Time Series (TS) forecasting has stagnated owing to algorithm restrictions, therefore systems developed using these methods can only perform so well. TS remains a challenge despite recent advances in Deep Learning (DL...
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Time Series (TS) forecasting has stagnated owing to algorithm restrictions, therefore systems developed using these methods can only perform so well. TS remains a challenge despite recent advances in Deep Learning (DL) in Natural Language Processing (NLP) and Reinforcement Learning (RL). This paper reviews the literature on these algorithms, highlights studies using them, and shows their limits. Neural Ordinary Differential Equations (NODEs) with continuous-time and continuous-depth tackle TS forecasting issues. Liquid Time-Constant (LTC) networks, a more advanced and reliable implementation of these NODEs, provides fluidity. We propose a new design that uses the LTC's liquid adaptability and is more adaptable to manage immediate changes. These algorithms are more steady, adaptive, and versatile than DL, which may help overcome its TS forecasting shortcomings.
This research implements hybrid deep learning network models for weather image classification. The study proposes to apply a combined model, namely VGG16-LightGBM. In its architecture model, a pre-train convolutional ...
This research implements hybrid deep learning network models for weather image classification. The study proposes to apply a combined model, namely VGG16-LightGBM. In its architecture model, a pre-train convolutional neural network (CNN) name as VGG16 is employed for feature extraction of images and the LightGBM algorithm is used to make classification. The results on accuracy of proposed models were compared with four other models, namely Xception, Inception V3,Vgg19, Vgg16 which are all implemented by transfer learning mechanism on the same dataset. The experimental results proved that the VGG16-LightGBM gives the best performance with the highest accuracy of 81,28%, outperforms the transfer learning technique of other 4 pre-train models in the problem of weather image classification.
Drawing a graph in the plane with as few crossings as possible is one of the central problems in graph drawing and computational geometry. Another option is to remove the smallest number of vertices or edges such that...
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When two face images are shown to a subject, the subject’s preference choice is influenced by many factors. Although it is a challenging task, one way to detect a subject’s preference is to analyze the eye movement ...
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We fabricated integrated silicon coupled-ring resonators and measured photonic band structures for synthetic frequency lattices with different configurations. A phase difference between two modulation signals introduc...
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With the advent of the Internet of Things, a world was born in which everything could be uniquely identified and monitored, tracked, and managed by computer programs. Items can self-configure using a predefined commun...
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Knowledge graph (KG) completion aims to find out missing triples in a KG. Some tasks, such as link prediction and instance completion, have been proposed for KG completion. They are triple-level tasks with some elemen...
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The integration of Fog computing and 5G communication may enhance Cyber Physical Systems (CPSs) for effective time identification of cyber attacks among consumer electronics. An unsupervised Intrusion Detection System...
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The integration of Fog computing and 5G communication may enhance Cyber Physical Systems (CPSs) for effective time identification of cyber attacks among consumer electronics. An unsupervised Intrusion Detection System (IDS) based on Generative Adversarial Networks (GANs) and Recurrent Neural Networks (RNNs) is presented in this study. This system is tailored to the resource limitations of consumer electronics within CPSs. By leveraging the processing power of fog nodes and the low-latency capabilities of 5G networks, cyber attacks can be swiftly identified. The use of a trained Long Short-Term Memory (LSTM) network encoder improves detection rates by enhancing reconstruction loss computation. Experimental results demonstrate that this approach, implemented through distributed fog computing infrastructure, offers better detection rates with a 15.2% reduction in detection latency and a 24.2% decrease in overall energy consumption compared to baseline methods. This innovative system could serve as an effective alternative for securing consumer electronic devices integrated into CPSs. IEEE
As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is ...
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Bilinear singular systems can be used in the investigation of different types of engineering *** the past decade,considerable attention has been paid to analyzing and synthesizing singular bilinear *** importance lies...
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Bilinear singular systems can be used in the investigation of different types of engineering *** the past decade,considerable attention has been paid to analyzing and synthesizing singular bilinear *** importance lies in their real world application such as economic,ecological,and socioeconomic *** are also applied in several biological processes,such as population dynamics of biological species,water balance,temperature regulation in the human body,carbon dioxide control in lungs,blood pressure,immune system,cardiac regulation,*** singular systems naturally represent different physical processes such as the fundamental law of mass action,the DC motor,the induction motor drives,the mechanical brake systems,aerial combat between two aircraft,the missile intercept problem,modeling and control of small furnaces and hydraulic rotary multimotor *** current research work discusses the Legendre Neural Network’s implementation to evaluate time-varying singular bilinear systems for finding the exact *** results were obtained from two methods namely the RK-Butcher algorithm and the Runge Kutta Arithmetic Mean(RKAM)*** with the results attained from Legendre Neural Network Method for time-varying singular bilinear systems,the output proved to be *** such,this research article established that the proposed Legendre Neural Network could be easily implemented in *** can obtain the solution for any length of time from this method in time-varying singular bilinear systems.
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