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International Joint Conference on Neural Networks (IJCNN)

作者： Bruno Siedekum Thuma Pedro Silva de Vargas Cristiano Garcia Alceu de Souza Britto Jean Paul Barddal Polytechnic School Pontifícia Universidade Católica do Paraná (PUCPR) Graduate Program in Informatics (PPGIa) Pontifícia Universidade Católica do Paraná (PUCPR) Instituto Federal de Santa Catarina (IFSC) Campus Caçador Departamento de Informática Universidade Estadual de Ponta Grossa (UEPG)

Feature extraction regards transforming unstructured or semi-structured data into structured data that can be used as input for classification and sentiment analysis algorithms, among other applications. This task becomes even more challenging and relevant when textual data becomes available over time as a continuous data stream since the lexicon and semantics can be ever-evolving. Data streams are, by definition, potentially infinite sequences of data that may have ephemeral characteristics, that is, where the data behavior changes, it leads to a phenomenon named concept drift. Textual data streams are specialized data streams, in which texts arrive over time from a continual data source, such as social media, raising challenges in which feature extractors are of great help. In this paper, we benchmark different feature extraction algorithms, i.e., Hashing Trick, Word2Vec, BERT, and Incremental Word-Vectors; in textual data stream classification, considering different stream lengths. The evaluation was performed over a binary and a multiclass classification task, considering two different datasets. Results show that pre-trained models, such as BERT, achieve interesting results, while Hashing Trick also performs competitively. We also observe that incremental methods such as Word2Vec and Incremental Word-Vectors are the most prepared for changing scenarios, yet, they are much more computationally intensive compared to the former when applied to larger streams.

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A Novel Approach for Optimum-Path Forest Classification Using Fuzzy Logic

arXiv

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arXiv 2022年

作者： de Souza, Renato William R. de Oliveira, João Vitor Chaves Passos, Leandro A. Ding, Weiping Papa, João P. de Albuquerque, Victor Hugo C. The Graduate Program in Applied Informatics University of Fortaleza CE Fortaleza Brazil The Pontifical Catholic University of Rio de Janeiro Brazil São Paulo State University Brazil The School of Information Science and Technology Nantong University Nantong China

In the past decades, fuzzy logic has played an essential role in many research areas. Alongside, graph-based pattern recognition has shown to be of great importance due to its flexibility in partitioning the feature space using the background from graph theory. Some years ago, a new framework for both supervised, semi-supervised, and unsupervised learning named Optimum-Path Forest (OPF) was proposed with competitive results in several applications, besides comprising a low computational burden. In this paper, we propose the Fuzzy Optimum-Path Forest, an improved version of the standard OPF classifier that learns the samples’ membership in an unsupervised fashion, which are further incorporated during supervised training. Such information is used to identify the most relevant training samples, thus improving the classification step. Experiments conducted over twelve public datasets highlight the robustness of the proposed approach, which behaves similarly to standard OPF in worst-case scenarios. © 2022, CC BY.

关键词： Fuzzy logic

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Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review

Critical Factors of Immersive Learning in Higher Education: ...

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International Conference on ICT For Smart Society (ICISS)

作者： Sunardi Meyliana Arief Ramadhan Yusep Rosmansyah Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Departement School of information system Bina Nusantara University Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of electrical engineering and informatics Bandung Institute of Technology Bandung Indonesia

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education.

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Drought propagation in an Andean basin in South America: a spatio-temporal analysis approach with monitoring indices

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Arabian Journal of Geosciences 2025年第3期18卷 1-21页

作者： Ocampo-Marulanda, Camilo Mera-Franco, Julián David Lizcano, Víctor Augusto Carvajal-Escobar, Yesid Canchala, Teresita Zorrilla-Millan, Daniel Felipe Caicedo-Bravo, Eduardo Water Resources Engineering and Soil Research Group (IREHISA) School of Natural Resources and Environmental Engineering Universidad del Valle Cali Colombia Graduate Program in Biometrics and Applied Statistics Department of Statistics and Informatics Federal Rural University of Pernambuco Recife Brazil School of Exact Sciences and Engineering Universidad Sergio Arboleda Bogotá Colombia Programa de Ingeniería Ambiental Facultad de Ingeniería Universidad Mariana Pasto Colombia School of Electrical and Electronic Engineering Universidad del Valle Cali Colombia

Drought is a climatic phenomenon that has increased in severity over time. This study analyzed drought spread in the Alto Patía basin (13,047 km2) in South America, applying the Standardized Precipitation Index (SPI), Vegetation Health Index (VHI), and Streamflow Drought Index (SDI) to assess meteorological, agricultural, and hydrological drought, respectively. Synchronous and asynchronous correlations were estimated, and Wavelet analysis was conducted to verify relationships among drought types. Results show that the lag between drought types varies based on the intensity, temporal continuity, and spatial extent of meteorological drought. A lag of zero months (1–2 months) was observed between meteorological and hydrological drought when moderate (severe) meteorological drought partially (fully) covers the basin. Agricultural drought, however, does not always correspond to meteorological drought during isolated rainfall events. As the first study in Colombia to systematically connect these drought types, this research addresses a gap in understanding how drought impacts progress across systems in regions with complex rainfall patterns. These findings offer critical insights into the interdependencies among drought types, supporting enhanced drought monitoring and early warning systems in similar climatic regions. By identifying specific lags between meteorological and hydrological droughts, this study provides practical guidance for land management, water conservation, and crop planning, offering a foundation for future research on drought resilience strategies.

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Design and Deployment of an Efficient Communication Service for Multi-UAV Systems

Design and Deployment of an Efficient Communication Service ...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Pedro H. M. Pereira Faezeh Pasandideh Maik Basso Joao Paulo J. da Costa Edison Pignaton de Freitas Graduate Program in Electrical Engineering Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil Hamm-Lippstadt University of Applied Sciences (HSHL) Hamm Germany Institute of Informatics Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil Department of "Technology and Systems" PK NRW Germany

Recent advances in the areas of microelectronics, information technology, and communication protocols have made possible the development of smaller devices, with increasing processing capacity and low energy consumption. This context contributed to the growth of applications based on the use of one or multiple Unmanned Aerial Vehicles (UAVs). Networks composed of multiple UAVs are being used as a matter of improving the effectiveness, accuracy, and minoring the time of missions. However, these applications demand a high rate of data exchange, such as the localization information of each UAV, which can be a challenge, due to the limited transmission power of certain drone platforms. This article proposes a communication service for multi-UAV systems based on dividing the UAV-fleet into groups using the communication protocol IEEE 802.11 ac. Each group has its local network, whose participants can be chosen based on the UAV’s localization or task assignment. UAVs/Drones within the same group constantly communicate, exchanging pose information and specific mission-related data. On the other hand, communication between different groups is only established by messenger drones in pre-set times. The communication service from its detailed implementation to its simulated and field validation experiments is presented in this paper. The results of three different network topologies provide evidence that the proposed communication service for multi-UAV is efficient and can be used for drone cooperative missions.

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The Intelligence Decision Making on Asset Management using Fuzzy Clustering 2

The Intelligence Decision Making on Asset Management using F...

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2nd International Conference on Innovative and Creative Information Technology, ICITech 2021

作者： Sediyono, Eko Hartomo, Kristoko Dwi Susetyo, Yeremia Alfa Setiwan, Adi Information System Graduate Program Universitas Kristen Satya Wacana Salatiga Indonesia Universitas Kristen Satya Wacana Dept. of Information System Salatiga Indonesia Universitas Kristen Satya Wacana Dept. of Informatics Salatiga Indonesia Universitas Kristen Satya Wacana Faculty of Science and Mathematics Salatiga Indonesia

ISBN: (纸本)9781728197463

This paper discusses research that produces a land asset information system called e-asset. The research method used is an experimental method by creating an asset information system. The system created is given to the Salatiga City Regional Finance Agency (BKD) for internal purposes. Fuzzy Algorithm has been implemented in the system so as to make it easier for users from BKD staff to find the location of land assets. The test results by the user stated that the user was satisfied with the satisfaction level of 3.41 (from scale 0 to 5). © 2021 IEEE.

关键词： Decision making

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Drone Application Model for Image Acquisition of Plantation Areas and Oil Palm Trees Counting 7

Drone Application Model for Image Acquisition of Plantation ...

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7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Sastrohartono, Hermantoro Suryotomo, Andiko Putro Saifullah, Shoffan Suparyanto, Teddy Perbangsa, Anzaludin Samsinga Pardamean, Bens Instiper Yogyakarta Faculty of Agricultural Technology Department of Agricultural Engineering Yogyakarta55281 Indonesia Universitas Pembangunan Nasional Veteran Yogyakarta Department of Informatics Yogyakarta55283 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

The area of oil palm plantations in Indonesia increased by 7% from 14 million ha in 2017 to 15 million ha in 2021. The vast land requires the support of effective and efficient management techniques to maintain sustainable productivity. The high-performance computing technologies, Internet of Things (IoT), Big Data, Artificial Intelligence, spatial modeling, and drones are the answers to these needs. This study aims to design an application that can obtain plantation image data and analyze the calculation of the number of oil palm trees. The image of oil palm plantations is obtained from processing photo data from drones through a mosaic and composite process. This study also employed Scrum and UML as a system model development method. The image of the oil palm plantation area is used to build a tree counting model using the Viola-Jones algorithm. The oil palm tree count information generated by this system can then be used by management for fertilizing, harvesting, and monitoring the condition of oil palm trees. © 2022 IEEE.

关键词： Drones

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Predicting Neurological Outcomes of Comatose Cardiac Arrest Patients Using Transformer Neural Networks with EEG Data

Predicting Neurological Outcomes of Comatose Cardiac Arrest ...

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Computers in Cardiology (CinC)

作者： Jefferson Dionisio Che Lin Lian-Yu Lin Wen-Chau Wu Smart Medicine and Health Informatics Program National Taiwan University (NTU) Taipei Taiwan Department of Electrical Engineering NTU Graduate Institute of Communication Engineering NTU School of Medicine NTU Center for Advanced Computing and Imaging in Biomedicine NTU Center for Biotechnology NTU Department of Internal Medicine NTU Hospital Taipei Taiwan Institute of Medical Device and Imaging NTU Department of Radiology College of Medicine NTU Graduate Institute of Clinical Medicine NTU

As part of the 2023 PhysioNet Challenge, our team FINDING_MEMO utilized Transformer to predict outcomes using patient EEG data since it excels at dealing with sequential data like EEG. We mainly used the Transformer encoder block's multi-head self-attention to generate representations from the input and leverage several hidden layers to form the final prediction. Using the latest EEG from every patient, our team achieved the challenge score of 0.42 with the hidden validation set (ranked 36th out of 73 invited teams) and obtained a result of 0.37 (ranked 29th out of 36 qualified teams). Our results show a consistent performance across varying EEG recording durations in both the validation and test set. Our team also had the second-best score when evaluated, with only 12 hours of available recordings in the test set. Such promising results showcase the models' generalizability and clinical potential in predicting outcomes for comatose patients, especially for limited available EEG recordings.

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An FPGA Accelerator for Bayesian Network Structure Learning with Iterative Use of Processing Elements

An FPGA Accelerator for Bayesian Network Structure Learning ...

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IEEE International Conference on Field-programmable Technology (FPT)

作者： Yasuhiro Nitta Hideki Takase Graduate School of Informatics Kyoto University Kyoto Japan JST PRESTO Program Graduate School of Informatics Kyoto University Kyoto Japan

ISBN: (纸本)9781665423021;9781665446228

A Bayesian network is one of the graphical models that represent the causality or correlation relationship among multiple observed phenomena. The structure learning of this network is generally NP difficult, and the computational time to obtain an approximate solution becomes huge. This paper proposes an FPGA accelerator for structure learning of Bayesian networks. The proposed method employs a dataflow type architecture and executes processes without dependency in dynamic programming in parallel. By iteratively using processing elements at each processing stage, we can efficiently use limited resources while taking advantage of the parallel performance of FPGAs. We implemented the proposed method for Xilinx Alveo U200 using high-level synthesis. Evaluation results showed that we achieved up to 12.6 times faster than single-core execution of software and up to 2.98 times faster than on multi-core execution. We further applied the proposed method to the Local-to-Global algorithm and achieves 8.6 times faster than the software execution in the structure learning of a practical network with 37 nodes.

关键词： Training Limiting Graphical models Heuristic algorithms Software algorithms Parallel processing Software

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Dynamic programming Algorithm using Furniture Industry 0/1 Knapsack Problem 5

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Anditta, Mietha Amartya, Natashya Warnars, Laurens Spits Warnars, Harco Leslie Hendric Spits Ramadhan, Arief Siswanto, Teddy Mantoro, Teddy Noordin, Nurulhuda Bina Nusantara University School of Computer Science Computer Science Departement Jakarta11530 Indonesia Bina Nusantara University School of Information Systems Information Systems Departement Jakarta11530 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Departement Jakarta11530 Indonesia Telkom University Pjj Informatika Bandung40257 Indonesia Trisakti University Information Systems Department Jakarta11440 Indonesia Sampoerna University Faculty of Engineering and Technology Jakarta12760 Indonesia Universiti Teknologi Mara School of Computing Sciences College of Computing Informatics and Mathematics Selangor Malaysia

ISBN: (纸本)9798350369489

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset. © 2023 IEEE.

关键词： Dynamic programming

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