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

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Isnan, Mahmud Trinugroho, Joko Pebrianto Pardamean, Bens Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Binus Graduate Program Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features. © 2023 IEEE.

关键词： Proteins

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The technological potential of agro-industrial residue from grape pulping(Vitis spp.)for application in meat products:A review

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Food Bioscience 2022年第5期49卷 40-49页

作者： Marcelo Edvan dos Santos Silva Cristiani Viegas Brandao Grisi Suzana Pedroza da Silva Marta Suely Madruga Fabio Anderson Pereira da Silva Post-Graduate Program in Food Science and Technology Department of Food EngineeringTechnology CentreFederal University of ParaibaJoao PessoaParaiba58051-900Brazil Post-Graduate Program in Agrifood Technology Federal University of ParaibaCampus Universitario IIIBananeirasPBBrazil Federal University of Pernambuco’s Agreste Av.Bom PastorBoa Vista55296-901GaranhunsPEBrazil

This review aimed to investigate the technological potential of grape pomace for application in the meat industry,approaching the compounds of interest and presenting the challenges of its application in meat *** pomace contains bioactive and nutritional compounds with beneficial properties for maintaining the physical-chemical,sensory,and nutritional quality of meat and its *** studies have reported the presence of fiber,amino acids,essential fatty acids and minerals capable of increasing the nutritional value of meat products and promoting a nutrient supply when associated with animal ***,the bioactive composition of grape pomace has anthocyanins,flavanols and phenolic acids capable of increasing the shelf life of meat products,inhibiting microbial development,and delaying the oxidative processes responsible for the deterioration of sensory and nutritional *** use of nano and microencapsulation technologies and the use of active packaging as carriers of grape pomace extracts enhance their application in meat and meat *** technologies,applied individually or together,promote the controlled release of bioactive compounds during the storage and mask undesirable flavors promoted by phenolic *** addition to the technological potential,the use of grape pomace by the meat industry meets the current demand for the use of natural additives and is in accordance with the sustainable development objectives established by the United Nations Organization,reducing environmental impacts and allowing the obtaining molecules with high added value from industrial waste.

关键词： Natural antioxidant Grape bagasse Meat products Bioactive compounds Oxidation Waste reuse

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Opportunities and Challenges from Using Animal Videos in Reinforcement Learning for Navigation

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IFAC-PapersOnLine 2023年第2期56卷 9056-9061页

作者： Vittorio Giammarino James Queeney Lucas C. Carstensen Michael E. Hasselmo Ioannis Ch Paschalidis Division of Systems Engineering Boston University Boston MA 02215 USA Center for Systems Neuroscience Department of Psychological and Brain Sciences and Graduate Program for Neuroscience Boston University Boston MA 02215 USA College of Engineering and Hariri Institute for Computing & Computational Science & Engineering Boston University Boston MA 02215 USA

We investigate the use of animal videos (observations) to improve Reinforcement Learning (RL) efficiency and performance in navigation tasks with sparse rewards. Motivated by theoretical considerations, we make use of weighted policy optimization for off-policy RL and describe the main challenges when learning from animal videos. We propose solutions and test our ideas on a 2D navigation task. We show how the use of animal videos improves performance over RL algorithms that do not leverage such observations.

关键词： Learning for Control Reinforcement Learning Deep Learning RL with demonstrations Imitation Learning Bio-inspired Control

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Affordable ESP32-based monitoring system for microbial fuel cells: real-time analysis and performance evaluation (ESP32-based data logger as a monitoring system for microbial fuel cell)

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International Journal of Energy and Water Resources 2024年第2期8卷 199-212页

作者： Indriyani, Y.A. Efendi, R. Rustami, E. Rusmana, I. Anwar, S. Djajakirana, G. Santosa, D.A. Study Program of Soil Sciences Graduate Schools IPB University Bogor Indonesia Department of Mechanical Engineering Faculty of Engineering Universitas Sulawesi Tenggara Kendari Indonesia Department of Physics Faculty of Mathematics and Natural Sciences IPB University Bogor Indonesia Department of Biology Faculty of Mathematics and Natural Sciences IPB University Bogor Indonesia Department of Soil Science and Land Resource Faculty of Agriculture IPB University Bogor Indonesia Biotechnology Center Research and Community Empowerment Institute IPB University Bogor Indonesia

Microbial fuel cells (MFCs) are bioelectrochemical instruments that exploit living microbes as biocatalysts for the conversion of chemical energy stored in the bonds of substrates into electrical energy. Monitoring of current or voltage generation in microbial fuel cells at rigid time intervals is noteworthy. Unfortunately, bioelectrochemical studies are often constrained due to the need for expensive and precise devices for electrochemical measurements. Utilization of low-cost microcontroller as monitoring system can be a considerable solution, compared with data logger that is a high-cost device, or compared with commercial multimeter that have to be used manually in collecting data. In this present work, microbial fuel cells electricity generation was successfully online monitored and the voltage data was also successfully collected using an ESP32 based-data logger. Statistically, there was no significant difference between ESP32 and multimeter in voltage reading, and the average of absolute and relative error values were 0.85 mV and 2.72%, respectively. It suggested that ESP32 could be a possible and low-cost monitoring system for microbial fuel cells bioelectricity generation. Additionally, ADS1115 (an external analogue to digital converter interface to ESP32) has up to four analogue channels, whereas multimeter has just one. In this research, microbial fuel cell powered by isolate KCf1 generated highest voltage value of 789 mV and power density of 541 mW m⁻². © The Author(s), under exclusive licence to Iranian Society of Environmentalists (IRSEN) and science and Research Branch, Islamic Azad University 2023.

关键词： Biofuel Electricity Electrochemistry Green technology Renewable energy

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Development and demonstration of a prototype system for the early detection of drowning

Development and demonstration of a prototype system for the ...

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2022 International Electrical engineering Congress, iEECON 2022

作者： Konishi, Nagato Ishigaki, Yo Nakada, Tsubasa Nemoto, Wataru Iinuma, Seizi Hoshino, Taisuke Ohkawara, Kazunori University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Japan Chief Executive Officer Plan I Corporation University of Electro-Communications Bioscience and Technology Program Department of Engineering Science Tokyo Japan

ISBN: (纸本)9781665402064

We have developed a small device that can measure 3-axis acceleration and pressure underwater, and collected data on drowning reactions by asking participants to reproduce the characteristic behavior of drowning. The data collected in this study is expected to aid the early detection of drowning reactions and reduce the number of accidents caused by drowning. © 2022 IEEE.

关键词： Accidents

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Fuzzy Intelligence Learning Based on Bounded Rationality in IoMT Systems: A Case Study in Parkinson's Disease

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IEEE Transactions on computational Social Systems 2023年第4期10卷 1607-1621页

作者： Zhang, Chao Ding, Juanjuan Zhan, Jianming Sangaiah, Arun Kumar Li, Deyu Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University School of Computer and Information Technology Taiyuan030006 China Hubei Minzu University School of Mathematics and Statistics Enshi445000 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102-2801 Lebanon

As a cause of interfering with routine activities, freezing of gait (FOG) is a severe syndrome of Parkinson's disease (PD) and usually performs as an abrupt and momentary inability to effective stepping forward. Advanced wearable acceleration sensors based on socially implemented Internet of medical things (IoMT) devices can remotely provide a platform for recognizing FOG. However, due to the diverse data acquisition modes that appear in classic IoMT devices, the obtained data may contain imprecise, hesitant, and incomplete ones. Meanwhile, the bounded rationality owned by neurologists usually has a big impact on using wearable acceleration sensors to predict illnesses. Therefore, the objective of this article lies in exploring a fuzzy intelligence learning approach based on bounded rationality in IoMT systems and providing a valid scheme for biomedical data analysis. Specifically, a brand-new three-way group decision-making approach by means of TODIM (an acronym in Portuguese for interactive multicriteria decision-making) with incomplete dual hesitant fuzzy (DHF) information and its applications in detecting FOG in PD using IoMT devices are systematically explored. First, taking advantage of DHF sets (DHFSs) when depicting realistic group decision information, the concept of multigranulation (MG) incomplete DHF information systems is built. Second, adjustable MG DHF probabilistic rough sets (PRSs) are further put forward via DHF similarity relations. Third, a three-way group decision-making approach is constructed by virtue of adjustable MG DHF PRSs and TODIM. Finally, the validity, effectiveness, and practicality of the constructed three-way group decision-making approach are investigated by a University of California, Irvine (UCI) dataset with several experimental analyses in the background of FOG detection in PD using IoMT devices. The experimental result indicates that the developed fuzzy intelligence learning approach achieves reasonable diagnostic conclusions

关键词： Diagnosis

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The Utilization of Fuzzy Logic and Bollinger Bands to Enhance Trading Decision-Making During the Bitcoin Halving Phase

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Procedia Computer science 2024年 245卷 272-281页

作者： Oscar Adam Darmawan Yaya Heryadi Lukas Lili Ayu Wulandhari Ilvico Sonata Computer Science Department Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta 12930 Indonesia

The volatile behavior of Bitcoin's price, especially during its halving periods, poses considerable obstacles for forecasting and decision-making in cryptocurrency trading. This paper presents a novel method that combines application fuzzy logic with Bollinger Bands to improve trading decision-making in times of high market volatility. This study conducted an experiment utilizing three fuzzy logic controllers and Bollinger Bands (BB) to determine the strength of buy, hold, and sell signals. This dataset includes the initial and final prices that are used to calculate the BB. The raw and computed values serve as the precise input parameters for the Fuzzy Inference System (FIS). The membership functions were categorized into four levels: very low, low, high, and very high, based on the input default settings utilized by traders. Rulesets were created using fuzzy logic to produce signals that indicate the level of strength of a trading advice. This study evaluate the effectiveness of this hybrid method in comparison to the traditional utilization of the Bollinger Band only indicator and Moving Average Convergence Divergence (MACD) indicator, which is widely favored by traders to identify possible market fluctuations. This methodology involves creating a trading simulation that is based on past Bitcoin halving events. The objective is to assess the efficacy of these strategies in managing heightened volatility. The application of fuzzy logic with the Bollinger Bands model yielded a success rate of 92.47% while analyzing 93 daily data points from the previous Bitcoin halving event on May 11, 2020.

关键词： fuzzy logic crypto price prediction bitcoin bollinger bands macd cryptocurrency

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YOLOS-MCI: Automating Microservices Coupling Index Calculation using Vision Transformers

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Procedia Computer science 2024年 245卷 924-933页

作者： Gintoro Yaya Heryadi Lukas Lili Ayu Wulandhari Ilvico Sonata Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta 12930 Indonesia

Microservice Architectures (MSA) provide flexibility and scalability in software development. However, accurately measuring the level of interdependence among Microservices continues to be a difficult task. Precisely evaluating this connection is essential for efficient MSA design, maintenance, and future development. Conventional techniques for assessing Microservice coupling are frequently done by hand, require a significant amount of time, and are susceptible to mistakes. This impedes the capacity to make well-informed judgments regarding the integration and adjustment of services. This study introduces a new method for automating the computation of the Microservice Coupling Index (MCI) by utilizing the You Only Look at One Sequence (YOLOS) object identification technique in combination with Vision Transformer (ViTs) technology. YOLOS is utilized for identifying constituents within Unified Modeling Language (UML) Component Diagrams, facilitating precise classification and effective assessment of coupling. The model exhibits varying performance over multiple Intersection over Union (IoU) thresholds and object sizes, with an average precision (AP) of 0.406 over IoU values ranging from 0.50 to 0.95. The maximum precision is achieved at an IoU of 0.50, with an AP of 0.709. The model demonstrates good performance in identifying smaller components, especially when evaluated at a 0.75 IoU threshold. However, it faces challenges in detecting small items, suggesting potential areas for improvement in future iterations. Initial results indicate that this automation greatly decreases the need for manual, labor-intensive tasks and enhances the precision of measuring coupling in MSA, hence facilitating effective decision-making in service integration and modification. Automating the computation of the coupling index has the potential to significantly influence the design and management of durable and readily controllable microservice architectures.

关键词： Coupling Microservices Microservices Coupling Index UML Component Diagrams Vision Transformer YOLO

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To Sharpen or not to Sharpen? On the Effect of Sharpening Filters Applied to Magnified Images 14

To Sharpen or not to Sharpen? On the Effect of Sharpening Fi...

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14th International Conference on Digital Image Processing, ICDIP 2022

作者： Emmendorfer, Leonardo R. de Olviveira, Vinicius Menezes Schein, Tatiana T. de Jesus, Junior C. Rodriguez, Bárbara D.A. Electrical Energy Processing Department Federal University of Santa Maria 97105-340 Brazil Center for Computational Science Federal University of Rio Grande 96201-900 Brazil Computational Modeling PhD Program Federal University of Rio Grande 96201-900 Brazil Computer Engineering Graduate Program Federal University of Rio Grande 96201-900 Brazil Institute of Mathematics Statistics and Physics Federal University of Rio Grande 96201-900 Brazil

ISBN: (纸本)9781510657564

Super-resolution algorithms aim to produce magnified high-resolution versions from low-resolution images. Some methods, however, are prone to generate blur during the process. Simple sharpening filters are adopted to alleviate this type of artifact. However, the actual effectiveness of this approach is not clear-cut in the literature. This work evaluates the effect of three simple sharpening filters on the quality of images obtained from super-resolution methods. Two metrics were considered in the evaluation: the Peak signal-to-noise ratio (PSNR) metric, and the Learned Perceptual Image Patch Similarity (LPIPS). One of the filters could consistently improve the LPIPS metric of magnified images from diverse benchmark sets on top of seven super-resolution methods. The increments obtained for the perceptual metric seem to occur due to the sharpening effect. Improvements on PSNR values were not as consistent. © 2022 SPIE.

关键词： Quality control

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ConvNeXt Model for Breast Cancer Image Classification

ConvNeXt Model for Breast Cancer Image Classification

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Devin Setiawan Andrea Stevens Karnyoto Indo Intan Bens Pardamean Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83.

关键词： Support vector machines Accuracy Head computational modeling Transfer learning Breast cancer Magnetic heads Data models Reliability Image classification

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