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A Hybrid CNN-SVM Approach for Malaria in Blood Cell Detection

A Hybrid CNN-SVM Approach for Malaria in Blood Cell Detectio...

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Software Engineering and Information Technology (ICoSEIT), International Conference on

作者： Andrea Stevens Karnyoto Mahmud Isnan Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

There is still a severe malaria problem worldwide, particularly in regions with limited access to diagnostic tools. It is crucial to develop a system for detecting malaria in blood cells. This paper presents a hybrid Convolutional Neural Network (CNN), and as a classifier, we use a Support Vector Machine (SVM) framework for the automated detection of malaria parasites in blood cell images. The proposed system leverages the strengths of CNNs in feature extraction and representation learning from images, combined with the discriminative power of SVMs for classification. Initially, CNN extracts intricate features from blood cell images, capturing essential patterns indicative of malaria infection. Subsequently, the extracted features are used to train an SVM classifier, enabling accurate discrimination between parasitized and uninfected blood cells. Experimental dataset evaluations were obtained from the Lister Hill National Center for Biomedical Communications website of the National Library of Medicine. The proposed model achieves a better f1-score, outperforming individual CNN or SVM models, around 0.015 compared to individual CNN models and 0.27 compared to individual SVM models. This hybrid CNN-SVM methodology offers a promising solution for accurately and efficiently detecting malaria parasites in blood cell images.

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Server Cloud Based Security Surveillance Systems: a Design Science Research

Server Cloud Based Security Surveillance Systems: a Design S...

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International Conference on Cyber and IT Service Management (CITSM)

作者： Alexander Nurenie Arief Ramadhan Edi Abdurachman Agung Trisetyarso Muhammad Zarlis Computer Science Department BINUS Graduate Program – Computer Science Bina Nusantara University Jakarta Indonesia School of Computing Telkom University Bandung Indonesia

In this research, the author addresses the prevalent issues faced by users of cloud services, especially those using Peer-to-Peer (P2P) technology, such as connection losses, security concerns, and poor video quality. The study does not delve into the complex problems at the data center level but instead proposes a novel Design Science Research approach. This new method, tested with five clients in varied locations, utilizes port forwarding to establish a direct server-client connection, bypassing traditional P2P frameworks. The results show that this approach significantly improves quality over the P2P system. Feedback from clients before and after the experiment indicates a positive response to this new method. This research offers insights into overcoming P2P-related issues in cloud services and contributes to the development of cloud-based server science, presenting a cost-effective, independent alternative to data center-dependent P2P services, with plans for ongoing research in this area.

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Comparative Analysis between Three-Leg Interleaved and Conventional Bidirectional Converters Applied in Battery of Wind Power-Battery Unit Supplying an Islanded Microgrid

Comparative Analysis between Three-Leg Interleaved and Conve...

引用

IEEE General Meeting Power& Energy Society

作者： André C. Do Nascimento Giusep M. S. Ribeiro Lindemberg R. de Lima Pedro A. de Alcântara Luana C. S. Soares Luciano S. Barros Camila M. V. Barros Electrical Engineering Graduate Program Federal Institute of Pará Belém Brazil Electrical Engineering Graduate Program Federal University of Paraíba João Pessoa Brazil Mathematics and Computational Modeling Graduate Program Federal University of Paraíba João Pessoa Brazil Computer Systems Department Federal University of Paraíba João Pessoa Brazil

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Battery energy storage systems (BESS) are crucial for microgrids, enhancing dynamic performance and mitigating uncertainties inherent to isolated environments characterized by intermittent power generation. When microgrid is supplied by wind power in connection with BESS, the bidirectional DC-DC converter associated with BESS assumes a critical responsibility—ensuring the control of a wind power supplied DC-link. This permits the Voltage Source Inverter (VSI) to synthesize consistent voltages for the connected AC load. However, challenges emerge, particularly in arduous stress on switches of the bidirectional converter attributed to elevated current ripple. This stress does not only prejudice the overall system’s functioning but can also potentially leading it to shutdown in case of switch failure. Traditionally, the conventional buck-boost converter topology has been the go-to choice for this application, nevertheless, exploring novel converter topologies, such as interleaved bidirectional DC-DC converter (IBDC) has garnered attention, promising enhanced performance. This paper’s goal is to conduct a comparative analysis of the operation of these converters in an islanded microgrid supplied by a single Permanent Magnet Synchronous Generators (PMSG) small-scale wind turbine with a BESS system. Load variation with Maximum Power Point Tracking (MPPT) operation of the PMSG is performed during MATLAB/Simulink environment simulations. The results suggest significant advantages for the interleaved three-phase converter, such as reduced battery charging current ripple, switch disturbance, and voltage ripple in the DC-link.

关键词： Maximum power point trackers Magnetic separation Microgrids DC-DC power converters Wind power generation Permanent magnets Batteries Topology Wind turbines Stress

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CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BASED ON ARTIFICIAL NEURAL NETWORKS AND TREE DECISION MODELS 15

CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BAS...

引用

15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on computer Science and Information Systems, MCCSIS 2022

作者： Yazdanpanah, Hamed Silva, Augusto C.M. Guedes, Murilo Morales, Hugo M.P. dos Coelho, Leandro S. Moro, Fernando G. Department of Research Robô Laura PR Curitiba Brazil Department of Computer Science University of São Paulo SP São Paulo Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana PR Curitiba Brazil Electrical Engineering Graduate Program Federal University of Parana PR Curitiba Brazil

ISBN: (纸本)9789898704405

Early recognition of clinical deterioration (CD) has vital importance in patients’ survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural network’s performance on EHRs data. Thus, in this article, an extremely boosted neural network (XBNet) is used to predict CD, and its performance is compared to eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate the models. Moreover, the principal component analysis (PCA) is employed to verify whether it can improve the adopted models’ performance. The performance of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are evaluated in CD prediction regarding the accuracy, precision, recall, F1-score, and geometric mean (G-mean) metrics in a 10-fold cross-validation approach. According to the experiments, the XGBoost model obtained the best results in predicting CD among Brazilian hospitals’ data. © 2022 15th International Conference on ICT, Society and Human Beings, ICT 2022, 19th International Conference on Web Based Communities and Social Media, WBC 2022 and 14th International Conference on e-Health, EH 2022 - Held at the 16th Multi Conference on computer Science and Information Systems, MCCSIS 2022. All rights reserved.

关键词： Neural networks

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Systematic Literature Review on Metaheuristic Algorithms in SNP Analysis for Diseases

Systematic Literature Review on Metaheuristic Algorithms in ...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Jason Sebastian Sulistyawan Kuncahyo Setyo Nugroho Bens Pardamean Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Single-nucleotide polymorphism (SNP) analysis has become a pivotal strategy for drug discovery within bioinformatics, especially for incurable diseases like cancer. With the increasing number of researchers starting to embrace metaheuristic methods, a review was done by gathering papers from the Google Scholar database from 2018 to 2023, which resulted in 20 papers after title, abstract, and content filtering. The findings show that the Genetic Algorithm and the Harmony Search Algorithm have become popular approaches in SNP analysis, particularly in studies on breast cancer, age-related macular degeneration, and colorectal cancer. However, the review shows that while researchers have proven most methods effective in finding disease-related SNPs, a more measurable study in SNP analysis is needed, due to the lack of elaboration on measurement metrics in the found studies.

关键词： Macular degeneration Filtering Databases Metaheuristics Time measurement Internet Drug discovery Intelligent systems Genetic algorithms Systematic literature review

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Scheduling Production in the Flexible Packaging Industry Using Mathematical Models and Genetic Algorithms

Scheduling Production in the Flexible Packaging Industry Usi...

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Informatics Engineering, Science & Technology (INCITEST), International Conference on

作者： Masmur Tarigan Ford Lumban Gaol Tuga Mauritsius Widodo Budiharto Computer Science Department / Computer Science Graduate Program Esa Unggul University/Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia Department of Information System Management Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Minimizing time wastage is a crucial objective for all production operations. Reliable manufacturing scheduling The objective is to mitigate time-based production waste caused by excessive processing, waiting, and transportation. Industry 4.0 promotes technological innovation across all industrial sectors to increase efficiency and effectiveness while preserving high competitiveness. The implementation of artificial intelligence in Industry 4.0 is expected to result in a reduction in time wastage. Non-dominance sequencing genetic algorithms (NSGA), genetic algorithms (GA), and evolutionary algorithms (EA) are the three most common scheduling approaches, according to literature-indexed research summaries from the past five years. The examination of production scheduling in the flexible packaging business, which involves the utilization of different machines and processes and is produced on demand, had not been conducted before in this study. A mathematical scheduling model will be developed in this research to ascertain the shortest production time span in the flexible packaging industry. As a form of technological innovation, this mathematical model will be utilized in experiments employing a genetic algorithm approach. For the genetic algorithm to generate a minimum makespan in the flexible packaging industry's production scheduling process.

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Air Pollution Level Prediction in Jakarta Using Artificial Neural Network

Air Pollution Level Prediction in Jakarta Using Artificial N...

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International Conference on Artificial Intelligence and Data Sciences (AiDAS)

作者： Roger Ferdinan Krestella Margareta Stevan Christyan Maria Susan Anggreainy Afdhal Kurniawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Air pollution has emerged as a critical issue in several major urban areas, including Jakarta. To address this problem, the study explores the utilization of the Artificial Neural Network (ANN) method. Three distinct approaches are investigated in this research: the Support Vector Classifier (SVC), Deep Artificial Neural Network (Deep ANN), and Long Short-Term Memory (LSTM). The available Air Quality data were utilized to train and assess the performance of the proposed ANN models. The research findings reveal that the Deep ANN model surpasses the other methods, achieving an impressive accuracy of approximately 96.57% and a cross-entropy value of 0.1103. In predicting air quality in Jakarta, Deep ANN has demonstrated superior performance when compared to SVC and LSTM. These results highlight the significant potential of deploying Deep Artificial Neural Network (ANN) methodologies for air quality prediction. Such an approach could play a pivotal role in the development of monitoring and early warning systems aimed at effectively addressing air quality issues in the Jakarta region.

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A Review on Machine Learning Methods for Water Quality Prediction

A Review on Machine Learning Methods for Water Quality Predi...

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International Conference on Artificial Intelligence and Data Sciences (AiDAS)

作者： Vickenzio Leeputra Dharma Nathanael Kenneth Nurtanio Fakhru Sidik Nugroho Maria Susan Anggreainy Afdhal Kurniawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Water is an important substance for the human body. Clean water is important for not just the human body, but also for the environment. In this paper, Prisma is used to filter many of the reference paper where the paper left are used for the research. Machine learning is one of many ways to predict water quality. Using algorithms to process large amounts of data and patterns, water quality can be predicted. Prediction on water temperature, pH, and others can be used to find bad quality water and potentially find the cure for it. There are also different kinds of environment in predicting water quality, where depending on the environment certain machine learning method is better than the others. This paper contains machine learning methods, water parameters, and areas of water for water quality prediction.

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Analyzing Microservices and Monolithic Systems: Key Factors in Architecture, Development, and Operations

Analyzing Microservices and Monolithic Systems: Key Factors ...

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International Conference of computer and Informatics Engineering (IC2IE)

作者： Juan Christian Steven Afdhal Kurniawan Maria Susan Anggreainy Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Microservices and monolithic systems are two prevalent architectural approaches in software development. This study provides a complete review and analysis of the key components involved in design, development, and operation in software development. A systematic review of the literature was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This review examined various sources debating monolithic systems vs microservices, highlighting their benefits, drawbacks, and implementation in enterprises. The paper addresses important research questions, aiming to further analyze this architectural approach. Findings show that microservices offer benefits such as scalability, flexibility, and independent deployment, while monolithic systems provide simplicity and ease of development. However, challenges related to network communication, data consistency, and operational complexity were also found with microservices. This research focuses on discussing the trade-offs and factors to consider when deciding between monolithic systems and microservices, which provides in-depth information for practitioners in decision-making for software development. This research aims to help readers understand the effects of using monolithic or microservice-based systems in software development.

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Predicting Stock Market Movements Using Long Short-Term Memory (LSTM)

Predicting Stock Market Movements Using Long Short-Term Memo...

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International Conference on Artificial Intelligence and Data Sciences (AiDAS)

作者： Achmad Fauzan Maria SusanAnggreainy Nicholas Nathaniel Afdhal Kurniawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

This paper explores using artificial intelligence (AI) to predict stock market movements and build optimal portfolios. The research methodology involves using LSTM networks to predict stock performance. The study aims to combine AI with human expertise to develop an intelligent trading system. The findings emphasize the importance of selecting appropriate AI approaches for accurate predictions and optimal portfolio management. The results of this study state that with LSTM, we can predict stock prices that are very close to their real prices. The average LSTM method in predicting stock prices is about 97.2938%. Average error obtained when using LSTM was 2.3223%.

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