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An Adaptive Decision-Making Approach for Better Selection of Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation

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Procedia computer Science 2023年 220卷 470-477页

作者： Rima Kaafarani Leila Ismail Oussama Zahwe ICCS-Lab Computer Science Department American University of Culture and Education 1507 Beirut Lebanon Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia Intelligent Distributed Computing and Systems (INDUCE) Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to business requirements. Several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using the top two blockchain platforms selected based on our proposed decision-making map approach which selects the top suitable platforms for healthcare insurance frauds detection application. Our classification shows that the largest percentage of platforms can be used for all types of application domains, the second biggest percentage for financial services, and a small number is to develop applications in specific domains. Our decision-making map and performance evaluations reveal that Hyperledger Fabric surpassed Neo in all metrics for detecting healthcare insurance frauds.

关键词： Blockchain Decision-Map Health Insurance Frauds Detection Hyperledger Fabric Neo Smart Contract Smart Healthcare

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From Rule-Based Models to Deep Learning Transformers Architectures for Natural Language Processing and Sign Language Translation systems: Survey, Taxonomy and Performance Evaluation

arXiv

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arXiv 2024年

作者： Shahin, Nada Ismail, Leila Intelligent Distributed Computing and Systems Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Emirates Center for Mobility Research United Arab Emirates University Abu Dhabi United Arab Emirates

With the growing Deaf and Hard of Hearing population worldwide and the persistent shortage of certified sign language interpreters, there is a pressing need for an efficient, signs-driven, integrated end-to-end translation system, from sign to gloss to text and vice-versa. There has been a wealth of research on machine translations and related reviews. However, there are few works on sign language machine translation considering the particularity of the language being continuous and dynamic. This paper aims to address this void, providing a retrospective analysis of the temporal evolution of sign language machine translation algorithms and a taxonomy of the Transformers architectures, the most used approach in language translation. We also present the requirements of a real-time Quality-of-Service sign language machine translation system underpinned by accurate deep learning algorithms. We propose future research directions for sign language translation *** Codes I.2, I.2.7, I.4, I.4.9 © 2024, CC BY.

关键词： Neural machine translation

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Efficient Training Approaches for Performance Anomaly Detection Models in Edge Computing Environments

arXiv

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arXiv 2024年

作者： Fernando, Duneesha Rodriguez, Maria A. Arroba, Patricia Ismail, Leila Buyya, Rajkumar Laboratory School of Computing and Information Systems The University of Melbourne Parkville Australia The CCS Center for Computational Simulation Universidad Politécnica de Madrid Madrid Spain Lab Department of Computer Science and Software Engineering United Arab Emirates University United Arab Emirates

Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are susceptible to performance anomalies caused by resource hogging (e.g., CPU or memory), resource contention, etc., which can negatively impact their Quality of Service and violate their Service Level Agreements. Existing research on performance anomaly detection for edge computing environments focuses on model training approaches that either achieve high accuracy at the expense of a time-consuming and resource-intensive training process or prioritize training efficiency at the cost of lower accuracy. To address this gap, while considering the resource constraints and the large number of devices in modern edge platforms, we propose two clustering-based model training approaches: (1) intra-cluster parameter transfer learning-based model training (ICPTL) and (2) cluster-level model training (CM). These approaches aim to find a trade-off between the training efficiency of anomaly detection models and their accuracy. We compared the models trained under ICPTL and CM to models trained for specific devices (most accurate, least efficient) and a single general model trained for all devices (least accurate, most efficient). Our findings show that ICPTL's model accuracy is comparable to that of the model per device approach while requiring only 40% of the training time. In addition, CM further improves training efficiency by requiring 23% less training time and reducing the number of trained models by approximately 66% compared to ICPTL, yet achieving a higher accuracy than a single general model. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System

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Procedia computer Science 2023年 220卷 331-338页

作者： Alain Hennebelle Huned Materwala Leila Ismail Independent Researcher Melbourne Australia Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR.

关键词： Artificial Intelligence (AI) Cloud Computing Diagnosis Digital Health Edge Computing eHealth Internet of Things (IoT) Logisitic Regression Machine Learning Prognosis Random Forest Risk Factors Smart Connected Healthcare Type 2 Diabetes Mellitus

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Enabling Communication-Efficient Federated Learning via Distributed Compressed Sensing

Enabling Communication-Efficient Federated Learning via Dist...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Yixuan Guan Xuefeng Liu Tao Ren Jianwei Niu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

Federated learning (FL) trains a shared global model by periodically aggregating gradients from local devices. Communication overhead becomes a principal bottleneck in FL since participating devices usually suffer from limited bandwidth and unreliable connections in uplink transmission. To address this problem, the gradient compression methods based on compressed sensing (CS) theory have been put forward recently. However, most existing CS-based works compress gradients independently, ignoring the gradient correlations between participants or adjacent communication rounds, which constrains the achievement of higher compression rates. In view of the above observation, we propose a novel gradient compression scheme named FedDCS, guided by distributed compressed sensing (DCS) theory. Following the design philosophy of separate encoding and joint decoding in DCS, FedDCS compresses gradients for participants in each round separately while reconstructing them at the central server jointly via fully exploiting correlated gradients from the previous round, which are known as side information (SI). Benefiting from this design, reconstruction performance is significantly improved with fewer decoding errors also iterations under the identical compression rate, and the total uploading bits to achieve model convergence are considerably reduced. Theoretical analysis and extensive experiments conducted on MNIST and Fashion-MNIST both verify the effectiveness of our approach.

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A survey on exploring the challenges and applications of wireless body area networks (WBANs)

Cyber Security and Applications

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Cyber Security and Applications 2024年 2卷

作者： Rajasekaran, Arun Sekar Sowmiya, L. Maria, Azees Kannadasan, R. Department of ECE SR University Telangana Warangal 506371 India Department of ECE KPR Institute of Engineering and Technology Coimbatore India School of Computer Science and Engineering Inavolu Beside AP Secretariat VIT-AP University Andhra Pradesh 522237 India Department of Software systems School of Computer Science and Engineering VIT University-Vellore Tamilnadu 632014 India

Networks play an important role in the day-to-day life of every individual. Networks are involved in the transmission of necessary information between the sender and receiver in the channel. Wireless Body Area Network (WBAN) is a major advancement in the field of network communication. Due to the arrival of the Micro Electromechanical System (MEMS) and several intelligent sensors, collaboration with WBAN makes accurate predictions of parameters in the human body. WBAN has numerous applications in medical and non-medical fields. WBANs have demonstrated remarkable capabilities in real-time health monitoring, facilitating the collection of vital physiological data from individuals in diverse environments. Firstly, their low-power and energy-efficient design ensures prolonged device operation, making them suitable for continuous monitoring over extended periods. Additionally, the miniaturization of sensors and the integration of wireless communication technologies enable seamless data transmission to centralized healthcare systems. Furthermore, the integration of artificial intelligence and machine learning algorithms in WBAN systems has enabled personalized health analytics, allowing for more precise and context-aware health monitoring. This paper gives a survey of the WBAN standard, security in WBAN, several authentication approaches, routing, and MAC protocols. In addition, this paper describes the challenges faced by WBAN, such as network partitioning, changes in postures, lifetime issues, and quality of service (QoS). At last, the advancement in WBAN is for future improvement in the area of body area networks. © 2024

关键词： Quality of service Routing protocol Security Sensors Wireless body area network

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PID Controller Machine Learning Algorithm Applied to the Mathematical Model of Quadrotor Lateral Motion 6

PID Controller Machine Learning Algorithm Applied to the Mat...

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6th IEEE International Conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2021

作者： Kucherov, Dmytro Kozub, Andrei Tkachenko, Valerii Rosinska, Galina Poshyvailo, Olexii Faculty of Cybersecurity Computer and Software Engineering National Aviation University Department of Computerized Control Systems Kyiv Ukraine National Space Facilities Control and Test Center State Space Agency of Ukraine Kyiv Ukraine

ISBN: (纸本)9781665438223

The paper discusses the problem of automatic tuning of the PID controller. The auto-tuning algorithm of the PID controller based on one machine learning method, which is equivalent to the steepest descent, is proposed. A feature of the proposed approach is the vector adjustment of the PID controller parameters, where the angular direction of descent is taken into account at first, and the step sizes are made if taking into account the increased accuracy of the approximation near the optimum point. Modeling performed in the Matlab environment confirms the effectiveness of the proposed approach. © 2021 IEEE.

关键词： Three term control systems

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Big data analytics: A single window IoT-enabled climate variability system for all-year-round vegetable cultivation 4

Big data analytics: A single window IoT-enabled climate vari...

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4th International Conference on Science and Sustainable Development: Advances in Sciences and Technology for Sustainable Development, ICSSD 2020

作者： Nwankwo, Wilson Ukhurebor, Kingsley Eghonghon Software Engineering AndCyberphysical Systems Unit Department of Computer Science Edo University Iyamho Edo State Nigeria Climatic Environmental and Telecommunication Unit Department of Physics Edo University Iyamho Edo State Nigeria

Vegetables constitute a major food source with huge nutritional values as well as major source of income. The cultivation of vegetables is dictated by climate and seasonal changes across Nigeria. Edo State lies within the South of Nigeria and enjoys the two popular seasons (rainy and dry) like many other parts of the country. However, the variability in soil distribution and weather conditions across different locations is a determining factor as to the category of not just vegetables to grow but other crops. In this paper, Edo state is used as a flagship project for its diverse potentials and uniqueness in respect of known variability in soil and weather conditions. The State is divided into three geo-referenced agricultural districts. A prototype system is proposed to provide vegetable farmers with real-time information on vegetablefarming requirements. The proposed system is an Internet of things (IoT)-enabled climate variability system with interfaces to popular mobile networks, existing Geographical Information System (GIS) in the State, and remote sensing stations respectively. Each geo-referenced point is a nexus to areas with similar weather variability and soil distribution. Historical data is collected from the existing GIS and a provision is made to constantly enrich the historical data with new information from the geo-referenced points including crops grown, trends in cultivation, queries from farmers, etc. The information generated from the geo-referenced locations are routed via GPS to the central analytics server in the cloud and appropriate algorithms are used to carry out data analysis for real-time prediction and messages to farmers through the Internet and Short Message Services (SMS). With this system, it is submitted that subsistent and mechanized farmers would benefit through the guidance of an analytics system thereby boosting vegetablefarming regardless of the season of the year. © Published under licence by IOP Publishing Ltd.

关键词： Internet of things

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Shared Weighted Continuous Wavelet Capsule Network for Electrocardiogram Biometric Identification 18

Shared Weighted Continuous Wavelet Capsule Network for Elect...

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18th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2021

作者： Monday, Happy Nkanta Li, Jian Ping Nneji, Grace Ugochi James, Edidiong Christopher Leta, Yobsan Bayisa Nahar, Saifun Haq, Amin Ul University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu Sichuan 611731 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu Sichuan 611731 China University of Missouri School of Computer and Information Systems St LouisMO United States

ISBN: (纸本)9781665413640

In recent times, researchers are showing more interest in the subject of biometric identification, which uses biological traits to confirm a user's identity. Traditional authentication techniques are prone to damage, fraud, and negligence. We investigate a unique biometric based on electrocardiogram (ECG) signals generated from the heart as a biometric security attribute for access control verification. In this research, we propose a shared weighted continuous wavelet capsule network for ECG biometric identification, in which a continuous wavelet transform (CWT) is utilized to convert one-dimensional time-domain ECG signals into scalograms of two-dimensional images to obtain good quality training data. Then, a siamese capsule network framework is utilized to predict the right match or mismatch of ECG query samples using the extracted specific attributes from the scalograms. The dataset utilized in this work is collected from the Physionet MIT-BIH Normal Sinus Rhythm database. Experimental result shows that the proposed approach properly predicted ECG query samples with 99.2% accuracy, which makes our model more robust. © 2021 IEEE.

关键词： Electrocardiograms

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A Feature Fusion Attention-based Deep Learning Algorithm for Mammographic Architectural Distortion Classification

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Rehman, Khalil Ur Jianqiang, Li Yasin, Anaa Bilal, Anas Basheer, Shakila Ullah, Inam Jabbar, Muhammad Kashif Tian, Yibin Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Beijing University of Technology College of Software Engineering Beijing100124 China Hainan Normal University College of Information Science and Technology Hainan Haikou571158 China Princess Nourah Bint Abdulrahman University Department of Information Systems College of Computer and Information Science Riyadh11671 Saudi Arabia Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan

Architectural Distortion (AD) is a common abnormality in digital mammograms, alongside masses and microcalcifications. Detecting AD in dense breast tissue is particularly challenging due to its heterogeneous asymmetries and subtle presentation. Factors such as location, size, shape, texture, and variability in patterns contribute to reduced sensitivity. To address these challenges, we propose a novel feature fusion-based Vision Transformer (ViT) attention network, combined with VGG-16, to improve accuracy and efficiency in AD detection. Our approach mitigates issues related to texture fixation, background boundaries, and deep neural network limitations, enhancing the robustness of AD classification in mammograms. Experimental results demonstrate that the proposed model achieves state-of-the-art performance, outperforming eight existing deep learning models. On the PINUM dataset, it attains 0.97 sensitivity, 0.92 F1-score, 0.93 precision, 0.94 specificity, and 0.96 accuracy. On the DDSM dataset, it records 0.93 sensitivity, 0.91 F1-score, 0.94 precision, 0.92 specificity, and 0.95 accuracy. These results highlight the potential of our method for computer-aided breast cancer diagnosis, particularly in low-resource settings where access to high-end imaging technology is limited. By enabling more accurate and timely AD detection, our approach could significantly improve breast cancer screening and early intervention worldwide. © 2013 IEEE.

关键词： Deep neural networks

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