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Environmental Disinfection Based on Mobile Intelligent Networking Highly Efficient Ultraviolet Light Machines

Aerosol and Air Quality Research 2024年第2期24卷 1-10页

作者： Chung, Chun-Yen Sung, Wen-Tsai Wang, Lin-Chi Siami, Lailatus Santos, John Paul Computer and Game Development Program Kun Shan University Tainan710303 Taiwan Department of Electrical Engineering National Chin-Yi University of Technology Taichung411030 Taiwan Department of Marine Environmental Engineering National Kaohsiung University of Science and Technology Kaohsiung81157 Taiwan Department of Environmental Engineering Chung Yuan Christian University Taoyuan32023 Taiwan Department of Civil Engineering Chung Yuan Christian University Taoyuan32023 Taiwan

Since 2019, the world has suffered from the outbreak of the "Coronavirus Disease (COVID)-19 pandemic. This study used the principle of Ultraviolet (UV)-C disinfection to produce a mobile high-power UV-C sterilizer. It focused on combining intelligent control systems, human sensors, and Internet of Things (IoT) systems to carry out disinfection tasks in unmanned indoor spaces. Because of its mobile nature, the sterilizer could be moved at any time in various learning, research, and activity rooms for disinfection. In addition to manually controlling the sterilizer, a Wi-Fi or Narrowband (NB)-IoT-enabled remote control was provided to start the disinfection process. In order to prevent ultraviolet light from harming the human body, sensors were employed self-acting to detect the presence of people and turn off the ultraviolet light when people were detected in the area. At the same time, a camera on the sterilizer would capture an image and notify the relevant personnel through a LINE notification, to prevent the human body from being exposed to ultraviolet light, causing harm to the teachers and students on campus. During operation, voice broadcast reminders and a status indicator could provide information about the operation status. After disinfection, users could view the disinfection process through a mobile device application or a webpage, which would automatically notify relevant personnel of the disinfection status through a LINE notification. In this way, the disinfection progress could be updated at anytime, anywhere and the current disinfection status in the classroom could be displayed in real-time, allowing teachers and students on campus to carry out teaching and learning in a safe and secure environment. © The Author(s).

关键词： Internet of things

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Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Classification 4

Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Cl...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Fitrianah, Devi Suryaningrum, Kristien Margi Sagala, Noviyanti Tri Maretta Ayumi, Vina Lim, Siew Mooi School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Mathematics Statistics Department Jakarta Indonesia Universitas Mercu Buana Informatics Engineering Program Faculty of Computer Science Jakarta Indonesia Tunku Abdul Rahman University College Faculty of Computing and Information Technology Computer Science Department Kuala Lumpur Malaysia

ISBN: (纸本)9781665473279

Study on the identification and classification of fish is challenging and valuable because of its role in advancing the marine and agricultural fields. This research has benefits interms of monitoring fish populations and ecosystems in a particular area. Furthermore, this research helps monitor fish that are considered threatened or endangered so that it makes iteasier to map prohibited areas for fishing. This research aims to know performance of MobileNetV2 and VGG16 with parameter tuning process by identifying the value of batch size, epoch, learning rate, and optimizer for fish image dataset. The proposed research phase consists of five main stages, including experimental setup, dataset construction, dataset preprocessing, dataset training and modelling and evaluation. As the result, VGG16 obtained the highest accuracy value. For VGG16 without fine-tuning, the testing accuracy is 98.07%. For VGG16 with fine-tuning, the testing accuracy is 96.56%. © 2022 IEEE.

关键词： Fish

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Human-computer Interaction and Knowledge Transfer: Study of E-Learning Achievement 7

Human-Computer Interaction and Knowledge Transfer: Study of ...

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7th International Conference of computer and Informatics Engineering, IC2IE 2024

作者： Alamsyah, Doni Purnama Othman, Norfaridatul Ahkmaliah Kerta, Johan Muliadi Morika, Doni Ramdhani, Yudi Bina Nusantara University Binus Business School Creativepreneurship Department Jakarta Indonesia Universiti Teknikal Malaysia Melaka Department of Management Technology Melaka Malaysia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University School of Design Design Interior Department Jakarta Indonesia Satu University Information System Program Bandung Indonesia

ISBN: (纸本)9798331505530

Human-computer interaction influences the effectiveness of e-learning knowledge transfer, so it is necessary to pay attention to user engagement and achievement. The aim of this research is to examine the factors that support e-learning knowledge transfer through achievement and user engagement (self-efficacy, self-confidence, self-esteem). The research method was carried out by surveying e-learning users, namely students, data from students was collected through quantitative questionnaires. The data is then processed using the SmartPLS tool through two tests, namely the PLS Algorithm and Boostrapping. The model was tested using inner and outer tests, and the data was analyzed based on the research hypothesis design. Student interactions with e-learning influence several behaviors that can support e-learning knowledge transfer performance. The research results prove that student e-learning achievement has a positive impact on e-learning knowledge transfer. Several supporting factors play a role in increasing e-learning achievement, namely self-efficacy, self-confidence and self-esteem of students. However, the supporting factors for students are only self-efficacy and self-confidence which are directly able to increase e-learning knowledge transfer. It is known that students who are less able to respect themselves in interacting with e-learning are unable to support knowledge transfer. The rules of human-computer interaction play a role in supporting e-learning knowledge transfer, studies on user behavior need to be considered, especially self-efficacy and self-confidence to support online learning academic performance. © 2024 IEEE.

关键词： Knowledge transfer

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A Novel Machine Learning Classifier Based on Genetic Algorithms and Data Importance Reformatting

arXiv

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arXiv 2024年

作者： Alkhayyat, A.K. Hewahi, N.M. Big Data Science and Analytics Program College of Science University of Bahrain Bahrain Department of Computer Science College of Information Technology University of Bahrain Bahrain

In this paper, a novel classification algorithm that is based on Data Importance (DI) reformatting and Genetic Algorithms (GA) named GADIC is proposed to overcome the issues related to the nature of data which may hinder the performance of the Machine Learning (ML) classifiers. GADIC comprises three phases which are data reformatting phase which depends on DI concept, training phase where GA is applied on the reformatted training dataset, and testing phase where the instances of the reformatted testing dataset are being averaged based on similar instances in the training dataset. GADIC is an approach that utilizes the exiting ML classifiers with involvement of data reformatting, using GA to tune the inputs, and averaging the similar instances to the unknown instance. The averaging of the instances becomes the unknown instance to be classified in the stage of testing. GADIC has been tested on five existing ML classifiers which are Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Logistic Regression (LR), Decision Tree (DT), and Naïve Bayes (NB). All were evaluated using seven open-source UCI ML repository and Kaggle datasets which are Cleveland heart disease, Indian liver patient, Pima Indian diabetes, employee future prediction, telecom churn prediction, bank customer churn, and tech students. In terms of accuracy, the results showed that, with the exception of approximately 1% decrease in the accuracy of NB classifier in Cleveland heart disease dataset, GADIC significantly enhanced the performance of most ML classifiers using various datasets. In addition, KNN with GADIC showed the greatest performance gain when compared with other ML classifiers with GADIC followed by SVM while LR had the lowest improvement. The lowest average improvement that GADIC could achieve is 5.96%, whereas the maximum average improvement reached 16.79%. © 2024, CC BY.

关键词： Adversarial machine learning

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HQNet: An Efficient Convolutional Neural Network for Cervical Cancer Classification 7

HQNet: An Efficient Convolutional Neural Network for Cervica...

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7th International Conference on Biomedical Imaging, Signal Processing, ICBSP 2022

作者： Yang, Han Toe, Teoh Teik BNU-HKBU United International College Program of Computer Science and Technology China NTU Business AI Lab Nanyang Technological University China

ISBN: (纸本)9781450397858

With the rapid development of artificial intelligence, cancer cell classification recognition is also gradually intelligent. Although the classification and recognition methods for some common cancers are mature, Cervical Cancer, as a common cancer among middle- aged women, is often neglected in diagnosis because of the low incidence of malignant tumors. In recent years, the incidence of Cervical Cancer has been increasing year by year and has also gained attention. In this paper, an efficient deformable convolu- tional neural network system(HQNet) was constructed to identify Cervical Cancer cells with different degrees of development. © 2022 ACM.

关键词： Convolution

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Optimization of Certainty Factor Algorithm to Overcome Uncertainty in Expert System Identification of Pests and Diseases of Cocoa 2

Optimization of Certainty Factor Algorithm to Overcome Uncer...

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2nd International Conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2022

作者： Gunawan, Rakhmat Dedi Ahmad, Imam Parjito Anggono, Harry Vernando, Ego Hengki Jaya, Firman Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Stkip Pgri Information Technology Education Study Program Situbondo Indonesia

ISBN: (纸本)9781665465410

Lampung Province is one of the regions with the highest cocoa production, but many factors can interfere with cocoa production. One of them is the factor of pests and diseases of cocoa that cannot be identified and prevented beforehand. An expert system is a system that adopts expert knowledge or experts in a particular field. However, the diagnosis usually contains uncertainty in the form of answers and statements used. Certainty Factor (CF) algorithm, is one of the algorithms that is able to overcome uncertainty by providing a value for the level of trust from experts and users. However, because the CF value is based on the level of confidence in the symptoms experienced by the user, it creates a problem, namely the consistency of the user's answers. So, it is necessary to make improvements to the CF algorithm, to overcome the inconsistency of user answers through the use of Consistency Ratio (CR). Through the use of CR, inconsistencies are overcome through comparisons between the consistency index and the random index. Based on the results of the accuracy test, the proposed model is able to produce an accuracy rate of 86.67% in the diagnosis of pests and diseases on cocoa plants. © 2022 IEEE.

关键词： Cocoa

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Strategic Information System Planning in the Industry 4.0 Era: A Systematic Literature Review

Strategic Information System Planning in the Industry 4.0 Er...

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2022 IEEE Creative Communication and Innovative technology, ICCIT 2022

作者： Mahendra, Irfan Ramadhan, Arief Trisetyarso, Agung Abdurachman, Edi Zarlis, Muhammad Bina Nusantara University Computer Science Program Jakarta11480 Indonesia Nusa Mandiri University Information Technology Faculty Jakarta11450 Indonesia

ISBN: (纸本)9781665468763

Information systems and technology have an important role in building the Company's competitive advantage in facing the Industrial Era 4.0. Strategic Information System Planning (SISP) helps companies formulate reliable information systems that are aligned with business strategies. The academic world should contribute in this regard, but currently, the amount of research related to SISP is very limited. This research is a systematic literature review, which aims to find out more about SISP research trends, research motivations and backgrounds, industries that have been handled, and the methods used in developing SISP. This research is expected to be a trigger for the emergence of new research to support the successful development of SISP in companies in various industries. The results of this research show that several topics are widely researched and need to be improved in the future, namely the alignment of IS strategies with business strategies, the development of specific SISPs in various industries, and the reliability of IS strategies by considering the application of the latest information technology, such as blockchain, artificial intelligence, big data, augmented reality, Internet of Things, and cloud computing. In addition, it is also necessary to develop a SISP framework that is more accommodating to changes in the business environment that is increasingly fast, agile, and collaborative, which includes aspects of business architecture, data/information architecture, technology architecture, information system security, governance, and human resources. © 2022 IEEE.

关键词： Industry 4.0

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Optimal Balancing of Data Transfer Time and Cost in High-Performance Networks

Optimal Balancing of Data Transfer Time and Cost in High-Per...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Liudong Zuo Daqing Yun Computer Science Department California State University Dominguez Hills Carson CA USA Computer and Information Sciences Program Harrisburg University of Science and Technology Harrisburg PA USA

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

The proliferation of large-scale applications has led to the generation of vast datasets across diverse scientific domains. The subsequent need to transfer such expansive data across geographical distances is essential for collaborative data storage and analysis. While reserving bandwidth on dedicated links within high-performance networks (HPNs) has proved as an efficient means for such extensive data transfers, certain crucial challenges remain to be investigated. In this paper, we delve into the intricate tradeoff between cost and completion time of data transfers using bandwidth reservation on fixed paths with fixed bandwidth of the HPNs, the most common type of bandwidth reservation or data transfer paths. Our focus centers on the scheduling of two types of bandwidth reservation requests (BRRs) that encapsulate this tradeoff: (i) minimizing data transfer cost within prescribed deadlines, and (ii) achieving the earliest data transfer completion time while adhering to predefined cost constraints. We propose two algorithms to optimize the scheduling of individual BRRs of these two types. We then compare the proposed algorithms with existing ones from the perspective of different performance metrics, and efficacy of the proposed algorithms is verified through extensive simulations.

关键词： Measurement Costs Processor scheduling Computational modeling Memory Collaboration Bandwidth

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Proposed IT Governance Model for Smart Tourism Destinations based on COBIT 2019 Framework 7

Proposed IT Governance Model for Smart Tourism Destinations ...

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7th International Conference on Information Management and technology, ICIMTech 2022

作者： Hiererra, Siti Elda Gaol, Ford Lumban Ranti, Benny Supangkat, Suhono Harso BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia University of Indonesia Faculty of Computer Science Computer Science Department Depok16424 Indonesia Institute of Technology Bandung Smart Cities and Communities Innovation Centre Faculty of Digital Communication and Hotel & Tourism Bandung40116 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

Tourism is one of Indonesia's main economic drivers because it can absorb many workers and bring in foreign exchange through tourism activities. Research related to Smart Tourism Destinations (STDs) and the technology that supports them has gained popularity and has received special attention in the last ten years. STDs show that various technologies are applied to a tourist destination, one of which aims to increase added value for the surrounding community and tourists. This study employs the systematic literature review (SLR) method to investigate smart tourism destinations and governance topics. This research aims to identify and analyze the main components of Smart Tourism Destinations Governance. Two research questions were developed and incorporated into the search term. This research resulted in 24 research papers on smart tourism destinations and governance. The result of this study is a proposed IT Governance Model, which can be a guideline for tourism's DMOs (Destination Management Organizations), tourism enterprises, IT companies that support smart tourism concepts, and policymakers (government) to improve their service to society/community. The model can be used as a guide to governing the IT implementation in smart tourism destinations projects. Limitations of this study regarding the scalability of database and keyword usage and their methodologies. Theoretical implication for scholars in the next research is that research can be conducted on how to implement this model and whether this model can be easily adopted. This future work also regarding the IT Governance Maturity Model or the measurement of IT Gov readiness in smart tourism destinations. © 2022 IEEE.

关键词： Tourism

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A Simple Deep Learning Model for Classifying Oranges in Embedded Manufacturing Settings

SSRN

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SSRN 2023年

作者： Salaiwarakul, Anongporn Mungklachaiya, Sujitranan Program in Computer and Information Technology Faculty of Science and Technology Loei Rajabaht University Loei42000 Thailand Department of Computer Science and Information Technology Faculty of Science Naresuan University Phitsanulok65000 Thailand

Recently, deep learning has emerged as a powerful tool for automating various tasks in manufacturing industry. Deep learning has shown promise particularly in classifying food products, e.g., identifying sweet oranges in a factory setting. Traditionally, classification of oranges based on sweetness is a labor-intensive and time-consuming process, requiring manual inspection and tasting by trained personnel. However, by employing deep learning techniques, this process can be automated, giving rise to accurate and consistent results at a much faster rate. In this study, we propose a deep learning model, combined with computer vision, for classifying the sweetness of oranges in a factory setting. By training a deep neural network on a dataset of labeled images, we developed a simple Convolutional Neural Network (sCNN) model with few layers that can accurately predict the sweetness of new oranges based on their images. We evaluated the performance of the model and compare it with four CNN architecture-transfer learning models of orange classification. Upon comparison with four different CNN architecture-transfer learning models, namely, MobileNet-V2, Inception-V3, VGG-16, and ResNet-50, we found that while each of these models yields a satisfactory result, learning rates of 0.001 or 0.0001 yielded superior results in every model. Without the application of dropout and image augmentation, ResNet-50 yielded the best classification accuracy (81.43%) of the five models, and the model performance improved upon employment of these methods. However, the provided images were not suitable for Inception-V3 models. Nevertheless, Simple CNN shows that the augmentation method enables the model to learn, achieving an accuracy of 81.43% with a dropout of 0.3, which is close to the VGG-16's highest accuracy of 83.57% (dropout = 0.0) and MobileNet-V2’s 74.29% (dropout = 0.3). © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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