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2024 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2024

作者： Mendonca, Vanessa Santos, Mikael Munoz, Daniel M. Humberto Llanos, Carlos Federal Institute of Brasilia Department of Computer Science Brazil Mechatronics Graduate Program Department of Mechanical Engineering Brazil Federal Institute of Brasilia Department of Agronomy Brazil University of Brasilia Faculty of Gama Electronics Engineering Undergraduate Program Brasilia Brazil

ISBN: (纸本)9798350374575

Technological advancements are increasingly evident across various sectors, including automobiles, industry, and healthcare. In precision agriculture, significant progress has been made, with AgroTICs and Smart Agriculture gaining substantial traction in the market. However, a gap remains between cutting-edge technology and family farming, presenting a challenge from both social and applied research perspectives. However, there is still a gap between cutting-edge technology and family farming, which creates a challenge from a social and applied research point of view. In this context, this paper proposes a monitoring model based on Fuzzy Logic and sensor automation applied to estimate the health of a corn crop. The proposed Fuzzy inference system involves calculating an indicator of nutrients as well as the average color and area of corn plants. The nutrient indicator is automatically computed by an ESP32 microcontroller using sensor readings, while the average color and area inputs are manually entered via a mobile application. Additionally, the Fuzzy inference is integrated into the ESP32. The model underwent experimental validation on the health of the plantation, and the results were evaluated in four areas: one was designated for testing, and three were for validation. The model achieved an accuracy of 97.5% in Scenario 3, categorized as 'Very Favorable', and an accuracy of 65% in Scenarios 2 and 4, categorized as 'Unfavorable'. The implications of this research contribute to the advancement of AgroTICs among small producers, with the potential to enhance and automate the monitoring of their harvest production. © 2024 IEEE.

关键词： Agribusiness

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Automatic Speech Recognition in Indonesian Using the Transformer Model

Automatic Speech Recognition in Indonesian Using the Transfo...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Sonata, Ilvico Bina Nusantara University Binus Graduate Program Computer Sciiene Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350307306

Automatic Speech Recognition (ASR) is useful for converting speech into text. ASR is needed to display automatic subtitles on movies or when conducting video conferencing. The use of deep learning in ASR applications is currently still dominated by Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN). Several previous studies used the Transformer deep learning model in building the ASR model. The accuracy results obtained are better than the LSTM or RNN models. However, ASR research using the Transformer model is still very limited. This paper will discuss the use of the Transformer model for ASR in Indonesian. The dataset used is the Indonesian language private speech dataset. The experimental results show that the Word Error Rate (WER) and Character Error Rate (CER) produced by the proposed model using the Indonesian language primary dataset are almost the same as using the English language public dataset where the resulting WER and CER are 27.34 and 7.96 for Indonesian and 25.28 and 6.02 for English. © 2023 IEEE.

关键词： Speech recognition

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Bioinformatics exploration into potential mechanisms of Calamus draco for colorectal cancer treatment 9th

Bioinformatics exploration into potential mechanisms of Cala...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Kenneth, Christian Nirwantono, Rudi Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

As one of the cancer types with the highest incidence rates, colorectal cancer (CRC) would benefit from treatments with fewer side effects and reduced treatment-resistant potential. One of the options is to harness the anti-CRC potential of natural products. Previous studies have shown that Calamus draco exudate, dragon's blood, has anticancer activity in liver cancer and acute myeloid leukemia, but its bioactivity has not been studied in CRC. Here we conduct a bioinformatics study based on network pharmacology to explore the anti-CRC potential and mechanism of C. draco-derived compounds. The bioinformatics pipeline is composed of compound and target collection, biological network evaluation, and enrichment analysis. We found that there are 43 bioactive compounds from C. draco targeting 91 CRC-related targets, of which most compounds target MEN1, PTGS2, and IDH1. Further analyses show that the targets of C. draco are involved in the cellular response to hypoxia. By inhibiting those targets, C. draco bioactive compounds can potentially hinder angiogenesis and increase treatment response efficacy. © 2024 The Authors.

关键词： Lung cancer

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Neural controller design for a class of nonlinear systems with time-varying delay: A bibliometric analysis-based short literature review

Neural controller design for a class of nonlinear systems wi...

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2023 International Conference on computer science, Information Technology and engineering, ICCoSITE 2023

作者： Wijaya, Santo Gaol, Ford Lumban Wibowo, Antoni Zarlis, Muhammad Bina Nusantara University BINUS Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Information System Management Information System Management Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

Real-world practical systems inherently exhibit non-linearities in their dynamics. Also, it is known that a time-varying delay exists in the system state or input-output. Combined, it affects the stability of the closed-loop system. It also increases the complexity of the controller design. In-depth controller design research on the class of Nonlinear Systems with Time-Varying Delay (NSTVD) has been the focus of the control community for many years. However, there is a lack of Systematic Literature Review (SLR) and classifications of the papers on this topic. This paper aims to review controller design utilizing a neural network model for the class of NSTVD systems. The study employs Kitchenham's SLR method to gather, analyze and synthesize published papers from reliable databases between 2017 and 2021. The bibliometric analysis for the selected 38 papers reveals the prolific authors, countries, affiliations, publishers, co-authorship network, co-occurrences of keywords, and ten most-cited papers. Finally, this paper developed a conceptual map outlining six multi-layered findings: the addressed problem, control design method, nonlinear system properties, time-varying delay properties, system constraint properties, and actuator limit properties. A brief qualitative analysis of the ten most-cited papers is performed based on the map. The findings highlighted that the proposed methods have shown encouraging results in the simulation domain and can be used as a source of inspiration for future studies and implementation of the neural controller design of the NSTVD system. © 2023 IEEE.

关键词： Nonlinear systems

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A Proposal for Electrodes and Frequency Bands that Combine Performance and Experimental Cost in EEG analysis of Motions

A Proposal for Electrodes and Frequency Bands that Combine P...

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2023 Congress in computer science, computer engineering, and Applied Computing, CSCE 2023

作者： Kanemoto, Sho Nishiura, Ryota Tsuchiya, Seiji Watabe, Hirokazu Doshisha University Graduate School of Science and Engineering Department of Computer Science and Engineering Kyoto Japan

ISBN: (纸本)9798350327595

In this paper, we utilize acquired EEG motion data to verify the reproducibility of pyramidal tract crossings, which are highly associated with motion. Previous studies faced the challenge of unclear detection of motion-related features in the acquired EEG data. Furthermore, the EEG data from 32 channels often contained significant noise, resulting in low prediction accuracy. However, through the validation conducted in this study, we successfully identified electrode-based features associated with motion. By reducing the number of electrodes to six, based on the extracted features, we were able to improve the accuracy of motion prediction and reduce computational costs. The reduction in the number of electrodes also suggests a potential decrease in the burden on the subject wearing the EEG measuring device. © 2023 IEEE.

关键词： BCI EEG motions pyramidal tract crossings readiness potential RP

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Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation 61

Jointprop: Joint Semi-supervised Learning for Entity and Rel...

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61st Annual Meeting of the Association for Computational Linguistics, ACL 2023

作者： Yandan, Zheng Anran, Hao Tuan, Luu Anh School of Computer Science and Engineering Interdisciplinary Graduate Program-HealthTech Nanyang Technological University Singapore

ISBN: (纸本)9781959429722

Semi-supervised learning has been an important approach to address challenges in extracting entities and relations from limited data. However, current semi-supervised works handle the two tasks (i.e., Named Entity Recognition and Relation Extraction) separately and ignore the cross-correlation of entity and relation instances as well as the existence of similar instances across unlabeled data. To alleviate the issues, we propose Jointprop, a Heterogeneous Graph-based Propagation framework for joint semi-supervised entity and relation extraction, which captures the global structure information between individual tasks and exploits interactions within unlabeled data. Specifically, we construct a unified span-based heterogeneous graph from entity and relation candidates and propagate class labels based on confidence scores. We then employ a propagation learning scheme to leverage the affinities between labelled and unlabeled samples. Experiments on benchmark datasets show that our framework outperforms the state-of-the-art semi-supervised approaches on NER and RE tasks. We show that the joint semi-supervised learning of the two tasks benefits from their codependency and validates the importance of utilizing the shared information between unlabeled data. © 2023 Association for Computational Linguistics.

关键词： Extraction

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A ShuffleNet Architecture-Based Multi-Layer Feature Scaling and Fusion Approach for Underwater Net Cage Damage Detection in Aquaculture

A ShuffleNet Architecture-Based Multi-Layer Feature Scaling ...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Huang, Xiang-Rui Chen, Yi-Cheng Chen, Liang-Bi Hu, Wu-Chih National Penghu University of Science and Technology Master Program in Electrical Engineering and Computer Science Department of Electrical Engineering Penghu Taiwan National Penghu University of Science and Technology Department of Computer Science and Information Engineering Penghu Taiwan

ISBN: (纸本)9798331530839

In offshore aquaculture operations, personnel equipped with diving gear are often necessary to inspect the underwater net cages for damage, particularly on the sea floor. This manual inspection process is time-consuming and complex. To overcome this problem, this paper proposes a computer vision solution for identifying damage in underwater net cages to address the inefficiencies and challenges of traditional manual inspections. The proposed scheme utilizes a high-performance multi-branch computational architecture designed based on ShuffleNet architecture to detect net cage damage more efficiently. Experimental results demonstrate that this work performs well on the ImageNet ILSVRC-2010 dataset and achieves an accuracy of 88.54% in underwater net damage detection. © 2024 IEEE.

关键词： Aquaculture

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Hotel Information System Management Audit Using COBIT 2019 - APO12 2

Hotel Information System Management Audit Using COBIT 2019 -...

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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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TaxDiff: taxonomic-guided diffusion model for protein sequence generation

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science China(Information sciences) 2025年第4期68卷 385-386页

作者： Zongying LIN Hao LI Liuzhenghao LV Yu WANG Bin LIN Junwu ZHANG Zijun CHEN Calvin Yu-Chian CHEN Li YUAN Yonghong TIAN School of Electronic and Computer Engineering Peking University AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School Peng Cheng Laboratory School of Chemical Biology and Biotechnology Peking University

Protein design aims to generate protein variants with targeted biological functions, which is significant in multiple biological areas, including enzyme reaction catalysis, vaccine design, and fluorescence intensity. Protein design contains two paradigms: sequence generation and structure generation. Recently, EvoDiff [1] proposed a universal designing paradigm, combining structure and sequence generation using the diffusion framework, which improves the protein design efficiency.

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Energy Sector Stock Price Prediction Using the CNN, GRU & LSTM Hybrid Algorithm

Energy Sector Stock Price Prediction Using the CNN, GRU & LS...

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2023 International Conference on computer science, Information Technology and engineering, ICCoSITE 2023

作者： Sulistio, Bambang Warnars, Harco Leslie Hendric Spits Gaol, Ford Lumban Soewito, Benfano Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350320954

Nowadays, many people are starting to care about early investment. One of the most popular investments lately, especially for millennials, is a stock investment. In investing, there are advantages and risks of loss. One way to reduce the risk of loss is by using price predictions before investing in stocks. This paper proposes the use of deep learning in making stock predictions. We conducted research by calculating the performance of six deep-learning algorithms to predict stock closing prices. The application of the CNN-LSTM-GRU hybrid algorithm combination produces the best performance compared to other methods, based on the value: Root Mean Squared Error (RMSE) decreased by 1.100 by 14%, Mean Absolute Error (MAE) was successfully reduced by 0.798 by 13.4%, and R Square increased by 0.957 by 3.9%. In predicting stock prices on the Indonesian Stock Exchange, especially in the energy sector, CNN-LSTM-GRU is more appropriate for investors than using a single algorithm to make decisions in investing in stocks.. © 2023 IEEE.

关键词： Long short-term memory

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