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2nd International Conference for Innovation in technology, INOCON 2023

作者： Utamajaya, Joy Nashar Supangat, Suhono Harso Gaol, Ford Lumban Ranti, Benny BINUS University BINUS Graduate Program School of Information System Jakarta Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatic Bandung Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta Indonesia University of Indonesia Computer Science Department Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350320923

In general, public or private organizations or companies have used information-based technology as a support to improve business performance to be more effective and efficient in order to achieve a company's business goals. Given the large contribution of information technology in the application of hotel applications as a supporting information system, it is a vital system that must avoid risks that can hinder and cause harm to the hotel management business processes. Therefore, it is necessary to carry out risk management using the COBIT 5 framework to manage possible risks that may occur based on the APO12 (Manage Risk) domain. From the evaluation results of the data obtained through observation and interviews as well as the calculation of the results of the questionnaire based on 6 APO12 subdomains, the results of the risk management level capability assessment in hotel applications are still at level 3 or have reached the level of established process with the target to be achieved at level 4 resulting in a gap of 1 level. Based on the results obtained, it is necessary to propose recommendations that can be used by hotels in improving the application of information technology risk management so that in the future it can achieve the expected target level so that the APO12 level of capability can increase and become more optimal. © 2023 IEEE.

关键词： Risk management

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The Implementation of Octave-S in the Risk Management Measurement of information system in the Department of Communication, Informatics, and Statistics 4th

The Implementation of Octave-S in the Risk Management Measur...

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4th International Conference on Inventive Computation and information Technologies, ICICIT 2022

作者： Sukardi, Oggsa Gaol, Ford Lumban Matsuo, Tokuro Master of Information System Management Binus Graduate Programs Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Binus Graduate Program—Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789811974014

An information system is an important part in an organization to support business processes and to achieve its vision and mission. The information system nowadays has been one of the assets that ought to be protected by an organization. The Department of Communication, Informatics, and Statistics has the information system supporting its various operational activities. In order to protect the information system, OCTAVE-S method as the management method is used. It is expected that the OCTAVE-S method can manage the risks to the information system as the asset by specifying the possible risks and developing countermeasures and mitigation plans for each of the risks. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Risk management

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Combating Digital Deception: Machine Learning Approaches for Detecting Political Misinformation and Clickbait on Social Media 6

Combating Digital Deception: Machine Learning Approaches for...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Harindi, Dewmini Perera, Nirmal Liyanage, Chamath Dematagoda, Hirushini Fernando, Harinda Rathnayake, Samadhi Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

This paper presents machine learning-based strategies for mitigating the problem of misinformation in social media, with a special focus on political misinformation detection and clickbait detection. The following research work attempts to address the pressing issue of the rapid spread of false or misleading information by making use of deep recurrent neural networks (RNN s) combined with semi-supervised learning methodologies. For political misinformation detection, a hybrid model integrating Long Short-Term Memory (LSTM) networks with fully connected layers was developed, achieving 94.22 % accuracy on the CIC Truth Seeker dataset. Clickbait detection was made using a sequential architecture that combines LSTM and Gated Recurrent Units (GRU) layers with Simple RNN layers to achieve an accuracy of 96% on test data. This study underlines the effectiveness of different machine learning techniques in battling misinformation that would contribute to online dignity in political discourse and ensure better public communication in a digital world. © 2024 IEEE.

关键词： Self-supervised learning

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An Improved Deep Learning Framework for Automated Optic Disc Localization and Glaucoma Detection

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computer Modeling in Engineering & Sciences 2024年第8期140卷 1429-1457页

作者： Hela Elmannai Monia Hamdi Souham Meshoul Amel Ali Alhussan Manel Ayadi Amel Ksibi Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information System College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Glaucoma disease causes irreversible damage to the optical nerve and it has the potential to cause permanent loss of *** ranks as the second most prevalent cause of permanent *** glaucoma diagnosis requires a highly experienced specialist,costly equipment,and a lengthy wait *** automatic glaucoma detection,state-of-the-art glaucoma detection methods include a segmentation-based method to calculate the cup-to-disc *** methods include multi-label segmentation networks and learning-based methods and rely on hand-crafted *** the optic disc(OD)is one of the key features in retinal images for detecting retinal diseases,especially for glaucoma disease *** approach presented in this study is based on deep classifiers for OD segmentation and glaucoma ***,the optic disc detection process is based on object detection using a Mask Region-Based Convolutional Neural Network(Mask-RCNN).The OD detection task was validated using the Dice score,intersection over union,and accuracy *** OD region is then fed into the second stage for glaucoma ***,considering only the OD area for glaucoma detection will reduce the number of classification artifacts by limiting the assessment to the optic disc *** this task,VGG-16(Visual Geometry Group),Resnet-18(Residual Network),and Inception-v3 were pre-trained and *** also used the Support Vector Machine *** feature-based method uses region content features obtained by Histogram of Oriented Gradients(HOG)and Gabor *** final decision is based on weighted fusion.A comparison of the obtained results from all classification approaches is *** metrics including accuracy and ROC curve are compared for each classification *** novelty of this research project is the integration of automatic OD detection and glaucoma diagnosis in a global ***,the fusion-based decision system uses the g

关键词： Optic disc glaucoma fundus image deep learning

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A Fruit and Vegetable Detection Based on Pytorch Machine Learning Frame Work Model 2

A Fruit and Vegetable Detection Based on Pytorch Machine Lea...

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2nd International Conference on Emerging Trends in information technology and Engineering, ic-ETITE 2024

作者： Priyadharshini, U. Vijayan, R. School of Advanced Science Vellore Institute of Information Technology Vellore India School of Computer Science Engineering and Information System Vellore Institute of Information Technology Vellore India

ISBN: (纸本)9798350328202

Today, Agricultures has become a significant element of people's life. Among them fruits and vegetables are a great thing for healthy lives. Classification of fruits and vegetable are difficult for normal person because of some same similarities like shape and color. Also, Fruits and Vegetables classification is an important process in industries applications as well as local market. Lots of similarities available between fruits like apples, pears and peaches, and that kinds of fruits are generally used in daily routine. As well as some vegetables are same in shape, so it will be difficult to differentiate in this type of situation for old persons. The previous research for solution of this type of problem for classification of fruits CNN is used but the problem in this research is when they use the self-made datasets then their accuracy decreases as compare to public datasets accuracy increases. To solve this type of problem, here produce a kind of algorithms in the paper to classify fruits and vegetable, using deep leaning technique such as VGG16 and DenseNet169 model have capability to extract the feature of images very deeply using various small filters so it provide a better result. On this premise, it was used to classify fruit kinds against a complex background and integrated with data improvement technology for future usage. It might be helpful for human for automatic differentiate the both categories. This project is implemented in the python programming language and the dataset used Fruits and Vegetables Image Recognition Dataset for the classification. Which will help to differentiate between Fruits and Vegetables with high accuracy rate. For the implementation of this project used Python programming and this implementation using the tool like Jupiter and Kaggle Notebook and used GPU for highspeed execution. © 2024 IEEE.

关键词： Fruits

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The Effects of Implementing a Library information system on the Increase in Library Use 6th

The Effects of Implementing a Library Information System on ...

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6th International Conference on Intelligent Sustainable systems, ICISS 2023

作者： Sim, Jason Wijayakusuma, Alfakhri Rizqulloh Rivaldy, Rifky Gaol, Ford Lumban Matsuo, Tokuro Bina Nusantara University Alam Sutra School of Information System West Jakarta13960 Indonesia Computer Science Department Bina Nusantara University West Jakarta11480 Indonesia Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (纸本)9789819917259

This study aims to determine whether the implementation of information technology on library management systems brings a positive effect on the student during the learning process. information technology is a general term for any tech-nology that assists humans in creating, modifying, storing, communicating, and/or disseminating information. In the digital era, the use of information technology is very important. The existence of information technology in libraries can certainly increase the interest of people to keep reading books in the library because of the ease due to the implementation of information technology. The main objective of this paper is to see the impact of the use of information technology in the library management system of Bina Nusantara Alam Sutera University which has quite many students such as Binusian card as an access to the library gate and for borrowing books. Library management systems help libraries keep track of the books and their checkouts. A well-chosen system will increase the library’s efficiency, save valuable administration time, lead to a better educational experience for students, and help develop independent learning. The results of the investigation state that several major impacts occur in the library management system due to the use or implementation of information technology. The main impact that occurs is that it is easy for library employees to record the turnover of people in the library. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Libraries

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Enhancing Children's Health Awareness through Narrative Game-Based Learning using Digital technology: A Framework and Methodology 9

Enhancing Children's Health Awareness through Narrative Game...

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9th International HCI and UX Conference in Indonesia, CHIuXiD 2023

作者： Muniandy, Nithiyaa Kamsin, Amirrudin Sabri, Mas Idayu Faculty of Computer Science & Information Technology University Malaya Kuala Lumpur Department of Computer System & Technology Malaysia

ISBN: (纸本)9798350304084

Effective health education and awareness initiatives are essential in a world facing growing concerns about children's well-being and rising unhealthy behaviours. This research presents a Narrative Game-Based Learning (N-GBL) framework designed to enhance children's health awareness, addressing critical gaps in their health education. Through a systematic literature review following the PRISMA methodology, this study identifies 13 essential components that form the foundation of the N-GBL framework. These components are structured around the Input-Process-Output (IPO) model, emphasizing narratives, game mechanics, and user engagement strategies. The core purpose of the N-GBL framework is to deepen children's understanding of the consequences of their choices, fostering informed decision-making and to empower them to make healthier choices. This research contributes to developing effective health awareness games tailored to children's learning preferences, bridging existing gaps in their health education. The N-GBL framework offers a revolutionary approach to health education for children, significantly enhancing health awareness and reducing health issues resulting from unhealthy behaviours. Future work will involve expert validation and practical testing in primary schools to transform health education for children. © 2023 IEEE.

关键词： Decision making

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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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Heuristic Search with Cut Point Based Strategy for Critical Node Problem

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Journal of computer Science & technology 2024年第6期39卷 1328-1340页

作者： Zhi-Han Chen Shao-Wei Cai Jian Gao Shi-Ke Ge Chan-Juan Liu Jin-Kun Lin School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 101408China Key Laboratory of System Software(Chinese Academy of Sciences)and State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China College of Information Science and Technology Northeast Normal UniversityChangchun 130024China School of Computer Science and Technology Dalian University of TechnologyDalian 116024China SeedMath Technology Limited Beijing 100086China

The critical node problem(CNP)aims to deal with critical node identification in a graph,which has extensive applications in many *** CNP is a challenging task due to its computational complexity,and it attracts much attention from both academia and *** this paper,we propose a population-based heuristic search algorithm called CPHS(Cut Point Based Heuristic Search)to solve CNP,which integrates two main *** first one is a cut point based greedy strategy in the local search,and the second one involves the functions used to update the solution pool of the ***,a mutation strategy is applied to solutions with probability based on the overall average similarity to maintain the diversity of the solution *** are performed on a synthetic benchmark,a real-world benchmark,and a large-scale network benchmark to evaluate our *** with state-of-the-art algorithms,our algorithm has better performance in terms of both solution quality and run time on all the three benchmarks.

关键词： local search cut point heuristic search critical node problem

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An Optimal Distribution of RSU for Improving Self-Driving Vehicle Connectivity

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computers, Materials & Continua 2022年第2期70卷 3311-3319页

作者： Khattab Alheeti Abdulkareem Alaloosy Haitham Khalaf Abdulkareem Alzahrani Duaa Al_Dosary Department of Computer Networking System College of Computer Sciences and Information TechnologyUniversity of AnbarRamadiIraq College of Medicine University of AnbarRamadiIraq Department of Computer Science Faculty of Computer Science and Information TechnologyAlbaha UniversityAlbahaSaudi Arabia College of Computer Sciences and Information Technology University of AnbarRamadiIraq

Self-driving and semi-self-driving cars play an important role in our daily *** effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital ***,external infrastructures also play significant roles in the transmission and reception of control data,cooperative awareness messages,and caution *** this case,roadside units are considered one of themost important communication *** distribution of these infrastructures will overburden the spread of self-driving vehicles in terms of cost,bandwidth,connectivity,and radio coverage *** this paper,a new distributed roadside unit is proposed to enhance the performance and connectivity of these ***,this approach is based primarily on k-means to find the optimal location of each roadside *** addition,this approach supports dynamicmobility with a long period of connectivity for each ***,this system can adapt to various locations(e.g.,highways,rural areas,urban environments).The simulation results of the proposed system are reflected in its efficiency and ***,the system can achieve a high connectivity rate with a low error rate while reducing costs.

关键词： Self-driving cars roadside unit autonomous vehicles distributed systems connectivity

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