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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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Restaurant Customer Feedback Sentiment Analysis using Aspect Embedding Long Short-term Memory Model

Restaurant Customer Feedback Sentiment Analysis using Aspect...

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

作者： Heryadi, Yaya Wijanarko, Bambang Dwi Fitria Murad, Dina Tho, Cuk Hashimoto, Kiyota BINUS Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Information Systems Department Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket83120 Thailand

ISBN: (纸本)9798350320954

In the past ten years, there has been a proliferation of unstructured textual data from a variety of fields, allowing for the analysis of the sentiment polarity of the text authors in each of those datasets. To analyze sentiment polarity from unstructured data, machine learning tasks such as aspect-based sentiment analysis are used. The public dataset of restaurant patron feedback was used as the input for this study's empirical results of aspect-based sentiment analysis utilizing the Aspect Embedding Long Short-Term Memory model. The primary experiment's results show that the aspect embedding long short-term memory model can predict aspect sentiment polarization from customer feedback to restaurant service and aspect text representation (aspect text embedding) as input with excellent performance. For example, the Aspect Embedding Long Short-term Memory model, which uses ReLU as its activation function in the Dense layer tends to achieve higher performance (0.97 average accuracies) than the same model equipped with Sigmoid, and Tanh functions which achieve 0.954 and 0.956 average training accuracy respectively. © 2023 IEEE.

关键词： Sentiment analysis

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Comparing the Effect of Attention Mechanism and Aspect Embedding on the Performance of Long Short-term Memory Model for Aspect-Based Sentiment Analysis

Comparing the Effect of Attention Mechanism and Aspect Embed...

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

作者： Heryadi, Yaya Wijanarko, Bambang Dwi Murad, Dina Fitria Tho, Cuk Hashimoto, Kiyota BINUS Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Information Systems Department Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket83120 Thailand

ISBN: (纸本)9798350320954

The attention mechanism is one of the key enablers which have positioned transformer models as the state-of-the-art models in Natural Language Processing. By having the attention mechanism, the first version (vanilla) transformer model can retrieve the hidden state but at the same time can maintain a low requirement on the context vector dimension by selectively choosing to which encoder hidden states to give priority attention. The objective of this study is to evaluate the effect of modified Long Short-term Memory models architecture on the model performance as a predictive model. The first model is modified by adding an attention mechanism to its architecture;whilst, the second model is modified by adding additional input to its architecture. The performance of both models is compared by assigning each model the same task: to address the aspect-based sentiment analysis task. The experiment results show that adding an attention mechanism is not significantly increase the performance of the Long Short-term Memory model in comparison to adding input to the same model. In particular, the average training accuracy of the former model is 0.885;whilst, the average training accuracy of the latter model is 0.942. © 2023 IEEE.

关键词： Sentiment analysis

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Into 3D-Reconstructed Apparel over Virtual Try-On with Virtual Reality

Into 3D-Reconstructed Apparel over Virtual Try-On with Virtu...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Doungtap, Surasachai Petchhan, Jirayu Phanichraksaphong, Varinya Wang, Jenq-Haur International Graduate Program of Electrical Engineering and Computer Science Taiwan National Taiwan University of Science & Technology Dept. of Electrical Engineering Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

Virtualization technologies are still growing bigger and faster. Despite the greatness of its advancement, the costume industry is still very accessible when it comes to real trials. Off-the-shelf stuff are inadequate details for the desired individual to assess its in-depth utility for each garment trying on for a second, including custom stuff are much harder to try out right away. To this end, 2D image-based 3D reconstruction inclusive of touchable-virtualized space is accessible easier to stuff details for mans' decision making in purchasing. We establish the overall end-to-end pipeline from reconstruction until visualization for one instance to be triable on its stuff for a moment. As an expectation, our proposed approach can bring objects into the experimental area and use them immediately without any obstacle. © 2023 IEEE.

关键词： Virtual reality

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E-Govchurch as a Digital Transformation Initiative for Nation-wide Christianity Organization 1

E-Govchurch as a Digital Transformation Initiative for Natio...

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1st International Conference on Technology Innovation and Its Applications, ICTIIA 2022

作者： Paramita, Adi Suryaputra Prabowo, Harjanto Ramadhan, Arief Sensuse, Dana Indra Graduate Program Universitas Ciputra Surabaya Bina Nusantara University Surabaya Indonesia/Jakarta Information Systems Department/Doctor Of Computer Science Indonesia Doctor Of Computer Science Graduate Program Bina Nusantara University Jakarta Indonesia Universitas Indonesia Faculty Of Computer Science Depok Indonesia

ISBN: (数字)9781665488266

ISBN: (纸本)9781665488266

The process of digital transformation is part of the 4.0 industrial revolution. Through digital transformation, it is hoped that all processes inside an organization will function more efficiently. Internet technology is the major driver of an organization's digital transformation effort. Information Systems for the statewide scope of the two main religion-based organizations in Indonesia are required to satisfy the Indonesian government's National Medium-Term Development Plan for the years 2020-2024 in accordance with Indonesian President Decree No. 18 of 2020. The regulation mandates that the ministry of religious affairs provide information and data services. E-Govchurch, an abbreviation for Electronic-Governmental Church, is the suggested framework. E-Govchurch is a digital program for Information Systems that incorporates a framework for data governance. E-Govchurch will develop utilizing SSM, RBV, the Zachman Framework, and the DAMA framework. The purpose framework in research will lead the next phase of framework development process. © 2022 IEEE.

关键词： Information systems

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Aspect-based Sentiment Analysis using Long Short-term Memory Model for Leveraging Restaurant Service Management

Aspect-based Sentiment Analysis using Long Short-term Memory...

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

ISBN: (纸本)9798350320954

In general, the hospitality industry has been acknowledged as a major sector that gives a high contribution to economic development in many countries including Indonesia. For that reason, many initiatives have been implemented to help the growth of the hospitality industry in many countries including Indonesia to rebound from the harsh impact of the Covid-19 Pandemic. One such initiative is improving restaurant services as the main sector of the hospitality industry. This paper presents empirical results of sentiment analysis as a means to assess the quality of restaurant services as the first step to improving service quality. In particular, this study explores the aspect-based sentiment analysis method to identify some aspects of restaurant service which need improvement by learning the polarity of customers toward the restaurant services without having to meet the customers directly. By using the aspect-based sentiment analysis method, the customer sentiments comprising opinions, sentiments, evaluations, attitudes, and emotions from restaurant service can be analyzed using customers' online reviews as input. The main experiment findings showed that the Long Short-term Memory model can achieve high performance in predicting aspect polarization in restaurant service reviews. Other findings suggest that Sigmoid as an activation function achieved 0.97 average training accuracy and 0.69 average testing accuracy giving a better performance to the model in comparison to ReLU, Tanh, and ELU activation functions. © 2023 IEEE.

关键词： Sentiment analysis

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A Systematic Literature Review of Deep Learning Application in Multiclass Anomaly Detection for Chest Medical Imaging 6

A Systematic Literature Review of Deep Learning Application ...

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6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Philips Pardamean, Bens Elwirehardja, Gregorius Natanael Isnan, Mahmud Bina Nusantara University Binus Graduate Program - Master of Compuer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia

ISBN: (纸本)9798350345162

Medical imaging abnormality detection is challenging, but deep learning approaches have shown promise. This paper reviews the current state of the art in deep learning approaches for detecting abnormalities in chest medical imaging. To discover the trends, opportunities, and challenges associated with this field, 18 studies were selected from Google Scholar based on their titles, abstracts, and contents for extensive review to answer two research questions. The study found that the National Institutes of Health (NIH) Chest X-ray 14 dataset is the most used dataset for this task. Most research uses a single-modal approach, considering only image data as input, with X-ray being the more popular instrument. There are 8 out of 18 studies leverage the transfer learning approach, with ResN et50 being the most popular network. MobileNetV2 has demonstrated competitive results compared to more robust networks. Preprocessing techniques such as image enhancement and data augmentation are leveraged by 61.1 % of the reviewed studies and are shown to improve model performance. © 2023 IEEE.

关键词： abnormality detection attention mechanisms chest x-ray CT images deep learning transfer learning

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BERT and RoBERTa Models for Enhanced Detection of Depression in Social Media Text 9th

BERT and RoBERTa Models for Enhanced Detection of Depression...

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

作者： Kurniadi, Felix Indra Paramita, Ni Luh Putu Satyaning Pradnya Sihotang, Erna Fransisca Angela Anggreainy, Maria Susan Zhang, Rongfang Computer Science Department School of Computer Science Bina Nusantara University Jakarta11530 Indonesia Statistic Department School of Computer Science Bina Nusantara University Jakarta11530 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11530 Indonesia Department of Rehabilitation and Health Service University of North Texas United States

Depression is a disease that affects everyone, both young and old. This mental illness not only affects the surrounding environment but everyone. Depression is characterized by deep sadness, behavioral changes and many other actions that are risky for people. In this research we try to solve the problem of detecting depression using Natural Language Processing (NLP) approaches, these two methods are Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT Approach (RoBERTa), where these two methods are used to detect posts made in reddit. The dataset is taken from Kaggle. The results obtained found that the average use of BERT and RoBERTa resulted in a high accuracy value of around 98% and with a well balanced precision, recall and F1-Score ratio. This research shows that there is a possibility of using BERT and RoBERTa in depression detection. © 2024 The Authors.

关键词： BERT depression natural language processing RoBERTa

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Effects of a Combination of Phytoestrogens on Risk Factors for Cardiovascular Diseases in Climacteric Women: A Randomized, Placebo-controlled Trial

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Current Bioactive Compounds 2023年第2期19卷 20-28页

作者： de Barros, Evayne dos Santos, Paola Cristine de Bortoli Frigo, Maiara Weber, Jucieli Zanelatto, Carla Koehnlein, Eloá Angélica Course of Nutrition Federal University of Fronteira Sul Realeza Brazil Post Graduate Program in Food Science and Technology Federal University of Fronteira Sul Laranjeiras do Sul Brazil University center of Pato Branco Brazil

Background & Aims: The incidence of cardiovascular disease in climacteric women has increased, becoming the main cause of death in mature women. The use of phytoestrogens, isoflavones, and lignans derived from soy and flaxseed can be natural alternative treatments for this phase of life. This study investigates the utility of isoflavones and lignans derived from soy and flaxseed as natural alternatives to pharmaceuticals to alleviate the symptoms of menopause. Methods: A clinical, prospective, randomized, blind, placebo-controlled trial was performed. For-ty-eight women aged 40 to 65 years were divided into two groups, Phytoestrogen (PHY), which received a cereal bar containing isoflavones and lignans for 90 days, and Placebo (PLA), which received expanded rice flake bar. The sociodemographic, clinical, anthropometric data, blood pressure (BP) measurements, and biochemical and hormonal tests were performed. Results: Forty-three women were included in this study (PHY=21 and PLA=22). There was a significant reduction in diastolic BP in the PHY group after the intervention (p<0.001). The frequen-cy of BP fluctuations in the PHY group significantly decreased to 23.8% after the intervention period (p=0.027), with no significant differences for the other indicators studied. Conclusion: The combination of phytoestrogens showed a trend in reducing the blood pressure of climacteric women. However, no significant changes in cardiovascular risk factors were observed. Clinical Trial Registration: The clinical trial was registered on the REBEC-Brazilian Clinical Trials Registry, number 6z8qqy (https://***/). © 2023 Bentham science Publishers.

关键词： cereal bar estrogen deficiency isoflavones lignans lipid metabolic disorder Menopause

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Performance Variations in Digital Talent: Gender-Based Insights from STEM and AI Education Backgrounds 4

Performance Variations in Digital Talent: Gender-Based Insig...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Halim, Erwin Sari, Azani Cempaka Wairooy, Irma Kartika Ario, Muhamad Keenan Achmad, Said Fitrianah, Devi School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331506490

In the digital era, digital talent development has become increasingly vital for organizations and economies to thrive. While much focus has been placed on technical skills, this report emphasizes the often-overlooked role of soft skills in developing digital talent. Through an extensive literature review and data analysis, the study identifies that soft skills are crucial in enhancing digital talent performance. This research explores the factors that influence Digital Talent Performance in Indonesia, specifically comparing the performance levels of male and female digital talents. Data were collected from college students in the Jabodetabek area (cities surrounding Jakarta, Indonesia) using a purposive sampling method, with an online questionnaire distributed to 378 respondents. The study utilized Structural Equation Modeling (SEM) to analyze the collected data using SmartPLS 4.1. software. Key variables examined include Communication Skills, Teamwork, Emotional Intelligence, Academic Achievement, Problem-Solving, Digital Readiness, Digital Skills, Digital Technology, and Digital Talent Performance. Out of the nine hypotheses tested, one was not statistically significant. The results suggest that a combination of both technical and soft skills significantly affects digital talent performance, with important gender-based differences in performance levels emerging from the analysis. This research contributes to understanding Indonesia's complex dynamics of digital talent development. © 2024 IEEE.

关键词： digital skill digital talent soft skills STEM AI talent performance

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