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Analysis of Digital Twins Implementation in Smart City using Big Data and Deep Learning

Analysis of Digital Twins Implementation in Smart City using...

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international conference on ent Computing and Control Systems (ICICCS)

作者： Chinmaya Kumar Nayak S. Karunakaran P. Yamunaa S. Kayalvili Mohit Tiwari Manu Vasudevan Unni Faculty of Emerging Technologies Sri Sri University Sri Sri Vihar Ward No - 3 Godi Sahi Barang Odisha Computer Technology Kongu Engineering College Erode Tamilnadu India Department of EEE Peri Institue of Technology Computer Science & Engineering Velalar College of Engineering and Technology Erode Tamil Nadu Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering A-4 Rohtak Road Paschim Vihar Delhi St. Claret College P. O. Box 1355 MES Ring Rd Sharadhamba Nagar Jalahalli Bengaluru Karnataka

Digital twins are becoming more relevant for business and academic users due to advances in IoT, AI, and Big Data. Due to global urbanization, pollution, public safety, traffic congestion, and other challenges have arisen. New technologies make cities smarter to keep up with growth. In the Internet of things (IoT) age, many sensing devices acquire and/or produce a broad range of sensory data over long periods of time for a variety of businesses and applications. the use case determines the device’s data stream volume and speed. the efficacy of the analytics process used to analyze these streams of data to learn, predict, and act determines IoT’s worth as a business paradigm changer and quality-of-life technology. this study introduces Deep Learning (DL), a family of advanced machine learning techniques, to enhance IoT analytics and teaching. Introducing new results, challenges, and research opportunities. this study may assist academics and newbies comprehend how to use DL to smart cities. Analyzing and summarizing major IoT DL research initiatives. Check out smart IoT devices with DL embedded into their AI. Ultimately, the study will identify issues and suggest additional research. Each chapter concludes with experimental findings and the newest literature review.

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Real-World Streaming Process Discovery from Low-Level Event Data 1

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17th international conference on Learning and Intelligent Optimization, LION-17 2023

作者： Lefebure, Franck thuault, Cecile Cholet, Stephane Softbridge Technology GP Baie-Mahaut97122 France

ISBN: (数字)9783031445057

ISBN: (纸本)9783031445040

New perspectives on monitoring and optimising business processes have emerged from advances in process mining research, as well as techniques for their discovery, conformance checking and enhancement. Many identify process discovery as the most challenging task, as it is the first in line, and as the methods deployed could impact the effectiveness of subsequent analysis. More and more companies are ready to use process mining products, but not at any cost. they require them to be highly reliable, fast and to introduce a limited overload on their resources (including human). In a real-word setting, specific business constraints add to common issues, such as high data frequency and asynchrony. this also adds to the complexity of real-world processes and of process mining itself. Mainstream studies propose to use a finite set of high-level and business-oriented event logs, where key attributes (i.e., case, activity and timestamp) are known, and apply unscaled discovery techniques to produce control-flow process models. In this research, we propose an original approach we have designed and deployed to mine processes of businesses. It features fully streamed and real-time techniques to mine low-level technical event data, where key attributes do not exist and have to be forged. We will focus on the scope of process discovery, and expose our adoption of an organizational perspective, driven by (semi-) unsupervised discovery, streaming and scaling features. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data mining

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Solar Flare Prediction Using Hybrid CNN-Model

Solar Flare Prediction Using Hybrid CNN-Model

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international conference on Electronics, Materials Engineering and Nano-technology (IEMENTech)

作者： Ramandeep Kaur Priya Yadav U. Hariharan Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Mohali Punjab India

this paper introduces a hybrid convolutional neural network (CNN) approach to solve the issues given by unpredictable solar flares in space weather forecasting. the methodology, which uses the Solar Dynamics Observatory (SDO) benchmark dataset, combines the robust feature extraction capabilities of VGGNet-16 with the computational efficiency of LeNet. the SDO dataset’s input data is carefully preprocessed and shrunk to fit the input size of VGGNet-16, which processes the data to extract complex feature maps capturing minor solar activity patterns. these maps are then transformed to 32x32 to meet the input requirements of LeNet, enabling for accurate classification of solar flares into their appropriate classifications. the experimental results show that the hybrid CNN system outperforms standard approaches in terms of prediction accuracy. the combination of VGGNet-16 feature extraction with LeNet classification improves accuracy while also ensuring computational economy, which is critical for real-time applications. this study represents a big step forward in space weather forecasting, demonstrating the connection between deep learning and heliophysics. the suggested model advances our understanding of solar phenomena by exposing complex patterns underlying solar flares and bridging the gap between observational data and computer models. the discovery holds potential for real-time space weather monitoring, which is critical for satellite operations and terrestrial communication systems. the methodology’s efficacy highlights its potential for enhancing astrophysical data processing and space weather forecasting skills, emphasizing the synergy between the technological advancement and scientific research in the field of astrophysics.

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the Influence of Pancasila Education and Religion Education on Value Education for the Students during COVID-19 Pandemic (Case Study: Bina Nusantara University Students) 22

The Influence of Pancasila Education and Religion Education ...

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7th international conference on Big Data and Computing, ICBDC 2022

作者： Benawa, Arcadius Lusia, Erma Alwino, Alfensius Irawan, Iwan Witono, Petrus Hepi Character Building Development Center Mass Communication Program Communication Department Faculty of Economics & Communication Bina Nusantara University Jakarta Indonesia Tourism Department Faculty of Economics & Communication Bina Nusantara University Jakarta Indonesia Character Building Development Center Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Character Building Development Center Industrial Engineering Department School of Industrial Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781450396097

It must be admitted that with the outbreak of the COVID-19 pandemic, conventional face-to-face learning has been disrupted. Luckily, we have to thank to advances in science and technology, so that the learning system does not have to stop, but can continue even though by online learning, either through zoom meeting or google meet or other online media. In such a context, this research was conducted with the main objective of finding out whether compulsory curriculum subjects, especially Pancasila Education and Religion Education, can still have a significant influence on character building through Value Education? therefore, this simple research used quantitative methods by distributing questionnaires to 150 students as simple random sampling. From the data obtained, it was found the result that Pancasila Education and Religion Education still have a significant influence on Value Education which at Bina Nusantara University the values is focused to the value of SPIRIT (Striving for the excellent, Perseverance, Integrity, Respect, Innovation, and Teamwork). So, it can be concluded that the COVID-19 condition must not slacken the spirit to shape the character of students through Pancasila Education and Religion Education, so that core values of Bina Nusantara University remain embedded in students. © 2022 ACM.

关键词： Students

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Advancing Heart Health with Multifaceted Deep Learning for ECG Signal Analysis

Advancing Heart Health with Multifaceted Deep Learning for E...

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international conference on Electronics technology (ICET)

作者： Sijjad Ali Asad Ali Amir Hamza Maryam Jamil Muhammad Uzair Rana Zaki Abdul Bari M. Hunza Nabel Akram Hamid Sharif College of Computer Science and Software Engineering Shenzhen University Shenzhen China College of Electronics and Information Engineering Shenzhen University Shenzhen China School of Media and Communication Shenzhen University China College of Physics and Optoelectronics Engineering Shenzhen University Shenzhen China School of Government Shenzhen University Shenzhen China

ISBN: (数字)9798350363951

ISBN: (纸本)9798350363968

Cardiovascular diseases have the highest death rate in the whole world, which stresses the need for taking more preventive steps. this paper proposes a novel neural network model with multimodal machine learning techniques, able to detect cardiac arrhythmias rapidly and accurately. By using massive amounts of data, the model exhibits great performance, specifically in distinguishing normal and ventricular extra beats, which demonstrates its ability in feature extraction and classification. It is such a great achievement that helps to carry out disease surveillance and early diagnosis through an instant interpretation of complex ECG patterns. the study relies upon algorithmic modifications and real-time data processing, proposing a framework that combines wearable technology with AI diagnostics for reliable cardiac monitoring. the results contribute to the design of cardiac healthcare technology, which can be used to develop further advances in cardiac health monitoring and prevention of diseases.

关键词： Analytical models Visualization Heart beat Surveillance Prevention and mitigation Electrocardiography Robustness

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A Life-Long Learning Education Passport Powered by Blockchain technology and Verifiable Digital Credentials: the BlockAdemiC Project

A Life-Long Learning Education Passport Powered by Blockchai...

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19th international conference on Software Engineering and Formal Methods (SEFM)

作者： Terzi, Sofia Ioannis, Stamelos Votis, Konstantinos Tsiatsos, thrasyvoulos Aristotle Univ Thessaloniki Thessaloniki Greece Ctr Res & Technol Hellas Thessaloniki Greece

ISBN: (纸本)9783031124297;9783031124280

the academic and business domains are in constant transformation due to the technological advances. In this context, proof of knowledge, skills and training are becoming crucial for the students and employees. In a competitive environment like that, forgery of diplomas and certificates is a frequent problem that has not been faced properly yet. Additionally, the lack of a formal representation and acknowledgment of informal and life-long learning outcomes costs to all educational stakeholders and employee seekers. By using open source blockchain technology as a game changer throughout the educational process, security, privacy, integrity and immutability of the data related to diplomas, certificates and skills acquired by learners can be guaranteed. the result of building a system supported by blockchain for life-long learning, a positive impact in trust and transparency of the education achievements, institutions, students and companies is achieved.

关键词： Education Blockchain Verifiable credentials Anti-forgery Digital identity

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A Survey on Techniques and Methods of Recommender System 1

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5th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2022

作者： Raval, Aanal Borisagar, Komal Computer Engineering Department GSET GTU Ahmedabad India E.C. Engineering Department GSET GTU Ahmedabad India

ISBN: (数字)9783031163647

ISBN: (纸本)9783031163630

As prevalence is growing for social media, the value of its content is becoming paramounting. this data can reveal about a person’s personal and professional life. the behaviors done on social media either frequent or periodic can comprehend fondness and attentiveness of users on certain matters. But accompanying it, diverse data from multiple sources with high volumes sets its foot in. Here becomes operational the usage of recommender systems. these are capable of providing customized assistance to users based on respective quondam behavior and preferences. Machine Learning and deep learning methods have proven to be a boon in these tasks of predictions with notable accuracy. this paper discusses existing techniques with its fors and againsts, concerns and issues, extrapolating the results and solutions, which in turn can help in better interpretation of current developments to pave a path for pioneering researches. © 2022, IFIP international Federation for Information Processing.

关键词： Recommender systems

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A Bibliometric Analysis of Research on Techniques for Network Communications Security

A Bibliometric Analysis of Research on Techniques for Networ...

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international conference on Information technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Ismaeel Abiodun Sikiru Michel Dossou Africa Centre of Excellence in Mathematics and Computer Science Institute of Mathematics and Physical Sciences Université d'Abomey-Calavi Bénin Department of Information Technology University of Ilorin Ilorin Nigeria Research Unit in Photonics and Digital Broadcasting (URPHORAN) Université d'Abomey-Calavi Bénin

Presently, security is very paramount in all endeavors that aim to guarantee seamless network communications, including our confidentiality, integrity, availability, and trust. thus, the objective of this bibliometric analysis is to examine and map the scientific advances in network communications security (NCS) in the recent years, while also projecting for future direction on the subject. To achieve this objective, web of science database was searched for related literature. While adopting preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach for inclusive and exclusive criteria, R package was executed for data analysis. Our findings, under the analysis of authors, countries, publications, and word analysis, show that Zhang Y (h-index=9; 13.4%) and Wang X (h-index=9; 12.4%) are the most contributing authors towards NCS. Anna University, India, is the most- contributing affiliated institution in this work, with 13.6%. Country- wise, USA is the most cited affiliated-countries, 6470 (32.1%). Of the sources, though Security and Communication networks journal has most studies on the subject, 91 (22.2%), IEEE Access owns highest h-index of 25 with citations of 1963 (35.7%). Also, keyword analysis reveals that in the recent years, “security”, “machine learning”, and “5G” are the most-relevant keywords used by authors. this study also reveals open interest, such as trust management, data models, and quantum mechanisms, for research community in network communications.

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IOT Security and Machine Learning Algorithms: Survey and Analysis

IOT Security and Machine Learning Algorithms: Survey and Ana...

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international conference on Trends in Electronics and Informatics (ICEI)

作者： Rachakonda Venkatesh N. Malarvizhi Dept of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology India

the Internet of things (IoT) establishes a web extension by combining physical components to arrange them self into shared objects. this creative observation can be utilized to transform a physical object mean something in the computerized world. the Internet of things consists of devices that are constrained inside a resource such as rechargeable batteries, memory, as well as managing ability, and so forth. the Internet of things aims to transform physical reality into artificially intelligent objects in the coming years. the Internet of things (IoT) aims to combine all of that in the surroundings underneath a single infrastructure, giving us access to the objects around us maintaining us kept up-to-date on their situation. IoTs have a number of problems, including fading, energy use, data security, network security, etc. Machine Learning (ML) has also made substantial technological advances, opening up numerous study possibilities to address the present and future security challenges in IoT. ML is employed as a powerful tool to accomplish this objective, detecting threats and identifying abnormal behavior of intelligent networked devices. Following a thorough examination of the literature on IoT attacks and different machine learning methodologies, this survey article addresses the architecture of the IoT.

关键词： Surveys Privacy Transforms Machine learning computer architecture Network security Market research

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Evaluation of Visual Parameters to Control a Visual ERP-BCI Under Single-Trial Classification 17th

Evaluation of Visual Parameters to Control a Visual ERP-BCI ...

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17th international Work-conference on Artificial Neural Networks (IWANN)

作者： Fernandez-Rodriguez, Alvaro Ron-Angevin, Ricardo Velasco-Alvarez, Francisco Diaz-Pineda, Jaime Letouze, theodore Andre, Jean-Marc Univ Malaga Inst Univ Invest Telecomunicac Univ Malaga TELMA Dept Tecnol Elect Malaga 29071 Spain Thales Avion France Bordeaux France Bordeaux Univ INP Bordeaux ENSC Lab IMS UMR CNRS 5218 F-33400 Talence France

ISBN: (纸本)9783031430770;9783031430787

A brain-computer interface (BCIs) based on event-related potentials (ERPs) is a technology that provides a communication channel between a device and a user through their brain activity. these systems could be used to assist and facilitate decision making in applications such as an air traffic controller (ATC). thus, this work attempts to be an approximation to determine whether it is possible to detect the stimulus through a single presentation of a stimulus (single-trial classification) and furthermore, to evaluate the effects of the type of stimulus to be detected, or not knowing the position of the stimulus appearance in an ERP-BCI. this experiment has involved six participants in four experimental conditions. Two conditions varied only in the type of stimulus used, faces (a type of stimulus that has shown high performance in previous ERP-BCI proposals) versus radar planes;and two conditions varied in the prior knowledge of where the stimulus would appear on the screen (knowing vs. not knowing). the results suggest that the use of single-trial classification could be adequate to correctly detect the desired stimulus using and ERP-BCI. In addition, the results reveal no significant effect on either of the two factors. therefore, it seems that radar planes may be as suitable stimuli as faces and that not knowing the location of the target stimulus is not a significant problem, at least in a standard BCI scenario without distracting stimuli. therefore, future studies should consider these findings for the design of an ATC using an ERP-BCI for stimulus detection.

关键词： Brain-computer Interface (BCI) Event-Related Potential (ERP) Single-Trial Classification Air Traffic Controller (ATC)

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