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An Assessment of the Fundamental Concepts of Blockchain Technology and its Application in Cryptocurrency using Bibliometric Methods

An Assessment of the Fundamental Concepts of Blockchain Tech...

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Advance computing and Innovative technologies in Engineering (ICACITE), international conference on

作者： Kamakshi Mehta Arun B Prasad Guptnath Trivedi Anita Gehlot Shiv Ashish Dhondiyal B. Raja Mannar TAPMI Business School Manipal University Jaipur Institute of Law Nirma University Ahmedabad Dr. Rajendra Prasad Central Agricultural University Pusa Samastipur Bihar Uttaranchal Institute of Technology Uttaranchal University India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Business Administration St.Theresa Intl College Thailand

In past few years, blockchain has attracted attention as a potential area of study on a worldwide scale. It is a decentralized database system that is both autonomous and unreliable. Since its first implementation, blockchain technology has been used by a growing number of nations and academic institutions. In order to get a broad understanding of blockchain technology and the present research focus, this article makes use of the quantitative analysis tools CiteSpace. For this study, we relied heavily on data from the Web of sciences core collection dataset. The studies included 1000 publications published between 2015 and 2022. To be more precise, the patterns and fields of publishing may provide information on the directions and foci of recent blockchain studies. This bibliometric study included the most prominent and prolific writers, universities, nations, and publications. Smart contracts, Bitcoin, Ethereum, Dogecoin and cryptography were the most popular theoretical and research issues related to blockchain technology. To identify developing tendencies and new frontiers in blockchain, a keyword-based cluster analysis was conducted. The findings of this study demonstrate the growing interest in blockchain technology and its potential applications in various domains, including cryptocurrency. The analysis also identified key research areas and gaps in the literature that can guide future research in this field. Overall, this paper provides a comprehensive overview of the current state of research on blockchain technology and cryptocurrency and contributes to the growing body of knowledge on this topic.

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Fraud detection in health insurance claims using machine learning and deep learning techniques 12

Fraud detection in health insurance claims using machine lea...

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12th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2021

作者： Akshatha, P. Manasa, G.L. Nayanashree, H.C. Poornima, A.S. Department of Computer Science Engineering Siddaganga Institute of Technology Tumkur India

A claim is what a doctor or hospital submits to the insurance company so they can get paid. A health insurance claim is a request that a health insurance policyholder submits to the Insurance Company in order to get the services that are covered in their health insurance *** tackle the problem of fraud in the medical insurance claims, health insurance companies make use of traditional rule-base models, but these models do not suffice anymore due to several factors such as the large volume of claims to be processed which makes the medical billing process prone to error, slow and sometimes inefficient. Hence there is a need to develop a novel fraud detection model for insurance claims processing. In this paper we are using the power of Machine Learning(ML) and Deep Learning(DL) algorithms to efficiently identify the fraudulent health insurance claims from the healthcare transaction dataset. Here we have considered a data set consisting of both legitimate and fraudulent claims. Initially we have implemented ML algorithms like SVM(Support Vector Machine), Logistic regression and Random forest for detection of anomalies and classification of health insurance claims into legitimate and fraudulent claims. Also DL algorithm MLP (Multilayer perceptron) is implemented. We have measured various metrics like Accuracy, Recall, Precision etc., for evaluating the performance of these ML and DL algorithms. All the models are good, but comparatively MLP has better performance with highest accuracy of 88.6 %. Study is also performed by varying the number of fraudulent records for the initial data set. This model helps in easy detection of the occurrence of fraudulent activities also is capable of fair fraud identification when applied to datasets with different fraud occurrence rates. The model can be used for decreased fraudulent activities in health insurance claims, it can become an efficient and effective application for Insurance clients and companies. © Grenze Scientific

关键词： Support vector machines

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Multi-resolution CNN for Lower Limb Movement Recognition Based on Wearable Sensors 1

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15th Multi-disciplinary international conference on Artificial Intelligence, MIWAI 2022

作者： Hnoohom, Narit Chotivatunyu, Pitchaya Mekruksavanich, Sakorn Jitpattanakul, Anuchit Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Mahidol University Nakorn Pathom Thailand Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9783031209925

ISBN: (纸本)9783031209918

Human activity recognition (HAR) remains a difficult challenge in human-computer interaction (HCI). The Internet of Healthcare Things (IoHT) and other technologies are expected to be used primarily in conjunction with HAR to support healthcare and elder care. In HAR research, lower limb movement recognition is a challenging research topic that can be applied to the daily care of the elderly, fragile, and disabled. Due to recent advances in deep learning, high-level autonomous feature extraction has become feasible, which is used to increase HAR efficiency. Deep learning approaches have also been used for sensor-based HAR in various domains. This study presents a novel method that uses convolutional neural networks (CNNs) with different kernel dimensions, referred to as multi-resolution CNNs, to detect high-level features at various resolutions. A publicly available benchmark dataset called HARTH was used to evaluate the recognition performance to collect acceleration data of the lower limb movements of 22 participants. The experimental results show that the proposed approach improves the F1 score and achieves a higher score of 94.76%. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Wearable sensors

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Assisting People During COVID-19 with data Visualization and Undemanding Design 4th

Assisting People During COVID-19 with Data Visualization an...

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4th international conference on Human Interaction and Emerging technologies: Future Applications, IHIET – AI 2021

作者： Karlsen, Ida Christine Gopalakrishnan, Sundarrajan Kazmi, Syeda Miral Sandula, Godavari Applied Computer Science Høgskolen i Østfold B.R.A Veien 4 Halden1747 Norway

ISBN: (纸本)9783030732707

This paper presents a minimalistic design for an interactive data visualization platform to present statistical information on COVID-19, through informing, designing and prototyping, and evaluation based on user experience (UX) and usability guidelines. This paper aims to present the creative process involved in designing a digital platform for statistical information, with the user in mind, which offers a comprehensive and simple experience. As a result, the platform got positive reviews from the participants involved and is proven to be easier to navigate than other contemporary sources on COVID-19. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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Caching Mechanisms Evolution in CCN Architectures 1

引用

20th international conference on Hybrid Intelligent Systems, HIS 2020 and 12th World Congress on Nature and Biologically Inspired computing, NaBIC 2020

作者： Fekih, Amna Fantar, Sonia Gaied Youssef, Habib Computer Science Department Higher Institute of Computer Science and Communication Techniques University of Sousse Hammam Sousse4011 Tunisia Computer Science Department Higher School of Sciences and Technologies University of Sousse Hammam Sousse4011 Tunisia PRINCE Research Laboratory University of Sousse Hammam Sousse4011 Tunisia

ISBN: (数字)9783030730505

ISBN: (纸本)9783030730499

Information-Centric Networks (ICN) see the future architecture of the internet (radical change) by using an information-driven model and integrating from the design the essential functionalities to reach the best performances. Current approaches to ICN employ in-network caching to minimize the latency of information retrieval. Any content distribution over the network will be decided by the caching strategy adopted either along the delivery path or off-path caching. In ICN, caching is a key component essential for improving the quality of service by bringing content as close as possible to customers and reducing network traffic thereby minimizing bottlenecks and waiting time for customers. Software-Defined Networks is characterized by a total separation between the forwarding and control planes, easy network programmability, support for existing applications and architectures, and even for the deployment of new architectures with an open exchange. ICN and SDN are both very active axes proposing solutions for the future architecture of the Internet but with two different starting points. Their integration certainly triggers a promising hybrid solution but generates challenges such as in-network caching and data-centric security functionalities from an SDN perspective. In return, ICN is a radical solution that requires an organizer/supervisor to be deployed on the architectures of existing networks. Because of this, in this paper, we present a comprehensive overview of the existing in-network caching mechanisms for CCN and SD-CCN architectures. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Quality of service

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Vehicular Cloud data Collection for Intelligent Transportation Systems

Vehicular Cloud Data Collection for Intelligent Transportati...

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international conference on Emerging Research in Electronics, Computer science and Technology (ICERECT)

作者： S. Thiru Hina Hashmi S Hemavathi Daxa Vekariya Arvind Kumar Pandey Wael M. F. Abdel-Rehim Department of MME University of Jeddah Jeddah Kingdom of Saudi Arabia Department of Computing Sciences & IT Teerthanker Mahaveer University Moradabad India Civil Engineering K. Ramakrishnan College of Technology Trichy Department of CSE PIET Vadodara India Department of School of Engg.&IT Arka Jain University Jamshedpur India Computer Science and Engineering King Salman International University Sinai Egypt

Millions many sensors will be connected to the Internet via the Networks of Things (IoT), enabling new mobile cloud technologies and products. With the introduction of IoT, future Internet of Vehicle (IoV) will replace the present Auto Informal Services (Van - to - Driver), where customers may place orders for various services by integrating their cars, sensors, and tablets to a huge network. Vehicular Cloud computing (VCC) is merely being imagined with the goal of supplying traffic services that enhance our daily traveling in order to provide Routes with broadband service. These plans include resources and support the expansion of the Internet of Things (IoT), which is a crucial component of IoV. The utilization of Vehicular Cloud (VC), however, is necessary for ITS (Intelligent Transportation System) to properly support a top travel service (VC). In order to acquire data for the benefit of ITS, we integrate the novel VCC technique in this study. We show via modeling results that a flexible VC may provide useful data collection with just a small percentage of autos involved.

关键词： Computer science Cloud computing Costs data collection data models Sensors Broadband communication

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A Taxonomy towards Blockchain computing for Resource security

A Taxonomy towards Blockchain computing for Resource securit...

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international conference on Innovative Sustainable Computational technologies (CISCT)

作者： Sivaram Rajeyyagari Regonda Nagaraju Meenu Chopra Cosmena Mahapatra Ansuman Samal Prabhishek Singh College of Computing and Information Technology Shaqra University Shaqra Saudi Arabia St.Martin's Engineering College Secunderabad Telangana India Vivekananda Institute of Professional Studies Delhi India Siksha O Anusandhan Deemed to be University Bhubaneswar Odisha India School of Computer Science Engineering & Technology Bennett University Greater Noida India

ISBN: (纸本)9781665474177

In the least tough of terms, blockchain can be portrayed as documents you can solely add to, proper now not reduce, or change. Subsequently, the time body is "chain" due to the reality you are making a chain of data. Not being there for the talent to replacement the principal blocks make it so secure. Moreover, blockchains are understanding-driven, so no one issue can take manipulate the data. With blockchain, you do not be given that a depended upon untouchable ought to oversee or assist trades. Blockchain development achieves decentralized safety and have faith in extra approaches than one. Regardless, new blocks are always taken care of straightly and successively. That is, they are constantly introduced to the "end" of the blockchain.

关键词： Taxonomy Blockchains Safety Security

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Verifiable Machine Learning Models in Industrial IoT via Blockchain 14th

Verifiable Machine Learning Models in Industrial IoT via B...

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12th international Advanced computing conference, IACC 2022

作者： Stodt, Jan Ghajar, Fatemeh Ghovanlooy Reich, Christoph Clarke, Nathan Institute for Data Science Cloud Computing and IT Security Furtwangen University of Applied Sciences Furtwangen Germany Offenburg University of Applied Sciences Offenburg Germany Plymouth University Portland Square PlymouthPL4 8AA United Kingdom

ISBN: (纸本)9783031356438

The importance of machine learning (ML) has been increasing dramatically for years. From assistance systems to production optimisation to healthcare support, almost every area of daily life and industry is coming into contact with machine learning. Besides all the benefits ML brings, the lack of transparency and difficulty in creating traceability pose major risks. While solutions exist to make the training of machine learning models more transparent, traceability is still a major challenge. Ensuring the identity of a model is another challenge, as unnoticed modification of a model is also a danger when using ML. This paper proposes to create an ML Birth Certificate and ML Family Tree secured by blockchain technology. Important information about training and changes to the model through retraining can be stored in a blockchain and accessed by any user to create more security and traceability about an ML model. © 2023, Springer Nature Switzerland AG.

关键词： Blockchain

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An Ingenious Method to Detect COVID in X-Ray Images Using Machine Learning Techniques

An Ingenious Method to Detect COVID in X-Ray Images Using Ma...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Palak Kumari Neha Rani N. Suresh Kumar School of Computing Science and Engineering Galgotias University Greater Noida India

Covid-19 is a fatal disease caused by the Covid-19 virus. It is very big problem for the whole world. The World Health Organization (WHO) has declared a pandemic. In May 2020, more people throughout the world had a favorable experience. The COVID illness is rapidly growing, and we are unable to stop it. We addressed the COVID-19 data science research initiatives employing a number of approaches, including statics, machine learning (ML), modelling, simulation, data visualization, and artificial intelligence (AI). We all suffering from COVID-19. in this case higher value of case comes from negative and lower false positive rate. The global impact of the COVID-19 outbreak was enormous. To tackle the pandemic, many projects have been launched, including those in the field of deep learning. This paper proposes a deep neural network modification based on the Xception model. The model is used to detect COVID-19 using chest X-ray images. Batch normalization and two stacks of two dense layers each are used in the proposed model. The layer addition is intended to avoid overfitting the proposed model. The proposed as a result, we compare the model’s loss, accuracy, and performance speed, and the results show that the quality of the machine learning model has higher prediction accuracy and loss, but it takes longer to execute than traditional machine learning languages. Machine learning algorithms in general, and convolutional neural networks (CNNs) in particular, have shown promise in medical picture analysis and categorization. The architecture of this study has been presented for the diagnosis of COVID-19.

关键词： COVID-19 Deep learning Machine learning algorithms Pandemics Computational modeling Predictive models data models

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A Methodology for data-Driven Estimation of Forging Load 1

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2nd international conference on Recent advances in Manufacturing, RAM 2021

作者： Chatterjee, Kaustabh Dixit, Uday S. Zhang, Jian Petrov, Pavel A. Department of Mechanical Engineering Indian Institute of Technology Guwahati Guwahati781039 India Shantou Institute of Light Industrial Equipment Research Shantou University Shantou-515063 China Department of Material Forming and Additive Technologies Moscow Polytechnic University str. B. Semenovskaya 38 Moscow107023 Russia

ISBN: (数字)9789811677878

ISBN: (纸本)9789811677861

Metal forming industry often requires to estimate forging load. Analytical and numerical techniques are not sufficient to provide an accurate estimation of the forging load. Interestingly, a large amount of shop floor forging data exists that are yet to be effectively utilised. Modelling using data-driven techniques can take care of the uncertainty associated with flow stress and friction condition. This article proposes a methodology that uses the existing information of axisymmetric forged products for the estimation of forging load by suggesting a proper value of complexity factor. Appropriate complexity factor is essential for empirically estimating the forging load in open as well as closed die forging. Estimation of forging load for open die forging is carried out for four different materials of cylindrical shape. In case of closed die forging, estimation of forging load is carried out for two different products. Forging load estimation is carried out for different sizes and for different friction conditions. The proposed methodology of using the existing data provides reasonable accuracy in the prediction of forging load. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Axisymmetric product Closed die forging Complexity factor Forging load Friction Open die forging

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