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An Improved Particle Swarm Optimization with Dual Update Strategies Collaboration Based Task Allocation 12th

An Improved Particle Swarm Optimization with Dual Update Str...

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

作者： Xia, Shuang Zhang, Xiangyin Li, Xiuzhi Zhang, Tian Faculty of Information Technology Beijing University of Technology Beijing100124 China

ISBN: (纸本)9783030788100

the task allocation problem is a hot topic in the field of multiple unmanned aerial vehicle (UAV). In this paper, we consider the task allocation in rescue scenarios and establish the optimization model. then, an improved particle swarm optimization with dual update strategies collaboration (DUCPSO) is proposed to solve it. In order to simplify the solution of the problem, a real vector coding method is adopted. In addition, the ring topology update method and the mutation update method are introduced to enhance the diversity of the population, and the effective collaboration of the two strategies is realized through the adaptive strategy conversion probability. the simulation results show that the proposed algorithm can effectively obtain the optimal task allocation scheme. Compared with other algorithms, the proposed algorithm is more feasible and efficient. © 2021, Springer Nature Switzerland AG.

关键词： Antennas

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Spiking Adaptive Dynamic Programming with Poisson Process 12th

Spiking Adaptive Dynamic Programming with Poisson Process

引用

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

作者： Wei, Qinglai Han, Liyuan Zhang, Tielin The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Systems Engineering Macau University of Science and Technology Macau999078 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Research Center for Brain-Inspired Intelligence Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783030788100

A new iterative spiking adaptive dynamic programming (SADP) algorithm based on the Poisson process for optimal impulsive control problems is investigated with convergence discussion of the iterative process. For a fixed time interval, a 3-tuple can be computed, and then the iterative value functions and control laws can be obtained. Finally, a simulation example verifies the effectiveness of the developed algorithm. © 2021, Springer Nature Switzerland AG.

关键词： Dynamic programming

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Emoji Score and Polarity Evaluation Using CLDR Short Name and Expression Sentiment 12th

Emoji Score and Polarity Evaluation Using CLDR Short Name an...

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12th international conference on Soft Computing and Pattern Recognition, SoCPaR 2020 and 16th international conference on Information Assurance and Security, IAS 2020

作者： Gupta, Shelley Singh, Archana Ranjan, Jayanthi Department of Computer Science and Engineering Amity School of Engineering and Technology Noida India Department of Information Technology Amity School of Engineering and Technology Noida India GITAM University Hyderabad Telangana India

ISBN: (纸本)9783030736880

the detection and analysis of sentiment have received great importance by professionals from ecommerce, medical services, physiologists, and social critics. the computerization has resulted in tremendous increase of online users. the users have adapted posting their day to day life events, expressions, judgement, memories, etc. on social media platforms like Facebook, WhatsApp, and Twitter as a part of life. Online postings consist of text and huge number of emojis as well. To evaluate the correct sentiment of such online postings, an accurate approach is required which evaluates the score and polarity of emoji used in an online sentiment. In the current paper, we have proposed an approach to evaluate score and polarity of 4158 emojis. Emoji evaluation is a task of determining the score and polarity of emoji as positive, negative, and neutral based on the sentiment of sentence and the short name description of emoji. the accuracy of the proposed approach is evaluated through machine learning approaches like Linear Regression, SVM and Random Forest. Our approach outperforms in SVM with an accuracy of 85%. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision trees

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Feature Selection Approach Based on a Novel Variant of Hybrid Differential Evolution and PSO for Sentiment Classification 12th

Feature Selection Approach Based on a Novel Variant of Hybri...

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12th international conference on Soft Computing and Pattern Recognition, SoCPaR 2020 and 16th international conference on Information Assurance and Security, IAS 2020

作者： Dixit, Abhishek Mani, Ashish Bansal, Rohit Department of Computer Science Amity School of Engineering and Technology Amity University Noida Uttar Pradesh India Department of EEE Amity School of Engineering and Technology Amity University Noida Uttar Pradesh India Department of Management Studies Rajiv Gandhi Institute of Petroleum Technology Raebareli Uttar Pradesh India

ISBN: (纸本)9783030736880

Over the last few years, digital text data is increasing with the advancement in use of technologies. therefore, sentiment analysis is an important step for improving the performance of any classifier. But with the increase in data, this problem has become more complex. To improve the overall performance of classifier and reducing the data-dimensionality, Feature selection is a key sub-stage for the sentiment analysis. In this paper, we propose a feature selection approach, which is based on a novel variant of hybrid differential evolution algorithm and PSO for sentiment classification. Experimental results show that our approach achieves 97.56% and 98.54% of average accuracy by using Naïve Bayes (NB) classifier for the two Twitter datasets, viz., TestData_2015 and twitter sanders, respectively. the results also show that our proposed approach is showing better results with state-of-art algorithms on Twitter datasets. Statistical results also showcase the effectiveness of our proposed model. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature Selection

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Towards a Proactive System of DDoS Attacks Detection in the Cloud 12th

Towards a Proactive System of DDoS Attacks Detection in the ...

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12th international conference on Soft Computing and Pattern Recognition, SoCPaR 2020 and 16th international conference on Information Assurance and Security, IAS 2020

作者： Ouhssini, Mohamed Afdel, Karim Agherrabi, Elhafed LabSIV Department of Computer Science Faculty of Science Ibn Zohr University Agadir Morocco LabIRF-SIC Department of Mathematics Faculty of Science Ibn Zohr University Agadir Morocco

ISBN: (纸本)9783030736880

Nowadays, cloud computing becoming a vital technology for organization and users by providing services through the internet. Flexibility, Efficiency, high scalability, and cost effective are the main advantages of cloud computing. However, cloud computing still suffers against attacks that threat its availability, especially DDoS attacks. this type of attack makes service unavailable by sending a huge number of flows generated by different distributed devices. High false positive rate, high time detection and high computing cost are the main challenges of exiting system of attack detection in the cloud. In this paper, we present new approach to detect DDoS attack in the cloud based on time series analysis and machine learning techniques. Statistical methods and deep learning are used to forecast the number of normal flows in the next minute, if the number of received flows is far exceeded the forecasted number, a preprocessing process is trigged followed by classifier to differ between DDoS attack and normal flows. Our system is evaluated by using public dataset CIDDS-001 and satisfactory results obtained an accuracy of an accuracy of 100%, a f1-score of 99.96% and a time detection of 0.29 s. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cloud computing

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An Ensemble Deep Learning Framework for Enhancing Sentiment Analysis 8th

An Ensemble Deep Learning Framework for Enhancing Sentiment ...

引用

8th international conference on advances in Computing and Data sciences, ICACDS 2024

作者： Rajpoot, Abha Kiran Agrawal, Hunar Sajjan Agrawal, Gaurav Singh, Jagendra Tyagi, Vipin Galgotias University Greater Noida India Sharda University Greater Noida India Bennett University Greater Noida India Jaypee University of Engineering and Technology Guna India

ISBN: (纸本)9783031709050

Since sentiment analysis has been a crucial field for various types of studies, this field needs to develop and improvise its efficiency and accuracy. the problem has been analyzed over time in many different ways, and multiple approaches have been used to solve it. So far, we haven’t utilized the combined power of modern models and contextual information obtained from texts, nor has it analyzed how contextual information could be effectively controlled. By combining deep learning models with rule-based techniques, the proposed approach effectively classifies sentiments for a range of topics. Remarkably, it uses an embedded representation and attention mechanism to process valence shifting cases efficiently. the method, that employs sentiment dictionaries related to specific domains and custom-designed rules, shows better results on three datasets. Probability Proportion Difference (PPD) and Categorical Probability Proportion Difference (CPPD) are used in feature selection, these methods outperformed Information Gain and the standard method. Comparable sentiment detection abilities are revealed through a comparative analysis with Categorical Probability Difference (CPD). Tests performed on datasets shows the superior sentiment classification performance of the CPPD method over other methods. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Stock Market Movement Prediction by Gated Hierarchical Encoder 12th

Stock Market Movement Prediction by Gated Hierarchical Encod...

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

作者： Chen, Peibin Tan, Ying Department of Machine Intelligence School of Electronics Engineering and Computer Science Peking University Beijing100871 China

ISBN: (纸本)9783030788100

Stock movement prediction is an important but challenging topic in the stock market. Previous methods mainly focus on predicting up or down of one stock, ignoring the significant up and down of the whole stock market that is more related to the final return. In this paper, a novel framework Gated Hierarchical Encoder (GHE) is proposed, which consists of two components: hierarchical feature learning and dynamic gate. Hierarchical feature learning helps the model do prediction from coarse to fine, while dynamic gate dynamically ensembles results from different branches. Experiments show that compared with MLP and LSTM, GHE achieves higher return on multiple stock markets, and predicts more accurately on the significant up and down. © 2021, Springer Nature Switzerland AG.

关键词： Investments

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Artificial Vision Technique to Detect and Classify Cocoa Beans 17th

Artificial Vision Technique to Detect and Classify Cocoa Bea...

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17th international Work-conference on Artificial Neural Networks (IWANN)

作者： Zhinin-Vera, Luis Zhiminaicela-Cabrera, Jonathan Pretel, Elena Suarez, Pamela Chang, Oscar Anton Castro, Francesc Lopez de la Rosa, Francisco Yachay Tech Univ Sch Math & Computat Sci Urcuqui 100650 Ecuador Yachay Tech Univ Sch Biol Sci & Engn Urcuqui 100119 Ecuador Univ Tecn Machala Fac Agr Sci Machala 070151 Ecuador MIND Res Grp Model Intelligent Networks Dev Urcuqui Ecuador Univ Castilla La Mancha LoUISE Res Grp I3A Albacete 02071 Spain Univ Castilla La Mancha Inst Invest Informat Albacete I3A Ave Espana S N Albacete 02071 Spain Univ Castilla La Mancha Dept Elect Elect Automat & Commun Engn Ave Espana S-N Albacete 02071 Spain

ISBN: (纸本)9783031430770;9783031430787

this article discusses the use of Artificial Intelligence (AI) to classify cocoa beans as healthy or diseased based on established classification criteria, given the challenges faced by the cocoa industry due to the impact of diseased beans on quality and grading. the proposed method uses YOLOv5 and achieved an 94.5% accuracy rate. the article also outlines the development of an affordable and easy-to-implement prototype system that cocoa farmers can use to grade and assure bean quality. the results suggest that the proposed system is successful, and increasing the amount of data improves its reliability, which could help farmers improve their competitiveness in the market.

关键词： cocoa beans classification computer vision smart farming

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GPU-Accelerated Enhanced Marching Cubes 33 for Fast 3D Reconstruction of Large Bone Defect CT Images

GPU-Accelerated Enhanced Marching Cubes 33 for Fast 3D Recon...

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

作者： Chin, Daniel Jie Yuan Mohamed, Ahmad Sufril Azlan Shariff, Khairul Anuar Ishikawa, Kunio School of Computer Sciences Universiti Sains Malaysia Gelugor Penang11800 Malaysia School of Materials and Mineral Resources Engineering Universiti Sains Malaysia Nibong Tebal Penang14300 Malaysia Dental Materials Science and Technology Division Faculty of Dental Medicine Airlangga University JI. Prof. Dr. Moestopo No. 47 Surabaya East Java60132 Indonesia Department of Biomaterials Faculty of Dental Science Kyushu University 3-1-1 Maidashi Higashi-ku Fukuoka812-8582 Japan

ISBN: (纸本)9783030902346

With the advancement in three-dimensional technologies, three-dimensional reconstruction of medical images serves as reliable assistance for doctors and surgeons in evaluating and diagnosing bone defects. Amongst the existing reconstruction methods, the Marching Cubes algorithm is highly popular in the surface rendering research study. there are many improvements made over the Marching Cubes algorithm, but due to the relatively small image datasets used during evaluation, it is difficult to judge the effectiveness of the improvements on large image datasets and the reconstructed models may not be viewable in lower-end specs digital devices like tablets and smartphones. thus, an enhancement over the extended Marching Cubes 33 with graphics processing unit acceleration to improve the reconstruction accuracy, execution time, and model portability for large image datasets is proposed in this study. the obtained results show that the proposed enhancement successfully increased the accuracy by 5.29%, decreased the execution time by 11.16%, and decreased the number of vertices and faces by 73.72%. this shows that it is possible to view bone defect models with a high similarity percentage on lower-end spec digital devices and print them out with a three-dimensional printer. © 2021, Springer Nature Switzerland AG.

关键词： Digital devices

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Arabic Speaker Identification System for Forensic Authentication Using K-NN Algorithm

Arabic Speaker Identification System for Forensic Authentica...

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

作者： Abdulwahid, Sarah Mahmoud, Moamin A. Abdulwahid, Nassren College of Graduate Studies Universiti Tenaga Nasional Kajang Malaysia Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Kajang Malaysia Department of Communication Engineering University of Technology Al-Sina’a Street Baghdad Iraq

ISBN: (纸本)9783030902346

For many years, there was an increasing necessity for being capable of the identification of a person based on his/her voice. Judges, law enforcement agencies, detectives, and lawyers, wanted to be able to use the forensic authentication of voice for investigating a suspect or confirming a judgment of guilt or innocence. this study aims to design and build a comprehensive identification of the forensic speakers for the Arabic language. the suggested system has been utilized for the recognition of forensic speaker’s isolated words for purposes of identification. It comprises two stages;the first stage is training the sentence of the forensic speaker in the case where it is not previously processed and stored;the second stage is testing;it is applied in the case where the sentence of the forensic speaker has been previously processed and stored. Every one of the phases involves utilizing audio features (standard division, mean, amplitude, and zero-crossing), pre-processing with the use of the MFCC, Hamming Window, vector quantization, and data mining classification approaches. the proposed system implementation provides removal of the noise in spoken sentences, processing speech sentences prior to the storing, and a correct classification with the use of a number of algorithms of data mining classification such as the Logistic Model Tree (LMT), and K-nearest neighbor (KNN) algorithms. KNN being given the highest accuracy of 91.53% and 94.56% respectively. © 2021, Springer Nature Switzerland AG.

关键词： Authentication

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