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Blockchain Empowered and Self-sovereign Access Control System

Blockchain Empowered and Self-sovereign Access Control Syste...

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IEEE international conference on Cloud computing Technology and science (CloudCom)

作者： Hanif Tadjik Jiahui Geng Martin Gilje Jaatun Chunming Rong University of Stavanger Stavanger Norway

ISBN: (纸本)9781665463683

Lack of trustworthiness, access policy flexibility, and user privacy preservation in centralized access control systems raise numerous security issues and reduce the collaboration maturity of global data sharing systems. In this paper, we propose a Self-Sovereign Identity-based, Decentralized, and Dynamic (SSIDD) access control system. SSIDD utilizes blockchain technologies to build trust for untrusted data sharing networks and ensures user privacy. Our access control provides high access policy flexibility and security for global inter-enterprise collaborations from a diverse industrial environment. SSIDD authenticates its users based on their Decentralized Identifiers (DID), which are under control of users and can be resolved into a DID document stored on the blockchain. Our data management technology keeps the data sharing systems safe against issues such as data breaches, identity thefts, and privacy violations. Besides, the authorization process of SSIDD is dynamic by adopting several smart contracts. The transparency of rules and agreements in smart contracts and the traceability of records on blockchain ledger provide a high level of security and trust. For proof of concept, we have developed and evaluated a prototype of SSIDD. Our evaluations show that the throughput and latency of our method are within an acceptable range.

关键词： Computers Authorization data privacy Cloud computing Smart contracts Collaboration Prototypes

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Training Echo-State Networks for Big data Prediction using Variation of Parameters

Training Echo-State Networks for Big Data Prediction using V...

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international IEEE conference on Signal-Image technologies and Internet-Based System

作者： Soukaina Seddik Hayat Routaib Anass El Haddadi Data Science and Competitive Intelligence Team Applied Science Laboratory LSA ENSAH Abdelmalek Essaadi University Al Hoceima Morocco

Recurring neural networks (RNNs) are a class of neural networks that are naturally suitable for processing time series and other sequential data. RNNs and specifically Reservoir computing (RC) has attracted much attention over the past decade, particularly in the context of time series prediction. However, most time series have a multi-scale structure. The Echo State Networks (ESN) are one of two pioneering methods of reservoir computing. ESN requires some experiences and insights to achieve the acclaimed accuracy in many tasks. In this paper, we present a practical application of case prediction using several time series data, multiple parameters of our ESN system are scaled to compare the accuracy of the proposed model.

关键词： Training Recurrent neural networks data analysis Computational modeling Time series analysis Predictive models Big data

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New Analytic Framework of Public Mental Health Prediction Using data science 1

New Analytic Framework of Public Mental Health Prediction Us...

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international conference on Smart technologies and Systems for Next Generation computing (ICSTSN)

作者： Marrapu, Hari Krishna Maram, Balajee Reddi, Prasadu GMR Inst Technol Dept IT Rajam Andhra Pradesh India GMR Inst Technol Dept CSE Rajam Andhra Pradesh India Anil Neerukonda Inst Technol & Sci Dept IT Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9781665421119;9781665421126

Nowadays the human-being is suffering from many issues in the society. In the current situation mental health problem is a prevalence issue. Usage of sensor data and dataset is an interesting issue dealing with prediction of mental health of the people living in society. The data science process can be carried out by collecting datasets from various health sector domains and differentiated into three categories such as low, medium, high. This is helpful in detecting weather the person is suffering from any mental issue or not. A sample test is conducted to assess the health conditions of healthy groups and people having mental illness with the help of data mining in data science field. This paper explains about a novel framework for prediction of public mental health condition which are helpful to handle the challenges in the field of data science. Evaluation of data science surveys is different from traditional data analysis due to the scientific approach method used during data classification and filtration. Here the role of "data science" framework is for promoting the usage of framework by the healthcare professionals to face challenges of mental health.

关键词： data science Public Health Mental Health Predictive modeling Pervasive computing Visual data Exploration data Mining

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Efficient Path Plannning for Mobile Sink using Salp Swarm Technique in IoT-Het Networks

Efficient Path Plannning for Mobile Sink using Salp Swarm Te...

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： Anil Sharma Manisha Sharma Department of Computing Science & IT Teerthanker Mahaveer University Moradabad India Department of Engineering & Technology Jaipur National University Jaipur India

ISBN: (纸本)9781665493970

For this reason, mobile sinks (MS) have already seen extensive application due to their ability to provide network-wide energy consumption parity and offer implicit load balancing. These nodes form what is known as a cluster topology, and the process of constructing one is known as clustering. An effective route finding system is provided in this study that does more than just solve the aforementioned problem; it also pinpoints where the MS must make pit stops for the transmission of data to be completely trustworthy. Specifically, the Salp Swarm Optimal solution for IoT networks is used, whose primary goal is to reduce the overall grouping time required by the suggested system. The target sites that make up the MS's route are decided using the circumferential visit approach. The results of the performance study demonstrate that the suggested strategy extends the life of the network by decreasing the required number of cluster updates and the time it takes to complete each update.

关键词： Energy consumption Network topology Load management Topology Particle swarm optimization

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Smart Healthcare Monitoring System for Early Diagnosis of Patient's Disease using Artificial Intelligence

Smart Healthcare Monitoring System for Early Diagnosis of Pa...

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： Dharamveer Singh Sagar Shalini Ninoria Department of Engineering & Technology Jaipur National University Jaipur India Department of Computing Science & IT Teerthanker Mahaveer University Moradabad India

ISBN: (纸本)9781665493970

The mental and physical health of an individual may be monitored using such a device. Many different types of illness may be traced back to stress, anxiety, and high blood pressure. Anxiety, stress, and pressure are all common among the elderly and need specialized care in this situation. Artifact identification for BP and PPG data was developed in this work and is now fully automated. It was suggested that the vital signs signal be processed in such a way that any anomalies caused by motion artefacts be removed automatically. Then, the systolic blood pressure and diastolic blood pressure connection was analyzed using eleven characteristics extracted from the oscillometric waveforms envelope. The blood pressure estimation technique suggested in this work has been shown to be accurate. The suggested design takes use of sophisticated regression to estimate the patient's diastolic and systolic levels of pressure from the parameters of the PPG signal.

关键词： Arrhythmia Wearable computers Anxiety disorders Medical services Human factors Rhythm Blood pressure

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Application TRIKE Methodology When Modeling Threats to APCs Information Security

Application TRIKE Methodology When Modeling Threats to APCs ...

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international Russian Automation conference (RusAutoCon)

作者： Chernov, D. Tula State Univ 92 Lenina Ave Tula 300012 Russia

ISBN: (纸本)9783030942021;9783030942014

In the modern world automation technologies of industrial processes have found wide application. Automated process control systems have become an important part of enterprises that operate in different economic fields and life support facilities all over the world. Nevertheless, the high growth of automation means raises the acute problem of providing APCs information security from external and internal threats. There are systematic reports in the media about new critical vulnerabilities in industrial equipment and attacks based on the exploitation of such vulnerabilities. When designing such systems it is necessary to estimate possible information security threats that already exist in the system or that are predicted to appear. That is why threat modeling is an important part of providing information security at industrial facilities using automation. In this work the author investigates the methodology of estimating threats to software security TRIKE. The research is aimed at finding general approaches to determine threat sources, tactics, and techniques for making implementation scenarios of threats to software information security, for applying them to provide cyber security of automated process control systems. In the frameworks of the subject main-streaming, the research gives a description of cyber-attacks at big industrial facilities, and their basic vulnerabilities are emphasized. In the article the analysis of TRIKE methodology has been done. It is aimed at determining basic threat modeling stages applicable to industrial automation systems. The approach of TRIKE methodology to generating a list of threats to information security is formalized. To achieve the aims the author constructs DFD data flow diagrams with the decomposition of peculiar elements of the algorithm for modeling threats to information security of automated process control systems using TRIKE methodology.

关键词： Automated process control systems Information security Threat Threat model TRIKE data flow diagrams Attack library

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Attribute Based Encryption in Healthcare Application

Attribute Based Encryption in Healthcare Application

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Automation, computing and Renewable Systems (ICACRS), international conference on

作者： Suresh Kumar S Chidambaram G S. Vijayalakshmi Dhayanandh A T Computer Science and Engineering PSG College of Technology Coimbatore India

ISBN: (纸本)9781665460859

data volumes are increasing due to the growth of technologies such as IoT, Cloud computing, and mobile internet. data encryption is the solution, which protects data privacy by giving specific access to encrypted data can be better understood using Attribute-based (ABE) encryption. When patients use web-based commercial systems to store their personal health data, the privacy of personal health records (PHRs) can be a major problem. When it comes to establishing policies to control access and protect data confidentiality, common access control systems, such as Work-Based Access Control, have major limitations. In this research work a novel healthcare application has been developed based on the ABE scheme approach. Advanced Encryption Standard (AES) algorithm and Secure Hash Algorithm (SHA) are used for the encryption and decryption processes in the ABE scheme. This allows only designated authorized personnel, such as a patient or their respective doctors, to access personal data recorded in medical records, with a vision to improve the privacy and security of user details.

关键词： Access control data privacy Renewable energy sources Computational modeling Medical services Libraries Encryption

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Context-aware Resemblance Detection based Deduplication Ratio Prediction for Cloud Storage

Context-aware Resemblance Detection based Deduplication Rati...

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IEEE/ACM international Symposium on Big data computing (BDC)

作者： Yuqing Geng Wenlong Tian Ruixuan Li Weijun Xiao Chunping Ouyang Yongbin Liu Qifei Liu Jing Li Xuming Ye Zhiyong Xu School of Computer Science and Technology University of South China China School of Physical and Mathematical Sciences Nanyang Technological University Singapore Hunan Provincial Base for Scientific and Technological Innovation Cooperation Hengyang China School of Computer Science and Technology Huazhong University of Science and Technology China Electrical and Computer Engineering Virginia Commonwealth University USA Math and Computer Science Department Suffolk University USA

With the prevalence of cloud storage, people prefer to outsource their data to the cloud for flexibility and reliability. Undoubtedly, there are lots of redundancy among these data. However, high-end storage with deduplication costs heavy computation and increases the data management complexity. Potential customers need the redundancy proportion information of their outsourced data to decide whether high-end storage with deduplication is worthwhile. Thus, many researchers have previously attempted to predict the redundant ratio. However, existing mechanisms ignore the redundancy proportion among similar chunks containing many duplicate data. Although resemblance detection, detecting the duplicate parts among similar data, has become a hot issue, it is hardly applied to the conventional deduplication ratio estimation because of unacceptable calculation cost. Therefore, we analyze the limitations and challenges of deduplication ratio prediction in prediction scope and response time and further propose a novel prediction scheme. By leveraging the context-aware resemblance detection, and confidence interval theory, our method can achieve faster estimation speed with higher accuracy in deduplication ratio compared with the state-of-the-art work. Finally, the results show that our method can efficiently and effectively estimate the proportion of duplicate chunks and redundant data among similar chunks by conducting experiments on real workloads.

关键词： Cloud computing Costs Redundancy Estimation Prototypes Prediction methods Machine learning

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Optimized Resource Management over Connected Vehicles in RF-CRN Environment

Optimized Resource Management over Connected Vehicles in RF-...

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： Praveen Kumar Ajay Chakravarty Department of Engineering & Technology Jaipur National University Jaipur India Department of Computing Science & IT Teerthanker Mahaveer University Moradabad India

ISBN: (纸本)9781665493970

The increasing number of vehicles and the shrinking supply of spectrum resources are major roadblocks to the establishment of a Web that could be utilised in vehicles. In this research, we present a game-theoretic framework for the re - allocation of available spectrum. It is necessary to implement such a plan in the congested case. This approach offers users the cognitive capability to actualize dynamic and smart effective use of spectrum, in contrast to the standard technique of assigning spectral efficiency. There is no need for a centralised hub or for individuals to share channel data with one another to achieve this result. Inside a situation when there are more users than wifi resources, the technique may effectively filter out customers who have better communication circumstances. This contributes to the system's improved capacity for communication as a whole. The simulation results show that the algorithm is very effective because it can autonomously choose the channels with the best conditions and it can also efficiently prevent the interfering that may arise among various users.

关键词： Spectral efficiency Simulation Heuristic algorithms Interference Throughput Resource management Vehicle dynamics

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Clustering SARS-CoV-2 Variants from Raw High-Throughput Sequencing Reads data 11th

Clustering SARS-CoV-2 Variants from Raw High-Throughput Seq...

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11th international conference on Computational advances in Bio and Medical sciences, ICCABS 2021

作者： Chourasia, Prakash Ali, Sarwan Ciccolella, Simone Della Vedova, Gianluca Patterson, Murray Georgia State University AtlantaGA30303 United States University of Milano-Bicocca Milan Italy

ISBN: (纸本)9783031175305

The massive amount of genomic data appearing over the past two years for SARS-CoV-2 has challenged traditional methods for studying the dynamics of the COVID-19 pandemic. As a result, new methods, such as the Pangolin tool, have appeared which can scale to the millions of samples of SARS-CoV-2 currently available. Such a tool is tailored to take assembled, aligned and curated full-length sequences, such as those provided by GISAID, as input. As high-throughput sequencing technologies continue to advance, such assembly, alignment and curation may become a bottleneck, creating a need for methods which can process raw sequencing reads directly. In this paper, we propose several alignment-free embedding approaches, which can generate a fixed-length feature vector representation directly from the raw sequencing reads, without the need for assembly. Moreover, because such an embedding is a numerical representation, it can be passed to already highly optimized clustering methods such as k-means. We show that the clusterings we obtain with the proposed embeddings are more suited to this setting than the Pangolin tool, based on several internal clustering evaluation metrics. Moreover, we show that a disproportionate number of positions in the spike region of the SARS-CoV-2 genome are informing such clusterings (in terms of information gain), which is consistent with current biological knowledge of SARS-CoV-2. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Coronavirus

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