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UAV Path Planning Based on Variable Neighborhood Search Genetic Algorithm 12th

UAV Path Planning Based on Variable Neighborhood Search Gene...

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12th international conference on advances in Swarm Intelligence, ICSI 2021

作者： Zhang, Guo Wang, Rui Lei, Hongtao Zhang, Tao Li, Wenhua Song, Yuanming College of Systems Engineering National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha410073 China

ISBN: (纸本)9783030788100

this study proposed a new genetic algorithm with variable neighbourhood search (GAVNS) for UAV path planning in three-dimensional space. First, an 0–1 integer programming mathematical model is established by inspired from the vehicle routing planning model with time window (VRPTW), and then a heuristic rule based on space vector projection is designed to quickly initialize high-quality solutions that meet constraints of upper error limit and minimum turning radius. Second, it improves mutation operator with a reselected mutation strategy, and incorporates Variable Neighborhood Search strategy based on adding and deleting route during the search process;Finally, GAVNS is compared with general Genetic Algorithm on a set of experiments. It is demonstrated that GAVNS algorithm is both effective and efficient. Moreover, the introduction of variable neighborhood search strategy enhances the local search ability of Genetic Algorithm. © 2021, Springer Nature Switzerland AG.

关键词： Motion planning

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A Comparison of ML and DL Approaches for Crowd Analysis on the Hajj Pilgrimage

A Comparison of ML and DL Approaches for Crowd Analysis on t...

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7th international conference on advances in Visual Informatics, IVIC 2021

作者： Zamri, Muhammad Nur Hakim Bin Abdullah, Junaidi Bhuiyan, Roman Hashim, Noramiza Al Farid, Fahmid Uddin, Jia Husen, Mohd Nizam Abdullah, Norra Faculty of Computing and Informatics Multimedia University Cyberjaya Persiaran Multimedia Cyberjaya63100 Malaysia Technology Studies Department Endicott College Woosong University Daejeon Korea Republic of Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia Sdn Bhd Rawang Malaysia

ISBN: (纸本)9783030902346

In proportion to the growth in human population, there has been a substantial rise in the number of crowds in public places. the more crowded a place, the more risk of stampedes. therefore, crowd management is very critical to ensure the safety of the crowds. Crowd monitoring is an effective approach to monitor, control and understand the behavior of the density of the crowd. One of the efficient automated video monitoring techniques to ensure public safety is crowd density estimation. Crowd density analysis is used primarily in public areas that are usually crowded with people such as stadiums, parks, shopping malls and railway stations. In this research, crowd density analysis by machine learning is presented. the main purpose of this model is to determine the best machine learning algorithm with the highest performance for crowd density classification. this model is focusing on machine learning algorithms such as traditional machine learning algorithms and deep learning algorithms. For traditional machine learning algorithms, Histogram Oriented Gradients (HOG) and Local Binary Pattern (LBP) have been used to extract important features from the input crowd images before being fed into Support Vector Machine (SVM) for classification. For deep learning algorithms, custom Convolutional Neural Network (CNN) together with two famous CNN architectures named Residual Network (ResNet) and Visual Geometry Group Network (VGGNet) were implemented as other methods in this paper for comparison. Other than that, the performance evaluation of the algorithm was measured based on the accuracy of the models. the performance of all different models was recorded and compared. © 2021, Springer Nature Switzerland AG.

关键词： Support vector machines

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Can Argumentation Help to Forecast Conditional Stock Market Crisis with Multi-agent Sentiment Classification? 12th

Can Argumentation Help to Forecast Conditional Stock Market ...

引用

12th international conference on advances in Swarm Intelligence, ICSI 2021

作者： Hao, Zhi-yong Sun, Peng-ge Shenzhen Institute of Information Technology Shenzhen China

ISBN: (纸本)9783030788100

It is well known that investors in stock market making financial decisions are often affected by certain events, like the coronavirus disease pandemic. However, it is very hard to perceive stock market crisis by making use of variety information. In this paper, we investigate whether it is possible to exploit arguments from investor sentiment expressed through financial news and posts, to forecast conditional stock market crisis. thus, an argumentation enriched multi-agent sentiment classification method is proposed to make full use of variety tone and proliferation under certain events. In particular, the conditional stock market is investigated in our experiment to compare the predictive performance of the argumentation enriched multi-agent sentiment classification system with the existing multiple classifier system for the variance of CSI 300 Index. We find that the proposed argumentation enriched system outperforms the existing popular multiple classifier systems, while giving argumentative explanations through preliminary empirical evaluation. © 2021, Springer Nature Switzerland AG.

关键词： Multi agent systems

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Early Prediction of Diabetes Disease Based on Data Mining Techniques 4th

Early Prediction of Diabetes Disease Based on Data Mining Te...

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4th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2021

作者： Elsadek, Salma N. Alshehri, Lama S. Alqhatani, Rawan A. Algarni, Zainah A. Elbadry, Linda O. Alyahyan, Eyman A. Computer Science Department College of Science and Humanities Imam Abdulrahman Bin Faisal University P.O. Box 31961 Jubail Saudi Arabia

ISBN: (纸本)9783030925994

As a common yet chronic disease, Diabetes Mellitus (DM) affects millions of people all over the world. Some groups are more vulnerable to diabetes in comparison to others, such as people with a family history, and those suffering from obesity. Early detection of such people, in conjunction with preventive measures, can go a long way in saving the lives of people who are most likely to be infected with these diseases and avoid suffering. Consequently, Prediction solutions have been found using data mining techniques which help to discover hidden information about the disease and supports decision-making. this study aims to ensure that diabetes is predicated at the initial stages using two algorithms of machine learning: Random Forest and Multi-Layer Perceptron (MLP) using the WEKA environment to estimate the accuracy. the experiments were applied using a dataset obtained from the Machine Learning Repository of UCI. the dataset comprises 16 attributes and 520 instances collected via questionnaires from patients at Sylhet-based Sylhet Diabetes Hospital (Bangladesh). According to the findings, the RF method has been found to yield better results than MLP when it comes to enabling early prediction of diabetes with a high accuracy of 97.88%. © 2021, IFIP international Federation for Information Processing.

关键词： Data mining

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Neural network analysis of plc traffic in smart city street lighting network 13th

Neural network analysis of plc traffic in smart city street ...

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13th international conference on Computational Intelligence in Security for Information Systems, CISIS 2020

作者： Andrysiak, Tomasz Saganowski, Lukasz Institute of Telecommunications and Computer Science UTP University of Science and Technology in Bydgoszcz Al. Prof. S. Kaliskiego 7 Bydgoszcz85-796 Poland

ISBN: (纸本)9783030578046

In the article, we present a system allowing to detect different kinds of anomalies/attacks in street lighting critical infrastructure realized by means of Power Line Communication. there is proposed a two-phase method of anomaly detection. Firstly, all the outliers are detected and eliminated from the examined network traffic parameters with the use of the Cook’s distance. Data prepared this way is used in step number two to create models based on multi-layer perceptron neural network and autoregressive neural network which describe variability of the examined street lighting network parameters. In the following stage, resemblance between the expected network traffic and its real variability are analysed in order to identify abnormal behaviour which could demonstrate an attempt of an anomaly or attack. Additionally, we propose a recurrent learning procedure for the exploited neural networks in case there occur significant fluctuations in the real Power Line Communication traffic. the experimental results confirm that the presented solutions are both efficient and flexible. © the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Time series analysis

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Research and Implementation of a Remote Monitoring Platform for Antarctic Greenhouse 9th

Research and Implementation of a Remote Monitoring Platform ...

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9th international conference on computer Engineering and Networks, CENet2019

作者： Lin, Kaiyan Liu, Chang Wu, Junhui Chen, Jie Si, Huiping Modern Agricultural Science and Engineering Institute Tongji University Shanghai China

ISBN: (纸本)9789811537523

In response to the weak environmental infrastructure of Antarctic greenhouses, the inability to automate operation and low reliability, a remote monitoring platform for the Antarctic greenhouse was developed, which integrates functions such as database management, image acquisition and remote data communication. An infrared high-resolution and high-speed digital camera was used in the greenhouses and with its cradle head;it is convenient to monitor the scene in 360°. the monitory software was developed using the SDK provided by the manufacturer to realize the functions of image displaying, capturing and console control. Furthermore, a communication software module was developed to transfer operational data and image files from Antarctic greenhouse to data center in Shanghai, China. the entity–relationship model was adopted for database designing, and the SQL Server database was used for data storing. And after a common communication protocol was studied for transferring database tables and image files, a flow chart of communication was designed, and separate threads were used for the transfer of image files to improve the efficiency. Using the socket technology to transfer the operational data and image files to the data center of Shanghai, people could obtain environmental parameters, running status and the information of plant images in the greenhouse. After the system was installed in the greenhouse of the Great Wall Station in Antarctica, it realized the remote monitoring of the crop’s growth in the greenhouse. the operational data showed that the system works stably, and the transmission of data is accurate, providing a guarantee for the operation of the greenhouses in Antarctica. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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Dynamic Resource Aware Scheduling Schemes for IEEE 802.16 Broadband Wireless Networks 4th

Dynamic Resource Aware Scheduling Schemes for IEEE 802.16 Br...

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4th international conference on Advanced Computing and Intelligent Engineering, ICACIE 2019

作者： Deva Priya, M. Christy Jeba Malar, A. Sam Peter, S. Sandhya, G. Vishnu Varthan, L.R. Vignesh, R. Department of Computer Science & Engineering Sri Krishna College of Technology CoimbatoreTamil Nadu India Department of Information Technology Sri Krishna College of Technology CoimbatoreTamil Nadu India

ISBN: (纸本)9789811565830

the scheduling algorithms for IEEE 802.16 standard are designed with the predominant goals of throughput optimization, ensuring fairness and Quality of Service (QoS) provisioning. In this work, enhancements are proposed to the existing Weighted Fair Queuing (WFQ) and Deficit Weighted Round Robin (DWRR) scheduling algorithms to efficiently utilize the unused units. In WFQ, additional units may be assigned to a queue, thus reducing the service rate. Instead in Enhanced WFQ (EWFQ), multiple queues are served in a round by effectively utilizing the unexploited units. In DWRR, a queue is not serviced if the size of the packet at the front of the queue exceeds the available quantum. Enhanced DWRR (EDWRR) checks for packets with sizes less than the Deficit Counter (DC), sorts the queue and services a smaller packet in the current round. Further, if the queue that is currently served becomes empty, the DC is transferred to the ensuing active queue instead of making it zero. this helps in servicing more number of packets in a round. the proposed scheduling schemes are proficient in servicing specific traffic flows. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Scheduling algorithms

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A Liver Segmentation Algorithm with Interactive Error Correction for Abdominal CT Images: A Preliminary Study 4th

A Liver Segmentation Algorithm with Interactive Error Correc...

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4th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2021

作者： Nayantara, P. Vaidehi Kamath, Surekha Manjunath, K.N. Rajagopal, K.V. Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Radiodiagnosis and Imaging Kasturba Medical College Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9783030925994

An automatic method for segmenting the liver from the portal venous phase of abdominal CT images using the K-Means clustering method is described in this paper. We have incorporated an interactive technique for correcting the errors in the liver segmentation results using power law transformation. the proposed method was validated on abdominal CT volumes of fifteen patients obtained from Kasturba Medical College, Manipal. the average values of the various standard evaluation metrics obtained are as follows: Dice coefficient = 0.9361, Jaccard index = 0.8805, volumetric overlap error = 0.1195, absolute volume difference = 4.048%, average symmetric surface distance = 1.7282 mm and maximum symmetric surface distance = 38.039 mm. the quantitative and qualitative results obtained in our preliminary work show that the K-Means clustering technique along with power law transformation is effective in producing good liver segmentation outputs. As future work, we will attempt to automate the power law transformation technique. © 2021, IFIP international Federation for Information Processing.

关键词： K-means clustering

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A Text Information Hiding Method Based on Sentiment Word Substitution 9th

A Text Information Hiding Method Based on Sentiment Word Sub...

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9th international conference on computer Engineering and Networks, CENet2019

作者： Li, Fufang Tang, Han Liu, Liangchen Li, Binbin Feng, Yuanyong Chen, Wenbin Gao, Ying School of Computer Science and Educational Software Guangzhou University Guangzhou510006 China

ISBN: (纸本)9789811537523

Text information hiding based on synonym substitution is sensitive to synonym dictionary quality. Synonym word forest has a large number of irreplaceable old words, and the network new words are not added, using this dictionary to perform synonym substitution steganography on the carrier text which is easy to change the statistical features of the original text. To solve the above problem, this paper proposed a method that dynamically expands sentiment dictionary. Firstly, we collected and merged the three basic sentiment dictionaries that are widely used and using the conjunction rules to identify the candidate sentiment words. then the cosine similarity between the sentiment words is calculated by the distributed word vector representation tool Word2vec. the sentiment words have a high similarity value which is added to the sentiment word dictionary. the expanded sentiment dictionary is used as a text information hiding dictionary based on sentiment word substitution, and the 7/3 matrix encoding method is used. Experimental analysis shows that the conjunction rules can effectively identify sentiment words, and the extended sentiment dictionary with 42.8% embedding rate for sentiment word substitution steganography can effectively reduce the possibility of statistical analysis. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Matrix algebra

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Dynamic Multi-objective Optimization via Sliding Time Window and Parallel Computing 12th

Dynamic Multi-objective Optimization via Sliding Time Window...

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12th international conference on advances in Swarm Intelligence, ICSI 2021

作者： Fan, Qinqin Wang, Yihao Ersoy, Okan K. Li, Ning Chu, Zhenzhong Logistics Research Center Shanghai Maritime University Shanghai201306 China School of Electronic and Computer Engineering Purdue University West LafayetteIN47906 United States Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai200240 China Logistics Engineering College Shanghai Maritime University Shanghai201306 China

ISBN: (纸本)9783030788100

Tracking changing Pareto front (PF) in the objective space and Pareto set (PS) in the decision space is an important task in dynamic multi-objective optimization (DMO). Similarly, maintaining population diversity and reusing previous evolutionary information are useful to explore promising regions and to find high-quality solutions quickly in time-varying environments. To this end, a sliding time window based on parallel computing (STW-PC) is introduced in the present study. In the STW-PC, obtained time-sequence solution sets aim to preserve the diversity and facilitate a fast convergence since problems in successive time/environments are usually related. the parallel computing method is also employed to reduce the computational time. the STW-PC is incorporated into a multi-objective evolutionary algorithm and is compared with two competitors on 12 dynamic multi-objective optimization problems. the results show that the STW-PC can both improve the tracking performance of the selected algorithm in different degrees of changes, and significantly reduce the calculation time compared with transfer learning. © 2021, Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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