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Swarm Intelligence with Adaptive Neuro-Fuzzy Inference System-Based Routing Protocol for Clustered Wireless Sensor Networks

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Computational Intelligence and Neuroscience 2022年第0期2022卷 7940895页

作者： A-Matarneh, Feras Mohammed Alqaralleh, Bassam A. Y. Aldhaban, Fahad Alqaralleh, Esam A. Kumar, Anil Gupta, Deepak Joshi, Gyanendra Prasad Department of Computer Science University College of Duba University of Tabuk Tabuk71491 Saudi Arabia MIS Department College of Business Administration University of Business and Technology Jeddah21448 Saudi Arabia School of Engineering Princess Sumaya University for Technology Amman11941 Jordan Data Science Research Group School of Computing DIT University Dehradun India Department of Computer Science & Engineering Maharaja Agrasen Institute of Technology Delhi India Department of Computer Science and Engineering Sejong University Seoul05006 Korea Republic of

Wireless sensor network (WSN) comprises numerous compact-sized sensor nodes which are linked to one another. Lifetime maximization of WSN is considered a challenging problem in the design of WSN since its energy-limited capacity of the inbuilt batteries exists in the sensor nodes. Earlier works have focused on the design of clustering and routing techniques to accomplish energy efficiency and thereby result in an increased lifetime of the network. The multihop route selection process can be treated as an NP-hard problem and can be solved by the use of computational intelligence techniques such as fuzzy logic and swarm intelligence (SI) algorithms. With this motivation, this article aims to focus on the design of swarm intelligence with an adaptive neuro-fuzzy inference system-based routing (SI-ANFISR) protocol for clustered WSN. The proposed SI-ANFISR technique aims to determine the cluster heads (CHs) and optimal routes for multihop communication in the network. To accomplish this, the SI-ANFISR technique primarily employs a weighted clustering algorithm to elect CHs and construct clusters. Besides, the SI-ANFISR technique involves the design of an ANFIS model for the selection process, which make use of three input parameters, namely, residual energy, node degree, and node history. In order to optimally adjust the membership function (MF) of the ANFIS model, the squirrel search algorithm (SSA) is utilized. None of the earlier works have used ANFIS with SSA for the routing process. The design of SSA to tune the MFs of the ANFIS model for optimal routing process in WSN shows the novelty of the study. The experimental validation of the SI-ANFISR technique takes place, and the results are inspected under different aspects. The simulation results highlighted the significant performance of the SI-ANFISR technique compared to the recent techniques with a maximum throughput of 43838 kbps and residual energy of 0.4800J, respectively. © 2022 Feras Mohammed A-Matarneh et al.

关键词： Routing protocols

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Semantics, Web and Mining 1

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丛书名： Lecture Notes in computer Science

1000年

作者： Markus Ackermann Bettina Berendt Marko Grobelnik Andreas Hotho Dunja Mladenič Giovanni Semeraro Myra Spiliopoulou Gerd Stumme Vojtěch Svátek Maarten Someren

ISBN: (数字)9783540476986

ISBN: (纸本)9783540476979

Finding knowledge – or meaning – in data is the goal of every knowledge d- covery e?ort. Subsequent goals and questions regarding this knowledge di?er amongknowledgediscovery(KD) projectsandapproaches. Onecentralquestion is whether and to what extent the meaning extracted from the data is expressed in a formal way that allows not only humans but also machines to understand and re-use it, i. e. , whether the semantics are formal semantics. Conversely, the input to KD processes di?ers between KD projects and approaches. One central questioniswhetherthebackgroundknowledge,businessunderstanding,etc. that the analyst employs to improve the results of KD is a set of natural-language statements, a theory in a formal language, or somewhere in between. Also, the data that are being mined can be more or less structured and/or accompanied by formal semantics. These questions must be asked in every KD e?ort. Nowhere may they be more pertinent, however, than in KD from Web data (“Web mining”). Thisis due especially to the vast amounts and heterogeneity of data and ba- ground knowledge available for Web mining (content, link structure, and - age), and to the re-use of background knowledge and KD results over the Web as a global knowledge repository and activity space. In addition, the (Sem- tic) Web can serve as a publishing space for the results of knowledge discovery from other resources, especially if the whole process is underpinned by common ontologies.

关键词： Artificial Intelligence computer Communication Networks database Management Information Storage and Retrieval Information Systems Applications (incl. Internet) computers and Society

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 6

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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6th International Conference on Advances in Artificial Intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms' cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy. © 2022 Association for Computing Machinery.

关键词： Ethanol

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A Decision-Dependent Chance-Constrained Planning Model for Distribution Networks Under Extreme Weather Events

A Decision-Dependent Chance-Constrained Planning Model for D...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Anping Zhou Hongbo Sun Shoichi Kitamura Daniel Nikovski Dept. of Electrical and Computer Engineering Southern Methodist University Dallas TX USA Advanced Technology R&D Center Mitsubishi Electric Corporation Amagasaki Japan Data Analytics Group Mitsubishi Electric Research Laboratories Cambridge MA USA

Extreme weather events have posed tremendous challenges to the operation of distribution networks. In this paper, we propose a decision-dependent chance-constrained model for the optimal planning of diesel generators, renewable distributed generations (RDGs), energy storage systems, and switches under contingency. A promising moment-based ambiguity set that incorporates the information of decision variables is employed to depict the uncertainty arising from RDGs. By leveraging effective approximation methods such as the Bonferroni approximation method to handle the considered joint chance constraints, the proposed model is transformed into a tractable mixed-integer second-order conic programming problem, which means it can easily be implemented. Numerical experiments are put forward on the IEEE 33-bus test system to validate the effectiveness of the developed approach.

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Speech Synthesis with Mixed Emotions

arXiv

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arXiv 2022年

作者： Zhou, Kun Sisman, Berrak Rana, Rajib Schuller, Björn W. Li, Haizhou Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Electrical and Computer Engineering the University of Texas Dallas United States University of Southern Queensland Australia GLAM – the Group on Language Audio & Music Imperial College London United Kingdom Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany Shenzhen Research Institute of Big Data the School of Data Science The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore University of Bremen Bremen Germany

Emotional speech synthesis aims to synthesize human voices with various emotional effects. The current studies are mostly focused on imitating an averaged style belonging to a specific emotion type. In this paper, we seek to generate speech with a mixture of emotions at run-time. We propose a novel formulation that measures the relative difference between the speech samples of different emotions. We then incorporate our formulation into a sequence-to-sequence emotional text-to-speech framework. During the training, the framework does not only explicitly characterize emotion styles but also explores the ordinal nature of emotions by quantifying the differences with other emotions. At run-time, we control the model to produce the desired emotion mixture by manually defining an emotion attribute vector. The objective and subjective evaluations have validated the effectiveness of the proposed framework. To our best knowledge, this research is the first study on modelling, synthesizing, and evaluating mixed emotions in speech. © 2022, CC BY.

关键词： Speech synthesis

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data reinforcement control technique-based monitoring and controlling of environmental factors for IoT applications

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Arabian Journal of Geosciences 2022年第7期15卷 1-8页

作者： Mehbodniya, Abolfazl Haq, Mohd Anul Kumar, Anil Ismail, Mohd Erfy Dahiya, Priyanka Karupusamy, Sathishkumar Department of Electronics and Communications Engineering Kuwait College of Science and Technology (KCST) Doha Area Kuwait Department of Computer Science College of Computer and Information Sciences Majmaah University AlMajmaah Saudi Arabia Data Science Research Group School of Computing DIT University Dehradun India Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Gobi Arts & Science College Gobichettipalayam India

In recent years, environmental monitoring is essential to minimize environmental problems. However, the need for more and more aquatic ecosystems from pollution, increasing climate change, and direct environment loss, so that they can continue to be thoroughly monitored for risks. In this system, the implementation of a data reinforcement control technique (DRCT) using the Internet of Things (IoT) systems is presented to aquatic inquiry environments. In particular, it describes an automated system using data from the design and implementation of recording environments that monitor sensors and installation parameters in lakes and rivers. Design and low-cost real-time monitoring, alert worker development, polluted notification data are confirmed around. Various parameters such as air quality, temperature, humidity, and PIC controller sound intensity sensor, collection, and upload data to the cloud using the Wi-Fi module. It then forwards to a digital dashboard for confirmation on the web via a smartphone or cloud platform exception notification to alert the user. The industry will be the power of closed industrial electricity if they do not take any steps to reduce pollution within a specified period until they are paid under government regulations and pollution control and the amount of pollution in the city. The proposed system is simulated in the PROTEUS 8.1 simulator to analyze the system output.

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Delay-Induced Watermarking for Detection of Replay Attacks in Linear Systems

Delay-Induced Watermarking for Detection of Replay Attacks i...

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American Control Conference (ACC)

作者： Christoforos Somarakis Raman Goyal Erfaun Noorani Shantanu Rane Data Science and Applied Mathematics Group Merck & Co West Point PA USA Palo Alto Research Center - An SRI Company Palo Alto CA USA Department of Electrical and Computer Engineering the Institute for System Research (ISR) at the University of Maryland College Park College Park MD USA A. James Clark School of Engineering

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

A state-feedback watermarking signal design for the detection of replay attacks in linear systems is proposed. The control input is augmented with a random time-delayed term of the system state estimate, in order to secure the system against attacks of replay type. We outline the basic analysis of the closed-loop response of the state-feedback watermarking in a LQG controlled system. Our theoretical results are applied on a temperature process control example. While the proposed secure control scheme requires very involved analysis, it, nevertheless, holds promise of being superior to conventional, feed-forward, watermarking schemes, in both its ability to detect attacks as well as the secured system performance.

关键词： Linear systems System performance Process control Watermarking Control systems Temperature control Signal design

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Day-ahead electricity price forecasting using WPT, VMI, LSSVM-based self adaptive fuzzy kernel and modified HBMO algorithm

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Scientific Reports 2021年第1期11卷 1-21页

作者： Syah, Rahmad Rezaei, Mohammad Elveny, Marischa Majidi Nezhad, Meysam Ramdan, Dadan Nesaht, Mehdi Davarpanah, Afshin Data Science & Computational Intelligence Research Group Universitas Medan Area Medan Indonesia Computer Science and Engineering Department University of Texas at Arlington Arlington United States Data Science & Computational Intelligence Research Group Universitas Sumatera Utara Medan Indonesia Department of Astronautics Electrical and Energy Engineering (DIAEE) Sapienza University of Rome Rome 00184 Italy DS & CI Research Group Universitas Medan Area Medan Indonesia Optimisation and Logistics Group School of Computer Science University of Adelaide Adelaide Australia Department of Mathematics Aberystwyth University Aberystwyth SY23 3FL United Kingdom

Due to focal liberality in electricity market projection, researchers try to suggest powerful and successful price forecasting algorithms. Since, the accurate information of future makes best way for market participants so as to increases their profit using bidding strategies, here suggests an algorithm for electricity price anticipation. To cover this goal, separate an algorithm into three steps, namely;pre-processing, learning and tuning. The pre-processing part consists of Wavelet Packet Transform (WPT) to analyze price signal to high and low frequency subseries and Variational Mutual Information (VMI) to select valuable input data in order to helps the learning part and decreases the computation burden. Owing to the learning part, a new Least squares support vector machine based self-adaptive fuzzy kernel (LSSVM-SFK) is proposed to extract best map pattern from input data. A new modified HBMO is introduced to optimally set LSSVM-SFK variables such as bias, weight, etc. To improve the performances of HBMO, two modifications are proposed that has high stability in HBMO. Suggested forecasting algorithm is examined on electricity markets that has acceptable efficiency than other models. © 2021, The Author(s).

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Introduction

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2016年 9792 LNCS卷 1-8页

作者： González-Beltrán, Alejandra Osborne, Francesco Peroni, Silvio Oxford e-Research Centre University of Oxford Oxford United Kingdom Data Science Group Knowledge Media Institute The Open University Milton Keynes United Kingdom Digital and Semantic Publishing Laboratory Department of Computer Science and Engineering University of Bologna Bologna Italy

We provide the motivation and overview of the SAVE-SD workshop series and introduce the manuscripts that were accepted as part of these proceedings of the SAVE-SD 2016 edition. © Springer International Publishing...

ISBN: (纸本)9783319536361

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Using mapping languages for building legal knowledge graphs from XML files?

arXiv

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arXiv 2019年

作者： Junior, Ademar Crotti Orlandi, Fabrizio O’Sullivan, Declan Dirschl, Christian Reul, Quentin ADAPT Centre for Digital Content Platform Research Knowledge & Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Dublin 2 Ireland Wolters Kluwer Deutschland Munchen Germany Wolters Kluwer N.V. Chicago United States

This paper presents our experience on building RDF knowledge graphs for an industrial use case in the legal domain. The information contained in legal information systems are often accessed through simple keyword interfaces and presented as a simple list of hits. In order to improve search accuracy one may avail of knowledge graphs, where the semantics of the data can be made explicit. Significant research effort has been invested in the area of building knowledge graphs from semi-structured text documents, such as XML, with the prevailing approach being the use of mapping languages. In this paper, we present a semantic model for representing legal documents together with an industrial use case. We also present a set of use case requirements based on the proposed semantic model, which are used to compare and discuss the use of state-of-the-art mapping languages for building knowledge graphs for legal data. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping

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