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Sustainable Emerging Innovations in engineering and Technology (ICSEIET), International Conference on

作者： Bayu Krisna Mukti Mega Novita Muhammad Amirudin Sanjay Kumar Singh Imadudin Harjanto Priyanka Bhardwaj Adhi Kusmantoro Manidipa Roy Department of Electrical Engineering Faculty of Engineering and Informatics Universitas PGRI Semarang Semarang Central Java Indonesia Postgraduate Program of Science Education Universitas PGRI Semarang Semarang Central Java Indonesia Department of Electronics & Communication Engineering ABES Engineering College Ghaziabad Uttar Pradesh India

The Covid-19 pandemic has emphasized the critical role of mask-wearing in reducing virus transmission. However, declining public awareness necessitates effective monitoring solutions. This study explores the Single Shot Multibox Detector (SSD) approach, a Convolutional Neural Networks (CNN)-based object detection method, for mask detection. Specifically, we investigate the performance of the SSD model with the ResNet50 architecture as the base network. Through the analysis of a dataset comprising 1000 images of individuals wearing and not wearing masks, the SSD model with ResNet50 achieves an impressive accuracy of 98.23%, surpassing the VGG16-based model's accuracy of 93.56%. These findings underscore the superiority of the CNN-based SSD model with ResNet50 for mask detection. Moreover, they have significant implications for real-time monitoring and enforcing mask-wearing protocols, emphasizing the importance of public awareness and adherence. Leveraging advanced AI techniques, such as CNN-based object detection with SSD, strengthens public health initiatives. The development of robust and efficient mask detection systems contributes to creating a safer environment amid the ongoing Covid-19 pandemic. This research highlights the value of harnessing these advanced technologies to effectively address public health crises. By employing CNN-based SSD with ResNet50, society can enhance its ability to mitigate risks and ensure public safety.

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Advanced Data Instrumentation for Stunting Analysis in Physical-Cyber Architecture

Advanced Data Instrumentation for Stunting Analysis in Physi...

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International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Daniel Nugraha M. Riza Nurtam Hartuti Mistialustina Didin Saepudin Welly Surjono Ary Setijadi Prihatmanto Doctoral Program of School of Electrical Engineering and Infomatics Institut Teknologi Bandung Bandung Indonesia Universitas Widya Dharma Pontianak Kalimantan Barat Indonesia Electrical Engineering Department Faculty of Engineering Universitas Sangga Buana Bandung Indonesia Universitas Sangga Buana Bandung Indonesia School of Electical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia

In Indonesia, stunting is a serious problem that has drawn a lot of attention from the government. This study offers a novel strategy for preventing stunting by combining the 5-Desk Model used in integrated health posts (Posyandu) with the Cyber-Physical System (CPS) architecture. Chronic malnutrition and childhood diseases can cause stunting, which can result in long-term health issues and cognitive deficits. Despite advancements, it still poses a serious threat to almost every Indonesian province. For the purpose of gathering data in Posyandu, such as registration, growth measurement, recording, counseling, and health services, the 5-Desk Model provides an organized framework. This promotes the well being of the community and gives healthcare professionals the ability to thoroughly monitor important health indicators. This strategy is improved with CPS integration, which results in a four-layered, comprehensive system that includes gamification, business intelligence, information systems, and instrumentation. To maximize efforts to avoid stunting, this architecture facilitates accurate monitoring, real-time data collecting, data analysis, and stakeholder communication. This study imagines a data-driven future for Indonesian stunting prevention, where cooperation, technology, and data are combined to address this pressing problem and safeguard the future prospects and wellbeing of Indonesia’s youth. Implementing this goal and expanding stunting prevention initiatives will be the main goals of future research.

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Enhancing Localized Surface Plasmon Resonance Response for Albumin Detection by Optimizing the Lateral Size of Hexagonal Gold Nanoparticles

Enhancing Localized Surface Plasmon Resonance Response for A...

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IEEE International Conference on Semiconductor Electronics (ICSE)

作者： Silva Nurfasha Chandra Wulandari Lilik Hasanah Ahmad Aminuddin Yanurita Dwi Hapsari Mohammad Arifin Roer Eka Pawinanto Budi Mulyanti Study Program of Physics Universitas Pendidikan Indonesia Bandung Indonesia Study Program of Physics Institut Teknologi Bandung Bandung Indonesia Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Study Program of Industrial Automation and Robotics Engineering Education Universitas Pendidikan Indonesia Bandung Indonesia Study Program of Electrical Engineering Universitas Pendidikan Indonesia Bandung Indonesia

ISBN: (数字)9798350378313

ISBN: (纸本)9798350378320

The kidney is a human organ with numerous vital functions, mainly for blood filtration. Damage to the kidneys can be detected from albumin levels in urine, higher levels of albumin in urine indicate the kidney's filtration function is impaired. The latest breakthrough in the medical field is the use of localized sensor plasma resonance (LSPR) in the field of biosensors to detect albumin protein in urine. Using Finite-Difference Time-Domain (FDTD) simulation, the lateral size of hexagonal gold nanoparticles (AuNPs) is varied to increase the optimal LSPR. AuNPs are well-known as the most promising material for plasmonic applications. In several studies, the hexagonal geometry was reported to increase the surface area of nanoparticles as well as enhance the LSPR signal. Varying the geometry of the hexagonal AuNPs will affect their sensitivity and optical properties. In this study, the lateral size of the hexagonal AuNPs varied from 10 nm to 60 nm, with a controlled thickness of 30 nm. The absorption graph blue shifts with the peak decreasing until the graph has almost no peak as the lateral size decreases. The lateral size of 40 nm at a thickness of 30 nm was found to have the highest shifting response towards the change of refractive index, indicating good sensitivity for sensors. Simulation of hexagonal AuNPs-based LSPR detection of albumin concentrations of 0.35 –5.71 mM produces a sensitivity of 2.59 nm/mM, equivalent to 245.96 nm/RIU. The competitive performance and ease of detection procedures in real applications show the potential of the hexagonal AuNPs-based LSPR sensor to be further explored in developing alternative albumin detection techniques.

关键词： Nanoparticles Geometry Gold Sensitivity Filtration Absorption Refractive index

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Towards an Automated Dissemination Process of Cyber Threat Intelligence Data using STIX 6

Towards an Automated Dissemination Process of Cyber Threat I...

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6th International Workshop on Big Data and Information Security, IWBIS 2021

作者： Briliyant, Obrina Candra Tirsa, Nusranto Pratama Hasditama, Maulana Andika Politeknik Siber Dan Sandi Negara Cyber Security Engineering Program Department of Cyber Security Bogor Indonesia National Cyber and Crypto Agency Depok Indonesia Faculty of Electrical Engineering and Informatics Budapest Computer Engineering Budapest Hungary

ISBN: (数字)9781665424516

ISBN: (纸本)9781665424516

Cyber Threat Intelligence (CTI) is an actionable, evidence-based knowledge management system of cyber threats data. CTI consists of 3 (three) major parts, namely information gathering, analysis, and dissemination. To carry out dissemination in CTI, Structured Threat Information Expression (STIX) and Trusted Automated Exchange of Intelligence Information (TAXII) have been developed as open and interoperable standards for the CTI dissemination. An effective CTI is a CTI that can be integrated into the organization's security operations processes. Many organizations today runs CTI with a mechanism for reporting and distributing information in the dissemination process or known as cyber threat information sharing which has significant effects on security operations performance. In this study, user requirements analysis and design of cyber threat information sharing processes were carried out. From the results of the requirement analysis, a suitable business process re-engineering were proposed to accelerate the automation of CTI, especially in the information sharing part. Lastly, the designed processes were tested by simulating the processes using STIX and TAXII-based platform. The results of this study indicate that the designed processes can be run on STIX and TAXII-based platforms, ergo a step toward to a fully automated CTI. © 2021 IEEE.

关键词： Information dissemination

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Investigating the Impact of Data Augmentation for Fine-Grained Grocery Product Classification Based on Vision Transformer

Investigating the Impact of Data Augmentation for Fine-Grain...

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Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), International Conference on

作者： Rissa Rahmania Yaya Heryadi Lukas Lukas Wayan Suparta Ilvico Sonata Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Group (CEG) Faculty of Engineering Universitas Katolik Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

The existence of fine-grained image classification supporting smart retail provides effectiveness in recognizing products with high similarity. However, the generic classification method performs poorly in identifying products from a subordinate category. This paper aims to identify augmentation techniques to leverage the Vision Transformer (ViT) model to classify the fine-grained grocery product, which involves embedding patches and transformer encoders to extract the main features. First, we develop a fine-grained image dataset with ColorJitter, CutOut, and combining both augmentations. Secondly, we perform experiments and analysis of ViT size, embedded patch size and image size in the patch embedding process. Lastly, the ViT model are evaluated according to the image sizes 224, 384, and 512 in accuracy, loss, and confusion matrix. The highest accuracy was obtained at 0.9922. The ColorJitter and CutOut improved the confusion matrix in ViT-B/16 and ViT-L/16 with an image size of 384 and 512. The results show that both augmentations in the ViT model are able to distinguish fine-grained grocery products.

关键词： Surveys Computer vision Accuracy Image recognition Transformers Feature extraction Data augmentation Artificial intelligence Image classification

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Faults Identification of Induction Motor Based on Vibration Using Backpropagation Neural Network

Faults Identification of Induction Motor Based on Vibration ...

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2020 International Conference on Smart Technology and Applications, ICoSTA 2020

作者： Kuspijani, K. Watiasih, Richa Prihastono, Prihastono Bhayangkara Surabaya University Electrical Engineering Study Program Engineering Faculty Surabaya Indonesia

ISBN: (纸本)9781728130835

Vibration analysis is very important in predictive maintenance in the industry today. In this paper, has designed a vibration detection system that can identify the condition of the induction motor by online. Induction motor vibration data is read from two vibration sensors, accelerometer1 and accelerometer2, displayed using ATMEGA16 microcontroller and can directly identify the condition of the induction motor by using artificial intelligence-based analysis that is Backpropagation-Neural Network (B-NN). After testing, the system managed to perform fault identification induction motor with the rank of success of 95 percent. © 2020 IEEE.

关键词： Accelerometers

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An Efficient Three-Factor Authenticated Key Agreement Technique Using FCM Under HC-IoT Architectures

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Computers, Materials & Continua 2022年第7期72卷 1373-1389页

作者： Chandrashekhar Meshram Agbotiname Lucky Imoize Sajjad Shaukat Jamal Parkash Tambare Adel R.Alharbi Iqtadar Hussain Department of Post Graduate Studies and Research in Mathematics Jayawanti Haksar Government Post-Graduation CollegeCollege of Chhindwara UniversityBetul460001M.P.India Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr University44801BochumGermany Department of Mathematics College of ScienceKing Khalid UniversityAbhaSaudi Arabia Water Resources&Applied Mathematics Research Lab Nagpur440027India College of Computing and Information Technology University of TabukTabuk71491Saudi Arabia Mathematics Program Department of MathematicsStatistics and PhysicsCollege of Arts and SciencesQatar University2713DohaQatar

The Human-Centered Internet of Things(HC-IoT)is fast becoming a hotbed of security and privacy *** users can establish a common session key through a trusted server over an open communication channel using a three-party authenticated key *** of the early authenticated key agreement systems relied on pairing,hashing,or modular exponentiation processes that are computationally intensive and *** order to address this problem,this paper offers a new three-party authenticated key agreement technique based on fractional chaotic *** new scheme uses fractional chaotic maps and supports the dynamic sensing of HC-IoT devices in the network architecture without a password *** projected security scheme utilized a hash function,which works well for the resource-limited HC-IoT *** results show that our new technique is resistant to password guessing attacks since it does not use a ***,our approach provides users with comprehensive privacy protection,ensuring that a user forgery attack causes no ***,our new technique offers better security features than the techniques currently available in the literature.

关键词： Three-party authenticated key agreement anonymity fractional chaotic maps Chebyshev polynomial password table human-centered internet of things(HC-IoT)

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Review Paper on Bluetooth Based Home Automation Using Android Phone

Review Paper on Bluetooth Based Home Automation Using Androi...

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Sustainable Emerging Innovations in engineering and Technology (ICSEIET), International Conference on

作者： Priyanka Bhardwaj Manidipa Roy Shilpa Srivastava Sanjay Kumar Singh Vidushi Gupta Mega Novita Muhammad Amirudin Imadudin Harjanto ABES Engineering College Ghaziabad U.P India Postgraduate Program of Science Education Universitas PGRI Semarang Semarang Central Java Indonesia Department of Electrical Engineering Faculty of Engineering and Informatics Universitas PGRI Semarang Semarang Central Java Indonesia

Electronic devices and appliances have become quintessential part of households with the recent development and integration of a home automation system with numerous client modules, including a remote mobile host controller, as in offices, home appliances etc.. Through Bluetooth, the client modules communicate with the host controller such as an Android smart phone. This paper presents a prototype can be implemented in practice with maximum efficiency keeping the security feature in mind.

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TinyML for Safe Driving: The Use of Embedded Machine Learning for Detecting Driver Distraction

TinyML for Safe Driving: The Use of Embedded Machine Learnin...

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Metrology for Automotive (MetroAutomotive), IEEE International Workshop on

作者： Thommas Flores Marianne Silva Mariana Azevedo Thais Medeiros Morsinaldo Medeiros Ivanovitch Silva Max Mauro Dias Santos Daniel G. Costa Postgraduate Program in Electrical and Computer Engineering (PPgEEC) Federal University of Rio Grande do Norte Natal Brazil INEGI Faculty of Engineering University of Porto Porto Portugal Federal University of Technology Ponta Grossa Brazil

Among the main causes of road accidents, one of the most significant is related to driver distraction while driving, which is responsible for 18% of car accidents worldwide. This situation has demanded the development of mechanisms to automatically detect this dangerous behavior while driving. One of the computational solutions that has been considered viable to detect situations like this is the use of Convolutional Neural Networks (CNN), but some complex issues arise when deploying CNN models in microcontroller-based embedded devices with constrained processing and memory capabilities. In this context, this paper proposes a driver distraction detection system that achieves high accuracy (99.3%) and low latency (72ms) while requiring minimal computational resources (Peak- RAM of 164 KB and Flash of 52.7 KB). This solution exploiting Tiny Machine Learning (TinyML) algorithms was developed with the support of the Edge Impulse platform, used to perform the entire ML pipeline, from data pre-processing and ML model creation to deployment into an Arduino Portenta H7 board. By designing a driver assistance system that can be integrated into vehicles, it is expected that an affordable solution based on embedded machine learning is provided, tackling a real-world problem by potentially reducing accidents caused by driver distractions.

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Classifying Tweet Sentiment Analysis on Airlines using Random Forest Classifier, Naïve Bayes Classifier and K-Nearest Neighbors Classifier Algorithm

Classifying Tweet Sentiment Analysis on Airlines using Rando...

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Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), International Conference on

作者： Ivan Alexander Yaya Heryadi Lukas Wayan Suparta Ilvico Sonata Computer Science Departement Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

Opinion has always affected businesses and individuals especially from the Public. People react through social media and spread it incompletely. The situation was then accepted as public opinion. There are three categories of opinion i.e., positive, negative, and neutral. To investigate public opinion on a particular issue of interest, sentiment analysis might be employed. By offering a solution to the text classification challenge, this experiment focuses on the perception of airline tweets among the American public. We offer a method to classify tweets as positive, negative, or neutral using three machine learning classifiers: Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbors Classifier. We examined the performance of three machine learning classifiers in terms of accuracy, average precision, average recall, and average f-1 score. The results demonstrate that the Random Forest Classifier, which produces 82% accuracy, 80% average precision, 82% average recall, and 80% average f1-score, performs the best among the machine learning classifiers.

关键词： Training Sentiment analysis Accuracy Social networking (online) Text categorization Nearest neighbor methods Naive Bayes methods Random forests Robots Airline industry

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