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Ant colony optimization algorithm in the design of international trade early warning system

Ant colony optimization algorithm in the design of internati...

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2022 international conference on computer Application and Information Security, ICCAIS 2022

作者： Li, Cheng School of Business Wuhan Huaxia University of Technology Hubei Wuhan430000 China

ISBN: (纸本)9781510663459

Establishing and improving a scientific system and a system that fully extracts and utilizes commercial data plays an extremely important role in promoting the normal development of China's foreign trade and maintaining the fundamental interests of the country and the industry. This paper aims to design an early warning system for international trade based on ant colony optimization algorithm. A method of dividing the optimized weights and thresholds into small sets is proposed for the optimization algorithm. The early warning indicator system, early warning model and early warning system construction of TBT are introduced in detail, and the design of TBT early warning system is described and demonstrated in detail. In the verification part, the algorithm after ant colony optimization is compared with the ordinary BP network before the improvement, and it is found that the error of the improved method is smaller, which shows the superiority of the algorithm in solving the problem of international trade early warning. © 2023 SPIE.

关键词： international trade

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5th international conference on Information and Communication technology for Intelligent Systems, ICTIS 2021

5th International Conference on Information and Communicatio...

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5th international conference on Information and Communication technology for Intelligent Systems, ICTIS 2021

ISBN: (纸本)9789811641763

The proceedings contain 78 papers. The special focus in this conference is on Information and Communication technology for Intelligent Systems. The topics include: Iris Recognition System Based on Convolutional Neural network;database Management Systems—An Efficient, Effective, and Augmented Approach for Organizations;parallel Computation of Hadamard Product for Dynamic Dataset on Google Colab;cooperative Hypothesis Building for computer Security Incident Response;Analysis and Prediction of COVID-19 with Image Super-Resolution Using CNN and SRCNN-Based Approach;Hierarchical Multi Label Classification of News Articles Using RNN, CNN and HAN;enhancing the Date Rate by the Management of Bandwidth and Power Using Interference Temperature Constraint in Cognitive Radio networks;dialogue Extraction and Translation from Stories on Thirukural Using Verb Cue Quote Content Source Identifiers;a Survey on Using Machine Learning to Counter Hardware Trojan Challenges;green Cloud Computing-Oriented Algorithmic Approach-Based Virtual Machine Consolidation;an Impact of Radon Transforms and Filtering Techniques for Text Localization in Natural Scene Text Images;Algorithmic Approach to Select MOOCs Platform: An Institutional Perspective;decentralised Content Creation in Digital Learning: A Blockchain Concept;data Security in Internet of Things;Forecasting of COVID-19 Using Hybrid ARIMA-FB Prophet Algorithm;novel Approach to Improve Security Systems;Review of Forthcoming ICT-Enabled Applications Promoting Learning in Higher Education;a Comprehensive Travel Recommendation System;manufacturing Information Systems for Forestry Companies Based on Integrated Modular Approach «Many Companies—One Tool»;ICT-Based Enhancement of Employment Schemes: A Case Study in Rural Uttarakhand, India;sustainable Technologies for Improved Income Generation: A Case Study of Women Villagers in Himachal Pradesh;named Entity Recognizer for Konkani Text;preface.

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A Study of an Efficient Scheduling Algorithm for Simultaneous 5G Multimedia Traffic

A Study of an Efficient Scheduling Algorithm for Simultaneou...

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2022 international conference on computer and Drone Applications, IConDA 2022

作者： Mohd Ramli, Huda Adibah Abdalla Hashim, Aisha Hassan Mohamed Rasied, Thaahirah Shireen Asnawi, Ani Liza Abdul Rahman, Farah Diyana International Islamic University Malaysia Department of Electrical and Computer Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9781665492355

Simultaneous support for Fifth Generation (5G) multimedia traffic in time-changing and frequency-varying downlink channels is a challenging task given their stringent and competing desired Quality of Service (QoS). Packet scheduling is essential in the 5G network for delivering multimedia packets to the end users whilst simultaneously meeting their required QoS. An abundant number of packet scheduling algorithms have been developed. Yet, the performance of these packet scheduling algorithms in the downlink 5G, which has a different framework and characteristics may require further investigations. Moreover, most 5G packet scheduling algorithms support only one multimedia use case. Therefore, the performance of several legacy packet scheduling algorithms for simultaneous support of 5G multimedia traffic in the downlink 5G is investigated in this paper. An array of simulation was conducted, and the results demonstrate the efficiency of a packet scheduling algorithm comes at the expense of the degradation of another network metric. Therefore, network operators need to decide on which metric to be optimized when choosing a packet scheduling algorithm to be implemented in their networks. © 2022 IEEE.

关键词： Quality of service

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Research on Heterogeneous Information network Recommendation System for Online Education 3

Research on Heterogeneous Information Network Recommendation...

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3rd international conference on Artificial Intelligence and computer Information technology, AICIT 2024

作者： Liu, Yong Li, Xiaoling School of Computer and Intelligence Education Lingnan Normal University Zhanjiang China School of Software Engineering South China University of Technology Guangzhou China Office of International Exchange and Cooperation Lingnan Normal University Zhanjiang China

ISBN: (纸本)9798350365443

This paper explores challenges and solutions in adaptive online education, with a focus on Heterogeneous Information networks (HIN). Traditional recommendation methods' accuracy issues lead to an emphasis on improving learning resource recommendations. Advanced similarity calculation methods within HIN, addressing bias problems, are discussed. Information recommendation challenges, especially in extraction and utilization stages, are explored, along with proposed theoretical frameworks for online education data analysis. The paper introduces a HIN-based information recommendation framework, utilizing the Random Walk with Restart and in-degree sensitive parameters. Additionally, a HIN long-tail recommendation method using deep neural networks is presented to counteract biases towards mainstream recommendations. The paper concludes by highlighting the potential of the proposed models to improve accuracy and computational efficiency in online education resource recommendations. © 2024 IEEE.

关键词： Contrastive Learning

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A New Chaotic-Based Analysis of Data Encryption and Decryption 18th

A New Chaotic-Based Analysis of Data Encryption and Decrypti...

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18th international conference on Data Science and Artificial Intelligence, ICDSAI 2022

作者： Johora, Fatema Tuj Alamin-Ul-Islam Yesmin, Farzana Rahman, Md. Mosfikur Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Faculty of Science and Information Technology Daffodil International University Dhaka Bangladesh

ISBN: (纸本)9783031161773

Because the amount of exchange hypersensitive data via the Internet is growing at an exponential rate, network and data security have recently been the most pressing worry. On the subject of data security, many approaches are available, including "cryptography." When data is sent from the sender to the receiver, it is encrypted using an encryption method, and when it is received by the receiver, it is decrypted using a decryption algorithm to see the exact and true data that was sent by the sender previously. Data encryption can be done in a number of ways. With algorithms like AES, DES, and RSA for data encryption and decryption, this research proposes a novel CRSA (chaotic random seed algorithm) technique. CRSA was also compared to other algorithms to see how well they performed. The experimental data presented in this work are used to examine those algorithms as well as our new CRSA algorithm. Cryptography, encryption, decryption, random reed, millisecond, and data security are all terms used in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cryptography

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Boosting Bandwidth Convergence: Optimizing Resource Allocation in Satellite-Terrestrial Integrated networks 23

Boosting Bandwidth Convergence: Optimizing Resource Allocati...

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23rd IEEE international conference on Communication technology, ICCT 2023

作者： Li, Dongbo Zhang, Jiajun Tian, Puyang Yin, Zhisheng Cheng, Siyao Liu, Jie Harbin Institute of Technology IoT and Ambient Intelligence Research Center Faculty of Computing Harbin China International Research Institute for Ai Harbin Institute of Technology Shenzhen Cyber-Physical Systems Research Center Shenzhen China School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen China School of Communication Engineering Xidian University Xi'an China International Research Institute for Ai Harbin Institute of Technology Shenzhen Shenzhen China

ISBN: (纸本)9798350325959

Satellite-Terrestrial Integrated network (STIN) is a multidimensional network that integrates satellites and terrestrial netwoks, providing information services globally. However, facing time-varying network topology and transmission resources, STIN is limited by the uneven network resources in each network segment. Therefore, it is difficult for STIN to achieve optimal network performance during data transmission. In response to the above challenges, this paper proposes a soft-defined STIN based multipath selection and resource allocation method. We build a soft-defined network architecture for STIN, establish satellite-terrestrial dynamic network topology, and deploy distributed controller nodes. The proposed method models multiple factors that affect throughput performance in the network as a link transmission cost matrix, defining bandwidth resources, computing resources, and latency parameters for modeling. Finally, construct the optimal resource objective function for soft-defined STIN. Meanwhile, we propose a Boosting Bandwidth Convergence (BBC) method. This method merges all transmission paths using a recursive method, and obtains the optimal objective function solution. STK, Mininet, and Ryu are adopted as a joint simulation platform to simulate the three-layer network architecture of soft-defined STIN. The simulation results show that the proposed method outperforms default Ryu switch algorithm and Shortest Path Bridging (SPB) algorithm in terms of throughput, even in the presence of network interference. It exhibits comparable average delay but maintains consistent delay throughout the transmission process, unlike the fluctuating delays observed in the other two algorithms due to buffer accumulation. © 2023 IEEE.

关键词： resource allocation Satellite-Terrestrial Integrated network soft-defined network throughput

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Malware Detection using Artificial Intelligence 13

Malware Detection using Artificial Intelligence

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13th international conference on Computing Communication and networking Technologies, ICCCNT 2022

作者： Mohapatra, Niva Satapathy, Biswajit Mohapatra, Bighnaraj Mohanta, Bhabendu Kumar Department of CSE International Institute of Information Technology Odisha Bhubaneswar India Visakhapatnam530045 India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Malware has always been a topic of concern. It is a malicious code that has been developed for harming a single computer or a network of computers. The number of malware is increasing exponentially so security researchers are constantly inventing new techniques for protecting computer networks. This research paper is focused on the use of artificial intelligence techniques in malware detection. Artificial intelligence (AI) can't automatically resolve all potential threats but when it comes to modelling of both good and bad behavior, it can lead to a successful weapon against the most advanced threat. According to the experimental results obtained in this research, Random Forest proved to be the most efficient algorithm that was eventually used to enhance the performance of malware detection. It resulted in the highest F1 score among all other algorithms used in the detection model. © 2022 IEEE.

关键词： Malware

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Intrusion Detection System Using PSO and DE algorithms with BP Neural network 8

Intrusion Detection System Using PSO and DE Algorithms with ...

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8th international conference on Fuzzy Systems and Data Mining, FSDM 2022

作者： Wang, Ke Guan, Jingwei Luo, Song Guan, Zhi MoE Beijing China Department of Computer Science and Technology Eecs Peking University Beijing China Liangjiang International College Chongqing University of Technology Chongqing China School of Computer Science and Engineering Chongqing University of Technology Chongqing China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (纸本)9781643683461

Intrusion detection system (IDS) combines software and hardware to detect network attacks. In this paper, we propose a new intrusion detection method based on an improved BP neural network algorithm. We improve the BP neural network algorithm by combining it with the particle swarm optimization (PSO) algorithm and the differential evolution (DE) algorithm. We also propose a new framework based on common intrusion detection framework to accommodate our improved BP neural network. The experiments based on the CICIDS2017 dataset show our approach achieves better detection efficiency. © 2022 The authors and IOS Press.

关键词： Particle swarm optimization (PSO)

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Predicting China's Marriage Rate using Machine Learning Models and Cross-Validation Evaluation 16

Predicting China's Marriage Rate using Machine Learning Mode...

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16th international conference on Cyber-Enabled Distributed Computing and Knowledge Discover, CyberC 2024

作者： Zhang, Deyu Theeramunkong, Thanaruk Khamsemanan, Nirattaya Yunnan College of Foreign Affairs and Foreign Language Kunming China Thammasat University Sirindhorn International Institute of Technology Thailand

ISBN: (纸本)9798331506896

After China's accession to the WTO, the country has experienced rapid development over the past 20 years, but the marriage rate has been declining. This study aims to analyze marriage data using machine learning, identify the factors influencing the marriage rate, and make predictions. The data covers seven variables including GDP, housing prices, fertility rate, and education level in 31 regions of China from 2003 to 2022. The study employed five machine learning algorithms with five models: Ridge, Lasso, Elastic Net, Polynomial, and Weighted Least Squares, along with three types of Cross-Validation techniques - Holdout, LOOCV, and K-Fold. By comparing the five regression models and evaluating the marriage rate models through Holdout, LOOCV, and K-Fold Cross-Validation, the results show that the Polynomial regression exhibits higher predictive accuracy, with MSE of 3.473 for Holdout, 3.001 for LOOCV, and 3.018 for K-Fold. Following closely is Ridge regression with MSE of 3.565 for Holdout, 3.326 for LOOCV, and 3.334 for K-Fold. These two models outperform the less performing Lasso and Elastic Net regressions. Weighted Least Squares show stable but slightly inferior performance. By comparing the predicted values for 2022 with the actual values, it is confirmed that Polynomial and Ridge regression closely align with the actual values, predicting the trend of changes in real and predicted values for each province, highlighting their effectiveness in predictive tasks involving complex data patterns. © 2024 IEEE.

关键词： Adversarial machine learning

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8th Annual international conference on Information Management and Big Data, SIMBig 2021

8th Annual International Conference on Information Managemen...

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8th Annual international conference on Information Management and Big Data, SIMBig 2021

ISBN: (纸本)9783031044465

The proceedings contain 27 papers. The special focus in this conference is on Information Management and Big Data. The topics include: CoffeeSE: Interpretable Transfer Learning Method for Estimating the Severity of Coffee Rust;plant Disease Classification and Severity Estimation: A Comparative Study of Multitask Convolutional Neural networks and First Order Optimizers;auditing algorithms: Determining Ethical Parameters of Algorithmic Decision-Making Systems in Healthcare;Calibration of Traffic Simulations Using Simulated Annealing and GPS Navigation Records;a Data-Driven Score Model to Assess Online News Articles in Event-Based Surveillance System;Model Comparison for the Classification of Comments Containing Suicidal Traits from Reddit via NLP and Supervised Learning;automatic Detection of Deaths from Social networking Sites;deep Learning vs Compression-Based vs Traditional Machine Learning Classifiers to Detect Hadith Authenticity;automatic Detection of Levels of Intimate Partner Violence Against Women with Natural Language Processing Using Machine Learning and Deep Learning Techniques;Quality Model for Educational Mobile Apps Based on SQuaRE and AHP;Enterprise Architecture Based on TOGAF for the Adaptation of Educational Institutions to e-Learning Using the DLPCA Methodology and Google Classroom;clustering Analysis for Traffic Jam Detection for Intelligent Transportation System;government Public Services Presence Index Based on Open Data;investigating Generative Neural-network Models for Building Pest Insect Detectors in Sticky Trap Images for the Peruvian Horticulture;Crack Detection in Oil Paintings Using Morphological Filters and K-SVD Algorithm;AmLDA: A Non-VAE Neural Topic Model;classical Machine Learning vs Deep Learning for Detecting Cyber-Violence in Social Media;multiphase Model Based on K-means and Ant Colony Optimization to Solve the Capacitated Vehicle Routing Problem with Time Windows;automatic Data Imputation in Time Series Processing Using Neural N

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