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2021 IEEE EMBS International Conference on Biomedical and Health informatics, BHI 2021

作者： Elshatoury, Heba Cruciani, Federica Zumerle, Francesco Storti, Silvia F. Altmann, André Lorenzi, Marco Anbarjafari, Gholamreza Menegaz, Gloria Galazzo, Ilaria Boscolo Institute of Technology University of Tartu Tartu Estonia Dept. Computer Science University of Verona Verona Italy Dept. Medical Physic and Biomedical Engineering University College London London United Kingdom Epione Research Project Inria Sophia Antipolis Nice France

ISBN: (纸本)9781665403580

Despite the increasing effort being devoted to the investigation of the link between imaging endophenotypes (IDPs) and genetic determinants (GDs) in Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD), many issues remain open and deserve investigation. Among these, the role of functional connectivity (FC) is still blurred. This paper aims at shading some light on the topic relying on the ADNI repository (177 patients, out of which 82 MCI and 95 controls). The within/between-network connectivities were derived from individual FC matrices and used as IDPs. Conversely, the GDs consisted of two Polygenic Risk Scores (PRS) that have recently been proven to play a role in AD. A Partial Least Squares (PLS) model equipped with LASSO regularization was finally fitted to the data for associating IDPs and GDs. In the first component, all FC coefficients had the same sign, and were correlated with PRS2. Connectivities involving the dorsal attention (DAN) and frontoparietal control (CON) networks reached the highest weights, while within/between-network FC for the limbic (LIM) were less represented. Overall, the within-network FC values were less pronounced compared to the between-network ones. In the second component, most of the FC features had zero weights. Visual (VIS) and somatomotory (SMN) showed a correlated trend, while being anti-correlated with LIM, CON and default mode network as well as with PRS1. Our findings suggest that the two PRSs correlated with a possible pattern of aberrant within/between-network FC changes occurring in RSNs devoted to higher cognitive functions and more vulnerable in this pathology. © 2021 IEEE

关键词： Least squares approximations

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Coherent VCSEL Network Computing 27

Coherent VCSEL Network Computing

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27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and Computing, OECC/PSC 2022

作者： Chen, Zaijun Sludds, Alexander Davis, Ronald Christen, Ian Ateshian, Lamia Bernstein, Liane Heuser, Tobias Heermeier, Niels Lott, James Reitzenstein, Stephan Hamerly, Ryan Englund, Dirk Massachusetts Institute of Technology 77 Massachusetts Ave. CambridgeMA02139 United States Ming Hsieh Dept. of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States Fakultät Ii Institut für Festkörperphysik Sekretariat Technische Universität Berlin Berlin10623 Germany Ntt Research Inc. Physics & Informatics Laboratories SunnyvaleCA94085 United States

ISBN: (数字)9784885523366

ISBN: (纸本)9784885523366

关键词： Deep learning

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Techno-economic analysis of cognitive radio models in 5G networks 17

Techno-economic analysis of cognitive radio models in 5G net...

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17th International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2020

作者： Bouras, Christos Kollia, Anastasia Papazois, Andreas Computer Technology Institute and Press Diophantus PatrasP.C. 26504 Greece Computer Engineering and Informatics Dept. University of Patras PatrasP.C. 26504 Greece

The first commercial products of 5G will be released within 2020 and therefore, it becomes an absolute necessity to research whether the key enabling technologies are advantageous for the operators to invest in. One of the most fundamental technologies Cognitive Radio (CR), since there is not much research in the field. This paper develops a techno-economic framework for the CR technology and is contrasted with an already existing model for Software Defined Networks (SDN). A Sensitivity Analysis (SA) indicates the cost parameters that are the most expensive ones and should be reconsidered for the model's wide adoption and viability. © 2020 The Authors.

关键词： Cognitive radio

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Artificial Intelligence Oriented User Sentiment Evaluation System on Social Networks using Modified Deep Learning Principles

Artificial Intelligence Oriented User Sentiment Evaluation S...

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Mobile Computing and Sustainable informatics (ICMCSI), International Conference on

作者： S. Thangamayan Sandip Nivrutti Jagdale T R.Kalai Lakshmi Jayendra Gopal Thatipudi Penyameen. K Bhola Khan Department of Research and Development Saveetha School of Law Saveetha Institute of Medical and Technical Sciences Chennai Yashwantrao Mohite Institute of Management Karad Bharati Vidyapeeth(Deemed to be University) School Of Management Studies Sathyabama Institute Of Science And Technology Chennai Dept. of Computer Science University of North Texas Velammal College Of Engineering And Technology Madurai Department of Regional Economics MJP Rohilkhand University Bareilly India

Sentiment analysis, a pivotal aspect of natural language processing, is vital for deciphering the emotional nuances embedded in textual data. This project introduces an innovative workflow that amalgamates various techniques to elevate sentiment analysis performance. The workflow encompasses Fuzzy C-means (FCM) clustering for pattern identification, Differential Multi-layer Whale Optimization (DWO) for feature extraction, and a classification phase utilizing diverse deep learning models, including Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN). Additionally, Ant Lion Optimization (ALO) ensures system optimization, fostering robustness and adaptability. The proposed system is evaluated against eight distinct deep learning models, each offering unique architectures or configurations. Remarkably, the proposed workflow achieves an impressive accuracy of 96%, underscoring its efficacy in discerning complex sentiments. This high level of accuracy positions the system as a powerful tool for real-world applications where nuanced understanding of human sentiment is paramount. The project contributes to the evolving landscape of sentiment analysis, providing a comprehensive framework for extracting valuable insights from diverse textual datasets.

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Network security analysis for signal strength based packet filitering 3

Network security analysis for signal strength based packet f...

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3rd International Conference on Intelligent Sustainable Systems, ICISS 2020

作者： Sundaram, B. Barani Rajkumar, P. Ananthi, M. Sravan Kumar, D. Vijaykumar, Janga Karthika, P. Bule Hora University College of Informatics Department of Computer Science Bule hora Ethiopia KGiSL Institute of Technology Dept of Computer Science and Engineering Coimbatore Tamilnadu India Institute of Technology Dept of IT KGiSL Coimbatore Tamilnadu India Balaji Institute of Technology Science Dept of CSE Warangal India Bule Hora University College of Informatics Dept. of Information Technology Bule Hora144 Ethiopia Kalasalingam Academy of Research and Education Department of Computer Applications TamilNadu India

ISBN: (纸本)9781728170893

Signal Strength based position check is all the more encouraging when contrasted with other Sybil assault discovery methods since it is lightweight and doesn't need centralization. Hubs can locally decide their areas through got signal quality varieties and illuminate neighbors about area refreshes likewise. In position confirmation, network hubs check the situation of every hub and further guarantee that each actual area is limited by just a single character at a specific time. This paper will overview the Sybil assault recognition methods with their connected problem. At last will introduce in thoughts regarding the Sybil assault discovery through one-time restriction (just the passage into organization) with fundamentally decreases correspondence in the clouds dissimilar to different plans where per parcel area following or intermittent spread of area data is utilized to distinguish Sybil assaults. © 2020 IEEE.

关键词： Network security

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Accelerating deep neural networks for efficient scene understanding in automotive cyber-physical systems

arXiv

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arXiv 2021年

作者： Nousias, Stavros Pikoulis, Erion Vasilis Mavrokefalidis, Christos Lalos, Aris S. Industrial Systems Institute Athena Research Center Patras Science Park Greece Computer Engineering Informatics Dept. University of Patras Greece

Automotive Cyber-Physical Systems (ACPS) have attracted a significant amount of interest in the past few decades, while one of the most critical operations in these systems is the perception of the environment. Deep learning and, especially, the use of Deep Neural Networks (DNNs) provides impressive results in analyzing and understanding complex and dynamic scenes from visual data. The prediction horizons for those perception systems are very short and inference must often be performed in real time, stressing the need of transforming the original large pre-trained networks into new smaller models, by utilizing Model Compression and Acceleration (MCA) techniques. Our goal in this work is to investigate best practices for appropriately applying novel weight sharing techniques, optimizing the available variables and the training procedures towards the significant acceleration of widely adopted DNNs. Extensive evaluation studies carried out using various state-of-the-art DNN models in object detection and tracking experiments, provide details about the type of errors that manifest after the application of weight sharing techniques, resulting in significant acceleration gains with negligible accuracy losses. © 2021, CC BY.

关键词： Deep neural networks

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Chaotic American zebra search optimization algorithm for benchmark challenges

Chaotic American zebra search optimization algorithm for ben...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Vikram Kumar Kamboj Ardhala Bala Krishna Chaman Verma Maria Simona Raboaca Constantin Filote Maria Stepanova Schulich School of Engineering University of Calgary Alberta Canada Dept. of Electrcical and Electronics Engineering Malla Reddy Engineering College for Women Hyderabad India Dept. of Media and Edu. Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Suceava Romania Engineering Business and Management Faculty Bauman Moscow State Technical University Russia

In this research, we presented a modification to the recently developed swarm premised American zebra search optimization algorithm (AZOA) using chaos. The particular Singer mapping was proposed by the so-called chaotic AZOA and was already known to perform better in optimization. Twelve uni, multi modal benchmark functions, three-bar truss and ten bar truss optimal structural designs were tested. The chaotic AZOA with Singer map would perform more effectively, more consistently, and quicker than the classical AZOA and other recent metaheuristics in optimization, according to the results, which also supported the viability of the modifications. It was discussed if the chaotic AZAO could be optimized with the original AZOA, and the chaotic AZOA was suggested for use in applications for actual engineering challenges.

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The state-of-the-art review on resource allocation problem using artificial intelligence methods on various computing paradigms

arXiv

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arXiv 2022年

作者： Joloudari, Javad Hassannataj Mojrian, Sanaz Saadatfar, Hamid Nodehi, Issa Fazl, Fatemeh Shirkharkolaie, Sahar Khanjani Alizadehsani, Roohallah Dipu Kabir, H.M. Tan, Ru-San Rajendra Acharya, U. Department of Computer Engineering Faculty of Engineering University of Birjand Birjand*** Iran Department of Information Technology Mazandaran University of Science and Technology Babol Iran Department of Computer Engineering Qom University of Technology Qom Iran Department of Electronic Engineering Faculty of Electrical and Computer Engineering University of Birjand Birjand Iran Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC3216 Australia School of Science and Technology Singapore University of Social Sciences Singapore Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

With the increasing growth of information through smart devices, increasing the quality level of human life requires various computational paradigms presentation including the Internet of Things, fog, and cloud. Between these three paradigms, the cloud computing paradigm as an emerging technology adds cloud layer services to the edge of the network so that resource allocation operations occur close to the end-user to reduce resource processing time and network traffic overhead. Hence, the resource allocation problem for its providers in terms of presenting a suitable platform, by using computational paradigms is considered a challenge. In general, resource allocation approaches are divided into two methods, including auction-based methods (goal, increase profits for service providers-increase user satisfaction and usability) and optimization-based methods (energy, cost, network exploitation, Runtime, reduction of time delay). In this paper, according to the latest scientific achievements, a comprehensive literature study (CLS) on artificial intelligence methods based on resource allocation optimization without considering auction-based methods in various computing environments are provided such as cloud computing, Vehicular Fog Computing, wireless, IoT, vehicular networks, 5G networks, vehicular cloud architecture, machine-to-machine communication (M2M), Train-to-Train(T2T) communication network, Peer-to-Peer (P2P) network. Since deep learning methods based on artificial intelligence are used as the most important methods in resource allocation problems;Therefore, in this paper, resource allocation approaches based on deep learning are also used in the mentioned computational environments such as deep reinforcement learning, Q-learning technique, reinforcement learning, online learning, and also Classical learning methods such as Bayesian learning, Cummins clustering, Markov decision process. © 2022, CC BY-SA.

关键词： Reinforcement learning

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Topological modes observed in Si photonics SSH integrated circuit

Topological modes observed in Si photonics SSH integrated ci...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO/PR 2022

作者： Nakamura, R. Nakama, T. Balčytis, A. Ozawa, T. Ota, Y. Iwamoto, S. Ito, H. Baba, T. Dept of Electrical & Computer Eng. Yokohama National Univ. 79-5 Tokiwadai Hodogaya-ku Yokohama240-8501 Japan School of Engineering RMIT University MelbourneVIC3000 Australia Advanced Institute for Materials Research Tohoku Univ. Sendai980-8577 Japan Dept. of Applied Physics and Physico-Informatics Keio Univ. 3-14-1 Hiyoshi Kohoku-ku Yokohama223-8522 Japan Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan

We fabricated a sophisticated Si photonic integrated circuit for investigating topological photonics SSH model. In the selective excitation and observation of SSH coupled microrings, we observed the wavefunctions of e... 详细信息

关键词： Timing circuits

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Topological modes observed in Si photonics SSH integrated circuit

Topological modes observed in Si photonics SSH integrated ci...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2022

作者： Nakamura, R. Nakama, T. Balčytis, A. Ozawa, T. Ota, Y. Iwamoto, S. Ito, H. Baba, T. Dept of Electrical &Computer Eng. Yokohama National Univ. 79-5 Tokiwadai Hodogaya-ku Yokohama240-8501 Japan School of Engineering RMIT University MelbourneVIC3000 Australia Advanced Institute for Materials Research Tohoku Univ. Sendai980-8577 Japan Dept. of Applied Physics and Physico-Informatics Keio Univ. 3-14-1 Hiyoshi Kohoku-ku Yokohama223-8522 Japan Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan

ISBN: (纸本)9798350350012

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