Heart failure (HF) is now one of the most common diseases, causing approximately seventeen million death cases every year all over the world. HF occurs due to less pumping ratio of blood by the heart that a normal hum...
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Atrial Fibrillation (AF) with high mortality rate needs to be monitored and detected accurately. AF is indicated as varying pulse-to-pulse intervals in a PPG signal. To record Photoplethysmography (PPG) signal, wrist-...
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Quantum federated learning(QFL)enables collaborative training of a quantum machine learning(QML)model among multiple clients possessing quantum computing capabilities,without the need to share their respective local *...
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Quantum federated learning(QFL)enables collaborative training of a quantum machine learning(QML)model among multiple clients possessing quantum computing capabilities,without the need to share their respective local ***,the limited availability of quantum computing resources poses a challenge for each client to acquire quantum computing *** raises a natural question:Can quantum computing capabilities be deployed on the server instead?In this paper,we propose a QFL framework specifically designed for classical clients,referred to as CC-QFL,in response to this *** each iteration,the collaborative training of the QML model is assisted by the shadow tomography technique,eliminating the need for quantum computing capabilities of ***,the server constructs a classical representation of the QML model and transmits it to the *** clients encode their local data onto observables and use this classical representation to calculate local *** local gradients are then utilized to update the parameters of the QML *** evaluate the effectiveness of our framework through extensive numerical simulations using handwritten digit images from the MNIST *** framework provides valuable insights into QFL,particularly in scenarios where quantum computing resources are scarce.
A dual-frequency tuning method of Inductive Power Transfer (IPT) system is proposed to estimate the coupling coefficient and output voltage from the transmitter (Tx) during power transfer without using an additional c...
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Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routi...
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Project Risk management is the process of identifying, evaluating, avoiding, or reducing risks. Where there is no software project without risks existence are natural in the context of project planning and management....
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The rapid expansion of Intelligent Transportation System (ITS) services depends on the Electric Vehicle (EV) and Mobile Charging Station (MCS) consumer electronics industry, as well as the intelligent Consumer Interne...
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The rapid expansion of Intelligent Transportation System (ITS) services depends on the Electric Vehicle (EV) and Mobile Charging Station (MCS) consumer electronics industry, as well as the intelligent Consumer Internet of Things (CIoT) platform. The functioning environment of MCSs is inherently dynamic, influenced by inconstant user preferences, energy demands, and charging service availability. Adapting to these changes in near-real-time while ensuring cost efficiency and fairness poses a notable challenge. These consumer electronic devices share data with third parties, so privacy is a critical concern. This paper presents a secure, optimized approach for enhancing the performance and accuracy of charging/discharging scheduling of MCSs within the CIoT network while protecting consumers’ data. This study aims to develop an optimization mechanism that enables decentralized learning with minimal data transfer while preserving user privacy by embedding Federated Learning (FL) as a security layer in our system. Also, it aims to maximize the potential profit of these stations while optimizing their daily operational efficiency. We propose a fog-edge communication to enhance communication in the decentralized FL-based network. Evaluating the result demonstrated enhanced profit maximization for MCSs operating within the CIoT network to fulfill as many energy requests from EVs as feasible while reducing self-charging expenses. IEEE
In the Next Generation Radio Networks(NGRN),there will be extreme massive connectivity with the Heterogeneous Internet of Things(HetIoT)*** millimeter-Wave(mmWave)communications will become a potential core technology...
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In the Next Generation Radio Networks(NGRN),there will be extreme massive connectivity with the Heterogeneous Internet of Things(HetIoT)*** millimeter-Wave(mmWave)communications will become a potential core technology to increase the capacity of Radio Networks(RN)and enable Multiple-Input and Multiple-Output(MIMO)of Radio Remote Head(RRH)***,the challenging key issues in unfair radio resource handling remain unsolved when massive requests are occurring *** imbalance of resource utilization is one of the main issues occurs when there is overloaded connectivity to the closest RRH receiving exceeding *** handle this issue effectively,Machine Learning(ML)algorithm plays an important role to tackle the requests of massive IoT devices to RRH with its obvious capacity *** paper proposed a dynamic RRH gateways steering based on a lightweight supervised learning algorithm,namely K-Nearest Neighbor(KNN),to improve the communication Quality of Service(QoS)in real-time IoT *** supervises the model to classify and recommend the user’s requests to optimal RRHs which preserves higher *** experimental dataset was generated by using computersoftware and the simulation results illustrated a remarkable outperformance of the proposed scheme over the conventional methods in terms of multiple significant QoS parameters,including communication reliability,latency,and throughput.
When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized *** allows ML models t...
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When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized *** allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third *** paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data *** virtue of FL,models can be learned from all such distributed data sources while preserving data *** aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software ***,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL *** ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.
Deepfake detection is a rapidly evolving field with significant implications for the integrity of visual media. This review explores techniques, challenges, and future directions in deepfake detection. Traditional ima...
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