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Interdisciplinary Humanitarian Conference for Sustainability (IIHC), International

作者： Lamhot Naibaho Razauddin L. Vertivendan V. Karunakaran Ashish Oberoi N.C. Ajay Vishwath English Language Education Study Program Faculty of Letters and Languages Christian University of Indonesia Department of computer and information sciences Gulf Colleges Saudia Arabia Department of CSE Galgotias University Greater Noida Uttar Pradesh India Department of CSE Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore INDIA Department of CSE RIMT University Mandi Godindgarh India Aeronautical Department Parul Institute of Engineering and Technology Parul University

Motion planning for many unmanned aircraft is challenging because they have a larger configuration space than self-driving automobile development (Automated guided vehicles). Additionally, there are more significant uncertainties and disruptions in UAV environments, which makes inter automatic navigation more difficult. In this letter, I proposed a 2 recurrent neural networks (RL) oriented multi-UAV collision avoidance technique by modeling the unpredictability or noise in the environment. Our objective is to create a strategy that can construct a path without clashing with anything using local noisy data. But unlike supervised algorithms, RL lacks a steady training data set with floor labels, thus its collision avoidance strategies often show significant fluctuation and are difficult to reproduce. To solve these issues, we created a two-stage training method for RL-based collision avoidance. We first optimize the policy, and then we utilize a supervised learning methodology with a loss function that encourages the agents to adopt the well-known reciprocity obstacle detection mechanism. In the second step, we use transmission to fine-tune the policy. The complete computer simulation findings demonstrate that this approach can handle noisy local views with erratic sound levels and can design moment & accident paths under inadequate sensing. We review the impact of our policies in a variety of ways.

关键词： Training Uncertainty Supervised learning Training data Aircraft navigation Sensors Noise measurement

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Developing and evaluating prediction models of architectural work performance by combining earned value methods and support vector machine

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AIP Conference Proceedings 2021年第1期2447卷

作者： Maulyda Nurannisa Apif Miptahul Hajji Aisyah Larasati Yuh Wen Chen 1Industrial Engineering Study Program State University of Malang Malang Indonesia 2Civil Engineering Study Program State University of Malang Malang Indonesia 3Department of Information Management Da-Yeh University Chang Hwa Taiwan

The concept of project management helps minimize the risk of loss for project managers. This study aims to develop a model for evaluating the performance of architectural work by combining the Earned Value Management (EVM) and Support Vector Machine (SVM) models. The Earned Value method has advantages in forecasting project scheduling and planning. In this research, forecasting is done with the help of machine learning to increase the accuracy of project completion time prediction. This research uses the architectural data study object of the Lecture Building Project (GKB UM). This study indicates that the performance of the architectural work of GKB UM has been delayed with a schedule variance (SVt) value of -13.53 weeks. In developing the prediction model using the Support Vector Machine algorithm, the most optimal parameter results are using the Radial Basis Function (RBF) kernel type, complexity (C) 150, and gamma (y) 0.01 with a Root Mean Square Error (RMSE) value of 0.565. Thus the prediction of project completion has a difference of 4 weeks. Project delays were caused by weather problems, Tower Crane (TC) damage, incompatibility of Homogeneous Tile (HT) work, and changes in Bill of Quantity (BOQ).

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Classification of apples based on color feature extraction and Euclidean distance search

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AIP Conference Proceedings 2024年第1期3048卷

作者： Dian Nursantika Erna Piantari Information System Study Program Department of Engineering Universitas Terbuka Tangerang Indonesia Department of Computer Science Universitas Pendidikan Indonesia Bandung Indonesia

Apple is a popular fruit in the world, until it became an icon by one of the leading software and hardware developers. The colors of apples that are often found in Indonesia are red and green, humans are recognize the color of the apple through sight, based on previous knowledge and experience. How about the computer, is the computer able to recognize the color of the apple? Through this research, it is expected to solve these challenges, so the research results can be applied in various devices, for example for grade classification of apples, identification of types of apples, and educational games. The research consists of two parts, namely training and testing. Training is conducted to train images by means of preprocessing, feature extraction, and feature collection. Testing is carried out to determine the ability of computers to recognize apples with the stages of preprocessing, feature extraction, and feature matching using the Euclidean distance algorithm. The images used in this study were 40 training images and 65 testing images. The results of testing on the training image have an accuracy rate of 100%, while the accuracy level of the new image is 84% recognized as true and 16% recognized as false.

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Integration of Artificial Intelligence in Software Defined Networking Technology through Developing Machine Learning Algorithms

Integration of Artificial Intelligence in Software Defined N...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Lamhot Naibaho Pulkit Raj Saxena Kuldeep Sharma Badria Sulaiman Alfurhood L. Pallavi Bhasker Pant English Language Education Study Program Faculty of Letters and Languages Christian University of Indonesia College of Computing Science and Information Technology Teer Thanker Mahaveer University Moradabad Uttar Pradesh India Department of Computer Science and Engineering Lovely Professional University Phagwara Jalandhar Punjab India Department of Computer Sciences College of Computer Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia CSE B V Raju Institute of Technology Narsapur Telangana India Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

A future networking design called "software-defined networking" combines network programmability with centralized administration (SDN). Network administration is currently handled by SDN, which, at the regulator, which acts as the cable network processor and divides the data plane into a control plane and a data aircraft. Recent breakthroughs in artificial intelligence (Al) have shown a stronger inclination for the science community to benefit from their ability to give learning capabilities and enhance Indicator. The author of this research provides a comprehensive analysis of initiatives underway to incorporate Al with SDN. The study concluded that the three primary Al thread where scientific research was centered were computer science, conceptual, and fuzzy reasoning systems. The authors of this paper explore the several fields in which these approaches may be used, their potential future applications, and the innovations made possible by the integration of AI-based methods into the SDN paradigm. By choosing the right SDN controller, any large organization may lower network complexity, implementation expenses, and maintenance costs. The focus of this article is software defined networking's benefits and downsides (SDN). Following is a basic explanation of artificial intelligence and a list of some of its most significant applications in SDN.

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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification

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Procedia Computer Science 2024年 241卷 146-153页

作者： Hina Tufail Abdul Ahad Ira Puspitasari Ibraheem Shayea Paulo Jorge Coelho Ivan Miguel Pires Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga School of Software Northwestern Polytechnical University Xian Shaanxi P.R. China Department of Electronics and Communication Engineering Istanbul Technical University(ITU) Turkey Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.

关键词： Alzheimer disease (AD) Deep learning Convolutional Neural Network (CNN) Computer-aided diagnosis (CAD)

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Corrigendum to “Behavioral aspects of safety culture: Identification of critical safety-related behaviors of motorcyclists in Indonesia's urban areas via the application of behavioral-based safety programs” [IATSS Research journal 46 (October 2022) 353–369]

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IATSS Research 2022年第4期46卷 623-623页

作者： Andrijanto Makoto Itoh Felix Sandro Sianipar Risk Engineering Department Graduate School of Systems and Information Engineering University of Tsukuba Ibaraki 305-8573 Japan Industrial Engineering Program Study Faculty of Engineering Universitas Kristen Maranatha Bandung 40164 Indonesia Faculty of Engineering Information and Systems University of Tsukuba Ibaraki 305-8573 Japan

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Handwriting prediction using the support vector machine method in web-based applications

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AIP Conference Proceedings 2022年第1期2659卷

作者： Syaifudin Dian Pratiwi Trubus Rahardiansyah Anasthesia Karunia Charles Hafiz Wijaya Khoirun Nisa 1Information System Study Program Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia 2Informatic Engineering Study Program Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia 3Department of Law Universitas Trisakti Jakarta Indonesia

Current technological advances have progressively encouraged individuals to use web-based applications. To determine the owner of a handwriting, one of the manual methods is generally done by people. And it can be done by a web application to find out the person’s handwriting. The Support Vector Machine (SVM) Method is a strategy of artificial intelligence used to recognize handwriting. This study intends to create a handwriting recognition framework using the Support Vector Machine (SVM) Method on a web-based application, with extraction features that are important as the basis for describing and characterizing handwritten images. The results obtained from predictions using the Support Vector Machine are by using 16 data and there are 80 tests carried out in total. The results obtained indicate that there are 25 incorrect prediction tests and 55 correct prediction tests so that the level of accuracy obtained is 68.75%.

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Application of factor analysis method to support the users acceptance model of ERP systems implementation

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Journal of Physics: Conference Series 2021年第1期1836卷

作者： W Sardjono J Sudirwan W Priatna G R Putra Information Systems Management Department BINUS Graduate Program -Master of Information Systems Management Bina Nusantara University Jakarta Indonesia – 11480 School of Information Systems Department BINUS Undergraduate Program Bina Nusantara University Jakarta Indonesia 11480 Informatics Engineering Engineering Bhayangkara University Jakarta Raya Indonesia Computer Science Study Program Faculty of Mathematics & Natural Sciences Pakuan University Bogor Indonesia

An e-Commerce company has been using an Enterprise Resource Planning (ERP) system for several years, but is still constrained in its implementation, this is reflected in the number of issue/change request tickets submitted by users who have not been able to reach ideal conditions. To overcome this problem, we need an appropriate reference model, the ERP system user acceptance model in companies. The study conducted factors that influence the level of acceptance of SAP ERP users in the XYZ group to then determine a model that is able to explain practically the level of ERP system user acceptance, which in turn can produce concrete proposals to increase ERP system user acceptance. Research is quantitative descriptive using factor analysis and regression analysis methods. The research method uses a questionnaire with a total of 107 valid respondents coming from 8 company names under the XYZ group in 6 Southeast Asian countries. The conclusion of the study is that there are 24 indicators and 4 factors that influence user acceptance of the SAP ERP system. These four factors are the low contribution of the ERP system, the quality of ERP systems and data, the development of ERP systems, and also the low quality of ERP system supporters. It is hoped that by optimizing the value of the 4 factors, the XYZ group can increase user acceptance of the SAP ERP system so that the system can provide benefits expected by the company to be competitive advantage.

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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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Analysis and Design of UI and UX Web-Based Application in Maiproyek Startup Using User Centered Design Method in information System program of Telkom University

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IOP Conference Series: Materials Science and engineering 2021年第1期1077卷

作者： C Adhitya R Andreswari P F Alam Information System Study Program Faculty of Industrial Engineering Telkom University Jl. Telekomunikasi No.1 Terusan Buah Batu Sukapura Dayeuhkolot Bandung West Java Indonesia

To develop a good application, the aspect that needs to be considered is to design a User Experience and User Interface that suits the needs of the users themselves. One of the design methods that can be used is User Centered Design method. This method focuses on designing the user's needs where it aims to provide comfort for the user and provide a relevant interface for the user. The study was conducted in information Systems program of Telkom University. The design will then be tested on each lecturer and student in information Systems program of Telkom University. This test aims to test whether a design is in accordance with the desired objectives or not. The results of the research the author wants to achieve is to design a user interface and user experience design of a web-based application for a Maiproyek application. After conducting research, the results obtained by the author are usability scores obtained from the *** application. The scores obtained are calculated in the medium category and show that the design of the application tested to each respondent already has a good usability value.

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