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Ihoya: knowledge management system based on community using User-centered Design (UCD) method

International Journal of Information Technology (Singapore) 2025年第4期17卷 2067-2074页

作者： Rafi, Muhammad Puspitodjati, Sulistyo Apriani, Niken Fitria Hakim, Shidiq Al Siagian, Al Hafiz Akbar Maulana Riyanto, Slamet Rodiah, Rodiah Kushadiani, Siti Kania Faculty of Computer Science and Information Technology Universitas Gunadarma West Java Depok Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency West Java Bogor Indonesia

Hoya represents a significant component of Indonesia’s biodiversity with considerable potential;however, its utilization remains suboptimal. There exists a keen interest among researchers, collectors, and horticultural enthusiasts to delve into this fascinating plant. The wide variety of Hoya species in Indonesia substantially challenges their identification. Consequently, there is a pressing need for a system that facilitates the identification of the Hoya plant and its various species. In this study, we propose developing a knowledge management system (KMS) to identify Hoya and its species. This proposal is anchored in the user-friendly nature of mobile-based applications, which offer accessibility at any time. To ensure the creation of a high-quality application that meets user satisfaction, a User Interface and User Experience (UI/UX) design process is implemented, employing the User centered Design (UCD) methodology that concentrates on the preferences and requirements of users. Subsequently, the System Usability Scale (SUS) method is adopted to assess the design and verify its alignment with user needs. This research culminates in our developed KMS prototype, namely iHoya, encompassing mockup pages tailored to users’ preferences, requirements, and navigational flows. An evaluation of the prototype design using the SUS method, conducted through a survey of targeted users, categorizes the design as acceptable while indicating the necessity for certain enhancements. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Hoya Knowledge management system SUS UCD UI/UX

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Decision theory and risk simulation analysis for optimizing profit in paylater services

Decision theory and risk simulation analysis for optimizing ...

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2022 International Conference on ENTERprise Information Systems, centerIS 2022 - International Conference on Project MANagement, ProjMAN 2022 and International Conference on Health and Social Care Information Systems and Technologies, HCist 2022

作者： Dominic, Nicholas Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

The projected increase in PayLater utilization reaches up to five million people by 2025. To optimize the yearly profit from their PayLater service, fintech companies must examine all possible risks before a unanimous decision is taken. Therefore, we proposed a unified decision framework derived from decision theory and the Monte Carlo simulation technique. Two schemes were coined: (1) a decision-making scheme, and (2) a risk simulation scheme. Throughout experiments, the framework was able to estimate several alternative decisions and their impacts, analyze the causes of failure and delays in the development of the PayLater service, and execute Monte Carlo simulations in up to 10,000 trials. Outputs of this study will benefit decision-makers in the fintech initiative before launching their PayLater products. © 2023 Elsevier B.V.. All rights reserved.

关键词： Fault tree analysis

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data mining for the global multiplex weekly average income analysis

Data mining for the global multiplex weekly average income a...

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作者： Dominic, Nicholas Elwirehardja, Gregorius Natanael Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

The box office (BO) income had significantly declined up to 80% in 2020, as the COVID-19 pandemic emerged. To minimize further financial risks, multiplex (multiple cinema complexes) owners need to analyze their potential income for each movie, each week. Therefore, we developed a proper data mining strategy that allows multiplex owners to analyze and discover insights on how successfully produced movies could be. The methodology comprises (1) data loading and exploration, (2) data cleaning, (3) data selection, integration, and transformation using Pentaho, (4) data mining in which the results were stored in the MySQL database, and (5) pattern evaluation and presentation using Qlik Sense as the Business Intelligence (BI) dashboard. Based on our data mining methodology, we revealed that drama, comedy, action, and thriller are favorite genres. We also found that DreamWorks Animation and Pixar Animation Studios are both the most popular production houses, even Apatow Productions and Escape Artists still have the biggest revenue on average. © 2022 The Authors. Published by Elsevier B.V.

关键词： Animation

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Understanding the role of human-inspired heuristics for retrieval models

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Frontiers of computer science 2022年第1期16卷 11-21页

作者： Xiangsheng LI Yiqun LIU Jiaxin MAO Department of Computer Science and Technology Institute for Artificial IntelligenceBeijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijing100084China

Relevance estimation is one of the core concerns of information retrieval(IR)*** existing retrieval models gained much success in both deepening our understanding of information seeking behavior and building effective retrieval systems,we have to admit that the models work in a rather different manner from how humans make relevance ***’information seeking behaviors involve complex cognitive processes,however,the majority of these behavior patterns are not considered in existing retrieval *** bridge the gap between practical user behavior and retrieval model,it is essential to systematically investigate user cognitive behavior during relevance judgement and incorporate these heuristics into retrieval *** this paper,we aim to formally define a set of basic user reading heuristics during relevance judgement and investigate their corresponding modeling strategies in retrieval *** experiments are conducted to evaluate the effectiveness of different reading heuristics for improving ranking *** on a large-scale Web search dataset,we find that most reading heuristics can improve the performance of retrieval model and establish guidelines for improving the design of retrieval models with human-inspired *** study sheds light on building retrieval model from the perspective of cognitive behavior.

关键词： reading heuristics retrieval model cognitive behavior

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Personality-based and trust-aware products recommendation in social networks

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Applied Intelligence 2023年第1期53卷 879-903页

作者： Vatani, Nasim Rahmani, Amir Masoud Javadi, Hamid Haj Seyyed Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Mathematics and Computer Science Shahed University Tehran Iran

In recent years, with the development of technology, the shopping approach of people has moved towards pervasive online social shopping. As a result, how to create a recommendation algorithm that offers products based on the personal and different needs and tastes of people on social networks is a significant research issue. This article proposes a personality-based and trust-aware probabilistic product recommendation algorithm in social networks. We present a dynamic method for determining how similar people in social networks are. For this purpose, we consider the personality-based features of recommendation attributes of products in social networks. Then, the level of trust of products and types of correlations among the products is considered to create a probabilistic matrix of product recommendation. Moreover, for solving the cold start problem of products, we consider qualitative aspects of products while exploiting personality-based user behavior regarding their purchases. At last, the empirical experiments are conducted to analyze the impact of the algorithm’s different influence factors using the Amazon dataset. Moreover, the results of comprehensive experiments adopted to verify the proposed personalized recommendation algorithm’s effectiveness show that the proposed algorithm has the appropriate effectiveness and the higher accuracy. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Behavioral research

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Augmenting IoT Intrusion Detection System Performance Using Deep Neural Network

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computers, Materials & Continua 2023年第1期74卷 1351-1374页

作者： Nasir Sayed Muhammad Shoaib Waqas Ahmed Sultan Noman Qasem Abdullah M.Albarrak Faisal Saeed Department of Computer Science Islamia College PeshawarPeshawar25120Pakistan Department of Computer Science CECOS University of IT and Emerging SciencesPeshawar25000Pakistan Department of Electrical Engineering HITEC University TaxilaTaxila47080Pakistan Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK

Due to their low power consumption and limited computing power,Internet of Things(IoT)devices are difficult to ***,the rapid growth of IoT devices in homes increases the risk of *** detection systems(IDS)are commonly employed to prevent *** systems detect incoming attacks and instantly notify users to allow for the implementation of appropriate *** have been made in the past to detect new attacks using machine learning and deep learning techniques,however,these efforts have been *** this paper,we propose two deep learning models to automatically detect various types of intrusion attacks in IoT ***,we experimentally evaluate the use of two Convolutional Neural Networks(CNN)to detect nine distinct types of attacks listed in the NF-UNSW-NB15-v2 *** accomplish this goal,the network stream data were initially converted to twodimensional images,which were then used to train the neural network *** also propose two baseline models to demonstrate the performance of the proposed ***,both models achieve high accuracy in detecting the majority of these nine attacks.

关键词： Internet of things intrusion detection system deep learning convolutional neural network supervised learning

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Deep Learning Classification Techniques on Detecting Diabetic Retinopathy dataset 7

Deep Learning Classification Techniques on Detecting Diabeti...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Revathi, B. Usharani, C. Kezial Elizabeth, S.K. Nagaraj, P. Nithya, D. Ramco Institute of Technology Department of Artificial Intelligence And Data Science Virudhunagar India Mangayarkarasi College of Engineering Department of Computer Science And Engineering Madurai India Kalasalingam Academy of Research And Education Department of Computer Science And Engineering Virudhunagar Krishnankoil India Theni Kammavar Sangam College of Technology Department of Computer Science And Engineering Theni India

ISBN: (纸本)9798350359299

Deep learning algorithms can summarize images to understand how to carry out necessary tasks. The purpose of this study is to compare several deep learning methods. Both experience-based and explanation-based learning are possible in deep learning. The most widely utilized algorithms, such as Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), Generative Adversarial Networks (GAN), Radial Basis Function Networks (RBFN), and Deep Belief Networks (DBN), and the Diabetic Retinopathy dataset is utilized in this study to evaluate the effectiveness of the algorithms. A comparative study of the classifiers reveals that CNN performs more accurately than the other approaches. © 2024 IEEE.

关键词： Classification (of information)

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MULTI-TASK REINFORCEMENT LEARNING WITH MIXTURE OF ORTHOGONAL EXPERTS 12

MULTI-TASK REINFORCEMENT LEARNING WITH MIXTURE OF ORTHOGONAL...

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12th International Conference on Learning Representations, ICLR 2024

作者： Hendawy, Ahmed Peters, Jan D'Eramo, Carlo Department of Computer Science TU Darmstadt Germany Germany Center for Cognitive Science TU Darmstadt Germany Systems AI for Robot Learning Germany Center for Artificial Intelligence and Data Science University of Würzburg Germany

Multi-Task Reinforcement Learning (MTRL) tackles the long-standing problem of endowing agents with skills that generalize across a variety of problems. To this end, sharing representations plays a fundamental role in capturing both unique and common characteristics of the tasks. Tasks may exhibit similarities in terms of skills, objects, or physical properties while leveraging their representations eases the achievement of a universal policy. Nevertheless, the pursuit of learning a shared set of diverse representations is still an open challenge. In this paper, we introduce a novel approach for representation learning in MTRL that encapsulates common structures among the tasks using orthogonal representations to promote diversity. Our method, named Mixture Of Orthogonal Experts (MOORE), leverages a Gram-Schmidt process to shape a shared subspace of representations generated by a mixture of experts. When task-specific information is provided, MOORE generates relevant representations from this shared subspace. We assess the effectiveness of our approach on two MTRL benchmarks, namely MiniGrid and MetaWorld, showing that MOORE surpasses related baselines and establishes a new state-of-the-art result on MetaWorld. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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Towards a blockchain-SDN-based secure architecture for cloud computing in smart industrial IoT

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Digital Communications and Networks 2023年第2期9卷 411-421页

作者： Anichur Rahman Md Jahidul Islam Shahab S.Band Ghulam Muhammad Kamrul Hasan Prayag Tiwari Department of Computer Science and Engineering National Institute of Textile Engineering and Research(NITER)Constituent Institute of the University of DhakaSavarDhaka1350Bangladesh Department of Computer Science and Engineering Green University of BangladeshDhakaBangladesh Future Technology Research Center College of FutureNational Yunlin University of Science and TechnologyDouliouTaiwanChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Information and Communication Engineering Hankuk University of Foreign StudiesSouth Korea Department of Computer Science Aalto UniversityFinland

Some of the significant new technologies researched in recent studies include BlockChain(BC),Software Defined Networking(SDN),and Smart Industrial Internet of Things(IIoT).All three technologies provide data integrity,confidentiality,and integrity in their respective use cases(especially in industrial fields).Additionally,cloud computing has been in use for several years *** information is exchanged with cloud infrastructure to provide clients with access to distant resources,such as computing and storage activities in the *** are also significant security risks,concerns,and difficulties associated with cloud *** address these challenges,we propose merging BC and SDN into a cloud computing platform for the *** paper introduces“DistB-SDCloud”,an architecture for enhanced cloud security for smart IIoT *** proposed architecture uses a distributed BC method to provide security,secrecy,privacy,and integrity while remaining flexible and *** in the industrial sector benefit from the dispersed or decentralized,and efficient environment of ***,we described an SDN method to improve the durability,stability,and load balancing of cloud *** efficacy of our SDN and BC-based implementation was experimentally tested by using various parameters including throughput,packet analysis,response time,bandwidth,and latency analysis,as well as the monitoring of several attacks on the system itself.

关键词： Smart IIoT Blockchain SDN IoT Security Privacy OpenFlow SDN-Controller data security Cloud computing Cloud management

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Robust and Interpretable Vector Borne Disease Prediction in IoT-Enabled Edge Networks

Robust and Interpretable Vector Borne Disease Prediction in ...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Doulani, Khushbu Sharma, Samarth Adhikari, Mainak Vijayalata, Y. Indian Institute of Information Technology Lucknow Department of Computer Science Lucknow India Indian Institute of Information Technology Lucknow Department of Computer Science and Artificial Intelligence Lucknow India School of Data Science Indian Institute of Science Education and Research Thiruvananthapuram Thiruvananthapuram India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350364866

Vector-borne diseases have created critical world-wide public health hazards that require innovative approaches for prevention and management. Within the context of IoT-enabled edge networks, this study offers a unique framework to build a reliable and straightforward distant prediction model for vector-borne diseases. The suggested strategy incorporates the use of edge computing capabilities to predict the onset and spread of diseases caused by vectors by using real-time data streams from mobile applications. Employing widely used techniques from machine learning, including interpretable models, our system not only achieves high prediction accuracy but also provides insight into the most important factors influencing the spread of disease. Moreover, this approach overcomes the resource constraints essential to edge networks, ensuring the prediction model's dependability and scalability. © 2024 IEEE.

关键词： Prediction models

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