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A Secure Cloud-Based Infrastructure for Virtual Sensors in IoT Environments 6

A Secure Cloud-Based Infrastructure for Virtual Sensors in I...

引用

6th International Conference of Women in Data science at Prince Sultan University, WiDS-PSU 2023

作者： Makda, Tahira Jehan Luiza Barros, Ana Dilek, Selma University of Doha for Science and Technology College of Business Management Doha Qatar University of Ceará Computer Science Program State Fortaleza Brazil Hacettepe University Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9781665477239

Recent work has proven the effort of researchers to integrate small sensors and a cloud environment, delivering the Internet of Things (IoT). Sensors as a service are one of the leading research concerns in this context. Nevertheless, security is becoming one of the most significant attributes of the IoT as sensors become more human-independent and are being extensively used to monitor human lives. That way, IoT brings many key security challenges that need attention, some of which we address in this position paper. We present a cloud-based infrastructure that can deliver sensors and actuators as a service, providing secure communication between them and the control nodes on which IoT applications rely while implementing Big Data algorithms. For mapping our proposal, two scenarios related to Health Assistance are discussed, considering secure communications in a sensor network. In conclusion, we propose a scope for future research in this field considering digital twin concepts. Since domains exploiting IoT technologies can benefit from adopting Digital Twin, our goal is to evolve a virtual sensor and actuator system into this technology. © 2023 IEEE.

关键词： Actuators

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Design an Intelligent Candy Inspection System with AIoT 23

Design an Intelligent Candy Inspection System with AIoT

引用

5th International Electronics Communication Conference, IECC 2023

作者： Liao, Yuan-Hsun Li, Hsiao-Hui Chang, Po-Chun Hsu, Chiao-Ti Wang, Ruo-An Department of Computer Science Tunghai University Taiwan Program in Chain Store Management Tainan University of Technology Taiwan

ISBN: (纸本)9798400708855

In the global community, countries are paying more and more attention to the monitoring and control of food safety. The food industry is investing more resources in the production chain to improve the process. Food products may be affected by disturbances or contamination during production, processing, storage, and transportation. This causes a variety of defects in the food, which can damage the quality of the food. Food defects are classified as problems with the contents and appearance, mostly changes in food substances or damage. If the food is affected by pathogenic microorganisms and deteriorates, it will also change the appearance of the food. The problematic food will bring damage to the health of customers. Through appropriate inspection mechanisms, it is possible to prevent and reduce product recalls and processing costs. As well as satisfying consumer expectations and needs. The cost savings to the food industry are substantial. To overcome this important problem, this study utilizes artificial intelligence (AI), artificial intelligent Internet of Things (AIoT), software engineering, and data analysis techniques to build a software and hardware system with an intelligent fondant inspection system. The original manual inspection mechanism is further upgraded to an intelligent inspection system. We designed the inspection machine according to the needs of the production line and designed it as a platform for self-Training. In addition to saving more manpower for manufacturers, the system will continue to grow through self-Training and tuning model functions. We hope to achieve higher inspection accuracy and contribute to food safety. © 2023 ACM.

关键词： Food products

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Eye Disease Detection Model Using Convolutional Neural Network with RMSprop, SGD, and Adam Optimizers 11

Eye Disease Detection Model Using Convolutional Neural Netwo...

引用

11th International Conference on ICT for Smart Society, ICISS 2024

作者： Kurniadi, Dede Mulyani, Asri Riefky Rafliana Suwandy, Moch Institut Teknologi Garut Department of Computer Science Kabupaten Garut Indonesia Institut Teknologi Garut Informatics Study Program Kabupaten Garut Indonesia

ISBN: (纸本)9798350389654

Eye disorders such as cataracts, glaucoma, and diabetic retinopathy can cause abnormalities in eye function, including blindness. This research aims to develop a machine-learning model and find the best model for detecting eye diseases. Deep Learning methods based on the Convolutional Neural Network (CNN) model will be used to create eye disease detection models by adding the RMSProp, SGD, and Adam optimizers techniques. The dataset used consists of 4 classes, namely normal (1074 data), glaucoma (1007 data), cataract (1038 data), and diabetic retinopathy (1098 data), totaling 4217 fundus images. The evaluation results reveal the CNN model with the RMSProp optimizer has better model performance with a value of 88% accuracy, 88% precision, 87% recall, and 88% f1-score, and based on the AUC value on the ROC-AUC curve for each class is above 0.90, which means that the model can differentiate data between classes well and is included in the Excellent classification category. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimal Energy Mix Modeling Simulation Using Reinforcement Learning on Philippine Monthly Energy Utilization for 2020-2022 6

Optimal Energy Mix Modeling Simulation Using Reinforcement L...

引用

6th International Conference on Electrical, Control and Instrumentation Engineering, ICECIE 2024

作者： Penas Ii, Reynaldo Ted L. Naval, Prospero C. University of the Philippines-Diliman Artificial Intelligence Program Quezon City Philippines University of the Philippines-Diliman Department of Computer Science Quezon City Philippines

ISBN: (纸本)9798350380040

A traditional method of generating an optimal energy mix involves selecting from various power producers, each with different costs per kilowatt-hour (kWh). This energy is then transmitted and distributed among various end-user sectors. However, this study explores a novel approach by replacing the traditional involvement of transmission and distribution utilities with reinforcement learning (RL) techniques. The objective was to determine an optimal energy mix over a 36-month period in the Philippines, from 2020 to 2022. The study evaluated several RL methods, including Q-learning, Deep Q-Network (DQN), Double Q-learning, and Actor-Critic algorithms. Each algorithm was tested with different learning rates (α) and discount factors (γ) to find the most effective combination. Among these, the Actor-Critic algorithm, with α=0.10 and γ=0.90, emerged as the most optimal. The simulations showed that using each algorithm, no energy demand in kWh was left unmet, demonstrating its effectiveness in managing and optimizing energy distribution. This approach not only automated the decision-making process but also enhanced efficiency by continuously learning and adapting to changes in energy production and consumption patterns. © 2024 IEEE.

关键词： energy optimal cost optimal mix Philippines reinforcement learning

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Database Availability Security: Handling Denial of Service (DoS) Attacks

Database Availability Security: Handling Denial of Service (...

引用

International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Jeffrey Surianto Irma Kartika Wairooy Sandy Wirananda Brilly Andro Makalew Computer Science Department School of Computer Science Jakarta Indonesia Computer Science Department Mobile Application & Technology Program Jakarta Indonesia

ISBN: (数字)9798331533243

ISBN: (纸本)9798331533250

In the data based digital age, there are risks of attacks that cause denial of service (DoS) or distributed denial of service (DDoS) which pose threats to the security and availability of databases especially with those who are affected by this which is very disturbing. This research aims at analysing the methods of detecting and preventing DoS and DDoS attacks, with more focus on the security of databases. This research identifies and evaluates various strategies employed to counter such threats to cloud and Internet of Things (IoT) systems from the literature review conducted using the PRISMA approach. The analyses show that machine learning algorithms are capable of enhancing the detection of DoS/DDoS attack's efficiency and accuracy, and the cloud and IoT security solutions, including complex load balancing and powerful firewalls, are crucial for preventing and mitigating such attacks. This study also shows that, AI should be included in the security plans and security measures should be adaptable and proactive. This work is useful for scholars, developers, and cybersecurity experts who are interested in enhancing measures to counter and prevent advanced cybercrimes and ensuring continued and reliable functionality in data centric settings.

关键词： Firewalls (computing) Prevention and mitigation Distributed databases Telecommunication traffic Denial-of-service attack Load management Security Internet of Things computer crime Protection

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Digital Twin (DT) Smart City for Air Quality Management 8

Digital Twin (DT) Smart City for Air Quality Management

引用

8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Ariansyah, Dedy Isnan, Mahmud Rahutomo, Reza Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Air pollution is a pressing issue in cities, and managing air quality poses a challenge for urban designers and decision-makers. This study proposes a Digital Twin (DT) Smart City integrated with Mixed Reality technology to enhance visualization and collaboration for addressing urban air pollution. The research adopts an applied research approach, with a focus on developing a DT framework. A use case of DT development for Jakarta, the capital of Indonesia, is presented. By integrating air quality data, meteorological information, traffic patterns, and urban infrastructure data, the DT provides a comprehensive understanding of air pollution dynamics. The visualization capabilities of the DT, utilizing Mixed Reality technology, facilitate effective decision-making and the identification of strategies for managing air quality. However, further research is needed to address data management challenges to build a DT for Smart City at scale. © 2023 The Authors. Published by Elsevier B.V.

关键词： Augmented Reality Digital Twin Mixed Reality Smart City

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Based-on computer Vision Applications for Bus Stop Passenger Detection System 3

Based-on Computer Vision Applications for Bus Stop Passenger...

引用

3rd IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2023

作者： Chang, Yu-Cheng Tsai, Hua-Wen Huang, Chao-Yi Wu, Zong-Rong Department of Computer Information and Network Engineer Lunghwa University of Science and Technology Taiwan Department of Bachelor's Program in Intelligent Robot National Yunlin University of Science and Technology Taiwan

ISBN: (纸本)9798350333862

Passenger transport is one of the most common ways of commuting in Taiwan. It plays an important role in the transportation system due to its large number of stations, dense frequency, and cheap transportation. Due to the unfriendly transportation environment and a large number of passengers, a blind spot of passenger transportation exists, which leads to traffic accidents at the station. We research to make the Bus Stop Passenger Detection System. Taking the object detection of Wheelchairs into consideration, it is more convenient to assist the disabled to find the passenger transportation system, which makes Taiwan's transportation system more convenient. © 2023 IEEE.

关键词： Object detection

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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 ...

引用

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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Initial Implementation and Utilization of Instruction Dynamic Simulation in MEIMAT 21

Initial Implementation and Utilization of Instruction Dynami...

引用

21st International Symposium on Communications and Information Technologies, ISCIT 2022

作者： Ogita, Tadahiro Morimoto, Tomoyuki Onogi, Shunsuke Kawamata, Ryota Wang, Ziming Tsutsumi, Toshiyuki Graduate School of Science and Technology Meiji University Computer Science Program Kanagawa Japan School of Science and Technology Meiji University Department of Computer Science Kanagawa Japan

ISBN: (数字)9781665498517

ISBN: (纸本)9781665498517

We have been developing MEIMAT, meiji micro-processor (MPU) architecture design tools. Our MEIMAT has a feature to implement arbitrary instructions. However, the MEIMAT does not have a function to simulate those instructions. Therefore, we have newly developed a function of simulation to dynamically execute the designed instructions. The result of this dynamic simulation can be measured by the developed module viewers. It is also demonstrated that a higher performance MPU can be designed by our improved MEIMAT. © 2022 IEEE.

关键词： Memory architecture

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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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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Alexander, Ivan Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Binus Graduate Program Computer Science Computer Science Departement Jakarta11480 Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

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. © 2023 IEEE.

关键词： Tweets

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