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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： S. Esakki Muthu K. Kartheeban Dep. Of computer science and engineering Kalasalingam academy of research and education Srivilliputtur India

Supply chain is the universal link network to deliver the product from one place to another. Capability to trace and track the Agri-goods products is very important throughout various stages of the supplychain including manufacturing, processing, distribution, importation, and retail. Ensuring the safety, quality, and validation of agricultural products is very crucial. In traditional supply chain management, which often involves untrusted entities and data is centrally monitored and stored with all the transactions being processed through a centralized organization. However, this centralized system is vulnerable to hacking by third parties, which can result in data security issues and the potential for a single point failure. And the primary challenges include monitoring, the origin of agri-goods, tracing the logistics, and low level of security, trust, and transparency between the stakeholders which leads to implicit delays downstream of Agri goods, and decisions taken among different dep.rtments. In this study, we propose a consortium blockchain solution for tracing and tracking of agri-goods products. Blockchain has a rapidly evolving disruptive Technology and immutable chain structure that is linked sequentially according to chronological order. Which is achieved through the creation of smart contract algorithms between different entities and fostering communication among stakeholders in the agri-goods supply chain. The traceability of the agri-goods products, including their origin, safety, quality, and validation, is implemented through the integration of RFID (Radio frequency identifications) tags and readers. Additionally, the solution incorporates a blockchain-enabled ultra-light weight mutual authentication RFID protocol for provide the security and resist the possible vulnerabilities.

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Implementation and Design of Physical/Digital Switches Modified from Sonoff Product Based on Internet of Things 4

Implementation and Design of Physical/Digital Switches Modif...

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4th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2022

作者： Tsao, Yung-Chung Kuo, Yaw-Wen Wu, Chia-Chun Tsai, Yin-Te Hsu, Chihcheng Li, Chiyang Wang, Han-Ching National Chi Nan University Dep. of Electrical Engineering Nantou County Taiwan National Quemoy University Dep. of Industrial Engineering and Management Kinmen County Taiwan Providence University Dep. of Computer Science and Communication Engineering Taichung City Taiwan National Central University Dep. of Information Management Taoyuan Country Taiwan College of Science and Technology National Chi Nan University Nantou County Taiwan Xinhe Elementary School Xindian District New Taipei City Taiwan

ISBN: (数字)9781728195797

ISBN: (纸本)9781728195797

While more home appliances are designed and manufactured, Sonoff serial home assistant alliances are designed and manufactured by eWeLink for many cheap and convenient devices controlled with exclusive APPs. Sonoff home assistant alliances are popular and well-known to attract many developers who can develop unofficial software to enhance the closed APPs designed by eWeLink and make flexible applications based on original hardware such as Tasmota and parameterized open-source APPs. We follow the above-mentioned developers to design a firmware that controls and is controlled based on message queue telemetry transport (MQTT) protocol from a cloud platform and the internet of thing (IoT) based architecture. The Sonoff home assistant alliances installed by the designed firmware can be controlled with one or more groups through a clouding platform by existing physical power-sockets from one-to-one controlling by eWeLink APPs. Under the proposed architecture, those Sonoff serial home assistant alliances are easily used like traditional power sockets for old-age users, who only use physical switches with touch-panel with MCU-based controllers to control power sockets. We designed digital switches on the website by browsers and physical touch panels as traditional switches work indep.ndently and coexist to meet most users with software-based APPs. The proposed architecture can be implemented and dep.oyed as a proof of concept (PoC) for the usability and validity. © 2022 IEEE.

关键词： Internet of things

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Implementation and Design of Water Flow Meter of Smart Home Integrated with Environment Monitor System 4

Implementation and Design of Water Flow Meter of Smart Home ...

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4th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2022

作者： Tsao, Yung-Chung Kuo, Yaw-Wen Wu, Chia-Chun Tsai, Yin-Te Hsu, Chihcheng Ho, Liulin Wang, Han-Ching National Chi Nan University Dep. of Electrical Engineering Nantou County Taiwan National Quemoy University Dep. of Industrial Engineering and Management Kinmen County Taiwan Providence University Dep. of Computer Science and Communication Engineering Taichung City Taiwan National Central University Dep. of Information Management Taoyuan Country Taiwan College of Science and Technology National Chi Nan University Nantou County Taiwan Xinhe Elementary School Xindian District New Taipei City Taiwan

ISBN: (数字)9781728195797

ISBN: (纸本)9781728195797

More home appliances were designed and manufactured in the market, but fewer commercial products related to water-flow monitoring are sold in the current market. Water-related costs and expenses are cheaper than others, but water is one of the critical survival necessities for human beings. Thus, the home appliances or the smart home needs to pay attention to ubiquitous water resources as one of the limited natural earth resources. We follow the above-mentioned issues to achieve an internet of things (IoT) based architecture to design a flow-monitoring information collecting device (FMICD). Hall sensors were used to collect the water-flow consumption and to upload the information of the environment monitor system which was implemented in the previous research. In the proposed architecture, the FMICDs are designed and developed with the MCU-based controller binding to a water-flow meter (Model: YF-S201) equipped to monitor water consumption. We implemented FMICDs in the Xinhe Elementary School in the Xindian District of New Taipei City in Taiwan to validate the usability and feasibility of the study with a website (http://***:9999/***) to offer related users and staff to validate the usability in the study. © 2022 IEEE.

关键词： Internet of things

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TIoT: A Trust by Design Architecture for Secure and Private Internet of Things (IoT) 21

TIoT: A Trust by Design Architecture for Secure and Private ...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Alsheakh, H. Mahdi, Tareq Nasser Kadhm, Mustafa S. Al Shammari, Abeer Abdulsahib, Fatimah I. Taher, Hazeem B Algriree, Waleed Alobaidi, Marwa Q. Mohammed, Hayder Dhafer College of Science Mustansiriyah University Department of Computer Science Baghdad Iraq College of Pharmacy Mustansiriyah University Clinical Laboratory Science Baghdad Iraq Ministry of Science and Technology Baghdad Iraq Szechenyi Istvan Egyetem Department of Computer Science Gyor9026 Hungary University of Thi-Qar Department of Computer Science Thi-Qar Iraq University Putra Malaysia Dep. of Wireless Communication and Network Engineering Selangor Malaysia National Engineering School of Sfax Sfax University Sfax Tunisia College of Pharmacy Mustansiriyah University Baghdad Iraq

ISBN: (纸本)9798350374131

The Internet of Things (loT) facilitates the interconnection of a vast array of devices, services, and individuals for data exchange in multi-domain loT ecosystems, the social, physical, and cyber domains. That emerges various security, privacy, and trust issues, such as vulnerabilities, threats, and attacks. Addressing these challenges requires innovative solutions. New controls (solutions) are needed. Trust by design can be one of these controls, supporting security and privacy (among other system components). This paper proposes a Trusted loT (TloT), a trust-by-design model, and highlights the author's principal contributions. First, it advocates for implementing Tlo T through trust services. Second, it outlines an architecture for using trust by design in loT, including the design of its top-level structure and activities. Minor contributions include overviews of loT and trust, as well as looking at security and privacy in terms of security services and privacy services, respectively. © 2024 IEEE.

关键词： Internet of things

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A Bayesian take on Gaussian process networks 23

A Bayesian take on Gaussian process networks

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Enrico Giudice Jack Kuipers Giusi Moffa Dep. of Mathematics and Computer Science University of Basel Basel Switzerland Dep. of Biosystems Science and Engineering ETH Zurich Basel Switzerland Dep. of Mathematics and Computer Science University of Basel Basel Switzerland and Division of Psychiatry University College London London UK

Gaussian Process Networks (GPNs) are a class of directed graphical models which employ Gaussian processes as priors for the conditional expectation of each variable given its parents in the network. The model allows the description of continuous joint distributions in a compact but flexible manner with minimal parametric assumptions on the dep.ndencies between variables. Bayesian structure learning of GPNs requires computing the posterior over graphs of the network and is computationally infeasible even in low dimensions. This work implements Monte Carlo and Markov Chain Monte Carlo methods to sample from the posterior distribution of network structures. As such, the approach follows the Bayesian paradigm, comparing models via their marginal likelihood and computing the posterior probability of the GPN features. Simulation studies show that our method outperforms state-of-the-art algorithms in recovering the graphical structure of the network and provides an accurate approximation of its posterior distribution.

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Logistic Regression Classification for Assessing the Risk of Kidney Tumor

Logistic Regression Classification for Assessing the Risk of...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Dalia Alzu'bi Rabia Emhamed Al Mamlook Ahmad Nasayreh Mohammad Aljaidi Rami AlAzab Hasan Gharaibeh Qais Al-Na'amneh Dep. Information and Systems Engineering Concordia University Montreal Canada Dep. of Business Administration Trine University Indiana USA Department of Industrial Engineering University Zawia Tripoli Libia Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. Surgery and Urology Jordan University of Science and Technology Irbid Jordan Dep. of Cyber Security Applied Science Private University Amman Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

The kidneys have the important function of filtering waste products and toxins from the blood in the human body. Any problems affecting the kidneys can potentially impact their efficiency and overall function. Kidney Tumor (KT) is a disease that specifically affects kidney cells and causes the abnormal growth of tissue in one or both kidneys. Assessing the risk of developing a kidney tumor and identifying the key factors that contribute to this risk are crucial for ensuring patient well-being. Additionally, this information assists doctors and specialists in making faster and more accurate diagnoses, determining appropriate treatment methods, and potentially influencing the course of the disease to minimize its severity and impact. Machine learning algorithms have recently been introduced to evaluate disease risks, and in this study, we specifically focus on examining the risk of kidney tumor development and investigating the influencing factors. We employed a logistic regression model to predict if a patient is at risk of developing a kidney tumor or not, and further categorized the samples into high-risk and low-risk groups. Our model was trained and tested using a unique dataset obtained from the (KAUH) hospital in Jordan, consisting of well-balanced metadata from 120 patients with kidney issues. Our work demonstrated accuracy results ranging from 90% to 98%. Ultimately, specialists can utilize our model as an additional tool to enhance the speed and accuracy of patient diagnoses

关键词： Logistic regression Analytical models Accuracy Predictive models Genetics History Reliability

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Designing Participatory AI: Creative Professionals' Worries and Expectations about Generative AI

Designing Participatory AI: Creative Professionals' Worries ...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Inie, Nanna Falk, Jeanette Tanimoto, Steve It University of Copenhagen Copenhagen Denmark Dep. of Artificial Intelligence and Human Interfaces University of Salzburg Salzburg Austria Paul G. Allen School of Computer Science & Engineering University of Washington Seattle United States

ISBN: (纸本)9781450394222

Generative AI, i.e., the group of technologies that automatically generate visual or written content based on text prompts, has undergone a leap in complexity and become widely available within just a few years. Such technologies potentially introduce a massive disruption to creative fields. This paper presents the results of a qualitative survey (N = 23) investigating how creative professionals think about generative AI. The results show that the advancement of these AI models prompts important reflections on what defines creativity and how creatives imagine using AI to support their workflows. Based on these reflections, we discuss how we might design participatory AI in the domain of creative expertise with the goal of empowering creative professionals in their present and future coexistence with AI. © 2023 Owner/Author.

关键词： computer programming

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Feature Selection for Robust Spoofing Detection: A Chi-Square-based Machine Learning Approach

Feature Selection for Robust Spoofing Detection: A Chi-Squar...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Qais Al-Na'amneh Mohammad Aljaidi Hasan Gharaibeh Ahmad Nasayreh Rabia Emhamed Al Mamlook Sattam Almatarneh Dalia Alzu'bi Abla Suliman Husien Dep. of Cyber Security Applied Science Private University Amman Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Business Administration Trine University Indiana USA Department of Industrial Engineering University of Zawia Tripoli Libia Dep. of Data Science and Artificial Intelligence Zarqa University Zarqa Jordan Dep. Information and Systems Engineering Concordia University Montreal Canada

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Nowadays, the Internet of Things (IoT) system is vulnerable to spoofing attacks that can easily where attackers can easily pose as a legal entity of the network. A “spoofing attack” refers to a type of cyber-attack when an attacker purposefully impersonates or masquerades as someone or something else to deceive the target or obtain unauthorized access to systems, information, or resources. In such attacks, the attacker alters their name, IP address, or other attributes to fool the victim into thinking they are engaging with a legitimate entity. Spoofing attacks can take place via a variety of channels, including, ARP and DNS spoofing. Therefore. Spoofing attacks can have serious consequences. We proposed a new approach based on three machine learning models LightGBM, Gradient Boost, and XGBoost to classify attacks on spoofing, we used Chi-square to select the best features to get the highest performance, and we demonstrated that the results using Chi-square achieved higher results than without Chi-square and improved the result with a rate three percent of accuracy. In terms of results, LightGBM outperformed other models by achieving 89%, 91 %, 87 %, and 89 % for accuracy, precision, recall, and f1-score, respectively. This shows the potential and efficiency of Chi-square to achieve the best performance by selecting the best features, thus providing a secure system and identifying cyber-attacks on systems.

关键词： Electric potential Accuracy Law Machine learning Learning (artificial intelligence) Feature extraction Internet of Things

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A Bayesian Take on Gaussian Process Networks

arXiv

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arXiv 2023年

作者： Giudice, Enrico Kuipers, Jack Moffa, Giusi Dep. of Mathematics and Computer Science University of Basel Basel Switzerland Dep. of Biosystems Science and Engineering ETH Zurich Basel Switzerland Dep. of Mathematics and Computer Science University of Basel Basel Switzerland Division of Psychiatry University College London London United Kingdom

关键词： Approximation algorithms

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Deep Feature Extraction Framework Based on DNN for Enhancing Mirai Attachment Classification in Machine Learning

Deep Feature Extraction Framework Based on DNN for Enhancing...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Hasan Gharaibeh Mohammad Aljaidi Ahmad Nasayreh Qais Al-Na'amneh Ameera S. Jaradat Ghassan Samara Rabia Emhamed Al Mamlook Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. of Cyber Security Applied Science Private University Amman Jordan Dep. of Business Administration Trine University Indiana USA Department of Mechanical and Industrial Engineering University of Zawia Tripoli Libia

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Nowadays, the rapid growth of Internet of Things (IoT) devices has changed many parts of our lives by providing seamless connectivity and automation. However, this expansion has created new challenges and vulnerabilities, especially with regard to security. Mirai botnet, a strain of malware that has severely damaged the IoT ecosystem, is one of the prominent risks to IoT security. This research examines the impact of the Mirai botnet on IoT devices. In this study, we present a hybrid model to detect Mirai attacks. Deep neural networks (DNNs) are used to extract significant deep features, which are subsequently passed to the (Light Gradient Boosting Machine) LGBM algorithm for classification. A dataset from the Canadian Institute 2023 that included several kinds of Internet of Things assaults was used to assess the model. The model achieved excellent feature extraction and promising results with accuracy, recall, precision, and F1-score scoring up to 95 % for all. These accuracy results demonstrated the superior performance of the suggested model over competing techniques, which had scores of 71 %, 85%, and 52%, respectively, for Support Vector Machine (SVM), Light Gradient Boosting Machine (LGBM), and Stochastic Gradient Descent (SGD). Furthermore, the model outperforms the DNN model, which received a score of 65%.

关键词： Support vector machines Accuracy Machine learning algorithms Botnet Artificial neural networks Feature extraction Boosting

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