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20th International Conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2024

作者： Miri, Pouriya Kochovski, Petar Tuler de Oliveira, Marcela Stankovski, Vlado Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Department of Engineering Systems Delft University of Technology Delft Netherlands

ISBN: (纸本)9783031812255

The specification of experiments expressed as Complex Analytics Workflows is a complex task that involves many decision-making steps with various degrees of complexity. The use of the context, the expert knowledge, and the potential for its sharing and reuse in the context of experiment specification have not been addressed sufficiently until now. Moreover, to make such knowledge instrumental, it should be coupled with specific probabilistic measures, such as particular assurances, ranking, and verification of various options. The paper aims to present a novel semantic model for probabilistic reasoning in any experimentation context coupled with a functional system for knowledge generation, reuse, and sharing. The result of our work can be used within existing experimentation engines. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Markov processes

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A Deep Analysis of Medical Monitoring System Based on ESP32 IoT System 3

A Deep Analysis of Medical Monitoring System Based on ESP32 ...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Kumar Sharma, Govind Tr, Mahesh Dev Bhoomi Uttarakhand University Dept of Computer Science and Engineering Uttarakhand Dehradun India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350399264

However, to be intact is to emerge, and that is what we tried unsuccessfully to achieve here with in out Iot-enabled patient monitoring built on esp32 task. Efforts to oversee health with relevant Features and sensors, example MAX30102 which it emits light to perceive the concentration of person's blood and also in turn illustrate air pressure, are not new in this era. It assesses the vitals of the patient and sends the information through the internet to the parties involved. Its small size makes it portable and therefore also used everywhere. It is based on IoT principles and helps with illness prevention and accurate general health monitoring. Comparatively speaking to other projects that do comparable services as esp32, it is rather affordable and small. For the Esp8266 we would have had we do not need to utilise Shields. Since Uno is less expensive and has comparable characteristics to the Raspberry Pi series, we chose to utilise it. The effectiveness of the suggested approach depends on variables like minimum errors in Ejection fraction and heart readings. © 2023 IEEE.

关键词： Patient monitoring

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ICDAC: Intelligent Contracts Driven Access Control Model for IoT Device Communication

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SN computer science 2024年第8期5卷 1054页

作者： Rajesh, S.M. Prabha, R. Department of Information Science and Engineering Dr. Ambedkar Institute of Technology Affiliated to Visvesvaraya Technological University Karnataka Bangalore India Department of Computer Science and Engineering Dr. Ambedkar Institute of Technology Affiliated to Visvesvaraya Technological University Karnataka Bangalore India

In the Internet of Things (IoT) epoch, the seamless communication of smart devices through autonomous collaboration demands real-time IoT networks to possess a high level of intelligence. However, the resource-constrained nature of IoT limits the adoption of traditional intuitive access control models for communication in dynamic, complex, and large-scale applications. The frequent changes in the IoT environment urge creating a secured autonomous dynamic access control system for device-to-device communication to secure device-sensitive data and detect abnormal access behaviors to ensure the smart infrastructure is free from cyberattacks. Smart Home infrastructure with various sensors and validation systems is used as the target application in the proposed system. Addressing the challenges, the novel design of an intelligent access control model to protect the privacy of device information and data is presented by integrating Artificial Intelligence and Blockchain. The attribute-based access policies are designed to create synthetic datasets by encompassing the if-then rule. Smart Home devices and transaction data are secured under the hashed storage structure IPFS. The Deep Learning-based ResNet model is employed in the design of intelligent contracts, and its execution is focused on the required device only. The authentic and immutable nature of the blockchain enforces the end-to-end security of the proposed system with an enhanced accuracy of 96.8% in access decisions to accommodate the Smart Home Infrastructure. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： ABAC Blockchain D2D Intelligent contracts IPFS ResNet Smart home

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Information-Centric IoT-Based Smart Farming with Dynamic Data Optimization

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computers, Materials & Continua 2023年第2期74卷 3865-3880页

作者： Souvik Pal Hannah VijayKumar D.Akila N.Z.Jhanjhi Omar A.Darwish Fathi Amsaad Department of Computer Science and Engineering Sister Nivedita UniversityKolkataIndia Post Doctoral Researcher Sambalpur UniversitySambalpurIndia Department of Computer Science Anna Adarsh College for WomenChennaiIndia Department of Information Technology Vels Institute of ScienceTechnology and Advanced StudiesChennaiIndia School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya47500SelangorMalaysia FITTaylor’s UniversitySubang Jaya47500SelangorMalaysia Department of Information Security and Applied Computing Eastern Michigan UniversityUSA School of Information Security and Applied Computing Eastern Michigan UniversityUSA

Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies,including big data,the cloud,and the Internet of Things(IoT).Many researchers try to integrate IoT-based smart farming on cloud platforms *** define various frameworks on smart farming and monitoring system and still lacks to define effective data management *** IoT-cloud systems involve massive structured and unstructured data,data optimization comes into the ***,this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization(ICISF-DDO),which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved ***,a conceptual framework of the proposed scheme and statistical design model has beenwell *** information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data *** simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%,reliability ratio of 98.63%,a coverage ratio of 99.67%,least sensor error rate of 8.96%,and efficient energy consumption ratio of 4.84%.

关键词： Smart farming data optimization information management internet of things cloud

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An Automated Model for Fabric Weave Pattern Recognition and Classification Using Deep Learning Based Image Processing 7

An Automated Model for Fabric Weave Pattern Recognition and ...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Pasha, Quratulain Akram, Noreen Rafique, Ayesha Akber, Anam Abbasi, Madiha Sir Syed University of Engineering & Technology Telecommunication Engg. Department Karachi Pakistan Sir Syed University of Engineering & Technology Computer Science Department Karachi Pakistan

ISBN: (纸本)9798331534271

Fabric weave type identification has many uses that adds to importance of advance classification methods;e.g fabric weave should be known in order to identify the most suitable use. But the manual inspection of fabric causes human errors such as, stressed, less focused, eye focus and mishandling of the apparatus. There are lighting effects which also degrades the performance of manual inspection. In recent years, computer vision based approaches have achieved remarkable performance in the fields of object recognition and image processing, thus opening a new door to develop an automated system for the recognition of woven fabric patterns for production of fine quality fabric according to customer needs. In this paper, we have devised an automatic weave pattern recognition mechanism based on convolutional neural networks(CNN). Initially three basic weave patterns were chosen i.e Twill, Plain and Satin. The results were analysed for its possible adaptability in the task of fabric weave pattern recognition. The classification results give the average accuracy of 72% for all three samples whereas individually, Plain weave gives 100% accuracy, Twill gives 66% accuracy and satin weave gives 50% only, which indicates a room for improvement in future. © 2024 IEEE.

关键词： Convolutional neural networks

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Cough Sounds, Symptoms, CXR, and CT Images for COVID-19 Detection 1

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3rd International Conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Pandey, Priyanka Tyagi, Nidhi Pattnaik, Prasant Kumar Department of Computer Science and Engineering School of Engineering and Technology SIET Meerut Meerut India School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar India

ISBN: (数字)9789819705733

ISBN: (纸本)9789819705726

Since the coronavirus epidemic has reached every corner of the globe, we need to develop machine-based solutions to detect it. The global spread of novel coronaviruses has claimed millions of lives and prompted the development of various low-cost diagnostic assays for the presence of COVID-19 infection. The identification of chronic and communicable diseases is only one example of how current technology and transfer learning (TL) methodologies have improved human health and wellness. In order to combat the spread of this terrible infection, intelligence-based models and preventive measures and much research are required. This review presents a comprehensive analysis of the literature on TL and fuzzy ensemble techniques related to COVID-19 detection procedures. For the purpose of identifying COVID-19, researchers have used cough sounds, CT scans, X-ray pictures, and a symptom dataset. In this study, we explore the use of DL/ML, TL, fuzzy ensemble, and fuzzy inference methods for identifying COVID-19. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Intrusion Detection: A Comparison Study of Machine Learning Models Using Unbalanced Dataset

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SN computer science 2024年第8期5卷 1028页

作者： Ajagbe, Sunday Adeola Awotunde, Joseph Bamidele Florez, Hector Department of Computer Engineering Abiola Ajimobi Technical University Ibadan 200255 Nigeria Department of Computer Science University of Zululand Kwadlangezwa 3886 South Africa Department of Computer Science University of Ilorin Kwara State Ilorin 240003 Nigeria Universidad Distrital Francisco Jose de Caldas Bogotá Colombia

The worldwide process of converting most activities of both corporate and non-corporate entities into digital formats is now firmly established. Machine learning models are necessary to serve as a tool for preventing illegal intrusion onto different networks. The machine learning (ML) model's strengths and drawbacks pertain to intrusion detection (IDS) tasks. This study used an experimental methodology to assess the efficacy of various ML models, including linear SVC, LR, random forest (RF), decision tree (DT), and XGBoost, in detecting intrusion on the UNSW NB15 datasets. The objective is to compare the strengths and shortcomings of these models. Data exploration, Feature engineering, selection and a test set of 15%, a validation set of 15%, and a training set of 70% respectively were used for data splitting. Performance evaluation was carried out using accuracy, recall, precision F1-score and confusion matrix plotted. The outcome of the experiment shows a percentage of 92.71% (1, normal) and 7.29% (0, attack) for normal traffic and attack traffic respectively. Performance evaluation results showed that RF and XGBoost outperformed the other ML models. Hence, ML models can effectively be used to detect system attacks. We intend to expand this research in the future and use the paradigm in a real-world setting with further conclusions and justifications. © The Author(s) 2024.

关键词： Comparative analysis Contemporary machine learning Deep learning Detection experiment Intrusion detection systems (IDSs)

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Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection System

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Multimedia Tools and Applications 2024年第21期83卷 59887-59913页

作者： Abualigah, Laith Ahmed, Saba Hussein Almomani, Mohammad H. Zitar, Raed Abu Alsoud, Anas Ratib Abuhaija, Belal Hanandeh, Essam Said Jia, Heming Elminaam, Diaa Salama Abd Elaziz, Mohamed Abd Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa13133 Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Science Wenzhou-Kean University Wenzhou China Department of Computer Information System Zarqa University P.O. Box 13132 Zarqa Jordan School of Information Engineering Sanming University Sanming365004 China Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Benha12311 Egypt Computer Science Department Faculty of Computer Science Misr International University Cairo12585 Egypt Faculty of Computer Science & Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt

With the ever-expanding ubiquity of the Internet, wireless networks have permeated every facet of modern life, escalating concerns surrounding network security for users. Consequently, the demand for a robust Intrusion Detection System (IDS) has surged. The IDS serves as a critical bastion within the security framework, a significance further magnified in wireless networks where intrusions may stem from the deluge of sensor data. This influx of data, however, inevitably taxes the efficiency and computational speed of IDS. To address these limitations, numerous strategies for enhancing IDS performance have been posited by researchers. This paper introduces a novel feature selection method grounded in Support Vector Machine (SVM) and harnessing the innovative modified Aquila Optimizer (mAO) for Intrusion Detection Systems in Wireless Sensor Networks. To evaluate the efficacy of our approach, we employed the KDD'99 dataset for testing and benchmarking against established methods. Multiple performance metrics, including accuracy, detection rate, false alarm rate, feature count, and execution time, were utilized for assessment. Our comparative analysis reveals the superiority of the proposed method, with standout results in terms of feature reduction, detection accuracy, and false alarm mitigation, yielding significant improvements of 11%, 98.76%, and 0.02%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Network security

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Online Information-Based Product Evolution Course Mining and Prediction

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International Journal of Information technology and Decision Making 2024年第2期23卷 599-627页

作者： Chen, Yuh-Jen Chen, Yuh-Min Department of Accounting and Information Systems College of Business Intelligence National Kaohsiung University of Science and Technology Kaohsiung City Taiwan Institute of Manufacturing Information and Systems College of Electrical Engineering and Computer Science National Cheng Kung University Tainan City Taiwan

In today’s fiercely competitive business environment, product design and development play critical roles in an enterprise’s success;therefore, consumer demand must be understood. Enterprises used to understand their consumers’ demands through time-consuming questionnaire surveys and statistical analyses. As the Internet and the popularity of virtual communities have grown, more consumers are leaving comments about their perceptions of the appeal of products on online social media platforms, thus enabling enterprises to more objectively understand consumers’ product preferences and demands. Therefore, determining how to effectively assist enterprises in analyzing valuable information beneficial to product design and development that can be gleaned from the large amount of social media data available is critical to promoting an enterprise’s competitive advantage in the product market. However, previous studies have primarily focused on understanding consumer viewpoints of products through review articles or electronic word-of-mouth (eWOM;it is a common channel for spreading product appraisals) from online social media. For these review articles and eWOM that can imply consumer demand for product features, there were no relevant studies focused on analyzing consumer demand by using both online review articles and eWOM for product feature evolution. Therefore, for new product features, this study developed a mechanism for product evolution course mining from product-related review articles (such as product functions or specifications) and eWOM on online social media to realize the prediction of future product features or specifications, and then assist enterprises in rapidly and accurately grasping product development trends to effectively discern key reference information for product design and development. The study was achieved by (1) designing a process for online information-based product evolution course mining and prediction, (ii) developing techniques related to

关键词： Forecasting

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Gaps in value-driven connectivity: A case study on connectivity challenges in Northern Europe 35

Gaps in value-driven connectivity: A case study on connectiv...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Karppinen, Pasi Bandaranayake, Sameera Haukipuro, Lotta Elmistikawy, Yomn Saarnisaari, Harri University of Oulu Faculty of Information Technology and Electrical Engineering Oulu Finland University of Oulu Faculty of Science Oulu Finland Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden

ISBN: (纸本)9798350362244

Well-working Internet connectivity is a fundamental citizen right and a necessity in modern digital societies. Nevertheless, the digital divide persists and often affects rural or remote areas. To bridge the digital divide, we need to understand the specific rural use cases and challenges. This would increase understanding about problems weak Internet connectivity causes, what is required from the connectivity technology and what is needed from policymakers and regulation. The goal of this paper is to identify rural use cases and analyze consequences to future connectivity technology through a mixed-methods approach: we investigated the divide and its consequences by a questionnaire and interviews conducted in northern Europe. The study found that the main issues faced by rural internet users are related to capacity, availability, and vulnerability of internet services. These issues hinder their daily life, work activities, and expose them to system-level risks. The article discusses how these findings can inform the development of more inclusive and resilient internet connectivity solutions. © 2024 IEEE.

关键词： digital divide mobile communication rural connectivity social sustainability

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