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International Symposium on Grids and Clouds 2023 in conjunction with HEPiX Spring Workshop, ISGC and HEPiX 2023

作者： Suhardiman, Basuki Hermanto, Benni Rio Directorate of Information Technology Institut Teknologi Bandung Jl Ganesha 10 Bandung Indonesia Master’s Program in Computational Science Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung Jl Ganesha 10 Bandung Indonesia Biomedical Engineering Department School of Electrical Engineering and Informatics Institut Teknologi Bandung Indonesia

Forest fires have been detected to occur in Indonesia since 1998. The forest fires mostly resulted from human activities in order to expand their land, especially oil palm lands. Once a land clearing is carried out, it covers areas of tens to hundreds of thousands of hectares. The peak of the forest fires occurred in 2015 when almost half of the world was affected especially by smoke from the fire. In 2011, VIIRS (Visible Infrared Imaging Radiometer Suite) was launched by NOAA (National Oceanic and Atmospheric Administration). In their collected data, there is data on Indonesia issued daily for areas in the islands of Java, Sumatra, Kalimantan, Sulawesi, and the general areas of Indonesia. The VIIRS night data shows the possibility of some burning at night. We need to identify burnings that potentially become fires, which are potentially producing smoke. It is important to observe areas where large-scale forest fires frequently occur, so the authority could do some prevention beforehand. Using existing data released per day per area, we utilize an ANN (Artificial Neural Network) to identify a potential fire. The processes start with data cleaning and processing, Neural Network creation, and finally ANN training and testing. By using the ANN prediction model with the MinMax Scaler, we choose variables Temperature, Radiant Heat Intensity, and Source Footprint. Simulations are made to show the ranges of these variables that predict the possibility of’flame’ status occurring. We conclude that the ANN method can give more accurate results than the existing classification. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Fires

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Mapping the Literature of Artificial Intelligence in Medical Education: A Scientometric Analysis 7th

Mapping the Literature of Artificial Intelligence in Medical...

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7th International Conference on Inventive Communication and Computaional Technologies, ICICCT 2023

作者： Maulana, Fairuz Iqbal Zaın, Muhammad Yasır Lestari, Dian Purnomo, Agung Adi, Puput Dani Prasetyo Computer Science Department Bina Nusantara University Jakarta11480 Indonesia Informatics Engineering Study Program University of Madura Pamekasan Indonesia Faculty of Medicine Universitas Airlangga Surabaya60132 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Jakarta11480 Indonesia Telecommunication Research Center National Research and Innovation Agency Jakarta11480 Indonesia

ISBN: (纸本)9789819951659

This study aims to investigate the use of artificial intelligence in health education in the last ten years from 2012 to 2022 using the Scopus database. Researchers use bibliometric analysis combined with the quantification method of Vosviewer and Rstudio software used for literature analysis. The results of the article that were flushed showed data such as the year of publication, journal, country, keywords, and authors, to the highest number of citations. The general keywords used by researchers are artificial intelligence, medical, and education. Researchers limit findings through keywords as in the last ten years and English only so that 1681 articles were obtained. According to the study's findings, McGill University affiliation had the greatest number of papers, with 69 pieces, and medicine had a proportion of 39.7% (n = 1067). This bibliometric study will be useful for other researchers to examine the development of research on artificial intelligence in health education in the last ten years. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Medical education

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Propolis nanoemulsion extract from celebes stingless bee (Tetragonula biroi) phytochemistry and antibacterial analysis to periodontopathogen bacteria

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Journal of Oral Biology and Craniofacial Research 2025年第3期15卷 576-584页

作者： Amalia, Nadya Rafika Budhy, Theresia Indah Ridwan, Rini Devijanti Rianti, Devi Bramantoro, Taufan Luthfi, Muhammad Ramadhani, Nastiti Faradilla Pramusita, Adya Putranti, Nurul Aisyah Rizki Nugraha, Albertus Putera Situmorang, Putri Cahaya Shariff, Khairul Anuar Nugraha, Alexander Patera Dental Health Science Study Program Faculty of Dental Medicine Universitas Airlangga Surabaya Indonesia Department of Oral and Maxillofacial Pathology Faculty of Dental Medicine Universitas Airlangga Surabaya Indonesia Department of Oral Biology Faculty of Dental Medicine Universitas Airlangga Surabaya Indonesia Department of Dental Material Faculty of Dental Medicine Universitas Airlangga Surabaya Indonesia Department of Dental Public Health Faculty of Dental Medicine Universitas Airlangga Surabaya Indonesia Department of Dentomaxillofacial Radiology Faculty of Dental Medicine Universitas Airlangga Surabaya Indonesia Department of Orthodontic Faculty of Dental Medicine Universitas Airlangga Surabaya Indonesia Faculty of Medicine Universitas Airlangga Surabaya Indonesia Biology Study Program Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Medan Indonesia School of Materials and Mineral Resources Engineering Engineering Campus Universiti Sains Malaysia Penang Nibong Tebal 14300 Malaysia

Background: Propolis from Sulawesi's stingless bees (Tetragonula biroi) contains antioxidants, more flavonoids than propolis from Apis bees, and the antibacterial ability. Objective: to examine the antibacterial properties of Propolis Nanoemulsion Extract (PNE), which is extracted from the Celebes Stingless Bee (T. biroi), in relation to the periodontopathogen bacteria such as Fusobacterium nucleatum (Fn), Porphyromonas gingivalis (Pg), Aggregatibacter actinomycetemcomitans (Aa), and Provotella intermedia (Pi). This investigation also examines PNE's phytochemistry, particle size analysis (PSA), and zeta potential. Methods: The maceration process with 96 % ethanol was used to create PNE from Celebes stingless bee (T. biroi), which was then subjected to zeta potential measurement and PSA. Phytochemical analysis was used to identify phytochemical constituents in the PNE (T. biroi). Diffusion zone, minimum bactericidal concentration (MBC), and minimum inhibitory concentration (MIC) were used to assess antibacterial efficacy against Aa, Pg, Pi, and Fn. Furthermore, the statistical analysis was used to extract the data. Results: Phenols, alkaloids, and flavonoids were identified;however, triterpenoids and saponins were not. Between 151.28 and 182.2 diameter nanometers (***) was the range of the PNE's diameter. At 1.56 % propolis (T. biroi) concentration, the MIC, MBC, and diffusion zone analysis performed better than at 0.76 %, with a significant difference (p:0.01;p < 0.05) to Aa, Pg, Pi, and Fn. Conclusions: The highest antibacterial activity against Aa, Pg, Pi, and Fn as periodontopathogen bacteria is demonstrated by alkaloids, flavonoids, and phenols in PNE from Celebes (T. biroi) at a concentration of 1.56 %. © 2025 The Authors

关键词： Antibacterial Communicable disease Infectious disease Medicine Periodontal disease

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Vision Transformer Algorithm for Plant Disease Detection: A Systematic Literature Review

Vision Transformer Algorithm for Plant Disease Detection: A ...

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International Conference on informatics and Computing (ICIC)

作者： Reza Al Husna Lintang Yuniar Banowosari Tubagus Maulana Kusuma Tri Handhika Doctoral program Information Technology Universitas Gunadarma Jakarta Indonesia Department of Informatics Program Universitas Gunadarma Jakarta Indonesia Department of Technology and Engineering Universitas Gunadarma Jakarta Indonesia Department of Computational Mathematics Studies Universitas Gunadarma Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Advances in deep learning have significantly impacted various sectors, including agriculture, by improving efficiency and sustainability. Computer vision-based plant disease detection that utilizes deep learning models such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) has replaced manual methods that require specialized skills. Vision Transformers (ViTs) with self-attention mechanisms excel at recognizing complex patterns in images of diseased plants, enabling early detection and more effective management. This study conducted a systematic literature review to evaluate the use of feature fusion-based transformer algorithms in plant disease detection and to identify current research trends, challenges, and future opportunities. The results show that ViTs can improve accuracy and efficiency in plant disease diagnosis, particularly under diverse environmental conditions. However, the adoption of this technology faces obstacles such as the need for large annotated datasets, significant computational costs, and variability in field conditions that can affect model performance. This research highlights the need for further innovation to address these challenges and expand the accessibility and reliability of deep learning-based plant disease detection for the global agricultural sector.

关键词： Deep learning Plant diseases Computer vision Technological innovation Accuracy Computational modeling Transformers Feature extraction Computational efficiency Systematic literature review

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Development and Validation of Customer Intention Factors for Live-Stream Shopping 7

Development and Validation of Customer Intention Factors for...

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7th International Conference on New Media Studies, CONMEDIA 2023

作者： Fernando, Erick Hidayat, Desman Pengaribuan, Christian Haposan Prabowo, Yulius Denny Universitas Multimedia Nusantara Faculty of Engineering and Informatics Department of Information System Tangerang Indonesia Bina Nusantara University Binus Entrepreneurship Center Management Department Jakarta11480 Indonesia Sampoerna University Management Study Program Faculty of Business Jakarta12780 Indonesia Bina Nusantara University Binus Online Learning Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307504

Along with technological improvements, online shopping is currently developing quickly. Online shops started by selling electronics, clothing, food, and home appliances and continue to evolve, selling various things. Currently, digital marketing has a novel technique, namely live shopping, where sellers can present, promote and offer their products directly through live streaming on social media and e-commerce platforms. This research will focus on gaining insight into the factors influencing customers to watch and purchase through live streaming. This research is an early-stage study of exploring and searching for research models. The study used a literature review approach by reviewing similar previous research articles. Several variable components were used in this study. The findings of the factors include visibility capacity, meta-vocation capacity, purchase orientation capacity, social presence, price, perceived product quality, perceived enjoyment, purchase orientation, perceived usefulness, and intention to buy. These factors were tested and proved to be valid with Cronbach's Alpha value being more than 0.5 and reliable with the value of the loading factor a greater than 0.6. Criteria The factors of the findings for further analysis of the relationship between factors and other variables. © 2023 IEEE.

关键词： Domestic appliances

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Integrated microalgae culture with food processing waste for wastewater remediation and enhanced biomass productivity

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Chinese Chemical Letters 2023年第2期34卷 636-643页

作者： Angela Paul Peter Kit Wayne Chew Apurav Krishna Koyande Heli Siti Halimatul Munawaroh Amit Bhatnagar Yang Tao Chihe Sun Fubao Sun Zengling Ma Pau Loke Show Department of Chemical and Environmental Engineering Faculty of Science and EngineeringUniversity of Nottingham MalaysiaJalan Broga43500 SemenyihSelangor Darul EhsanMalaysia Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection Wenzhou UniversityWenzhou 325035China Study Program of Chemistry Department of Chemistry EducationFaculty of Mathematics and Science EducationUniversitas Pendidikan IndonesiaBandung 40154Indonesia Department of Separation Science LUT School of Engineering ScienceLUT UniversitySammonkatu 12FI 50130MikkeliFinland College of Food Science and Technology Nanjing Agricultural UniversityNanjing 210095China Key Laboratory of Industrial Biotechnology Ministry of EducationSchool of BiotechnologyJiangnan UniversityWuxi 214122China Department of Sustainable Engineering Saveetha School of EngineeringSIMATSChennai 602105India

Waste generation from food manufacturing facilities poses a serious hazard like environmental degradation, water pollution, and land pollution due to its high nutrient composition. Specifically, solid waste(powder) disposal requires additional energy sources in terms of scientific treatment, structured collection, and disposal packaging according to the safety regulation. Thus, this research discusses the viewpoint of integrating food processing waste as an organic carbon source with BG-11 medium for Chlorella vulgaris(FSP-E) growth. The food processing waste powders investigated in this study were obtained from milk, and biscuit manufacturing facilities. The culture medium was modified by combining both BG-11 and food processing waste powders to identify the optimal algal growth and biochemical *** to the microalgae grown in BG-11 alone(IBG), the combination of biscuit waste and IBG produced higher biomass concentration(44%), with increased lipid(11%), protein(20%), and carbohydrate(57%) contents. Chlorella vulgaris was able to uptake nutrients from the culture medium with combination of food processing waste and IBG thus enhancing its growth. The results obtained also indicate that an integrated culture system using food processing waste and synthetic sources can generate energy out of waste by improving the bio-composition of the microalgae biomass.

关键词： Medium contamination Food processing Waste powder Organic medium Waste remediation

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An Investigation of Ultrashort Pulses Propagation in Atmospheric Turbulence of FSO Communications 9

An Investigation of Ultrashort Pulses Propagation in Atmosph...

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9th International Conference on Electrical engineering, Computer Science and informatics, EECSI 2022

作者： Darusalam, Ucuk Priambodo, Purnomo Sidi Universitas Nasional Informatics Study Program Universitas Siber Asia of IEEE Photonics Society Informatics Faculty of Information and Communications Technology Jakarta Indonesia Universitas Indonesia IEEE Photonics Society Faculty Engineering Department of Electrical Engineering Depok Indonesia

ISBN: (数字)9786239213565

ISBN: (纸本)9781665470438

The atmospheric turbulence as the natural media of optical propagation for the free-space optical (FSO) communications is the great advantage to reach flexible connection and low-cost deployment but also is the major problem as well. Since, the phenomena of optical propagation in the atmosphere are random process, an optical signal is induced that produces severe effects to the performance. Thus, a method to reach best performance of FSO must be developed intensively to reduce the severe effects of atmospheric turbulence. One of the solutions to reduce those aforementioned effects is an ultrashort pulse implemented in FSO to modulate frequency carrier as the signal of optical propagation. In this paper, an investigation of an ultrashort pulse was performed through simulation. An ultrashort pulse has a great chance to be implemented in FSO since it has shorter width of pulse duration than a short pulse. An ultrashort pulse is generated using optical second harmonic pulse generator that produce a pulse at 50 fs of width. Based on the results of simulation, the ultrashort pulses showing better performance of FSO than short pulses have. © 2022 Institute of Advanced engineering and Science (IAES).

关键词： atmospheric turbulence free-space optical communications optical propagation optical scintillation temporal broadening turbulence effects ultrashort pulses

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The Scientific Progress and Prospects of Artificial Intelligence for Cancer Detection: A Bibliometric Analysis 24

The Scientific Progress and Prospects of Artificial Intellig...

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24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023

作者： Maulana, Fairuz Iqbal Adi, Puput Dani Prasetyo Lestari, Dian Ariyanto, Sandy Vikki Purnomo, Agung Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia National Research and Innovation Agency Telecommunication Research Center Jakarta11480 Indonesia Universitas Airlangga Faculty of Medicine Surabaya60132 Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia Bina Nusantara University BINUS Business School Undergraduate Program Entrepreneurship Department Jakarta11480 Indonesia

ISBN: (纸本)9798350313956

Using the Scopus database, this study aims to investigate the use of artificial intelligence for cancer detection in the last ten years from 2013 to 2022. The researchers used bibliometric analysis combined with VosViewer and Rstudio software quantification method for literature analysis. The results of the flushed articles show data such as publication year, journal, country, keywords, and authors, to the highest number of citations. Common keywords used by researchers are artificial intelligence, medical, and human. Researchers limited the findings through keywords such as in the last ten years, documents are journals, and English only so that 1868 articles were obtained. The results found that Harvard Medical School affiliation had the highest number of articles, with 101 articles, by subject area, Medicine had a proportion of 41.9% (n=1320). This bibliometric study will be useful for other researchers to examine the development of research on Artificial Intelligence for Cancer Detection in the last ten years. © 2023 IEEE.

关键词： Diseases

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Use of an Expert System to Diagnose and Provide Solutions for Pests and Diseases in Hydroponic Mustard Plants Using Certainty Factor and Forward Chaining Methods

Use of an Expert System to Diagnose and Provide Solutions fo...

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Science and Information Technology in Smart Administration (ICSINTESA), International Conference of

作者： Mahesa Bayu Baihaqi Aris Puji Widodo Ferry Jie Master of Information System Study Program Diponegoro University Central Java Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro University Central Java Indonesia School of Business and Law Edith Cowan University Australia

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

Mustard greens are a vegetable that is very popular with people, especially hydroponic mustard greens, which appear fresher. However, like other vegetables, mustard greens grown hydroponically are also susceptible to pests and diseases, which can cause the vegetables to lose their freshness and may even result in crop failure. This condition can cause losses for mustard greens farmers in running their businesses. This research proposes an information system that represents the expertise of hydroponic mustard greens farmers in diagnosing pests and diseases in mustard plants as well as solutions to overcome them. This is to make it easier for novice growers and anyone interested in learning about diseases or pests that impact hydroponic mustard greens. The expert system developed is web-based and functions as a user consultation medium in relation to mustard pests and diseases using the forward chaining and certainy factor method to explain the decision tree procedure for mustard plant disease symptoms. With this expert system, farmers do not need to look for people who can explain the problems they face. We found that there were 10 types of pests and diseases and 29 symptoms in the hydroponic mustard plants reviewed in this research.

关键词： Plant diseases Plants (biology) Hydroponics Expert systems Information technology Faces Diseases Business Information systems Farming

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Mammogram Breast Cancer Classification Based on Deep-Convolutional Neural Network

Mammogram Breast Cancer Classification Based on Deep-Convolu...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Avissa Aurellia Amadea Christy Atika Sari Eko Hari Rachmawanto Wellia Shinta Sari Mohammed S. Noori Mohamed Ayad Alkhafaji Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Study Program in Information Systems University of Dian Nuswantoro Semarang Indonesia Department of Computer Engineering Technology Northern Technical University Mosul Iraq Department of Computer Science National University of Science and Technology DhiQar Iraq

Based on WHO’s data, breast cancer is one of the most deadly diseases that has claimed many victims, especially women. This disease begins with the presence of an undetected and eventually turns into malignant (cancer). This happens due to ignorance of the importance of having a medical check-up even though in good health. Doctors and researchers can prevent the development of tumor cells through treatment that begins with radiological examinations to identify the possibility of a person being affected by this disease. One of the most frequently used techniques is Mammography. This technique can detect the presence of tumor cells using advanced technology and several methods in displaying the patient’s diagnostic results on low-dose X-rays in the form of mammogram images. The technology is inseparable from the methods used to identify the presence of tumor cells. In this study, we have proposed the CNN method based on the deep-CNN model to identify mammogram images in the detection of breast cancer cells with average evaluation results in terms of accuracy, precision, recall, specificity, and f-measure on mammogram image datasets of 99.52%, 99.72%, 99.31%, 99.72%, and 99.5%. These results showed that this method has a good performance in breast cancer detection.

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