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Constructing a Cryptocurrency-Price Prediction Model Using Deep Learning 8

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Constructing a Cryptocurrency-Price Prediction Model Using D...

8th International Conference on Engineering and Emerging Technologies, ICEET 2022

作者： Tran, Toai Kim Le, Thanh Thi Tuyet Bui, Thinh Tien Dang, Vang Quang Senkerik, Roman Ho Chi Minh City University of Technology and Education Faculty of Economics No 1 Vo Van Ngan Street Linh Chieu Ward Thu Duc City Viet Nam VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Dept. of Computer Science 17. Listopadu 2172/15 Ostrava-Poruba708 00 Czech Republic Tomas Bata University in Zlin Faculty of Applied Informatics T. G. Masaryka 5555 Zlin76001 Czech Republic

ISBN: (纸本)9781665491068

The purpose of this study is to discover the optimal Deep Learning model for Bitcoin prediction among the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). Our empirical results indicate that LSTM is the optimal model for predicting Bitcoin price and trend with the prediction accuracy of 88.9%. Our study serves as a stepping stone for novice cryptocurrency investors and future studies of more advanced and sophisticated algorithms. Finally, given that the ideal model for predicting the price of cryptocurrencies is still a topic of controversy, the findings of this study will serve as a valuable empirical resource for future studies. © 2022 IEEE.

关键词： Forecasting

Synergistic Integration of Photovoltaic and the Grid: An Intelligent ANFIS-Governed Interleaved Boost Converter Approach

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Synergistic Integration of Photovoltaic and the Grid: An Int...

Advancement in Renewable Energy and Intelligent Systems (AREIS), International Conference on

作者： Balaji V M. Ganga Barathi K Pramod Kumar Gouda Rajesh Prasad Mammula Venkatesh Department of Electrical and Computer Engineering MAI-NEFHI College of Engineering and Technology Asmara Eritrea Department of Biomedical Engineering Hindusthan College of Engineering and Technology Coimbatore India Dept. of EEE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Dept. of EEE Aditya Institute of Technology and Management Srikakulam Andhra Pradesh Dept. of EEE Stella Mary's College of Engineering Tamil Nadu India Dept. of EEE Avanthi's Research and Technological Academy Vizianagaram A. P India

ISBN: (数字)9798350387230

ISBN: (纸本)9798350387247

The transition towards Renewable Energy Sources (RES), mainly solar power is crucial in addressing the depletion of fossil fuel-based energy supplies and mitigating environmental concerns. This paper proposes an innovative approach to enhance efficiency of PhotoVoltaic (PV) systems by integrating a two-phase Interleaved Boost (IB) converter with an Adaptive Neuro Fuzzy Inference System (ANFIS) controller. The ANFIS controller optimizes the operation of the IB converter by adjusting the duty cycle based on two key input parameters: solar irradiation and temperature. This adaptive control method effectively transforms solar energy into electrical power, maximizing the PV system's power production. By using the ANFIS mechanism, the suggested system improves total output power, decreases overshoot, and produces a higher voltage output than conventional techniques, all of which lead to increased performance and dependability. The IB converter operates with an efficiency of 96.15% and minimized settling time.

关键词： Photovoltaic systems Renewable energy sources Radiation effects Systems operation Transforms Solar energy Production Stability analysis Temperature control Thermal stability

Extreme Age of Information for Wireless-Powered Communication Systems

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arXiv 2022年

作者： Miridakis, Nikolaos I. Shi, Zheng Tsiftsis, Theodoros A. Yang, Guanghua The School of Intelligent Systems Science & Engineering Jinan University Zhuhai Campus Zhuhai519070 China Dept. of Informatics and Computer Engineering University of West Attica Aegaleo 12243 Greece

The extreme or maximum age of information (AoI) is analytically studied for wireless communication systems. In particular, a wireless powered single-antenna source node and a receiver (connected to the power grid) equipped with multiple antennas are considered when operated under independent Rayleigh-faded channels. Via the extreme value theory and its corresponding statistical features, we demonstrate that the extreme AoI converges to the Gumbel distribution whereas its corresponding parameters are obtained in straightforward closed-form expressions. Capitalizing on this result, the risk of the extreme AoI realization is analytically evaluated according to some relevant performance metrics, while some useful engineering insights are manifested. Copyright © 2022, The Authors. All rights reserved.

关键词： Antennas

LMMS Reloaded: Transformer-based Sense Embeddings for Disambiguation and Beyond

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arXiv 2021年

作者： Loureiro, Daniel Jorge, Alípio Mário Camacho-Collados, Jose LIAAD - INESC TEC Dept. of Computer Science FCUP University of Porto Portugal School of Computer Science and Informatics Cardiff University United Kingdom

Distributional semantics based on neural approaches is a cornerstone of Natural Language Processing, with surprising connections to human meaning representation as well. Recent Transformer-based Language Models have proven capable of producing contextual word representations that reliably convey sense-specific information, simply as a product of self-supervision. Prior work has shown that these contextual representations can be used to accurately represent large sense inventories as sense embeddings, to the extent that a distance-based solution to Word Sense Disambiguation (WSD) tasks outperforms models trained specifically for the task. Still, there remains much to understand on how to use these Neural Language Models (NLMs) to produce sense embeddings that can better harness each NLM's meaning representation abilities. In this work we introduce a more principled approach to leverage information from all layers of NLMs, informed by a probing analysis on 14 NLM variants. We also emphasize the versatility of these sense embeddings in contrast to task-specific models, applying them on several sense-related tasks, besides WSD, while demonstrating improved performance using our proposed approach over prior work focused on sense embeddings. Finally, we discuss unexpected findings regarding layer and model performance variations, and potential applications for downstream tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

Pre-Decision-Making-empowered Double Auction for Data Services over Vehicles

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Pre-Decision-Making-empowered Double Auction for Data Servic...

IEEE International Conference on Communications in China (ICCC)

作者： Sicheng Wu Minghui Liwang Wei Gong Zhipeng Cheng Xiaoyu Xia Xianbin Wang School of Informatics Xiamen University Xiamen China College of Electronic and Information Engineering Tongji University Shanghai China School of Future Science and Engineering Soochow University Suzhou China School of Computing Technologies RMIT University Melbourne Australia Dept. of Electrical and Computer Engineering Western University Ontario Canada

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Double auction has been emerged as a popular mechanism to enable dynamic data sharing among moving vehicles for critical applications such as autonomous driving. This paper proposes a novel pre-decision-making-empowered double auction mechanism for data provisioning among moving vehicles (which are classified into data buyers and sellers), who may pass a considered road intersection. To help buyers obtain their required data (e.g., different data types and qualities), while supporting a responsive auction process between vehicles, the proposed mechanism features an unique pre-decision-making empowered two-stages auction to reduce the data trading latency. We further investigate DBSCAN-based clustering algorithm and VCG-based pricing rule for the first stage, to facilitate long-term contracts between buyers and sellers, as well as preference lists for buyers, in advance to practical transactions. Then, the second stage will follow the pre-decisions in the former stage, thus enabling a time-efficient data trading process. Simulations validate our superior performance in social welfare, time efficiency, and maintaining crucial auction properties such as truthfulness and individual rationality.

关键词： Solution design Roads Smart contracts Clustering algorithms Pricing Mathematical models Data models

LIFE: A generalizable autodidactic pipeline for 3D OCT-A vessel segmentation

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arXiv 2021年

作者： Hu, Dewei Cui, Can Li, Hao Larson, Kathleen E. Tao, Yuankai K. Oguz, Ipek Vanderbilt University Dept. of Electrical Engineering and Computer Science Vanderbilt University Dept. of Biomedical Engineering NashvilleTN United States

Optical coherence tomography (OCT) is a non-invasive imaging technique widely used for ophthalmology. It can be extended to OCT angiography (OCT-A), which reveals the retinal vasculature with improved contrast. Recent deep learning algorithms produced promising vascular segmentation results;however, 3D retinal vessel segmentation remains difficult due to the lack of manually annotated training data. We propose a learning-based method that is only supervised by a self-synthesized modality named local intensity fusion (LIF). LIF is a capillary-enhanced volume computed directly from the input OCT-A. We then construct the local intensity fusion encoder (LIFE) to map a given OCT-A volume and its LIF counterpart to a shared latent space. The latent space of LIFE has the same dimensions as the input data and it contains features common to both modalities. By binarizing this latent space, we obtain a volumetric vessel segmentation. Our method is evaluated in a human fovea OCT-A and three zebrafish OCT-A volumes with manual labels. It yields a Dice score of 0.7736 on human data and 0:8594±0:0275 on zebrafish data, a dramatic improvement over existing unsupervised algorithms. © 2021, CC BY.

关键词： Optical tomography

Towards interval temporal logic rule-based classification 1

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Towards interval temporal logic rule-based classification

1st Workshop on Artificial Intelligence and Formal Verification, Logic, Automata, and Synthesis, OVERLAY 2019

作者： Lucena-Sánchez, E. Muñoz-Velasco, E. Sciavicco, G. Stan, I.E. Vaccari, A. Dept. of Mathematics and Computer Science University of Ferrara Italy Dept. of Physics Informatics and Mathematics University of Modena and Reggio Emilia Italy Dept. of Applied Mathematics University of Málaga Spain Dept. of Mathematical Physical and Computer Sciences University of Parma Italy

Supervised classification is one of the main computational tasks of modern Artificial Intelligence, and it is used to automatically extract an underlying theory from a set of already classified instances. The available learning schemata are mostly limited to static instances, in which the temporal component of the information is absent, neglected, or abstracted into atemporal data, and purely, native temporal classification is still largely unexplored. In this paper, we propose a temporal rule-based classifier based on interval temporal logic, that is able to learn a classification model for multivariate classified (abstracted) time series, and we discuss some implementation issues. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： computer circuits

TOWARDS EFFICIENT MEDICAL IMAGE SEGMENTATION VIA BOUNDARY-GUIDED KNOWLEDGE DISTILLATION

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TOWARDS EFFICIENT MEDICAL IMAGE SEGMENTATION VIA BOUNDARY-GU...

2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Wen, Yang Chen, Leiting Xi, Shuo Deng, Yu Tang, Ximan Zhou, Chuan Key Laboratory of Digital Media Technology of Sichuan Province School of Computer Science and Engineering China Institute of Electronic and Information Engineering Guangdong University of Electronic Science and Technology of China China Dept. Biomedical Engineering King's College London London United Kingdom

ISBN: (纸本)9781665438643

In recent years, knowledge distillation for semantic segmentation has been extensively studied in order to obtain satisfactory performance while reducing computational costs. Compared with natural images, segmentation targets in medical images have fuzzy boundaries that are difficult to determine, but current knowledge distillation methods fail to explicitly transfer boundary information, resulting in poor boundary discrimination in compact models. Therefore, in this paper, we propose two knowledge distillation modules, namely boundary-guided deep supervision and output space boundary embeddng alignment, to explicitly transfer boundary information. The validity of our knowledge distillation approaches is demonstrated by extensive experiemnts on four public medical image data sets, namely, Montgomery County, CHAOS, GlaS and DRISHTI-GS. © 2021 IEEE

关键词： Distillation

ML-Quadrat & DriotData: A Model-Driven Engineering Tool and a Low-Code Platform for Smart IoT Services

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arXiv 2021年

作者： Moin, Armin Mituca, Andrei Challenger, Moharram Badii, Atta Günnemann, Stephan Germany DriotData UG Munich Germany Dept. of Computer Science Univ. of Antwerp Flanders Make Belgium Dept. of Computer Science Univ. of Reading United Kingdom Dept. of Informatics Munich Data Science Institute TUM Germany

In this paper, we present ML-Quadrat, an open-source research prototype that is based on the Eclipse Modeling Framework (EMF) and the state of the art in the literature of Model-Driven Software Engineering (MDSE) for smart Cyber-Physical Systems (CPS) and the Internet of Things (IoT). Its envisioned users are mostly software developers who might not have deep knowledge and skills in the heterogeneous IoT platforms and the diverse Artificial Intelligence (AI) technologies, specifically regarding Machine Learning (ML). ML-Quadrat is released under the terms of the Apache 2.0 license on Github1. Additionally, we demonstrate an early tool prototype of DriotData, a web-based Low-Code platform targeting citizen data scientists and citizen/end-user software developers. DriotData exploits and adopts ML-Quadrat in the industry by offering an extended version of it as a subscription-based service to companies, mainly Small- and Medium-Sized Enterprises (SME). The current preliminary version of DriotData has three web-based model editors: text-based, tree-/form-based and diagram-based. The latter is designed for domain experts in the problem or use case domains (namely the IoT vertical domains) who might not have knowledge and skills in the field of IT. Finally, a short video demonstrating the tools is available on YouTube: https://***/VAuz25w0a5k. © 2021, CC BY-NC-SA.

关键词： Internet of things