processing Society of Japan. Received his B.E. M.E. and Dr. of Eng. degrees from Tokyo Institute of Technology in 1980 1982 and 1991 respectively. He joined Fujitsu Ltd. in 1982 and was a researcher at the Institute of New Generation Computer Technology (ICOT) from 1982 to 1986 and at Fujitsu Laboratories Ltd. from 1986 to 1992. He is an Associate Professor of School of Information Science Japan Advanced Institute of Science and Technology (JAIST). HIS research interests include parallel computer architecture for databases and data engineering. He is a member of IPS I.E.I.C.E. JSAI IEEE and ACM.

An implementation method is proposed for parallel production systems on multicomputers, or message-passing computers, to speed up execution time. There have been proposed parallel production systems using hash mechanism, but they cause a skewed load distribution problem. To obtain more efficient balance of load, the method addressed here and named clustered parallel production systems (CPPS) adopts two load balancing strategies: hash and demand-driven. Taking account of the cost of termination detection, the execution time of the CPPS and simple hash method are estimated, and implement on an nCube2. The estimation meets the execution results. The CPPS provides much better load balance to improve scalability.

关键词： parallel production system multicomputer message-passing parallel computer load balancing

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