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2021 International Conference on System, Computation, Automation and Networking, ICSCAN 2021

作者： Kesavan, Suresh Manic Al Naimi, Imad Al Attar, Feras Rajinikanth, Venkatesan Kadry, Seifedine National University of Science and Technology College of Engineering Department of Electrical and Communication Engineering Muscat Oman OMR St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Chennai600119 India Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway

ISBN: (纸本)9781665439862

COVID19 is one of the hash lung infections;which causes severe pneumonia in humans and untreated infection will lead to death. The goal of this study is to employ an automated Infection-Segmentation-Scheme (ISS) to extract and evaluate the COVID19 lesion on CT scans of the Lungs. This work implemented a Convolution-Neural-Network (CNN) scheme called Res-UNet to study the CT slices of the lungs. The various phases of this research involve in;(i) 3D to 2D conversion and resizing, (ii) Implementation of CNN segmentation scheme, (iii) Comparison of mined COVID19 lesion with Ground-Truth (GT) and (iv) Validation. In this study, 200 CT images (10 patients x 20 slices/patient) of dimension 224× 224× 3 pixels are considered for the assessment and the Image-Quality-Measures (IQM), like Jaccard, Dice ad Accuracy are computed between extracted lesion and the GT. The experimental outcome confirms that the result of Res-UNet is better on sagittal-view of CT compared to axial and coronal. © 2021 IEEE.

关键词： Computerized tomography

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Blockchain-based federated learning with checksums to increase security in Internet of Things solutions

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Journal of Ambient Intelligence and Humanized computing 2023年第5期14卷 4685-4694页

作者： Prokop, Katarzyna Polap, Dawid Srivastava, Gautam Lin, Jerry Chun-Wei Faculty of Applied Mathematics Silesian University of Technology Kaszubska 23 Gliwice44-100 Poland Dept. of Math and Computer Science Brandon University BrandonMBR7A 6A9 Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Western Norway University of Applied Sciences Bergen Norway Dept. of Computer Science and Math Lebanese American University Beirut1102 Lebanon

Federated learning is becoming a practical solution for machine learning (ML) in industry. This is due to the possibility of implementing artificial intelligence (AI) systems and training its models on private data sets. However, this is not an ideal solution as it is possible to manipulate or even intercept the model during its transmission between the server and workers. In this paper, we propose a solution to ensure the security of the model transmitted between units in FL. The given model is encrypted with AES, DES, RSA algorithms, then a checksum is determined. This checksum with a private key is stored as a transaction on a blockchain. In the case of sending the model and its modification, the recipient can easily verify whether it is correct. The proposed solution has been described, tested, and compared to indicate its advantages and disadvantages. Conducted experiments were based on analyzing the communication time between participants, the accuracy of machine learning models, and attack detection. In terms of attack detection on the blockchain, we reached 81% thanks to the checksum mechanism. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Blockchain

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Random number generator influence on differential search performance 6

Random number generator influence on differential search per...

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6th IASTED International Conference on Modelling, Simulation and Identification, MSI 2016

作者： Alhalabi, Wadee S. Dragoi, Elena-Niculina Alsaadi, Areej A. Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Chemical Engineering Applied Informatics and Automation Chemical Engineering and Environmental Protection Gheorghe Asachi Technical University of Iasi Iasi700050 Romania

ISBN: (纸本)9780889869837

Differential Search is an optimization approach that simulates the Brownian like random walk movement of migration organisms. Its inner workings relay on Random Number Generators (RNG) to emulate the probability of the natural driven process and therefore, the properties and performance of the RNG used has an influence on the overall algorithm performance. This study focuses on assessing this influence and on the manner in which the best suited RNG must be selected in order to achieve the optimal results for different types of problems. The results showed that depending on the properties of the problem, different RNGs provide the best solutions. However, as the number of function evaluation rises in report to the problem dimensionality, the influence on the RNG becomes smaller.

关键词： Number theory

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Wearable computers: A study on ECG signal changes during everyday activities

Wearable computers: A study on ECG signal changes during eve...

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2011 International Conference on Biometrics and Kansei Engineering, ICBAKE 2011

作者： Szczepański, Adam Saeed, Khalid Riener, Andreas Ferscha, Alois Faculty of Physics and Applied Computer Science AGH University of Science and Technology Al. Mickiewicza 30 PL-30059 Cracow Poland Institute for Pervasive Computing Johannes Kepler University Linz Altenberger Strasse 69 4040 Linz Austria

ISBN: (纸本)9780769545127

This paper deals with the analysis of the ECG signal during the same everyday activities of 5 people working together. The purpose of the work is to determine the influence of such factors as eating, working or walking on the pulse and the average heartbeat shape. The inter-beat intervals and R values are measured and examined. Authors try to draw conclusions which will help them in developing the real-time ECG analysis and diagnostics system. This will help monitor the person all day long and alert the right people in case of emergency. © 2011 IEEE.

关键词： Electrocardiography

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Docker incorporation is different from other computer system infrastructures: A review

Docker incorporation is different from other computer system...

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2021 International Research Conference on Smart computing and Systems Engineering, SCSE 2021

作者： Kithulwatta, W.M.C.J.T. Jayasena, K.P.N. Kumara, B.T.G.S. Rathnayaka, R.M.K.T. Sabaragamuwa University of Sri Lanka Faculty of Graduate Studies Sri Lanka Sabaragamuwa University of Sri Lanka Fac. of Applied Sciences Dept. of Computing and Information Systems Sri Lanka Sabaragamuwa University of Sri Lanka Fac. of Applied Sciences Department of Physical Sciences and Technology Sri Lanka

ISBN: (纸本)9781665409650

Currently the computing world is getting complex, innovating and maturing with modern technologies. Virtualization is one of the old concepts and currently containerization has arrived as an alternative and innovative technology. Docker is the most famous and trending container management technology. Different other container management technologies and virtualization technologies are respective other corresponding technologies and mechanisms for Docker containerization. This research study aims to identify how Docker incorporation is different from other computer system infrastructure technologies in the perspective of architecture, features and qualities. By considering forty-five existing literatures, this research study was conducted. To deliver a structured review process, a thorough review protocol was conducted. By considering four main research questions, the research study was lined up. Ultimately, Docker architecture and Docker components, Docker features, Docker integration with other computing domains and Docker other computing infrastructures were studied. After synthesizing all the selected research studies, the cream was obtained with plenty of knowledge contribution to the field of computer application deployment and infrastructure. © 2021 IEEE.

关键词： Containers

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A rule-based named-entity recognition for malay articles

A rule-based named-entity recognition for malay articles

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9th International Conference on Advanced Data Mining and Applications, ADMA 2013

作者： Alfred, Rayner Leong, Leow Ching On, Chin Kim Anthony, Patricia Fun, Tan Soo Razali, Mohd Norhisham Bin Hijazi, Mohd Hanafi Ahmad Center of Excellence in Semantic Agents School of Engineering and Information Technology Universiti Malaysia Sabah Jalan UMS 88400 Kota Kinabalu Sabah Malaysia Department of Applied Computing Faculty of Environment Society and Design Lincoln University Christchurch New Zealand

ISBN: (纸本)9783642539138

A Named-Entity Recognition (NER) is part of the process in Text Mining used for information extraction. This NER tool can be used to assist user in identifying and detecting entities such as person, location or organization. Different languages may have different morphologies and thus require different NER processes. For instance, an English NER process cannot be applied in processing Malay articles due to the different morphology used in different languages. This paper proposes a Rule-Based Named-Entity Recognition algorithm for Malay articles. The proposed Malay NER is designed based on a Malay part-of-speech (POS) tagging features and contextual features that had been implemented to handle Malay articles. Based on the POS results, proper names will be identified or detected as the possible candidates for annotation. Besides that, there are some symbols and conjunctions that will also be considered in the process of identifying named-entity for Malay articles. Several manually constructed dictionaries will be used to handle three named-entities;Person, Location and Organizations. The experimental results show a reasonable output of 89.47% for the F-Measure value. The proposed Malay NER algorithm can be further improved by having more complete dictionaries and refined rules to be used in order to identify the correct Malay entities system. © Springer-Verlag 2013.

关键词： Character recognition

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Multi-parameter Discrete Grey Prediction with Few Observations 4

Multi-parameter Discrete Grey Prediction with Few Observatio...

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4th World Conference on Mechanical Engineering and Intelligent Manufacturing, WCMEIM 2021

作者： Niu, Wei Lei, Pan Wang, Wei Song, Xiaogang Cheng, Juan Xi'An Aeronautics Computing Technique Research Institute AVIC Xi'an China AVIC First Aircraft Institute Xi'an China Xi'An University of Technology Faculty of Computer Science and Engineering Xi'an China Xi'An Institute of Applied Optics Xi'an China

ISBN: (纸本)9781665421720

The fault prediction of weapon is getting more difficult for its complex structure, unique operating environment and multi-plicate faults. Currently although the main fault prediction methods have achieved certain success in practical application, they all fall short in some aspects. Based on the discrete grey prediction theory and with an analysis of the disadvantages of the discrete grey model, an adaptive prediction model with several characteristic parameters for small samples is proposed. This model modifies the initial value and takes into account the interrelations of the parameters and characteristics of prediction series. The data of a certain aircraft engine are taken as an example for prediction and analysis, and the results showed that the model had high availability and precision. © 2021 IEEE.

关键词： Aircraft engines

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Bi-directional Mapping System as a New IPv4/IPv6 Translation Mechanism

Bi-directional Mapping System as a New IPv4/IPv6 Translation...

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International Conference on Computer Modeling and Simulation, UKSIM

作者： Ra'ed AlJa'afreh John Mellor Mumtaz Kamala Basil Kasasbeh Department of Computing University of Bradford Bradford UK Faculty of Information Technology Applied Science University Amman Jordan

A new Internet protocol (IPv6) is being deployed with new features and improvements for the devices that are connected to the network. Unfortunately, the current Internet Protocol (IPv4) and the new Internet Protocol (IPv6) are incompatible with each other. During the time of migration from IPv4 to IPv6 networks, when both versions are available and the users of Internet want to connect without any restrictions, a transition mechanism is required. A number of transition mechanisms have been proposed by the Internet Engineering Task Force (IETF) in order to ensure smooth and independent changeover from IPv4 to IPv6 networks. In this paper, we propose a new flexible transition scheme which is known as the Bi-Directional Mapping System (BDMS) that deals with the IPv4/IPv6 address mapping transition as well as converting the received packets to their destination environment. Our new transition scheme (BDMS) is simple, efficient, and easy to implement. BDMS reduces the packet size compared with tunneling mechanisms and reduces the costs of the IPv6 network compared with the dual stack mechanisms.

关键词： Bidirectional control Internet IP networks Protocols Tunneling Costs Computer networks Computational modeling Computer simulation Informatics

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Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination Algorithm (KB-FPA)

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Computers, Materials & Continua 2024年第6期79卷 3757-3782页

作者： Nidhika Chauhan Navneet Kaur Kamaljit Singh Saini Sahil Verma Kavita Ruba Abu Khurma Pedro A.Castillo University Institute of Computing Department Chandigarh UniversityPunjab140413India Department of Computer Science and Engineering Chandigarh UniversityPunjab140413India Universidade Federal do Piauí TeresinaPiauí64049-550Brazil MEU Research Unit Faculty of Information TechnologyMiddle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Department of Computer Engineering Automatics and RoboticsUniversity of GranadaGranada18071Spain

Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates *** paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing *** motivation stems from the pressing issue of accommodating fluctuating levels of user demand *** adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy *** reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing *** enhance the resource allocation process,we introduce a novel knowledge-based optimization *** this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination Algorithm(FPA),Spark Lion Whale Optimization(SLWO),and Firefly *** findings reveal that our proposed algorithm,Knowledge Based Flower Pollination Algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption *** paper underscores the profound significance of resource allocation in the realm of cloud *** addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing *** contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.

关键词： Cloud computing resource allocation energy consumption optimization algorithm flower pollination algorithm

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Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

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Computers, Materials & Continua 2024年第6期79卷 4805-4824页

作者： Muhammad Sohail Irshad Tehreem Masood Arfan Jaffar Muhammad Rashid Sheeraz Akram Abeer Aljohani Faculty of Computer Science&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Computer Science Applied CollegeTaibah UniversityMedina42353Saudi Arabia

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant *** and timely diagnosis increases the patient’s chances of ***,issues like overfitting and inconsistent accuracy across datasets remain *** a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib *** aim was to create a robust detection mechanism that consistently performs *** such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for *** findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated *** demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib *** insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG *** comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib ***,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

关键词： Convolution neural network atrial fibrillation area under curve ECG false positive rate deep learning classification

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