Considering the challenges faced by the current Central Offices, this paper presents a novel architecture and related technological aspects of the Next Generation Central Offices (NGCOs), as envisioned within the OCTA...
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Thanks to the high potential for profit, trading has become increasingly attractive to investors as the cryptocurrency and stock markets rapidly expand. However, because financial markets are intricate and dynamic, ac...
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In the year of 2006, the METI of Japan established the SSPS '"USEF model"as the target SSPS configuration to be realized and also established the road map to achieve by the year 2050 based on the conclus...
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The paper is dedicated to the area of feature selection, in particular a notion of attribute rankings that allow to estimate importance of variables. In the research presented for ranking construction a new weighting ...
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CBCT (Cone Beam Computed Tomography) has fast become a key to produce 3D images in orthodontic and maxilla-facial surgery. Our purpose in this study is to develop a new approach for automatic localization of the regio...
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Federated Learning (FL) is a revolutionary privacy-preserving distributed learning framework that allows a small group of users to cooperatively build a machine-learning model using their own data locally. Smart citie...
Federated Learning (FL) is a revolutionary privacy-preserving distributed learning framework that allows a small group of users to cooperatively build a machine-learning model using their own data locally. Smart cities are areas that can generate high volume and critical data, which has the potential to revolutionize federated learning. Nevertheless, it is highly challenging to select a trustworthy group of clients to collaborate in model training. The utilization of a random selection technique would pose many threats due to malicious clients’ targeted and untargeted attacks. Such vulnerability may cause attacks and poisoning in the produced model. To address this problem, we present a mutual trust client-server selection approach based on matching game theory and bootstrapping mechanisms for federated learning in smart cities. Our solution entails the creation of: (1) preference functions for federated servers and smart devices (i.e., IoT/IoV) that enables them to sort each other based on trust score, (2) light feedback-base technique that leverages the cooperation of multiple client devices to assign trust value to the newly connected federated server, and (3) intelligent matching algorithms consider trust preferences of both parties in their design. According to our simulation results, our technique outperforms the baseline selection approach VanillaFL in terms of increasing the trust level and hence the global accuracy of the federated learning model and optimizing the number of untrusted selected clients.
Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend ...
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A wheelchair is a specialized form of Personal Mobility Vehicle (PMV) designed to help people with disabilities move safely to their desired locations. Unlike conventional PMVs, wheelchairs for people with disabilitie...
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Community detection can help uncover and understand complex networks’ underlying patterns and structures. It involves identifying cohesive groups with similar entities while being separated from other groups. Social ...
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Fingerprint recognition is crucial for device and data security, especially with the widespread use of capacitive sensors in mobile devices. However, denoising wet fingerprints from these sensors poses challenges due ...
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