The growing number of connected Internet of Things (IoT) devices has led to the daily growth of network botnet attacks. The networks of compromised devices controlled by a single entity can be used for malicious purpo...
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Blockchain-based decentralized technology has gained a lot of attractions and interest in academia and industry. This peer-to-peer and transparent blockchain network makes it an acceptable technology. This decentraliz...
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Blockchain-based decentralized technology has gained a lot of attractions and interest in academia and industry. This peer-to-peer and transparent blockchain network makes it an acceptable technology. This decentralized network has many great specifications such as transparency, immutability, irreversibility and auditability. Bitcoin, the first and most successful blockchain application, achieves much success in recent years. This survey article attempts to outline an extensive survey of blockchain technology, investigating its structures, various protocol layers, features, value, challenges, and applications.
Human activity recognition (HAR) is an area of study that seeks to automatically and precisely detect an individual’s behavior by analyzing bio-signal data. Bio-signal data can be acquired using sensing technology in...
Human activity recognition (HAR) is an area of study that seeks to automatically and precisely detect an individual’s behavior by analyzing bio-signal data. Bio-signal data can be acquired using sensing technology integrated into smartphones and other worn intelligent gadgets. Nevertheless, collecting sensor data tagged with activity information employing individual smartphones can result in data being analyzed in varying contexts, potentially compromising the accuracy of machine learning prediction methodologies. This paper introduces a novel HAR approach via smartphone sensors. The proposed method incorporates a hybrid deep neural network architecture enhanced with an attention mechanism. The deep learning model under consideration is referred to as the Att-CNN-BLSTM network. This particular network can autonomously extract significant features from smartphone sensor data. This feature extraction aims to effectively classify different human actions with a high degree of accuracy. In order to assess the efficacy of the hybrid model, a series of investigations were undertaken utilizing a publically accessible HAR dataset. This dataset was employed for training and testing purposes, employing a 5-fold cross-validation approach. We additionally carried out an analytical comparison utilizing state-of-the-art deep learning models from prior research. The findings from our experimentation demonstrate superior performance of the Att-CNN-BLSTM model over other advanced AI algorithms, with the highest accuracy rate of 90.54% and top F1-score of 86.88% attained.
Android systems have been successfully developed to meet the demands of users. The following four methods are used in Android systems for memory management: backing swap, CompCache, traditional Linux swap, and low mem...
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Vaccine boosters have been recommended to mitigate the spread of the coronavirus disease 2019 (COVID-19) pandemic. A mathematical model with three vaccine doses and susceptibility is formulated. The model is calibrate...
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This article addresses the persistent challenges involved in the complex system reliability redundancy optimization of the overspeed protection system of a gas turbine (OPSGT), which is an essential complex system for...
This article addresses the persistent challenges involved in the complex system reliability redundancy optimization of the overspeed protection system of a gas turbine (OPSGT), which is an essential complex system for ensuring the turbine's safety. The optimal functionality of this complex reliability-redundancy is achieved by means of a novel nature-inspired computational approach named the Modified Wild Horse Optimization Algorithm (MWHO), inspired by the behavior of wild horses. The MWHO algorithm efficiently delivers optimal solutions to this complex NP-hard problem, showcasing its potential in solving various complex system reliability optimization problems. This efficiency is easily verified through extensive numerical simulations, demonstrating that the outcomes produced by MWHO exhibit outstanding performance when compared to various existing metaheuristics. Hence, the MWHO not only introduces a fresh perspective in tackling associated reliability-redundancy optimization challenges but also establishes itself as a competitive and efficient algorithm in the complex realm of system reliability optimization.
The Standard Model (SM) description of the CP violation can be tested by over-constraining the angles of the Unitary Triangle. Discrepancies between precise measurements of the Cabibbo–Kobayashi–Maskawa (CKM) angle ...
Combinatorial optimization is one of the fields where near term quantum devices are being utilized with hybrid quantum-classical algorithms to demonstrate potentially practical applications of quantum computing. One o...
Combinatorial optimization is one of the fields where near term quantum devices are being utilized with hybrid quantum-classical algorithms to demonstrate potentially practical applications of quantum computing. One of the most well studied problems in combinatorial optimization is the Max-Cut problem. The problem is also highly relevant to quantum and other types of “post Moore” architectures due to its similarity with the Ising model and other reasons. In this paper, we introduce a scalable hybrid multilevel approach to solve large instances of Max-Cut using both classical only solvers and quantum approximate optimization algorithm (QAOA). We compare the results of our solver to existing state of the art large-scale Max-Cut solvers. We demonstrate excellent performance of both classical and hybrid quantum-classical approaches and show that using QAOA within our framework is comparable to classical approaches. Reproducibility: Our solver is publicly available at https://***/angone/MLMax-cut.
Robotic systems based on Deep Reinforcement Learning have shown great potential to enable assembly systems with higher flexibility and robustness. This paper presents a concept of a Case-Based Reasoning system to auto...
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Robotic systems based on Deep Reinforcement Learning have shown great potential to enable assembly systems with higher flexibility and robustness. This paper presents a concept of a Case-Based Reasoning system to automate the implementation process, based on the assumption that similar assembly tasks have similar solutions as used as heuristics in the current manual procedure. For retrieving similar cases a digital description of the assembly task and a method to measure the similarity is introduced. The retrieved cases are then used to warmstart a Bayesian Hyperparameter Optimization. The approach is evaluated on two simulated robot task.
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