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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Hamzeh Ghorbani Alla Krasnikova Parvin Ghorbani Simin Ghorbani Harutyun S. Hovhannisyan Arsen Minasyan Natali Minasian Mehdi Ahmadi Alvar Harutyun Stepanyan Mohammazreza Azodinia Faculty of General Medicine University of Traditional Medicine of Armenia (UTMA) Yerevan Armenia Department of Cardiology Faculty of Medicine Yerevan State medical university Yerevan Armenia Department of Cardiology Faculty of Medicine Ahvaz Jundishapur University of Medical Sciences Ahvaz Iran Department of Nursing and Midwifery Faculty of Nursing Ahvaz Jundishapur University of Medical Sciences Ahvaz Iran Department of Internal Disease Propaedeutics Yerevan State Medical University Yerevan Armenia Faculty of General Medicine Yaroslavl State Medical University Yaroslavl Russia Department of Computer Engineering Faculty of Engineering Shahid Chamran University Ahwaz Iran Doctoral School of Applied Informatics and Applied Mathematics Obuda University Budapest Hungary

This research paper aims to predict coronary artery disease (CAD) using data from 350 patients collected at one of the hospitals in Armenia. CAD is a critical parameter which can have a significant impact on patients' life and survival. The study considers several input variables, including level of cholesterol (LOC), patient's age (PA), type of chest pain (TCP), number of arteries blocked (NAB), sex (S), and family history (FH), to make accurate predictions. To achieve this crucial task of CAD prediction, the researchers employed three powerful classification algorithms: Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR). Among these, the Random Forest algorithm stands out for its robustness and numerous advantages, including high accuracy, ability to handle outliers effectively, provision of feature importance insights, and reduced risk of overfitting. The research findings presented in this article demonstrate the impressive performance of the Random Forest algorithm, showcasing an accuracy value of 0.95 and a precision value of 0.94. These results indicate the model's ability to make precise and reliable predictions, essential when dealing with a life-or-death parameter like CAD. By conducting a comparative analysis based on statistical parameters, the researchers establish that Random Forest outperforms both SVM and LR. Thus, the conclusion drawn from the study suggests that the ranking of the algorithms based on their performance is as follows: RF > SVM > LR.

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End-to-End Learning for VCSEL-based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities

arXiv

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arXiv 2022年

作者： Srinivasan, Muralikrishnan Song, Jinxiang Grabowski, Alexander Szczerba, Krzysztof Iversen, Holger K. Schmidt, Mikkel N. Zibar, Darko Schröder, Jochen Larsson, Anders Häger, Christian Wymeersch, Henk The Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden Chalmers University of Technology GothenburgSE-41296 Sweden OpenLight Photonics Mountain ViewCA94087 United States The Department of Photonic Engineering Technical University of Denmark 2800 Kgs Lyngby Denmark The Department of Applied Mathematics and Computer Science Technical University of Denmark. 2800 Kgs Lyngby Denmark

Optical interconnects (OIs) based on vertical-cavity surface-emitting lasers (VCSELs) are the main workhorse within data centers, supercomputers, and even vehicles, providing low-cost, high-rate connectivity. VCSELs must operate under extremely harsh and time-varying conditions, thus requiring adaptive and flexible designs of the communication chain. Such designs can be built based on mathematical models (model-based design) or learned from data (machine learning (ML) based design). Various ML techniques have recently come to the forefront, replacing individual components in the transmitters and receivers with deep neural networks. Beyond such component-wise learning, end-to-end (E2E) autoencoder approaches can reach the ultimate performance through co-optimizing entire parameterized transmitters and receivers. This tutorial paper aims to provide an overview of ML for VCSEL-based OIs, with a focus on E2E approaches, dealing specifically with the unique challenges facing VCSELs, such as the wide temperature variations and complex models. © 2022, CC BY.

关键词： Optical communication

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A new approach to dealing with interval data in the TOPSIS method

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Procedia computer Science 2022年 207卷 4545-4555页

作者： Aleksandra Kaczyńska Neeraj Gandotra Wojciech Sałabun Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Żołnierska 49 71-210 Szczecin Poland Yogananda School of AI Computers and Data Science Shoolini University Solan 173229 HP India

Dealing with uncertainty is a significant direction of contemporary research on decision support systems. Despite many sophisticated methods of dealing with uncertain data, such as fuzzy sets and their numerous generalizations, methods based on interval numbers are still very popular. This is because interval data are much less complex and easier to process, and they are also easier to determine. However, there is a research gap regarding whether such a simple approach can adequately model uncertainty. This paper proposes a new approach to handling interval data in the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method based on the cartesian product of boundaries. This approach is compared with the most popular extension proposed by Jahanshahloo. In this work, two study cases taken from the literature were analyzed. Finally, the results of both approaches were compared, and conclusions were drawn.

关键词： TOPSIS interval data decision-making MCDA

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Adaptive Intelligent System for Monitoring and Control the Parameters of Pipelines Electrochemical Corrosion Protection

Adaptive Intelligent System for Monitoring and Control the P...

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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Khussanov, Zhakhongir Prokhorov, Oleksandr Prokhorov, Valeriy Tevyashev, Andriy Turdybekova, Dilfuza Shatalov, Oleksii 'Structural And Biochemical Materials' M. Auezov South Kazakhstan University Regional Laboratory Of Engineering Shymkent Kazakhstan National Aerospace University 'Kharkiv Aviation Institute' Department Of Computer Science And Information Technologies Kharkiv Ukraine Natioanal University Of Radio Electronics Problematic Scientifically Adv. Lab. For Radio Monitoring And Proc. Of Radio Technical Information Kharkiv Ukraine National University Of Radio Electronics Department Of Applied Mathematics Kharkiv Ukraine M. Auezov South Kazakhstan University Shymkent Kazakhstan National University Of Radio Electronics Department Of Artificial Intelligence Kharkiv Ukraine

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The paper is dedicated to the creation of adaptive intelligent system that provides control and management of parameters of cathodic protection stations, taking into account changes in external conditions in some sections of pipelines. Multicriteria task of optimization of operation modes of cathodic protection stations is considered, as optimization is performed both by the criterion of optimality of protective potential distribution (uniformity of protective total potential distribution along the pipeline length) and by the criterion of minimum total protective current of stations. The system provides increase of reliability of electrochemical protection system as a whole and accordingly prevents possible emergencies at pipeline system, as well as reduces costs of current repair of pipelines, due to reliability and uninterrupted protection. © 2022 IEEE.

关键词： Adaptive control systems

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A Robust Slot Filling Model based on LSTM and CRF for IoT Voice Interaction

A Robust Slot Filling Model based on LSTM and CRF for IoT Vo...

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IEEE Globecom Workshops

作者： Mourad Jbene Smail Tigani Rachid Saadane Abdellah Chehri Laboratory Engineering system SIRC/LAGeS-EHTP Hassania School of Public Works Casablanca Morocco Engineering Unit Euro-Med Research Center Euro-Med University Fes 51 Morocco Department of Mathematics and Computer Science Royal Military College of Canada Kingston CANADA

ISBN: (纸本)9781665459761

The Internet Of Things (IoT) is an emerging paradigm representing a network of infrastructure connecting different types of devices in different places. Recently, IoT-based Conversational systems gained more attention, providing users with a new human-computer interaction mode. These new systems allow users to interact with the IoT ecosystem through a virtual assistant. They also heavily rely on Natural Language Processing (NLP) techniques for intelligent and efficient communication. Understanding the user’s needs is the first step toward a more humanized IoT interaction system. This can be achieved using Natural language understanding (NLU) modules. Slot Filling is one of the core sub-tasks in NLU. It is an active research area focusing on extracting attribute values from the user’s utterance. In this study, we propose an LSTM-based model for slot filling. The model benefits from contextual embeddings extracted from the BERT transformer model, the attention mechanism, and the conditional random field (CRF) that is known to model strong dependence among adjacent tags in the output sequence. Experiments show that our model achieved competitive results on two widely used benchmark datasets.

关键词： Virtual assistants Semantics Focusing Transformer cores Transformers Filling Natural language processing

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Tripartite entanglement measure under local operations and classical communication

arXiv

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arXiv 2022年

作者： Ge, Xiaozhen Liu, Lijun Cheng, Shuming Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong College of Mathematics and Computer Science Shanxi Normal University Linfen041000 China The Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai201804 China Institute for Advanced Study Tongji University Shanghai200092 China

Multipartite entanglement is an indispensable resource in quantum communication and computation, however, it is a challenging task to faithfully quantify this global property of multipartite quantum systems. In this work, we study the concurrence fill, which admits a geometric interpretation to measure genuine tripartite entanglement for the three-qubit system in [S. Xie et al., Phys. Rev. Lett. 127. 040403 (2021)]. First, we use the well-known three-tangle and bipartite concurrence to reformulate this quantifier for all pure states. We then construct an explicit example to conclusively show the concurrence fill can be increased under local operations and classical communication (LOCC) on average, implying it is not an entanglement monotone. Moreover, we give a simple proof of the LOCC-monotonicity of three-tangle and find that the bipartite concurrence and the squared can have distinct performances under the same LOCC. Finally, we propose a reliable monotone to quantify genuine tripartite entanglement, which can also be easily generalised to the multipartite system. Our results shed light on studying genuine entanglement and also reveal the complex structure of multipartite systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum communication

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A review of coagulation explaining its definition, mechanism, coagulant types, and optimization models;RSM, and ANN

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Current research in Green and Sustainable Chemistry 2023年 6卷

作者： El-taweel, Reem M. Mohamed, Nora Alrefaey, Khlood A. Husien, Sh Abdel-Aziz, A.B. Salim, Alyaa I. Mostafa, Nagwan G. Said, Lobna A. Fahim, Irene Samy Radwan, Ahmed G. School of Engineering and Applied Sciences Nile University Giza Egypt Industrial Engineering Program SESC Research Center Nile University Giza Egypt October High Institute for Engineering Technology Giza Egypt Environmental and Sanitary Engineering Division Public Works Department Faculty of Engineering Cairo University Giza Egypt Nanoelectronics Integrated Systems Center (NISC) Nile University Giza Egypt Engineering Mathematics and Physics Dept. Faculty of Engineering Cairo University Giza 12613 Egypt

The textile business is one of the most hazardous industries since it produces several chemicals, such as dyes, which are released into water streams with ef-fluents. For the survival of the planet's life and the advancement of humanity, water is a crucial resource. One of the anthropogenic activities that pollute and consume water is the textile industry. Thus, the purpose of the current effort is to Apply coagulation as a Physico-chemical and biological treatment strat-egy with different techniques and mechanisms to treat the effluent streams of textile industries. The discharge of these effluents has a negative impact on the environment, marine life, and human health. Therefore, the treatment of these effluents before discharging is an important matter to reduce their adverse ef-fect. Many physico-chemical and biological treatment strategies for contaminants removal from polluted wastewater have been proposed. Coagulation is thought to be one of the most promising physico-chemical strategies for removing con-taminants and colouring pollutants from contaminated water. Coagulation is accompanied by a floculation process to aid precipitation, as well as the collection of the created sludge following the treatment phase. Different commercial, and natural coagulants have been applied as a coagulants in the process of coagulation. Additionally, many factors such as;pH, coagulant dose, pollu-tants concentration are optimized to obtain high coagulants removal capacity. This review will discuss the coagulation process, coagulant types and aids in addition to the factors affecting the coagulation process. Additionally, a brief comparison between the coagulation process, and the other processes;princi-ple, advantages, disadvantages, and their efficiency were discussed throgh the review. Furthermore, it discusses the models and optimization techniques used for the coagulation process including response surface methodology (RSM), ar-tificial neural network (ANN), and several

关键词： Coagulants Coagulation Mathematical models optimization Textile Textile effluents

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Singularly near optimal leader election in asynchronous networks

arXiv

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arXiv 2021年

作者： Kutten, Shay Moses, William K. Pandurangan, Gopal Peleg, David Faculty of Industrial Engineering and Management Technion - Israel Institute of Technology Haifa Israel Department of Computer Science University of Houston HoustonTX United States Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel

This paper concerns designing distributed algorithms that are singularly optimal, i.e., algorithms that are simultaneously time and message optimal, for the fundamental leader election problem in asynchronous networks. Kutten et al. (JACM 2015) presented a singularly near optimal randomized leader election algorithm for general synchronous networks that ran in O(D) time and used O(m log n) messages (where D, m, and n are the network's diameter, number of edges and number of nodes, respectively) with high probability.1 Both bounds are near optimal (up to a logarithmic factor), since Ω(D) and Ω(m) are the respective lower bounds for time and messages for leader election even for synchronous networks and even for (Monte-Carlo) randomized algorithms. On the other hand, for general asynchronous networks, leader election algorithms are only known that are either time or message optimal, but not both. Kutten et al. (DISC 2020) presented a randomized asynchronous leader election algorithm that is singularly near optimal for complete networks, but left open the problem for general networks. This paper shows that singularly near optimal (up to polylogarithmic factors) bounds can be achieved for general asynchronous networks. We present a randomized singularly near optimal leader election algorithm that runs in O(D + log2 n) time and O(m log2 n) messages with high probability. Our result is the first known distributed leader election algorithm for asynchronous networks that is near optimal with respect to both time and message complexity and improves over a long line of results including the classical results of Gallager et al. (ACM TOPLAS, 1983), Peleg (JPDC, 1989), and Awerbuch (STOC 89). Copyright © 2021, The Authors. All rights reserved.

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Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms

arXiv

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arXiv 2022年

作者： Goswami, Somdatta Li, David S. Rego, Bruno V. Latorre, Marcos Humphrey, Jay D. Karniadakis, George Em Division of Applied Mathematics Brown University ProvidenceRI United States Department of Biomedical Engineering Yale University New HavenCT United States Center for Research and Innovation in Bioengineering Valencia Polytechnic University Valencia Spain School of Engineering Brown University ProvidenceRI United States

Thoracic aortic aneurysm (TAA) is a localized dilatation of the aorta resulting from compromised wall composition, structure, and function, which can lead to life-threatening dissection or rupture. Several genetic mutations and predisposing factors that contribute to TAA have been studied in mouse models to characterize specific changes in aortic microstructure and material properties that result from a wide range of mechanobiological insults. By contrast, assessments of TAA progression in vivo are largely limited to measurements of aneurysm size and growth rate. It has been shown, however, that aortic geometry alone is not sufficient to predict the patient-specific progression of TAA but computational modeling of the evolving biomechanics of the aorta could predict future geometry and properties from initiating insults. In this work, we present an integrated framework to train a deep operator network (DeepONet)-based surrogate model to identify contributing factors for TAA by using synthetic finite element-based datasets of aortic growth and remodeling (G&R) resulting from prescribed mechanobiological insults. For training data, we investigate multiple types of TAA risk factors and spatial distributions within a computationally efficient constrained mixture model to generate axial-azimuthal maps of aortic dilatation and distensibility. The trained network is then capable of predicting the initial distribution and extent of the insult from a given set of dilatation and distensibility information, which in turn can be used to determine subsequent aortic geometry and mechanical properties. Two DeepONet frameworks are proposed, one trained on sparse information and one on full-field grayscale images, to gain insight into a preferred neural operator-based approach. Performance of the surrogate models is evaluated through multiple simulations carried out on insult distributions varying from fusiform (analytically defined) to complex (randomly generated). We show that thi

关键词： Blood vessels

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A Fast Fourier Convolutional Deep Neural Network For Accurate and Explainable Discrimination Of Wheat Yellow Rust And Nitrogen Deficiency From Sentinel-2 Time-Series Data

arXiv

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arXiv 2023年

作者： Shi, Yue Han, Liangxiu González-Moreno, Pablo Dancy, Darren Huang, Wenjiang Zhang, Zhiqiang Liu, Yuanyuan Huang, Mengning Miao, Hong Dai, Min School of Electronic and Electrical Engineering The University of Leeds Leeds United Kingdom Department of Computing and Mathematics Manchester Metropolitan University ManchesterM1 5GD United Kingdom Department of Forest Engineering University of Córdoba Córdoba Spain Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Department of Computer Science The University of Manchester Manchester United Kingdom Beijing University of Technology Beijing China College of Mechanical Engineering Yangzhou University Yangzhou China

Accurate and timely detection of plant stress is essential for yield protection, allowing better-targeted intervention strategies. Recent advances in remote sensing and deep learning have shown great potential for rapid non-invasive detection of plant stress in a fully automated and reproducible manner. However, the existing models always face several challenges: 1) computational inefficiency and the misclassifications between the different stresses with similar symptoms;and 2) the poor interpretability of the host-stress interaction. In this work, we propose a novel fast Fourier Convolutional Neural Network (FFDNN) for accurate and explainable detection of two plant stresses with similar symptoms (i.e. Wheat Yellow Rust And Nitrogen Deficiency). Specifically, unlike the existing CNN models, the main components of the proposed model include: 1) a fast Fourier convolutional block, a newly fast Fourier transformation kernel as the basic perception unit, to substitute the traditional convolutional kernel to capture both local and global responses to plant stress in various time-scale and improve computing efficiency with reduced learning parameters in Fourier domain;2) Capsule Feature Encoder to encapsulate the extracted features into a series of vector features to represent part-to-whole relationship with the hierarchical structure of the host-stress interactions of the specific stress. In addition, in order to alleviate over-fitting, a photochemical vegetation indices-based filter is placed as pre-processing operator to remove the non-photochemical noises from the input Sentinel-2 time series. The proposed model has been evaluated with ground truth data under both controlled and natural conditions. The results demonstrate that the high-level vector features interpret the influence of the host-stress interaction/response and the proposed model achieves competitive advantages in the detection and discrimination of yellow rust and nitrogen deficiency on Sentinel-2 time

关键词： Remote sensing

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