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7th computer science On-line conference, CSOC 2018

作者： Shaheen, Ameen Sleit, Azzam Al-Sharaeh, Saleh Computer Science Department King Abdullah II School for Information Technology The University of Jordan Amman11942 Jordan

ISBN: (纸本)9783319911915

Traveling Salesman Problem is a well-known NP-Hard problem, which aims at finding the shortest path between numbers of cities. Chemical Reaction Optimization (CRO) is a recently established meta-heuristic algorithm for solving optimization problems which has successfully solved many optimization problems. the main goal of this paper is to investigate the possibility of parallelizing CRO for solving the TSP problem called (PCRO). PCRO is compared with Genetic Algorithm (GA), which is a well-known meta-heuristic algorithm. Experimental results show relatively better performance for PCRO in terms of execution time, Speedup, optimal cost and Error rate. © 2019, Springer international Publishing AG, part of Springer Nature.

关键词： Traveling salesman problem

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A framework of optimizing the deployment of IoT for precision agriculture industry 24

A framework of optimizing the deployment of IoT for precisio...

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24th KES international conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020

作者： Chehri, Abdellah Chaibi, Hasna Saadane, Rachid Hakem, Nadir Wahbi, Mohamed University of Quebec in Chicoutimi QuébecG7H 2B Canada SIRC/LaGeS-EHTP EHTP Km 7 Route El Jadida Oa sis Morocco Université du Québec en Abitibi-Témiscamingue Val d'OrQCJ9P 1Y3 Canada

the massive growth of wireless communications in recent years is mostly due to new connectivity demands and advances in technology development of low power) transceivers. An example of the unique demands is the increasing exchange of data in Internet services, which has led to wireless network deployment for data transmissions. the coordination of the IoT devices, smart systems, and agriculture can contribute directly to the development of the farmer's practices by building their farm more intelligent and digital. However, enhancing farming practices requires inspecting farm equipment and farmer's experiences, which can be analyzed through the interconnectedness of IoT objects to collect farm data over the Internet to launch smart digital agriculture. It is challenging to control all farming processes (especially in real-time), this remaining as the main limitation of traditional farming. In this work, we focus on how wireless sensors can play a vital role in smart farm systems and allow processing the large amount of data generated in batches or real-time to analyze it, retrieve insights from it, and create a Smart Digital Farm. this paper proposes hierarchical-logic mapping and deployment algorithms to tackle the problem of poor network connectivity and sensing coverage in random IoT deployment. © 2020 the Authors. Published by Elsevier B.V.

关键词： Wireless sensor networks

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Enhancing energy efficiency of wireless sensor network for mining industry applications 24

Enhancing energy efficiency of wireless sensor network for m...

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24th KES international conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020

作者： Chehri, Abdellah Saadane, Rachid Hakem, Nadir Chaibi, Hasna University of Quebec in Chicoutimi QuébecG7H 2B Canada SIRC/LaGeS-EHTP EHTP Km 7 Route El Jadida Oa sis Morocco Université du Québec en Abitibi-Témiscamingue Val d'OrQCJ9P 1Y3 Canada

Recent advances in sensing modules and radio technology will enable small but smart sensors to be deployed for a wide range of environmental monitoring applications. they collect data from different environment or infrastructures in order to send them to the cloud using different communications platforms. these data can be used to provide smarter services. However, they are various issues and challenges related to the ubiquitous sensors that should be solved. In this paper we interest on analysis of wireless sensor network from an energy management perspective. the idea behind the energy-efficiency wireless sensor networks is that each node can only transmit to a limited number of other nodes directly. the limited resources of nodes imply that the transmission range is limited. In order to transfer the data to the final destination, the traffic must be relayed using intermediate nodes, creating a multi-hop route. the total energy consumption associated with an end-to-end transmission over such a route can be significantly reduced if the nodes are correctly configured. In this paper, underground mine monitoring system is presented with an overview of the related issues and challenges such as reliability, cost, and scalability. © 2020 the Authors. Published by Elsevier B.V.

关键词： Adaptive modulation

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An Optimization Algorithm for the Sale of Overage Data in Hong Kong’s Mobile Data Exchange Market 17th

An Optimization Algorithm for the Sale of Overage Data in Ho...

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17th international conference on Information technology: New Generations, ITNG 2020

作者： Blocher, Jordan Harris, Frederick C. Department of Computer Science and Engineering University of Nevada Reno RenoNV United States

ISBN: (纸本)9783030430191

Internet service providers are offering shared data plans where multiple users may purchase and share a single pool of data. In the Chinese economy, users have the ability to sell unused data on the Hong Kong Exchange Market, called "2cm", currently maintained by AT&T internet services. We propose a software-defined network for modeling this wireless data exchange market;a fully connected, pure "point of sale" market. A game-theoretical analysis identifies and defines rules for a progressive second price (PSP) auction, which adheres to the underlying market structure. We allow for a single degree of statistical freedom—the reserve price—and show that data exchange markets allow for greater flexibility in acquisition decision-making and mechanism design with an emphasis on optimization of software-defined networks. We have designed a framework to optimize this strategy space using the inherent elasticity of supply and demand. Using a game theoretic analysis, we derive a buyer-response strategy for wireless users based on second price market dynamics and prove the existence of a balanced pricing scheme. We examine shifts in the market price function and prove that the desired properties for optimization to a Nash equilibrium hold. © Springer Nature Switzerland AG 2020.

关键词： Electronic data interchange

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A Speech Recognition Mechanism for Enabling Interactions Between End-Users and Healthcare Applications 17th

A Speech Recognition Mechanism for Enabling Interactions Bet...

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17th international conference on Information technology: New Generations, ITNG 2020

作者： Couto, Henrique Lima, Ítalo Souza, Rychard Araújo, André Times, Valéria Advanced Studies in Data Science and Software Engineering Federal University of Alagoas Penedo Brazil Center for Informatics Federal University of Pernambuco Recife Brazil

ISBN: (纸本)9783030430191

the constant evolution of software development technologies has provided new interactions mechanisms for improving the usability of software systems and increasing the productivity of healthcare professionals. In this sense, speech recognition uses methods and technologies that allow the capture and transcription of spoken language automatically. However, few studies only have used health professionals to prototype and validate graphical user interfaces with speech recognition for helping in the development of healthcare applications. this paper specifies a computational solution that makes use of speech recognition to assist healthcare professionals in recording patients’ clinical care data. Six physicians have participated in our study by prototyping activities and specifying workflow to be carried out in patient care. After that, the software architecture is specified and the proposed solution, which has been implemented based on the prototyping task performed by the end users, is detailed. To evaluate the proposed solution, we have conducted interviews with health professionals and the results showed a reduction in time and effort for recording patient information. In addition, using a quantitative approach, aspects of learnability, memorability, efficiency and satisfaction were investigated, where the proposed healthcare application tool obtained an average evaluation of 88% with respect to usability. © Springer Nature Switzerland AG 2020.

关键词： Human computer interaction

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Rare lazy learning associative classification using cogency measure for heart disease prediction 4th

Rare lazy learning associative classification using cogency ...

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4th international conference on Research in Intelligent and Computing in Engineering, RICE 2019

作者： Ibrahim, S. P. Siddique Sivabalakrishnan, M. Research Scholar School of Computing Science and Engineering Vellore Institute of Technology Chennai Campus India School of Computing Science and Engineering Vellore Institute of Technology Chennai Campus India

ISBN: (纸本)9789811527791

Discovery of class association rules from the enormous amount of database is considered as a vital task in data mining. Nowadays, the development of computer technologies, for example, database management system and data storage has given the stage for gathering and dealing with a lot of information. Finding frequent class association rules combines two significant data mining techniques such as association rule mining and classification for maximum accuracy. Since this hybrid mining yields a large number of class rules during database scan, the processing time will increases significantly. Moreover, many of these rules may be irrelevant and sometimes it misses the most important rules during classifier construction. Rare lazy learning associative classification is the process of recognizing rare class rules without generating classifier construction by focusing on the useful features from patient data of the test instance which is important for rule generation. this method is profoundly appropriate for biomedical field like heart disease prediction system which requires higher accuracy. the proposed algorithm uses simple local caching mechanism on heart disease data and constructing classifier using cogency measure that will make the lazy associative classification fast and achieve higher accuracy than traditional algorithms. Experimental results show that the proposed cogency-based algorithm is more efficient than traditional algorithms for heart disease with high prediction accuracy and the proposed algorithm may also be useful in anomaly detection, fraud detection, detection of network failures and many more bio medical areas. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Association rules

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Preface

Advances in Intelligent Systems and Computing

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advances in Intelligent Systems and Computing 2021年 1276卷 v-vi页

作者： Mandal, Jyotsna Kumar Mukherjee, Imon Bakshi, Sambit Chatterji, Sanjay Sa, Pankaj K. Department of Computer Science and Engineering University of Kalyani KalyaniWest Bengal India Department of Computer Science and Engineering National Institute of Technology Rourkel RourkelaOdisha India

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the Regression of MNIST Dataset Based on Convolutional Neural Network 4th

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4th international conference on Advanced Machine Learning Technologies and Applications, AMLTA 2019

作者： Wang, Ziheng Wu, Su Liu, Chang Wu, Shaozhi Xiao, Kai School of Aerospace Engineering and Applied Mechanics Tongji University No. 1239 Siping Road Yangpu District Shanghai China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai China

ISBN: (纸本)9783030141172

the MNIST dataset of handwritten digits has been widely used for validating the effectiveness and efficiency of machine learning methods. Although this dataset was primarily used for classification and results of very high accuracy (99.3%+) on it have been obtained, its important application of regression is not directly applicable, thus substantially deteriorates its usefulness and the development of regression methods for such types of data. In this paper, to allow MNIST to be usable for regression, we firstly apply its class/label with normal distribution thereby convert the original discrete class numbers into float ones. Modified Convolutional Neural Networks (CNN) is then applied to generate a regression model. Multiple experiments have been conducted in order to select optimal parameters and layer settings for this application. Experimental results suggest that, optimal outcome of mean-absolute-error (MAE) value can be obtained when ReLu function is adopted for the first layer with other layers activated by the softplus functions. In the proposed approach, two indicators of MAE and Log-Cosh loss have been applied to optimize the parameters and score the predictions. Experiments on 10-fold cross-validation demonstrate that, desired low values of MAE and Log-Cosh error respectively at 0.202 and 0.079 can be achieved. Furthermore, multiple values of standard deviation of the normal distribution have been applied to verify the applicability when data of label number at varied distributions is used. the experimental results suggest that a positive correlation exists between the adopted standard deviation and the loss value, that is, the higher concentration degree of data will contribute to the lower MAE value. © 2020, Springer Nature Switzerland AG.

关键词： Normal distribution

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METS VR: Mining Evacuation Training Simulator in Virtual Reality for Underground Mines 17th

METS VR: Mining Evacuation Training Simulator in Virtual Rea...

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17th international conference on Information technology: New Generations, ITNG 2020

作者： Andersen, Kurt Gaab, Simone José Sattarvand, Javad Harris, Frederick C. Computer Science and Engineering University of Nevada Reno RenoNV United States Mining and Metallurgical Engineering University of Nevada Reno RenoNV United States

ISBN: (纸本)9783030430191

the mining industry is one of America’s most important suppliers of raw materials such as metals for the manufacturing, construction and high-tech industry. It is very profitable, but requires the mine to be fully active. Mining is also a comparably dangerous line of work and lives can be lost on the work site due to an emergency. As of right now, mines will generally conduct quarterly training to go over evacuation drills and how to handle emergency situations. However, the mine needs to halt production during drills, which is a major disadvantage of this training method. this in turn becomes a loss of profit for the mine. With the Mining Evacuation Training Simulator (METS), the mining industry will have a virtual training scenario for their workers. A more immersive experience is critical to the quality of training as the teaching content actualizes the real world experience. METS is a cost efficient training and safe alternative to the current model of evacuation training. With METS the mine will still be able to remain in production while a few employees at a time perform evacuation and emergency training. © Springer Nature Switzerland AG 2020.

关键词： Profitability

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the Grid-Based Spatial ARIMA Model: An Innovation for Short-Term Predictions of Ocean Current Patterns with Big HF Radar Data 15th

The Grid-Based Spatial ARIMA Model: An Innovation for Short-...

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15th international conference on Computing and Information technology, IC2IT 2019

作者： Pongto, Ratchanont Wiwattanaphon, Nopparat Lekpong, Peerapon Lawawirojwong, Siam Srisonphan, Siwapon Kee, Kerk F. Jitkajornwanich, Kulsawasd Department of Computer Science Faculty of Science King Mongkut’s Institute of Technology Ladkrabang Bangkok10520 Thailand Bangkok10210 Thailand Department of Electrical Engineering Faculty of Engineering Kasetsart University Bangkok10900 Thailand College of Media and Communication Texas Tech University LubbockTX79409 United States

ISBN: (纸本)9783030198602

Marine natural disasters have direct impacts on countries as well as their residents living on and near the coast. Warning and monitoring system can aid in reducing the loss of lives in the event of a disaster. HF (high frequency) radar, an IoT-enabled ocean surface current monitoring system, implementation is one of the first attempts towards achieving this goal. Although HF systems can monitor sea current patterns in terms of speed and direction for each of the pixels of the coverage area, it fails to predict future values, which are essential to many applications such as oil-spill trajectory prediction (using the GNOME suite: General NOAA Operational Modeling Environment), water quality control and management, and optimized sea navigation. In this paper, we propose a model, called the grid-based spatial ARIMA (auto-regressive integrated moving average), to estimate the forecast values. As a result, the full potential of the HF systems can be utilized. the method considers not only observations of POI (point of interest), but also its neighboring pixels when predicting future values. the proposed method is implemented and compared with other existing approaches, including baseline, kNN, traditional ARIMA model, and LSTM (long short-term memory) techniques. the experimental results showed that our approach outperformed other methods in V comp prediction (with RMSEs of 6.23265) with a configuration of (2, 0, 1) as (p, d, q) and a historical dataset of 1 day and 7Â h prior. this configuration was found to be the best combination. © 2020, Springer Nature Switzerland AG.

关键词： Pixels

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