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A full privacy-preserving distributed batch-based certificate-less aggregate signature authentication scheme for healthcare wearable wireless medical sensor networks (HWMSNs)

A full privacy-preserving distributed batch-based certificat...

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作者： Rabie, Osama Bassam J. Selvarajan, Shitharth Hasanin, Tawfiq Mohammed, Gouse Baig Alshareef, Abddulrhman M. Uddin, Mueen Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Cybersecurity Center King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Kebri Dehar University Kebri Dehar Ethiopia School of Built Environment Engineering and Computing Leeds Beckett University LeedsLS1 3HE United Kingdom Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India College of Computing and Information Technology University of Doha for Science and Technology Doha24449 Qatar

The dynamic connectivity and functionality of sensors has revolutionized remote monitoring applications thanks to the combination of IoT and wireless sensor networks (WSNs). Wearable wireless medical sensor nodes allow continuous monitoring by amassing physiological data, which is very useful in healthcare applications. These text data are then sent to doctors via IoT devices so they can make an accurate diagnosis as soon as possible. However, the transmission of medical text data is extremely vulnerable to security and privacy assaults due to the open nature of the underlying communication medium. Therefore, a certificate-less aggregation-based signature system has been proposed as a solution to the issue by using elliptic curve public key cryptography (ECC) which allows for a highly effective technique. The cost of computing has been reduced by 93% due to the incorporation of aggregation technology. The communication cost is 400 bits which is a significant reduction when compared with its counterparts. The results of the security analysis show that the scheme is robust against forging, tampering, and man-in-the-middle attacks. The primary innovation is that the time required for signature verification can be reduced by using point addition and aggregation. In addition, it does away with the reliance on a centralized medical server in order to do verification. By taking a distributed approach, it is able to fully preserve user privacy, proving its superiority. © 2023, The Author(s).

关键词： Sensor nodes

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AI and Machine Learning Based E - Learning System For Secondary Education 7

AI and Machine Learning Based E - Learning System For Second...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Wijayawardena, G.C.S. Subasinghe, S.G.T.S. Bismi, K.H.P. Gamage, Anjalie Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9781665421683

One of the key functions directly shifted to online platforms under COVID-19 is education. The paper is about an E-learning system for secondary education in Sri Lanka. Learners and teachers can access information, resources, and tools through an E-Learning system, which is a Learning Management System that integrates a number of online activities. The main functions provided through the proposed system are chatbot, final grade prediction and weak area prediction of the students. Chatbots are becoming increasingly popular in a wide range of applications, especially in those that provide intelligence support to the user, according to recent research. So, in order to speed up the aid process, these systems are often integrated with Chatbots, which can quickly and accurately read the user's questions. This paper describes the implementation of a Chatbot prototype in the educational domain: a system for providing support to students. In the beginning, the goal was to design a special architecture and communication model that would help students get the proper answers. The final grade prediction component plays major role in the system. Because when the students are graded by their marks, they can review which areas that they have to improve and work on them. This is helpful for students as well as teachers. Weak area prediction also plays a significant role, because it can help to find out the weak areas of each subject and generate Individual Student Progress Plans to predict the students' weak subjects and the subject areas of the students. This motivates students to get higher marks easily because this part is mainly focused on weak areas of students and improve those weak areas by providing several learning activities. These are the major parts of this system to have a good E-learning system for both students and Teachers. © 2022 IEEE.

关键词： Students

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Leveraging Deep Learning Models for Machine-Generated Text Detection Using Transformer-Based Models

Leveraging Deep Learning Models for Machine-Generated Text D...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Suthanthiradevi, P. Revathy, G. Rejini, K. Muthu Lakshmi, V. School of Computing Srm institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering Chennai117 India Amrita college of Engineering and Technology Department of Computer Science Engineering Nagercoil India St. Joseph's College of Engineering Department of Computer Science and Engineering OMR Chennai India

ISBN: (纸本)9798331543617

GPT is a large language model (LLM) derived from natural language processing that can generate a human-like text using machine learning. However, these models raise questions about authenticity and reliability of material, particularly in fields such as journalism, social media, and academia, despite their usefulness for automating text-based tasks. Detecting machine-generated text is thus an important difficulty in ensuring content integrity. This study investigates the use of huge language models as a technique for recognizing machine-generated material. The author proposes a comprehensive detection model by evaluating the language patterns, syntactic structures, and stylistic traits that separate AI-generated literature from human writing. In addition, this research investigate the possibilities of fine-tuning models designed expressly for text identification tasks and evaluate their performance using LLM - Detect AI Generated Text datasets. In digital ecosystems, LLMs are effective at detecting AI-generated text, providing a novel approach for content moderation, academic integrity checks, and synthetic media detection. An increasingly AI-powered future will require a model that can discriminate between human and machine-generated writing in real-time. According to experimental findings, the CNN architecture's design combined with the use of DistilBERT embeddings allows for the effective and efficient classification of AI generated text data, achieving an exceptional 98% accuracy rate. © 2024 IEEE.

关键词： Adversarial machine learning

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Artificial Intelligence Adoption in QSR Industry 22

Artificial Intelligence Adoption in QSR Industry

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6th International Conference on E-Education, E-Business and E-Technology, ICEBT 2022

作者： Lee, Angela S.H Bengeri, Atul Kan, Chong Chin Department of Computing and Information Systems School of Engineering and Technology Sunway University Malaysia Linc Education Singapore Singapore

ISBN: (纸本)9781450397216

According to research conducted by Allied Market Research (Kale and Deshmukh, 2020), the Quick-service restaurant (QSR) industry has been enjoying significant growth over the last several years and the trend is expected to continue at least over the next five years. This is due to the agility of most QSR brands in adapting to consumer needs, quality food products and service innovation. Offering the convenience of brands reach for their consumers such as a restaurant within the reach of the customer, as well as providing online ordering and food delivery convenience. The convenience of the omnichannel digital services offered by typical QSR restaurant brands to their customer provides convenience for service delivery and at the same time provides an opportunity to collect, collate, check, consume and analyse the data to examine, explore and provide hindsight, insight and foresight for the QSR industry to be ahead of the competition. Not only are these digital customer channels a great way of providing a better customer experience, but they also help QSR operators in capturing massive data such as sales transactions, customer details, product performance, and daypart (breakfast, lunch and dinner) sales insights. With the ability to analyse and perform analytics on the data and from a data strategy perspective, it is paramount to have a robust big data platform, data warehouse and ability to consolidate data from all the IT systems. This is to enable the development of BI/dashboards to provide better business insights, such as real-time and daypart product sales. Data could also be used to develop new digital products for more positive customer engagements such as upselling and loyalty programmes or for implementing of "smart kitchen"that is able to connect various datasets for maximum kitchen efficiency and cost optimisation. Due to a lack of data science approach and machine learning capability, most QSR brands are missing this sales uplifting opportunity. To develo

关键词： Data Analytics

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Automation 5.0: The Key to systems Intelligence and Industry 5.0

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： Automation machinery intelligence

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Improving Performance of Recurrent Neural Networks Using Simulated Annealing for Vertical Wind Speed Estimation

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Energy engineering 2023年第4期120卷 775-789页

作者： Shafiqur Rehman HilalH.Nuha Ali Al Shaikhi Satria Akbar Mohamed Mohandes Interdisciplinary Research Center for Renewable Energy and Power Systems(IRC-REPS) KFUPMDhahran31261Saudi Arabia HUMIC Engineering School of ComputingTelkom UniversityBandung40257Indonesia Electrical Engineering Department King Fahd University of Petroleum&Minerals(KFUPM)Dhahran31261Saudi Arabia

An accurate vertical wind speed(WS)data estimation is required to determine the potential for wind farm *** general,the vertical extrapolation of WS at different heights must consider different parameters fromdifferent locations,such as wind shear coefficient,roughness length,and atmospheric *** novelty presented in this article is the introduction of two steps optimization for the Recurrent Neural Networks(RNN)model to estimate WS at different heights using measurements from lower *** first optimization of the RNN is performed to minimize a differentiable cost function,namely,mean squared error(MSE),using the Broyden-Fletcher-Goldfarb-Shanno ***,the RNN is optimized to reduce a non-differentiable cost function using simulated annealing(RNN-SA),namely mean absolute error(MAE).Estimation ofWS vertically at 50 m height is done by training RNN-SA with the actualWS data a 10–40 m *** estimatedWS at height of 50 m and the measured WS at 10–40 heights are further used to train RNN-SA to obtain WS at 60 m *** procedure is repeated continuously until theWS is estimated at a height of 180 *** RNN-SA performance is compared with the standard RNN,Multilayer Perceptron(MLP),Support Vector Machine(SVM),and state of the art methods like convolutional neural networks(CNN)and long short-term memory(LSTM)networks to extrapolate theWS *** estimated values are also compared with realWS dataset acquired using LiDAR and tested using four error metrics namely,mean squared error(MSE),mean absolute percentage error(MAPE),mean bias error(MBE),and coefficient of determination(R2).The numerical experimental results show that the MSE values between the estimated and actualWS at 180mheight for the RNN-SA,RNN,MLP,and SVM methods are found to be 2.09,2.12,2.37,and 2.63,respectively.

关键词： Vertical wind speed estimation recurrent neural networks simulated annealing multilayer perceptron support vector machine

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Investigating the Impacts of Digital Food Supply Chain Traceability on Sustainability: An Australian Study 34

Investigating the Impacts of Digital Food Supply Chain Trace...

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34th Australasian Conference on Information systems, ACIS 2023

作者： Kurnia, Sherah Li, Mingye Liu, Libo Cui, Tingru Li, Wen Tortorella, Guilherme Luz Palmer, Martin Ali, Mazen School of Computing and Information Systems University of Melbourne Melbourne Australia Department of Mechanical Engineering University of Melbourne Melbourne Australia Department of Chemical Engineering University of Melbourne Melbourne Australia College of Information Technology University of Bahrain Bahrain

Digital food supply chain traceability (DFSCT) has the potential to address global sustainability concerns. However, limited studies investigate the use of technologies to support food supply chain traceability. Furthermore, empirical studies that show the impacts of DFSCT on sustainability are lacking. This research in progress aims to investigate the impact of DFSCT on the economic, environmental, and social benefits, and to explore how these three benefits affect each other. Through a survey of 310 organisations in the Australian food industry, our study findings show the importance of DFSCT in enhancing organisations’ sustainability performance, with empirical evidence demonstrating the positive impact of social and environmental performance on economic performance. This paper offers modest contributions to research and practice related to the role of technology in supporting traceability and sustainable supply chain management. Copyright © 2023 Kurnia, Li, Liu, Cui, Li, Tortorella, Palmer and Ali.

关键词： Sustainable development

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A Comparison Study on Generative Adversarial Networks (GANs) for Satellite Image Upscaling 12

A Comparison Study on Generative Adversarial Networks (GANs)...

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12th IEEE Conference on systems, Process and Control, ICSPC 2024

作者： Li, Brandon Kah Wei Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Chua, Hui Na Wong, Richard T.K. Hamzah, Muzaffar Sunway University No. 5 Jalan Universiti School of Engineering and Technology Department of Computing and Information Systems Selangor Darul Ehsan Bandar Sunway47500 Malaysia School of Engineering and Technology Selangor Darul Ehsan Bandar Sunway47500 Malaysia University of Aleppo Faculty of Informatics Engineering Syria Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu88450 Malaysia

ISBN: (纸本)9798350391398

Satellite images that are publicly available on the internet such as Google Maps are mostly low-quality and blurry which is not very useful for research or decision-making due to the lack of details. Recently image upscaling has been increasing in popularity to address several real-world problems where one approach is using Convolutional Neural Networks (CNNs). However, CNNs have difficulty in restoring fine details and this has led to the use of Generative adversarial networks (GANs) for image scaling. In this paper, three GAN models are trained which are Enhanced Super-Resolution Generative Adversarial Networks (Real-ESRGAN), Image Restoration Using Swin Transformer (SwinIR-Medium), and Dual Aggregation Transformer (DAT-2). The models are evaluated with well-known image metrics to determine each model's effectiveness in upscaling low-resolution satellite images. The results show that all models can reconstruct the image to some degree where the best out of the three is DAT-2. Further fine-tuning of the best model by changing the loss functions has shown improvements in structural integrity for the upscaled image. © 2024 IEEE.

关键词： Generative adversarial networks

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Machine Learning and OpenCV: A Powerful Combination for Driver Drowsiness Detection

Machine Learning and OpenCV: A Powerful Combination for Driv...

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2024 International Conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Siva Kumar, A. Gopirajan, P.V. Ramasamy, Jayaraj Jackson, Beulah School Of Computing Srm Institute Of Science And Technology Department Of Data Science And Business Systems Kattankulathur Chennai603203 India School Of Computing Srm Institute Of Science And Technology Department Of Computational Intelligence Kattankulathur Campus Chennai603203 India Botho University Faculty Of Engineering And Technology Department of IT Botswana Vel Tech Rangarajan Dr. Sagunthala R & D Institute Of Science And Technology Department Of Electronics And Communication Engineering Chennai India

ISBN: (纸本)9798350352689

Driver fatigue is a significant contributor to road accidents worldwide, and there is a need for efficient driver drowsiness detection systems to prevent such accidents. Computer vision techniques like OpenCV have recently been utilized to develop such strategies. This proposed work investigates the use of machine learning models and OpenCV Python package for driver drowsiness detection. The proposed system uses a camera to continuously capture the driver's face and applies image processing techniques to identify drowsiness. The system detects blinks by monitoring the driver's eyes and measuring the frequency and duration of eyelid closures. The real-time experiments conducted with the proposed method showed high accuracy in detecting drowsiness, and the system can be implemented in various settings, including cars and heavy vehicles. Ultimately, the system has some potential to reduce accidents caused by driver fatigue, thereby enhancing road safety. © 2024 IEEE.

关键词： Support vector machines

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Automated 3D Urban Landscapes Visualization Using Open Data Sources on the Example of the City of Zagreb

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KN - Journal of Cartography and Geographic Information 2022年第2期72卷 139-152页

作者： Komadina, Adrian Mihajlović, Željka Department of Electronics Microelectronics Computer and Intelligent Systems Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb 10000 Croatia

Geographical location visualization is important to create virtual environments when performing simulations. This study uses publicly available data sources to create three-dimensional (3D) views of geographical locations, focusing on the city of Zagreb as a case study, and presents an automated solution that integrates data from four different sources to achieve this. Publicly available data sources (OpenStreetMap, Google Maps Elevation API, MapBox Static Images API) were used, and GIS Zrinjevac which is unique to Zagreb. Based on these sources, individual 3D objects were generated and displayed virtually in the form of an interactive 3D map. The proposed solution attempts to balance the differing levels of detail achieved using other techniques. The solution was implemented in the form of an application created using the Unity game engine. The results were analyzed to evaluate the solution’s validity and the created application’s performance. The analysis showed that the total execution time dependence is quadratically polynomial to the amount of retrieved data, and linear to the number of height map points. Examining the application’s update rate showed that the buildings and individual models used for polygon and point data had the greatest impact. Finally, possible improvements, alternative approaches, and advantages and disadvantages of the proposed solution are compared with other techniques used in this research area. © 2022, Deutsche Gesellschaft für Kartographie e.V.

关键词： Cartographic data Data visualization GIS Zrinjevac Google maps elevation API OpenStreetMap Virtual environment

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