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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Ade Cahyo Priyo Utomo Christy Atika Sari Eko Hari Rachmawanto Heru Pramono Hadi Salama A. Mostafa Wisam Subhi Talib Al-Dayyeni Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Study Program in Information Systems University of Dian Nuswantoro Semarang Indonesia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Malaysia School of Information Technologies and Engineering ADA University Baku Azerbaijan

Citrus Limon L. (Lemon) is a type of fruit that is currently widely consumed, because it contains abundant vitamin C, fiber, and antioxidants. This fruit have high potential in the agribusiness sector and is widely cultivated by traditional farmers. However, during harvest time, traditional farmers generally still use manual methods using the human eye in distinguishing the maturity level of lemons which is less efficient because it has a low accuracy. Digital image processing is one solution to this problem. In the research on the classification of lemon maturity levels using digital image processing with the feature extraction method of the Mean RGB, HSV, and LBP methods and the K-Nearest Neighbor classification algorithm in this study, a total of 120 lemon images were used which were divided into 80 training image data and 40 image data. testing. The results of model performance measurements in the form of the highest accuracy level in the Mean RGB method of 100%, the highest accuracy in the HSV method of 98%, and the highest accuracy in the LBP method of 82.5%.

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Spatial Distances and Knowledge Acquisition Strategy for Patenting by Indonesian Academic Inventors

Spatial Distances and Knowledge Acquisition Strategy for Pat...

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Advancement in Data Science, E-learning and Information Systems (ICADEIS), International Conference on

作者： I Wayan Agus Arimbawa Wirarama Wedashwara Kassahun Gelana Rahmat Fauzi Ade Rahhmat Iskandar Dept. of Informatics Engineering University of Mataram Mataram Indonesia Technology Management Economics and Policy Program Seoul National University Seoul South Korea School of Industrial and Systems Engineering Telkom University Indonesia

Knowledge acquisition is important for innovation and adaptation of organizations including universities. New knowledge and capabilities can be acquired from inside or outside of the institution. There are large body of literature discuss the interaction of universities and the other Triple-helix model’s agents, but rarely found and discuss intra helices interactions. This research explores the characteristics of Indonesian academic inventors’ knowledge acquisition strategy for patenting in Indonesia. There are two points of view of this research: firstly, the study discusses the knowledge acquisition strategy of Indonesian universities in regard of the knowledge diversity among two collaborating universities and secondly, how is the magnitude of knowledge gaps between two entities influencing the decision for inhouse or out of university *** the data from Sinta, knowledge diversity index is measured using Shannon entropy index of the academic inventor’ expertise, university’s department, and university’s division. The research recognition’s gap between two collaborating inventors is measured using the difference of their Scopus’ h-indices. The findings show some interesting facts of how academic inventors strategize their collaboration patterns. First, interuniversity collaborations for patenting reveal more diversity on the academic inventors’ knowledge than those are in intra-university collaborations. Second, collaborations for patenting with different universities expect better knowledge spill-overs than collaborations within a university. But, this knowledge spill-over only significant at individual level but not at institutional level. It seems that individual recognition is more important than institutional recognition when choosing a collaboration partner.

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Asynchronous Data Communication Architecture on Real-Time Face Recognition for Hybrid Laboratory System

Asynchronous Data Communication Architecture on Real-Time Fa...

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International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Nina Lestari Trisna Purnama Slamet Risnanto Budi Fitriadi Ach Maulana Habibi Yusuf Ary Setijadi Prihatmanto Doctoral Program of School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Electrical Engineering Department Faculty of Engineering Universitas Sangga Buana Bandung Indonesia Faculty of Engineering Universitas Sangga Buana Bandung Indonesia Universitas Komputer Indonesia Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

The Internet of Things (IoT) and Artificial Intelligence (AI) have reshaped educational technology, increasing the chances of developing more effective and engaging learning environments. This study proposes an architecture for asynchronous data communication with real-time face recognition, a virtual and remote labs confluence. The architecture uses IoT hardware and software to comprise critical components such as IoT cameras, message brokers, FTP servers, and AI processing servers. The aim is to optimize resource usage and maximize response time efficiency. The architecture employs message queuing to manage high volumes of face image data, facilitating immediate verification and rapid access. Our findings demonstrate that the system can achieve face recognition with 91.7% accuracy and an average system communication latency of 2.631 seconds, effectively leveraging asynchronous communication to optimize resource usage and computational efficiency. The research comprehensively measures the data communication metrics and face recognition metrics. It also explains how asynchronous data communication can be strategically deployed in a hybrid lab environment to enhance educational and operational efficacy.

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“Is It Future or Is It Past?”: From Self-contained Microtasks to AI-driven Collaborative Crowdsourcing

“Is It Future or Is It Past?”: From Self-contained Microta...

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Human-Computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Daniel Schneider Marcos Antonio De Almeida Ramon Chaves Benjamim Fonseca Hesam Mohseni António Correia Postgraduate Program in Informatics (PPGI) Federal University of Rio de Janeiro Rio de Janeiro Brazil Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal Faculty of Information Technology University of Jyväskylä Jyväskylä Finland

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

Interest in artificial intelligence (AI)-driven crowd work has increased during the last few years as a line of inquiry that expands upon prior research on microtasking to represent a means of scaling up complex tasks through AI mediation. Despite the increasing attention to the macrotask phenomenon in crowdsourcing, there is a need to understand the processes, elements, and constraints underlying the infrastructural and behavioral aspects in such form of crowd work when involving collaboration. To this end, this paper provides a first attempt to characterize some of the research conducted in this direction to identify important paths for an agenda comprising key drivers, challenges, and prospects for integrating human-centered AI in collaborative crowdsourcing environments.

关键词： Crowdsourcing Human computer interaction Collaboration Artificial intelligence Mediation Robots Optimization

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An Intelligent System Based on Evolutionary Algorithm for Scheduling University Course Timetable

SSRN

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SSRN 2022年

作者： Bimantara, I. Made Satria Yuhana, Umi Laili Supriana, I. Wayan Pardede, Eric Department of Informatics Institut Teknologi Sepuluh Nopember 60117 Indonesia Informatics Study Program Faculty of Mathematics and Natural Sciences Udayana University Indonesia Department of Computer Science and Information Technology La Trobe University BundooraVIC3086 Australia

University Course Timetabling (UCT) is a common problem in educational institutions. The preparation of the class schedule must pay attention to available resources without violating set constraints. This research applies Evolutionary Algorithm such as Genetic Algorithm into timetabling system, to replace human expertise in generating optimal lecture schedules. Genetic Algorithm (GA) uses chromosomes to represent the solution of the UCT. One chromosome represents a set of schedule solutions that AG generates. Genes and alleles become components of chromosomes that represent the value of each entity to be optimized. We modeled chromosomes into four gene types, each of which has three allele types, namely day, hour, and room alleles which will be optimized with GA. The N-point and cycle crossovers were implemented into the new chromosome encoding scheme and do not take place at the gene level, but at the allele level by grouping similar allele types before the crossover process is carried out. The success rate is measured by the percentage of the optimal number of schedules generated from the trials. The proposed method provides a success rate of 80 - 85% and can produce an optimal schedule with an average computation time of 8 minutes. GA can reduce human resources and save time in the process of preparing lecture schedules. © 2022, The Authors. All rights reserved.

关键词： Genetic algorithms

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Scalable Numerical Embeddings for Multivariate Time Series: Enhancing Healthcare Data Representation Learning

arXiv

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arXiv 2024年

作者： Huang, Chun-Kai Hsieh, Yi-Hsien Chien, Ta-Jung Chien, Li-Cheng Sun, Shao-Hua Su, Tung-Hung Kao, Jia-Horng Lin, Che Taiwan Data Science Degree Program NTU Taiwan Department of Electrical Engineering NTU Taiwan Division of Gastroenterology and Hepatology NTU Hospital Taiwan Hepatitis Research Center NTU Hospital Taiwan Graduate Institute of Clinical Medicine College of Medicine NTU Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taiwan Smart Medicine and Health Informatics Program NTU Taiwan

Multivariate time series (MTS) data, when sampled irregularly and asynchronously, often present extensive missing values. Conventional methodologies for MTS analysis tend to rely on temporal embeddings based on timestamps that necessitate subsequent imputations, yet these imputed values frequently deviate substantially from their actual counterparts, thereby compromising prediction accuracy. Furthermore, these methods typically fail to provide robust initial embeddings for values infrequently observed or even absent within the training set, posing significant challenges to model generalizability. In response to these challenges, we propose SCAlable Numerical Embedding (SCANE), a novel framework that treats each feature value as an independent token, effectively bypassing the need for imputation. SCANE regularizes the traits of distinct feature embeddings and enhances representational learning through a scalable embedding mechanism. Coupling SCANE with the Transformer Encoder architecture, we develop the Scalable nUMerical eMbeddIng Transformer (SUMMIT), which is engineered to deliver precise predictive outputs for MTS characterized by prevalent missing entries. Our experimental validation, conducted across three disparate electronic health record (EHR) datasets marked by elevated missing value frequencies, confirms the superior performance of SUMMIT over contemporary state-of-the-art approaches addressing similar challenges. These results substantiate the efficacy of SCANE and SUMMIT, underscoring their potential applicability across a broad spectrum of MTS data analytical tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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lab: an R package for generating analysisready data from laboratory records

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PeerJ Computer Science 2023年 9卷 e1528页

作者： Tseng, Yi-Ju Chen, Chun Ju Chang, Chia Wei Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Computational Health Informatics Program Boston Children’s Hospital BostonMA United States Department of Information Management National Taiwan University Taipei Taiwan

Background: Electronic health records (EHRs) play a crucial role in healthcare decision-making by giving physicians insights into disease progression and suitable treatment options. Within EHRs, laboratory test results are frequently utilized for predicting disease progression. However, processing laboratory test results often poses challenges due to variations in units and formats. In addition, leveraging the temporal information in EHRs can improve outcomes, prognoses, and diagnosis predication. Nevertheless, the irregular frequency of the data in these records necessitates data preprocessing, which can add complexity to time-series analyses. Methods: To address these challenges, we developed an open-source R package that facilitates the extraction of temporal information from laboratory records. The proposed lab package generates analysis-ready time series data by segmenting the data into time-series windows and imputing missing values. Moreover, users can map local laboratory codes to the Logical Observation Identifier Names and Codes (LOINC), an international standard. This mapping allows users to incorporate additional information, such as reference ranges and related diseases. Moreover, the reference ranges provided by LOINC enable us to categorize results into normal or abnormal. Finally, the analysis-ready time series data can be further summarized using descriptive statistics and utilized to develop models using machine learning technologies. Results: Using the lab package, we analyzed data from MIMIC-III, focusing on newborns with patent ductus arteriosus (PDA). We extracted time-series laboratory records and compared the differences in test results between patients with and without 30-day in-hospital mortality. We then identified significant variations in several laboratory test results 7 days after PDA diagnosis. Leveraging the time series– analysis-ready data, we trained a prediction model with the long short-term memory algorithm, achieving an area un

关键词： Extraction

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A Study on Medicinal Plant Leaf Recognition Using Artificial Intelligence 3

A Study on Medicinal Plant Leaf Recognition Using Artificial...

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3rd International Conference on informatics, Multimedia, Cyber, and Information System, ICIMCIS 2021

作者： Ayumi, Vina Ermatita, Ermatita Abdiansah, Abdiansah Noprisson, Handrie Purba, Mariana Utami, Marissa Universitas Sriwijaya Doctoral Program in Engineering Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Palembang Indonesia Universitas Mercu Buana Faculty of Computer Science Jakarta Indonesia Program of Informatics Management Politeknik Anika Palembang Indonesia Muhammadiyah University of Bengkulu Faculty of Engineering Bengkulu Indonesia

ISBN: (纸本)9781665427333

Medicinal plant recognition manually takes a lot of time and money. Moreover, to reduce these resources, some researchers propose to implement artificial intelligence technology. This paper aims are to conduct a systematic literature review of medicinal plant leaf recognition published in the last two years (2019-2020) from IEEE, Springer and Science Direct. We obtained 15 studies in the field of medicinal plant leaf recognition using artificial intelligence. The dataset used for medicinal plant leaf recognition is mostly used private dataset, however, there are public dataset named Leaf, Flavia, Swedish dataset. We also found robust method that can be used for medicinal plant leaf recognition is Multichannel Modified Local Gradient Pattern (MCMLGP) and Gray Level Co-Occurrence Matrix (GLCM) as feature extraction;and Convolutional Neural Network (CNN), Multi-Layer Perceptron trained with Backpropagation algorithm (MLP-BP), Support Vector Machine (SVM), and Transfer Learning (VGG19) as classifier. © 2021 IEEE.

关键词： Image processing

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Digitization and Gamification in Cultural Heritage: The portuguese context in the framework of national and international policies and some practical examples 16

Digitization and Gamification in Cultural Heritage: The port...

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16th Iberian Conference on Information Systems and Technologies, CISTI 2021

作者： Cordeiro, Patricia Alexandra Nunes Sousa, Joao Paulo Carvalho, Aida Dei Feup Digital Media Doctoral Program Porto Portugal Esact Ipb Department of Mathematics and Informatics Mirandela Portugal Esact Ipb Citur Departamento de Artes e Humanidades Mirandela Portugal

ISBN: (纸本)9789895465910

This article begins with an attempt to contextualize the use of digital media and computation in works related to cultural heritage, in Portugal, in the frame of the regulations and policies that have been developed internationally and nationally. It then develops on the contextualization of the implementation of digitalization strategies regarding museum's collections in Portugal and its struggles both to implement these strategies as well as to invest in the creation and implementation of applications of digital tools for audience's engagement. Following some examples that have been developed for different purposes such as preservation, cataloguing, education and teaching, in different fields of cultural heritage such as archeology, ethnology, natural and historical sites, the article will then focus on the contributions of the more recent uses of technology to enhance or create interactive heritage experiences in cultural heritage, such as, photogrammetry, 3D modelling, and movement analysis. Finally, the last part of this article explores a proposal of gamification of intangible cultural heritage, portraying the winter festivity Carnival of Podence inscribed, in the Representative List of Intangible Cultural Heritage of Humanity, in December 2019. © 2021 AISTI.

关键词： Digital storage

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Medical Knowledge-Enabled Multi-Task Learning for Gastric Cancer Survival Prediction

Medical Knowledge-Enabled Multi-Task Learning for Gastric Ca...

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IEEE International Symposium on Biomedical Imaging

作者： Degan Hao Qiong Li Yudong Zhang Shandong Wu Intelligent Systems Program University of Pittsburgh Pittsburgh PA USA The First Affiliated Hospital With Nanjing Medical University Nanjing PR China Dept. of Radiology Dept. of Biomedical Informatics and Dept. of Bioengineering University of Pittsburgh Pittsburgh PA USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Accurate cancer survival prediction can inform patient prognostication and treatment planning. Multi-source clinical data, including radiological images and pathological markers, demand effective machine learning strategies to make smart use of these data to enhance patient outcome. In this work, we proposed a novel strategy of translating multi-task learning to clinical applications for gastric cancer survival prediction, by leveraging the established medical knowledge of abdominal CT images and pathological staging markers to construct meaningful auxiliary tasks for joint learning with the target task. In implementing our method for survival prediction, we also customized a convolutional neural network to account for censored data. We evaluated our method with extensive experiments on a large cohort of 1,061 gastric cancer patients for both overall and progression-free survival prediction, showing superior performance when compared to multi-input learning and two state-of-the-art prediction methods. As an additional benefit, our method also enables survival prediction even before surgery. Source code is available at https://***/usernameoliver/***.

关键词： Pathology Accuracy Source coding Surgery Prediction methods Multitasking Planning

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