Quantum random number generators have attracted the research community for providing secure communication. This paper presents a novel Quantum LFSR, designed using the novel construction of D-Flip-Flop (DFF) and XOR g...
Quantum random number generators have attracted the research community for providing secure communication. This paper presents a novel Quantum LFSR, designed using the novel construction of D-Flip-Flop (DFF) and XOR gates in QCA. The suggested Quantum LFSR comprises 216 cells with an area of 0.23μm2 and the total power dissipation is 0.31442 eV, 0.41729 eV, and 0.54389 eVat different standard tunnelling energy levels. The power parameters are analysed using QCA Pro, and the calculated outcomes show that the Quantum LFSR has low power consumption and reduced footprint compared to conventional designs.
Training general-purpose vision models on purely sequential visual data, eschewing linguistic inputs, has heralded a new frontier in visual understanding. These models are intended to not only comprehend but also seam...
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The security of IoT that is based on layered approaches has shortcomings such as the redundancy, inflexibility, and inefficiently of security solutions. There are many harmful attacks in IoT networks such as DoS and D...
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Such an analysis of different machine learning methods for predicting the achievement levels of students in Portuguese secondary education makes this essay. The research highlights the importance of accurate expectati...
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ISBN:
(数字)9798350351408
ISBN:
(纸本)9798350351415
Such an analysis of different machine learning methods for predicting the achievement levels of students in Portuguese secondary education makes this essay. The research highlights the importance of accurate expectations of learners' results for education system administrations and respective policymakers. The current study makes use of the “Student Performance in Portuguese Secondary education” dataset and employs machine learning algorithms, namely MLPRegressor, XGBoost, DecisionTreeRegressor, CatBoost, and KNeighborsRe-gressor, to the corpus. Performance metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), etc., are used to judge every model's performance. The conclusion that can be drawn from the data is that the MLPRegressor model leads among the competitors, having an MSE equivalent of 0.0103, which is superior to others. The findings of this study are of great significance for educational institutions and policymakers as they work to make appropriate contact with students' performance prediction.
Accurate estimation of the state of charge (SoC) of lithium-ion batteries (LIBs) in electric vehicles (EVs) is crucial for optimizing performance, ensuring safety, and extending battery life. However, traditional esti...
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Accurate estimation of the state of charge (SoC) of lithium-ion batteries (LIBs) in electric vehicles (EVs) is crucial for optimizing performance, ensuring safety, and extending battery life. However, traditional estimation methods often struggle with the nonlinear and dynamic behavior of battery systems, leading to inaccuracies that compromise the efficiency and reliability of electric vehicles. This study proposes a novel approach for SoC estimation in BMW EVs by integrating a metaheuristic algorithm with deep neural networks. Specifically, teaching-learning based optimization (TLBO) is employed to optimize the weights and biases of the deep neural networks model, enhancing estimation accuracy. The proposed TLBO-deep neural networks (TLBO-DNNs) method was evaluated on a dataset of 1,064,000 samples, with performance assessed using mean absolute error (MAE), root mean square error (RMSE), and convergence value. The TLBO-DNNs model achieved an MAE of 3.4480, an RMSE of 4.6487, and a convergence value of 0.0328, outperforming other hybrid approaches. These include the barnacle mating optimizer-deep neural networks (BMO-DNNs) with an MAE of 5.3848, an RMSE of 7.0395, and a convergence value of 0.0492; the evolutionary mating algorithm-deep neural networks (EMA-DNNs) with an MAE of 7.6127, an RMSE of 11.2287, and a convergence value of 0.0536; and the particle swarm optimization-deep neural networks (PSO-DNNs) with an MAE of 4.3089, an RMSE of 5.9672, and a convergence value of 0.0345. Additionally, the TLBO-DNNs approach outperformed standalone models, including the autoregressive integrated moving average (ARIMA) model (MAE: 14.3301, RMSE: 7.0697) and support vector machines (SVMs) (MAE: 6.0065, RMSE: 8.0360). This hybrid TLBO-DNNs technique demonstrates significant potential for enhancing battery management systems (BMS) in electric vehicles, contributing to improved efficiency and reliability in electric vehicle operations.
The overwhelming rise in computing and networking skills makes Mobile platforms as predominant means of Internet access. Specific portable apps are unusable due to the inadequate computing capacities of mobile platfor...
The overwhelming rise in computing and networking skills makes Mobile platforms as predominant means of Internet access. Specific portable apps are unusable due to the inadequate computing capacities of mobile platforms. The constrained conne ctivity and reaction times involved in offloading data into multiple servers, such as a cloud data center, make permitting applications needing real-time information collection and processing on mobile stages are very challenging. The hybrid statics/mobile computing network is the latest supply system which has the heterogeneous ability for sensing, computing, and networking with static/mobile devices nearby. This robust computing network can allow creative mobile information and calculation-intensive applications, including omnipresent context-aware health and wellbeing checks of older adults, remote precipitation and flood risk evaluation, shared object detection and monitoring, and shared multimedia content searching and sharing. A detailed experimental study is provided to validate and demonstrate the advantages of the autonomous capability of the proposed system.
Since Thailand has now completely stepped into an aged society there are more than 20% of the population aged 60 years and over. There was also an increase in those who had problems with their balance. One of the main...
Since Thailand has now completely stepped into an aged society there are more than 20% of the population aged 60 years and over. There was also an increase in those who had problems with their balance. One of the main difficulties for these people is walking on their own. This paper presents an automatic walking aid for people with balance or walking problems. A specially designed automatic walking aid can be adjusted to six levels of height. It has an anti-collision warning system at a distance of up to 40 centimeters and displays GPS coordinates via SMS when falling. The experimental results of the prototype automatic walking aid showed that the walking aid could work as designed and could help people with balance problems walk more comfortably.
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