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Article
Open AccessFederated Constrastive Learning and Visual Transformers for Personal Recommendation
This paper introduces a novel solution for personal recommendation in consumer electronic applications. It addresses, on the one hand, the data confidentiality during the training, by exploring federated learn...
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Article
Open AccessNature inspired optimization algorithms for medical image segmentation: a comprehensive review
Image segmentation is the process of splitting a digital image into distinct segments or categories based on shared characteristics like texture, color, and intensity. Its primary aim is to simplify the image ...
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Article
Open AccessMetaheuristic algorithms and their applications in wireless sensor networks: review, open issues, and challenges
Metaheuristic algorithms have wide applicability, particularly in wireless sensor networks (WSNs), due to their superior skill in solving and optimizing many issues in different domains. However, WSNs suffer f...
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Article
Open AccessDeepfakes: current and future trends
Advances in Deep Learning (DL), Big Data and image processing have facilitated online disinformation spreading through Deepfakes. This entails severe threats including public opinion manipulation, geopolitical...
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Article
Open AccessHigh-Precision Matching Algorithm for Multi-Image Segmentation of Micro Animation Videos in Mobile Network Environment
In the mobile network environment, the accuracy of related image matching algorithms is affected by factors such as bandwidth uncertainty and channel interference, resulting in significant limitations in image...
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Article
Open AccessWhen explainable AI meets IoT applications for supervised learning
This paper introduces a novel and complete framework for solving different Internet of Things (IoT) applications, which explores eXplainable AI (XAI), deep learning, and evolutionary computation. The IoT data ...
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Article
Group intrusion detection in the Internet of Things using a hybrid recurrent neural network
This paper introduces a novel framework for identifying a group of intrusions in the context of the Internet of Things (IoT). It combines both deep learning and decomposition. A set of data is first collected ...
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Chapter and Conference Paper
Knowledge Guided Deep Learning for General-Purpose Computer Vision Applications
This research targets general-purpose smart computer vision that eliminates reliance on domain-specific knowledge to reach adaptable generic models for flexible applications. It proposes a novel approach in wh...
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Article
Open AccessAn edge-driven multi-agent optimization model for infectious disease detection
This research work introduces a new intelligent framework for infectious disease detection by exploring various emerging and intelligent paradigms. We propose new deep learning architectures such as entity emb...
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Article
Efficient evolutionary computation model of closed high-utility itemset mining
HUIM has been an important issue in recent years, particularly in basket-market analysis, since it identifies useful information or goods for decision-making. Numerous research focused on extracting high-utili...
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Article
Open AccessDeep learning based decomposition for visual navigation in industrial platforms
In the heavy asset industry, such as oil & gas, offshore personnel need to locate various equipment on the installation on a daily basis for inspection and maintenance purposes. However, locating equipment in ...
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Chapter and Conference Paper
How Image Retrieval and Matching Can Improve Object Localisation on Offshore Platforms
Deep learning is gaining popularity in the realm of object localization. Existing deep learning methods have shown good accuracy and inference runtime, but they require a lot of training data. This needs a maj...
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Article
Linguistic frequent pattern mining using a compressed structure
Traditional association-rule mining (ARM) considers only the frequency of items in a binary database, which provides insufficient knowledge for making efficient decisions and strategies. The mining of useful i...
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Article
SS-ITS: secure scalable intelligent transportation systems
This paper introduces a secure and scalable intelligent transportation and human behavior system to accurately discover knowledge from urban traffic data. The data are secured using blockchain learning technol...
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Article
Open AccessCluster-based information retrieval using pattern mining
This paper addresses the problem of responding to user queries by fetching the most relevant object from a clustered set of objects. It addresses the common drawbacks of cluster-based approaches and targets fa...
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Article
Mining of High-Utility Patterns in Big IoT-based Databases
When focusing on the general area of data mining, high-utility itemset mining (HUIM) can be defined as an offset of frequent itemset mining (FIM). It is known to emphasize more factors critically, which gives ...
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Chapter and Conference Paper
Mining of High-Utility Patterns in Big IoT Databases
In general data mining, HUIM also known as high-utility itemset mining is an offshoot of frequent item set mining (FIM). HUIM is known to give more emphasis to many factors which can give HUIM a distinct edge ...
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Chapter and Conference Paper
A Graphic CNN-LSTM Model for Stock Price Predication
In this paper, we presented a novel model that combines Convolution Neural Network (CNN) and Long Short-term Memory Neural Network (LSTM) for better and accurate stock price prediction. We then developed a mod...
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Article
Open AccessIncrementally updating the high average-utility patterns with pre-large concept
High-utility itemset mining (HUIM) is considered as an emerging approach to detect the high-utility patterns from databases. Most existing algorithms of HUIM only consider the itemset utility regardless of the...
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Article
Open AccessA recurrent neural network for urban long-term traffic flow forecasting
This paper investigates the use of recurrent neural network to predict urban long-term traffic flows. A representation of the long-term flows with related weather and contextual information is first introduced...