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2010 IEEE International Conference on Intelligent Computing and Intelligent systems, ICIS 2010

作者： Xu, Mingdi Zhao, Heng Li, Junlin Zhang, Huanguo System Software Department Wuhan Digital Engineering Institute Wuhan China School of Computer Wuhan University Wuhan China

ISBN: (纸本)9781424465835

As to distributed processing environment, the intractable problem is that process's current state is indeterminate, which could bring some security issues such as diffusing action. We put forward a checkpoint of integrity measurement (CPIM) arithmetic. The CPIM arithmetic could control process's state when process encounters an integrity measurement checkpoint, which would solve the problem that the running state of parallel software is indeterminate. The virtue of CPIM arithmetic is simple and low-cost. We also provide implementation scheme and prototype for evaluating overhead and performance. ©2010 IEEE.

关键词： Integrity measuremtn Recovery Rollback

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Safety concerns on operating UAV using cooperative multiagent negotiation

Safety concerns on operating UAV using cooperative multiagen...

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作者： Corrêa, Mário A. Camargo Jr., João B. Gimenes, Ricardo A.V. Almeida Jr., Jorge R. Safety Analysis Group of Computer Digital System Engineering Department University of São Paulo São Paulo Brazil

ISBN: (纸本)8587978128

Abstract Developed countries have made significant efforts to integrate UAV operations in controlled aerial space due to a rising interest of using Unmanned Aerial Vehicles (UAV) for civilian purposes. This study focuses the use of multi-agent architecture to distribute the workload of detecting and solving conflicts between the aircraft and the UAV with no intervention from ground-based facilities. Midair collision risk was the main unsafe situation evaluated using an aircraft collision model based on air traffic density data. Copyright © 2006 IFAC.

关键词： Air traffic control

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Metadata in digital video repositories: An analysis on author-generated metadata

Metadata in digital video repositories: An analysis on autho...

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2010 International Conference on Information Society, i-Society 2010

作者： Dantas, Joana Socrates Silveira, Regina Melo Ruggiero, Wilson Vicente LARC-Laboratory of Computer Architecture and Networks Department of Computer and Digital System Engineering Escola Politécnica da Universidade São Paulo Brazil

ISBN: (纸本)9780956426338

The growth of digital content generation and its access by the general public have, for the past few years, promoted the importance of resources' metadata, and enhanced the debate on their generation and quality. The improvement of new technologies and users needs have led to new metadata generation practices, which include the author-generated metadata. By studying the current literature on the subject we analyzed the author-generated metadata quality used on a given IPTV service. From the results of this study we propose an alternative methodology for insertion of metadata by the service user, where an auto-complete engine is applied aiming to enhance the system's metadata quality. © 2010 i-Society.

关键词： Metadata

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Cuckoo-based malware dynamic analysis

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International Journal of Performability engineering 2019年第3期15卷 772-781页

作者： Wang, Lele Wang, Binqiang Liu, Jiangang Miao, Qiguang Zhang, Jianhui National Digital Switching System Engineering and Technological Research Center Zhengzhou450002 China Nanjing Information Technology Institute Nanjing210000 China Department of Computer Science Xidian University Xi'an710071 China

Aiming at the problems of the huge number of malware currently in the big data environment, the insufficient ability of automatic malware analysis available, and the inefficiency of the classification of malicious attributes, in this paper, we propose a Cuckoo-based malware dynamic analysis system that can be extended, analyzed quickly, and has application value. The system proposes a semantic feature model based on deep learning, uses a deep recursive neural network model to describe the multi-layered aggregation relationship of program semantics, and builds a malware semantic aggregation model. The model can automatically complete the acquisition and analysis of behavioural features of unknown program samples and perform attribute discrimination on unknown program samples efficiently and accurately. © 2019 Totem Publisher, Inc.

关键词： Semantics

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Cloud-based ecosystem proposal for applying an intelligent virtual assistant in online education 2

Cloud-based ecosystem proposal for applying an intelligent v...

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2nd Sustainable Cities Latin America Conference, SCLA 2021

作者： De Armas De Armas, Claudia Lopez, Fernando L. Gutierrez Kofuji, Sergio Takeo Bressan, Graca Tori, Romero Universidade de Sao Paulo Department of Electronic System Engineering Sao Paulo Brazil Universidade de Sao Paulo Department of Computer Engineering and Digital Systems Sao Paulo Brazil

ISBN: (纸本)9781665437684

Intelligent Virtual Assistants (IVA) or advanced chatbots are bringing many benefits in services. Achieving the robot-man interaction in an effective and affective way is not an easy task. However, it is possible to arrive at a model with the existing technological advances to create this expected interaction. In education, specifically in online learning, there are problems with student retention and learning effectiveness. Considering the problems presented, it is possible to use the great potential of IV As to support students and teachers during these courses and avoid some usual challenges. There are some platforms and services in the cloud with tools ready for the development of these IV As. Hence, this article aims to design a modular ecosystem for an IVA applied in education. Also, do a comparative study on platforms and services in the cloud: Amazon Web Service, Google, and Microsoft. The objective is to select the most complete and feasible according to our needs: Developing and hosting an IVA that allows interaction via text and user voice. As a result, the Microsoft service was selected. With the proposal of infrastructures, technologies, artificial intelligence elements, and technical direction was developed a modular ecosystem. © 2021 IEEE.

关键词： Ecosystems

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Deep Learning for Multivariate Prediction of Building Energy Performance of Residential Buildings

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computers, Materials & Continua 2023年第6期75卷 5947-5964页

作者： Ibrahim Aliyu Tai-Won Um Sang-Joon Lee Chang Gyoon Lim Jinsul Kim Department of ICT Convergence System Engineering Chonnam National UniversityGwangju61186Korea Graduate School of Data Science Chonnam National UniversityGwangju61186Korea Interdisciplinary Program of Digital Future Convergence Service Chonnam National UniversityGwangju61186Korea Department of Computer Engineering Chonnam National UniversityYeosu59626Korea

In the quest to minimize energy waste,the energy performance of buildings(EPB)has been a focus because building appliances,such as heating,ventilation,and air conditioning,consume the highest ***,effective design and planning for estimating heating load(HL)and cooling load(CL)for energy saving have become *** this vein,efforts have been made to predict the HL and CL using a univariate ***,this approach necessitates two models for learning HL and CL,requiring more computational ***,the one-dimensional(1D)convolutional neural network(CNN)has gained popularity due to its nominal computa-tional complexity,high performance,and low-cost hardware *** this paper,we formulate the prediction as a multivariate regression problem in which the HL and CL are simultaneously predicted using the 1D *** the building shape characteristics,one kernel size is adopted to create the receptive fields of the 1D CNN to extract the feature maps,a dense layer to interpret the maps,and an output layer with two neurons to predict the two real-valued responses,HL and *** the 1D data are not affected by excessive parameters,the pooling layer is not applied in this ***,the use of pooling has been questioned by recent *** performance of the proposed model displays a comparative advantage over existing models in terms of the mean squared error(MSE).Thus,the proposed model is effective for EPB prediction because it reduces computational time and significantly lowers the MSE.

关键词： Artificial intelligence(AI) convolutional neural network(CNN) cooling load deep learning energy energy load energy building performance heating load prediction

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Design and optimization on dynamic power system for self-powered integrated wireless sensing nodes

Design and optimization on dynamic power system for self-pow...

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2005 International Symposium on Low Power Electronics and Design

作者： Ma, Dongsheng Wang, Janet M. Somasundaram, Mohankumar N. Hu, Zongqi Integrated System Design Laboratory Electrical and Computer Engineering Department University of Arizona Tucson AZ 85721 United States Digital VLSI Design Laboratory Electrical and Computer Engineering Department University of Arizona Tucson AZ 85721 United States

This paper presents an integrated power system for low-power wireless sensor networks with dynamic efficiency optimization technique. By adaptively resizing power transistors and adjusting switching frequency, system efficiency is enhanced significantly. Theoretical analysis is elaborated to support the proposed technique. A prototype integrated power system for self-powered photovoltaic wireless sensing node was designed and simulated with TSMC 0.35μm CMOS process. With a power range of 0.5μW to 10mW, power efficiency stays above 71%. Tolerance between theoretical and simulated optimal power transistor sizes is less than 6.7%, while that of optimal switching frequencies is less than 5%. The paper gives another solution to minimizing system power in the perspective of power processing. Copyright 2005 ACM.

关键词： Power electronics

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Automated Detection of Harmful Insects in Agriculture: A Smart Framework Leveraging IoT, Machine Learning, and Blockchain

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4787-4798页

作者： Rahman, Wahidur Hossain, Muhammad Minoar Hasan, Md. Mahedi Iqbal, Md. Sadiq Rahman, Mohammad Motiur Fida Hasan, Khondokar Moni, Mohammad Ali Mawlana Bhashani Science and Technology University Department of Computer Science and Engineering Tangail1902 Bangladesh Uttara University Department of Computer Science and Engineering Dhaka1230 Bangladesh Prime University Department of Management and Information System Mirpur Dhaka1216 Bangladesh Bangladesh University Department of Computer Science and Engineering Dhaka1207 Bangladesh School of Professional Studies CanberraNSW2052 Australia Artificial Intelligence and Cyber FuturesInstitute Charles Sturt University AI & Digital Health Technology BathurstNSW2795 Australia

Paddy cultivation is a significant global economic sector, with rice production playing a crucial role in influencing worldwide economies. However, insects in paddy farms predominantly impact the growth rate and ecological equilibrium of the agricultural field. Hence, the precise and timely identification of insects in agricultural settings presents a potential strategy for addressing this issue. This study aims to implement an automated system for paddy farming by employing a real-time framework that incorporates the Internet of Things (IoT), blockchain technology, and Deep Learning (DL) algorithms. The primary emphasis of the DL-based system is on the timely identification of pests. In contrast, integrating the IoT and blockchain technologies facilitates stablishing a fully automated system with security within the agricultural domain. The DL-based system includes a secondary dataset of paddy insects, and then preprocessing, feature extraction, and identification have been performed. Besides, an IoT-based system is embodied with a camera module and microprocessor, accompanied by some apparatus required to automate the whole system. In addition, the research also includes the blockchain to secure each individual data transmission among the several IoT components and the cloud server. While examining the proposed solution, various experimental data have been systematically documented and analyzed. The proposed framework attained a peak accuracy of 98.91% using the VGG19 model and ensemble classifiers to detect the pest with a specificity of 99.14% and a precision of 98.21%. The study additionally quantifies the mean duration of the cloud response when integrated with IoT, yielding an average time of 1.71 s after pest identification. Nevertheless, the system has exhibited a high level of efficacy in the context of real-time monitoring and automation of paddy farms. © 2020 IEEE.

关键词： Blockchain

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Enhancing Parkinson’s Disease Prediction Using Machine Learning and Feature Selection Methods

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computers, Materials & Continua 2022年第6期71卷 5639-5657页

作者： Faisal Saeed Mohammad Al-Sarem Muhannad Al-Mohaimeed Abdelhamid Emara Wadii Boulila Mohammed Alasli Fahad Ghabban College of Computer Science and Engineering Taibah UniversityMedina41477Saudi Arabia School of Computing and Digital Technology Birmingham City UniversityBirminghamB47XGUnited Kingdom Information System Department Saba’a Region UniversityMareebYemen Computers and Systems Engineering Department Al-Azhar UniversityCairo11884Egypt RIADI Laboratory National School of Computer SciencesUniversity of ManoubaManouba2010Tunisia

Several millions of people suffer from Parkinson’s disease ***’s affects about 1%of people over 60 and its symptoms increase with *** voice may be affected and patients experience abnormalities in speech that might not be noticed by listeners,but which could be analyzed using recorded speech *** the huge advancements of technology,the medical data has increased dramatically,and therefore,there is a need to apply data mining and machine learning methods to extract new knowledge from this *** classification methods were used to analyze medical data sets and diagnostic problems,such as Parkinson’s Disease(PD).In addition,to improve the performance of classification,feature selection methods have been extensively used in many *** paper aims to propose a comprehensive approach to enhance the prediction of PD using several machine learning methods with different feature selection methods such as filter-based and *** dataset includes 240 recodes with 46 acoustic features extracted from3 voice recording replications for 80 *** experimental results showed improvements when wrapper-based features selection method was used with K-NN classifier with accuracy of 88.33%.The best obtained results were compared with other studies and it was found that this study provides comparable and superior results.

关键词： Filter-based feature selection methods machine learning parkinson’s disease wrapper-based feature selection methods

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system for coronavirus classification based on a new textural descriptor 21

System for coronavirus classification based on a new textura...

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21st IEEE International Conference on Sciences and Techniques of Automatic Control and computer engineering, STA 2022

作者： Boubakri, Fathia Gargouri, Norhene Masmoudi, Alima Damak Sfax Tunisia Digital Research Center of Sfax Technopole of Sfax Computer Imaging and Electronic System Group Tunisia Sfax Engineering School University of Sfax Computer Imaging and Electronic System Group CEM Laboratory Department of Electrical Engineering P.O. Box 1169 Sfax3038 Tunisia

ISBN: (纸本)9781665482615

The combination of Chest X-Ray imaging and Artificial Intelligence (AI) has proven its efficiency in coronavirus disease (COVID-19) detection [1]. The present paper proposes an efficient COVID-19 detection system based on a new textural features descriptor: Monogenic Local Binary Pattern Variance (MLBPV). An Artificial Neural Network (ANN) model is used for Regions Of Interest (ROIs) classification. Evaluating MLBPV, it outperforms other tested models by achieving an Area Under Curve (A-{z}) of 0.96263 and an accuracy of 99.9805%. Comparing our method with previous ones proves that ours provides the best performance. This model may be implemented in digital X-Ray machine for radiography and help radiologists. © 2022 IEEE.

关键词： COVID-19

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