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Aerospace Electrical Engineering and Informatics (ICAAEEI), International Conference of Adisutjipto on

作者： Ernando Rizki Dalimunthe Horas Nico L. P. Sibuea Dirga Rama Setiadi Akhmad Jayadi Jaka Persada Sembiring Elka Pranita Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandar Lampung Indonesia Informatic Management Study Program Politenik Negeri Lampung Bandar Lampung Indonesia

ISBN: (数字)9798331507930

ISBN: (纸本)9798331507947

The quadcopter has a fairly simple mechanical design because it does not require other drives to perform various movements such as roll, pitch, and yaw. The simplicity of this mechanical design has a considerable impact on the complexity of its control. Therefore, it is necessary to have a good control method to maintain the stability of the quadcopter. The research aims to maintain stability of quadcopter by using a combination of the Fuzzy Logic Controller (FLC) with the Proportional-Integral-Derivative (PID) method, where FLC is used to initialize the input of the PID method to find the quadcopter stability point. The application of fuzzy methods for the initial tuning of PID parameters has proven effective in enhancing the system's fundamental performance, such as overshoot, rise time, and settling time, compared to the conventional PID approach. The proposed method can reduce overshoot by over 20%, with reductions reaching up to 88% in yaw motion. Additionally, the Fuzzy-PID approach significantly improves stability, shortening the time to achieve steady-state by less than 1 seconds for all movements while PID did more than 2s for pitch and roll motions and over 1s for yaw motion.

关键词： Fuzzy logic Power demand PI control Attitude control Stability analysis Regulation Steady-state PD control Tuning Quadrotors

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Fitness Landscape Optimization Makes Stochastic Symbolic Search By Genetic programming Easier

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IEEE Transactions on Evolutionary Computation 2024年

作者： Huang, Zhixing Mei, Yi Zhang, Fangfang Zhang, Mengjie Banzhaf, Wolfgang Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Michigan State University BEACON Center for the Study of Evolution in Action and Ecology Evolution and Behavior Program Department of Computer Science and Engineering East LansingMI48864 United States

Searching for symbolic models plays an important role in a wide range of domains such as neural architecture search and automatic program synthesis. Genetic programming is a promising stochastic method for searching effective symbolic models within an acceptable time. The genetic programming performance is closely related to the hardness of the fitness landscape. A better fitness landscape with less local optima normally implies that it is easier to search for better solutions. In recent years, there have been many studies enhancing genetic programming performance by forming better fitness landscapes. However, the better design of the fitness landscape highly relies on specific domain knowledge and consumes a lot of expert effort. This paper proposes a fitness landscape optimization method to automatically design better fitness landscapes for genetic programming search than the manually designed ones. We optimize the landscapes by optimizing the neighborhood structures of symbolic solutions. We verify the effectiveness of the proposed method in both supervised learning and combinatorial optimization problems. The results show that the proposed method significantly reduces the hardness of fitness landscapes. By simply searching against the automatically optimized fitness landscapes, a genetic programming method can have a very competitive performance with state-of-the-art methods. © 1997-2012 IEEE.

关键词： Genetic programming

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Utilisation of convolutional neural network on deep learning in predicting digital image to tree damage type

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International Journal of Internet Manufacturing and Services 2024年第1期10卷 77-90页

作者： Safe’i, Rahmat Andrian, Rico Maryono, Tri Nopriyanto, Zuhri Forestry Master Study Program Faculty of Agriculture University of Lampung Lampung Indonesia Department of Computer Science Faculty of Mathematics and Natural Science University of Lampung Lampung Indonesia Department of Agrotechnology Faculty of Agriculture University of Lampung Lampung Indonesia

Damage is often defined as a condition where there is a change in an object or shape. Damage does not only occur to objects, it can occur to living things, including trees. Damage to trees is seen in the physical shape of the tree that has changed shape, to find out it requires deep learning. One way that can be used is by modelling through computers through artificial intelligence, namely creating a deep learning model that can retrieve image information to recognise objects. This research aims to utilise convolutional neural network algorithm in deep learning to identify tree damage. The research stages carried out are input, feature extraction and classification, and output. The final result obtained is the successful identification of trees in deep learning on the model with an accuracy of 99.06% with a detection error of 0.94%. Detection errors occur due to similarities in terms of patterns, etc. This can be minimised by combining hyperparameters. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Error detection

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Smart Campus and Education: The study of Individual Performance in E-Learning Environments

Smart Campus and Education: The Study of Individual Performa...

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International Conference on ICT For Smart Society (ICISS)

作者： Doni Purnama Alamsyah Doni Morika Boby Siswanto Norfaridatul Akmaliah Othman Entrepreneurship Department BINUS Business School Bina Nusantara University Jakarta Indonesia Design Interior Study Program School of Design Bina Nusantara University Jakarta Indonesia Computer Science Study Program School of Computer Science Bina Nusantara University Jakarta Indonesia Department of Technology Management Faculty of Technology Management and Technopreneurship Universiti Teknikal Malaysia Melaka Melaka Malaysia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

The Learning Management System (LMS) is a component of the Smart Campus and is implemented with e-learning. E-learning can be effective if it gives satisfaction to the user and improves academic performance. Therefore, it is important to evaluate online learning users to support learning outcomes. The purpose of this study is to examine the relationship model of individual performance with support for user satisfaction and flexibility in an e-learning environment. This study was carried out using a survey to 615 e-learning users. Users are among the universities students in the city of Bandung (Indonesia). Data were collected quantitatively through questionnaires distributed online. Data processing is done with SmartPLS, where there are two model tests, namely the PLS Algorithm and Bootstrapping. Results of this study model test found that user satisfaction can be increased by information quality from e-learning. Online learning has perceived flexibility that is assessed by users and can increase user satisfaction and individual performance. It was found that user satisfaction was able to support the individual performance achievements of students as e-learning users. The results from this study provide valuable insights into the role of user satisfaction as a mediator in controlling individual student performance, highlighting the significance of integrating Smart Campus solutions.

关键词： Surveys Learning management systems Electronic learning Urban areas Education Distributed databases Data processing Data models Information integrity

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Integrated-Regression and Whale Optimisation Algorithms to post stroke rehabilitation analysis: A case study for serious games

Integrated-Regression and Whale Optimisation Algorithms to p...

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2021 IEEE Symposium on computers and Informatics, ISCI 2021

作者： Ismail, Waidah Grosan, Crina Abdullah, Zul Hilmi Aldailamy, Ali Y. Zainal, Nurezayana Hendradi, Rimuljo Faculty Science and Technology Universiti Sains Islam Malaysia Bandar Baru Nilai Negeri Sembilan Malaysia International Halal and Fatwa CenterUniversiti Sains Islam Malaysia Bandar Baru Nilai Negeri Sembilan Malaysia Faculty of Information Technology Inti International University Bandar Baru Nilai Negeri Sembilan Malaysia Department of Computer Science Brunel University Uxbridge United Kingdom Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Malaysia Information System Study Program Universitas Airlangga Surabaya Indonesia

ISBN: (纸本)9781665402767

Stroke is one of the leading causes of death and long-term disability worldwide. The primary goal of post-stroke rehabilitation is to maximize the independence of the affected individuals by facilitating both neurological and compensatory functional recoveries in their daily lives. This paper presents an integrated study, using multiple linear regression (MLR) and whale optimization algorithm, for optimizing the post-stroke rehabilitation performance using Medical Interactive Rehabilitation Assistant (MIRA). MIRA is one of the serious games using virtual reality. The research objective finding the best games settings for MIRA. Analysis of variance and F-test were performed to justify the goodness of fit of the developed mathematical models, as well as the significance of the individual MIRA input settings of the games. Results demonstrated that the developed MLR models for average acceleration and distance were significant, with P=0.018 and P © 2021 IEEE.

关键词： Optimization

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Diabetes Prediction Model for Better Clarification by using Machine Learning 6

Diabetes Prediction Model for Better Clarification by using ...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Eben, J. Lysa Jayasudha, R. Ramya, S. Kaliappan, S. Aswal, Shobha Al-Salehi, Khalid Ali Salem Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai India Department of Mathematics Dr.N.G.P.Institute of Technology Coimbatore India Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Erode India Division of Research and Development Lovely Professional University Punjab Phagwara144411 India School of Computing Graphic Era Hill University Dehradun India Biomedical Engineering Study Program Faculty of Science and Technology Airlangga University Surabaya Indonesia

ISBN: (纸本)9798350398496

Diabetes mellitus is one of the most pressing health concerns because so many people are afflicted by its disabling symptoms. Factors such as age, excess body fat, insufficient physical activity, a history of diabetes in one's family, a sedentary lifestyle, an unhealthy diet, hypertension, etc., all increase the likelihood of developing diabetes mellitus. Health complications are more common in people with diabetes, including cardiovascular disease, renal failure, stroke, blindness, and nerve injury. To validate a diagnosis of diabetes, hospitals typically perform a battery of procedures on the patient. Big data analytics has many vital applications in the healthcare sector. Numerous large computer systems are used in the healthcare sector. With the help of big data analytics, researchers can sift through mountains of data in search of previously unseen patterns and insights. Current techniques have a poor degree of precision in classification and forecast. While previous research has focused on factors such as glucose, body mass index, age, insulin, etc., the proposed model takes these into account and also the other factors that may be more relevant to the development of diabetes. The newer sample is superior to the older one based on categorization accuracy. A workflow algorithm for diabetes prognosis is also required to improve the accuracy. © 2023 IEEE.

关键词： Forecasting

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Implementation of Malaria Detection Using MobileNetV2 Pretrained Model for Improved Deep Learning Based Diagnostics

Implementation of Malaria Detection Using MobileNetV2 Pretra...

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International Conference on Computing, Engineering, and Design (ICCED)

作者： Rahmat Hidayat Achmad Solichin Adhitia Erfina Sudin Saepudin Muhamad Muslih Mohd Zainuri Saringat Master of Computer Science Budi Luhur University Jakarta Indonesia Information Systems Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Tun Hussein Onn Malaysia University

ISBN: (数字)9798331529376

ISBN: (纸本)9798331529383

The Plasmodium parasite, which causes malaria, is an acute fever illness that infects people when a female Anopheles mosquito bites them. It is predicted that malaria would claim 619,000 lives in 2021, with 96% of those deaths occurring in the African continent. We can achieve this by using a microscope to examine thick and thin blood smears. The proficiency of a microscope examiner is crucial for doing microscopic examinations. Consider how time-consuming, ineffective, and costly it would be to examine thousands of malaria cases. Consequently, Creating an automated method for detecting malaria parasites is the aim of this study. We employ a MobileNetV2 pretrained model with CNN technology. Because it has been trained on dozens or even millions of data points, this pretrained model is incredibly light but dependable. There are two main benefits of automatic malaria parasite detection: firstly, it can offer a more accurate diagnosis, particularly in locations with limited resources; secondly, it lowers diagnostic expenses. The optimizer utilizes Adam Weight, the criteria uses NLLLoss, and the model is trained using 32 for batch_size. In the fourteenth epoch, we obtained the maximum accuracy score of 96.26% based on the training data. The outcomes of the predictions demonstrate how excellent this score is. EfficienceNet, DenseNet, AlexNet, and other pretrained models are among the alternatives that scientists are advised to try training with.

关键词： Training Adaptation models Accuracy Costs Malaria Microscopy Data visualization Training data Cost function Data models

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Database analysis and design learning tool based on problem/project-based learning

Database analysis and design learning tool based on problem/...

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International Conference on Data and Software Engineering (ICODSE)

作者： Ruth Nattassha Fazat Nur Azizah Informatics/Computer Science Study Program School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Institut Teknologi Bandung Bandung Jawa Barat ID

The study of database analysis and design is typically a core in an informatics/computer science undergraduate/postgraduate study program. However, a survey conducted by Connolly in 2005 concluded that in Europe, companies considered the skill of database design is the most lacking skill in IT graduate recruits. To overcome the problem, Connolly and Begg in 2006 proposed the problem/project-based learning as an approach to study database design. This paper presents our work on a learning tool on the database analysis and design, focusing on the data requirement analysis and conceptual data modeling phase. The tool is designed based on the principles of problem/project-based learning by Connolly and Begg. The learning tool is developed on top of Moodle Learning Management System (LMS) by creating a new plug-in for data modeling called DBDesign. We conducted some interviews to a number of teachers and students of informatics/computer engineering study programs to obtain some opinions about the usefulness of the learning tool in improving the database analysis and design skills. All respondents agree that the tool will help students in learning database analysis and design. Nevertheless, some features are considered to be less important than others to be provided in such database learning tool; therefore, further study is required on the features.

关键词： Databases Data models Erbium Syntactics Object oriented modeling Analytical models Interviews

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AAAI 2008 workshop reports

AAAI 2008 workshop reports

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作者： Anand, Sarabjot Singh Bunescu, Razvan Carvcdho, Vitor Chomicki, Jan Conitzer, Vincent Cox, Michael T. Dignum, Virginia Dodds, Zachary Dredze, Mark Furcy, David Gabrilovich, Evgeniy Göker, Mehmet H. Guesgen, Hans Hirsh, Haym Jannach, Dietmar Junker, Ulrich Ketter, Wolfgang Kobsa, Alfred Koenig, Sven Lau, Tessa Lewis, Lundy Matson, Eric Metzler, Ted Mihalcea, Rada Mobasher, Bamshad Pineau, Joelle Poupart, Pascal Raja, Anita Ruml, Wheeler Sadeh, Norman Shani, Guy Shapiro, Daniel Smith, Trey Taylor, Matthew E. Wagstaff, Kiri Walsh, William Zhou, Rong Department of Computer Science University of Warwick United Kingdom School of Electrical Engineering and Computer Science Ohio University United States Microsoft Live Labs United States computer science and engineering University at Buffalo United States Computer science and economics Duke University United States Intelligent Computing group of BBN Technologies Utrecht University Netherlands Department of Computer science Harvey Mudd College United States University of Pennsylvania United States University of Wisconsin United States Yahoo Research PricewaterhouseCoopers Center for Advanced Research United States Department of Computer science School of Engineering and Advanced Technology Massey University New Zealand Departent of Computer science Rutgers University United States Department of Computer Science Dortmund University of Technology. Germany ILOG Rotterdam School of Management Erasmus University Netherlands Donald Bren School of Information and Computer Sciences University of California Irvine United States Depatrment of Computer science University of Southern California United States IBM Almaden Research Center United States Department of Computer Information Technology Southern New Hampshire University United States Computer and Information Technology Program College of Technology Purdue University United States Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computer Science and Engineering University of North Texas United States School of Computing DePaul University United States Department of Computer Science McGill University Canada School of Computer Science University of Waterloo Canada Department of Software and Information Systems University of North Carolina Charlotte United States University of New Hampshire United States Microsoft Research Institute for the Study of Learning and Expertise Applied Reactivity Inc. United States NASA Ames Research Center Carnegie Mellon University West

AAAI was pleased to present the AAAI-08 Workshop program, held Sunday and Monday, July 13-14, in Chicago, Illinois, USA. The program included the following 15 workshops: Advancements in POMDP Solvers;AI Education Workshop Colloquium;Coordination, Organizations, Institutions, and Norms in Agent Systems, Enhanced Messaging;Human Implications of Human-Robot Interaction;Intelligent Techniques for Web Personalization and Recommender Systems;Metareasoning: Thinking about Thinking;Multidisciplinary Workshop on Advances in Preference Handling;Search in Artificial Intelligence and Robotics;Spatial and Temporal Reasoning;Trading Agent Design and Analysis;Transfer Learning for Complex Tasks;What Went Wrong and Why: Lessons from AI Research and Applications;and Wikipedia and Artificial Intelligence: An Evolving Synergy. Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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E-Translator Kawi to Bahasa 2

E-Translator Kawi to Bahasa

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2nd International Joint Conference on Advanced Engineering and Technology, IJCAET 2017 and International Symposium on Advanced Mechanical and Power Engineering, ISAMPE 2017

作者： Sudana, Oka Putra, Darma Sudarma, M. Hartati, Rukmi S. Pradnya Prastika, Ryan Wirdiani, Ayu Study Program of Doctoral Engineering Science Faculty of Engineering Udayana University Kabupaten Badung-Bali80361 Indonesia Information Technology Departement Faculty of Engineering Udayana University Kabupaten Badung-Bali80361 Indonesia Electrical and Computer EngineeringDepartement Faculty of Engineering Udayana University Jalan Raya Bukit Jimbaran Kabupaten Badung-Bali80361 Indonesia

Kawi Language is a kind of language which has developed in Java during the Hindu-Buddhist era in the ancient Indonesian archipelago. As the time passed, Kawi began to come to extinction. To overcome this, preservation of Kawi language can be done by enhancing the understanding of culture through the use of technology. Technology that can be used for cultural preservation is one of NLP based applications such as Kawi Language to Bahasa Translator Application which is developed in this research. The translator application is an application that translates from one language to another. The result of translation of the translator application is a language that is easy to understand by the people or commonly used language. This translator application is developed to processes Kawi Language text into bahasa based on search method which each word inserted. The stemming method used is Bobby Nazief & Mima Adriani Algorithm using Kawi Language rule. The translated Kawi Language text can be a word, sentence or paragraph with the result of the Text in Bahasa as well as words, sentences or paragraphs. © The Authors, published by EDP sciences, 2018.

关键词： Translation (languages)

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