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ICACS 2024 - Proceedings of the 2024 8th international conference on Algorithms, Computing and Systems

ICACS 2024 - Proceedings of the 2024 8th International Confe...

引用

8th international conference on Algorithms, Computing and Systems, ICACS 2024

ISBN: (纸本)9798400718304

The proceedings contain 24 papers. The topics discussed include: autonomous floating garbage collection device using computer vision and robotics;coordinated optimization of scheduling and path planning for delivery robots in intelligent manufacturing workshops;a preliminary study of the stressed and drowsy driving prediction models using semi-supervised learning;axial capacity prediction of concrete externally strengthened by low-cost natural fiber reinforced polymer composite utilizing machine learning intelligence;enhancing brain tumor diagnosis: a cutting-edge ensemble deep learning approach;a comparative study of sentiment analysis on twitter and reddit using deep learning techniques;generative artificial intelligence for future education: current research status, hot spots, and research trends;methodological considerations for anonymizing tabular data using generative adversarial networks;and data migration in large scale storage systems with varying file sizes.

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Robust Reward Placement under Uncertainty 33

Robust Reward Placement under Uncertainty

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33rd international Joint conference on Artificial intelligence (IJCAI)

作者： Petsinis, Petros Zhang, Kaichen Pavlogiannis, Andreas Zhou, Jingbo Karras, Panagiotis Aarhus Univ Dept Comp Sci Aarhus Denmark Hong Kong Univ Sci & Technol Guangzhou Artificial Intelligence Thrust Guangzhou Peoples R China Baidu Res Business Intelligence Lab Beijing Peoples R China Univ Copenhagen Dept Comp Sci Copenhagen Denmark

ISBN: (纸本)9781956792041

We consider a problem of placing generators of rewards to be collected by randomly moving agents in a network. In many settings, the precise mobility pattern may be one of several possible, based on parameters outside our control, such as weather conditions. The placement should be robust to this uncertainty, to gain a competent total reward across possible networks. To study such scenarios, we introduce the Robust Reward Placement problem (RRP). Agents move randomly by a Markovian Mobility Model with a predetermined set of locations whose connectivity is chosen adversarially from a known set. of candidates. We aim to select a set of reward states within a budget that maximizes the minimum ratio, among all candidates in., of the collected total reward over the optimal collectable reward under the same candidate. We prove that RRP is NP-hard and inapproximable, and develop Psi-Saturate, a pseudo-polynomial time algorithm that achieves an is an element of-additive approximation by exceeding the budget constraint by a factor that scales as O(ln |Iota Iota|/is an element of). In addition, we present several heuristics, most prominently one inspired by a dynamic programming algorithm for the maxmin 0-1 KNAPSACK problem. We corroborate our theoretical analysis with an experimental evaluation on synthetic and real data.

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An Overview of Optimized Inventory Management Models in Technology Companies: Historical Developments, Practical Applications, and AI-Driven Approaches 7

An Overview of Optimized Inventory Management Models in Tech...

引用

7th international conference on Algorithms, Computing and Artificial intelligence, ACAI 2024

作者： Arnawtee, Mohammed Mahde Mahmood Zaiter, Murooj Fadhil Zaiter Institute of radio electronics and information technologies Ural Federal University Dept. Big Data Analysis and Methods of Analysis Ekaterinburg Russia

ISBN: (纸本)9798331529314

This article provides a comprehensive overview of improved inventory management models in technology companies and how AI methodologies such as machine learning, predictive analytics, and optimization algorithms are revolutionizing inventory management by enabling real-time decision making, accurate demand forecasting, and automated inventory replenishment by integrating historical context with state-of-the-art AI techniques. This article provides a thorough understanding of how technology companies can leverage advanced inventory management models to maintain a competitive edge and challenges associated with implementing AI- driven inventory systems. The findings offer valuable insights for both industry practitioners and academics, contributing to the development of more resilient and adaptive inventory management strategies within the rapidly evolving technology sector. © 2024 IEEE.

关键词： Inventory control

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Experimental Study on Validation of Typical Human Machine Interaction Tasks Based on GOMS Modified Model 5

Experimental Study on Validation of Typical Human Machine In...

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5th international conference on robotics, Intelligent Control and Artificial intelligence, RICAI 2023

作者： Shi, Jin-Shou Li, Lin-Jie Chen, Nan Ma, Chao Li, Yaping Beijing Polytechnic Beijing China

ISBN: (纸本)9798350357950

On the basis of improving the basic model through experiments in the early stage and obtaining a modified GOMS model, in order to verify its effectiveness, this article adopts an experimental method based on typical human-machine interaction tasks in complex information systems, combining Analytic Hierarchy Process with GOMS model analysis. Based on task analysis, typical human-machine interaction task experiments are carried out, Verify the task analysis of the model by comparing human task execution time with the task execution time predicted by GOMS task analysis theory through experiments. The experimental data results show that the error between GOMS predicted time and actual time is between 0.31% -14.02%, which is relatively close and can be used for human-machine interaction task analysis, usability design and evaluation of information systems. © 2023 IEEE.

关键词： experimental comparison GOMS model human-computer interaction task analysis

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Design of Fertilizer analysis System for Precision Fertilizer Application 4

Design of Fertilizer Analysis System for Precision Fertilize...

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4th international conference on Artificial intelligence, robotics, and Communication, ICAIRC 2024

作者： Wei, Qiang Lang, Chunling Lu, Fei Chen, Yuer Huang, Yu Mo, Kaining Guangxi Science & Technology Normal University Laibin China

ISBN: (纸本)9798331531225

The traditional fertilizer application method relies on experience to apply fertilizer on a large scale, which has problems such as fertilizer waste and environmental pollution, and affects the sustainable development of agriculture. For this reason, this paper designs and realizes a set of precision fertilizer application control systems containing sensors, decision-making, and positioning systems. Among them, the sensor system senses the real-time crop and soil status through soil moisture sensors, temperature sensors, and crop growth sensors;the decision-making system uses a high-performance processor, a variety of data transmission interfaces, data processing, and storage modules, and decision-making algorithm execution units to process and analyze the sensor data and generate the decision of fertilizer application. The localization system is based on the hardware of LIDAR, development boards, etc., as well as the SLAM map building, and AMCL positioning algorithm to achieve accurate positioning;through these three systems to fully regulate the fertilization to achieve precision fertilization. © 2024 IEEE.

关键词： Fertilizers

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Sentiment analysis Twitter (X) Users Towards the Boycott of Pro-Israel Brand Using Support Vector Machine Method with SMOTE Approach

Sentiment Analysis Twitter (X) Users Towards the Boycott of ...

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2024 international conference on Control and Automation, Electronics, robotics, Internet of Things, and Artificial intelligence, CERIA 2024

作者： Mawarnie, Hana Patricia Widodo Nugraheni, Murien Universitas Negeri Jakarta Dept. of Informatics and Computer Jakarta Indonesia Universitas Negeri Jakarta Dept. of Information System and Technology Jakarta Indonesia

ISBN: (纸本)9798331511166

The Israel-Palestine conflict that occurred on October 7, 2023, resulted in a boycott of brands known to be pro-Israel. Twitter or X, as one of the largest social media platforms in the world, became a platform for people to voice their opinions regarding the boycott. This study aims to analyze the sentiment of Twitter or X users towards the boycott of pro-Israel brands using the Support Vector Machine (SVM) method with the SMOTE (Synthetic Minority Oversampling Technique) approach. The Support Vector Machine (SVM) method is a machine learning method used for classification and regression. SMOTE is a technique used to overcome data imbalance. The data used in this study is Indonesian language tweet data from October 7, 2023, to April 7, 2024. The data was collected using data crawling techniques. The results of the study from the 80:20 data split, show that the Support Vector Machine (SVM) polynomial kernel model with the SMOTE technique has better performance in analyzing the sentiment of Twitter or X users towards the boycott of pro-Israel brands, with an accuracy value of 98,47%. © 2024 IEEE.

关键词： Tweets

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Workspace analysis Based on a Serial-Parallel Hybrid Robot: Representing Robot Capabilities 17th

Workspace Analysis Based on a Serial-Parallel Hybrid Robot: ...

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17th international conference on Intelligent robotics and Applications

作者： Tian, Xinze Cao, Baoshi Qi, Le Li, Xueai Liu, Yang Sun, Kui Ni, Fenglei Xie, Zongwu Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Peoples R China

ISBN: (纸本)9789819607945;9789819607952

Calculating the dexterity value of a certain point is the foundation for demonstrating the dexterity of the robot. Furthermore, the criteria for calculating the dexterity value of the given point determines the authenticity of the capability map. Traditional methods that use Jacobian matrix to find inverse kinematic to determine whether a pose is reachable exhibit uncertainty around singularities. Thus, this article proposes an approach for calculating the dexterity of points based on a serial-parallel hybrid 7-degree-of-freedom robot. By improving the inverse kinematics solving process and selecting the optimal solutions, this method addresses the issues of low efficiency and inaccuracy in calculations near singular points. Additionally, this method does not require consideration of the configuration of the robot's end-effector axis, thus offering broader applicability. By analyzing the characteristics of the capability map and explaining the reasons behind their formation, verify the practicality and potential applications of this criteria.

关键词： Redundant Robot Hybrid Serial-Parallel Robot Inverse Kinematics Workspace Characteristics Capability Map

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Customer Churn Prediction: Leveraging Data analysis and Machine Learning Approaches 10

Customer Churn Prediction: Leveraging Data Analysis and Mach...

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10th international conference on Artificial intelligence and robotics, QICAR 2024

作者： Mardi, Arsalan Ghorbani, Hananeh Faculty of Electricity Computer Information Technology and Biomedical Engineering Qazvin Branch Islamic Azad University Qazvin Iran Faculty of Science and Engineering Bandar-e Anzali Branch Islamic Azad University Gilan Iran

ISBN: (纸本)9798350348873

The prediction of customer attrition in the banking industry has gained significant attention due to the proliferation of services and the need to retain customers in today's competitive market, particularly when customers abandon banking products and services. Customer churn, defined as the termination of a customer's relationship with a bank, has become a critical concern. Changing the banks' approach is crucial, emphasizing the importance of retaining and ensuring customer loyalty, especially when customers discontinue their usage of banking services and products. Banks must actively determine customer attrition by analyzing customer behavior and making efforts and investments to retain customers. This study uses different machine learning algorithms and leverages data from the banking sector to achieve precision in predicting customer loss. This endeavor leads to the empowerment of financial institutions, enabling the implementation of precision-targeted measures aimed at cultivating the preservation of customer loyalty. Before applying machine learning algorithms, data is processed for data cleansing and transformation to ensure data quality. The results indicate that LightGBM and RandomForest algorithms exhibit the best performance in detecting customer attrition. ©2024 IEEE.

关键词： Forecasting

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VisSpecDic: Visual analysis of Spectrum Situation Data Based on Density Incremental Clustering 7

VisSpecDic: Visual Analysis of Spectrum Situation Data Based...

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7th international conference on pattern Recognition and Artificial intelligence, PRAI 2024

作者： Xunchao, Cong Jinyue, Huang Zhengguo, Zhu Zilong, Ren Yunbo, Rao Jiansu, Pu University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350350890

The omnipresence of wireless communication highlights the importance of efficient radio spectrum management. Traditional monitoring, based on spectral analysis, is challenged by diverse and fluctuating signal environments. This research introduces the SpecVis system, featuring a Density Incremental Clustering (DIC) algorithm that integrates spectral and signal data for enhanced radio spectrum analysis. The DIC algorithm classifies signal data across frequency and bandwidth grids, using density peaks to identify signal locations and a filtering process to minimize noise interference. It also employs connectivity ratios to discern between fleeting and stable signals. The SpecVis system was evaluated within the FM broadcast band, accurately detecting all authorized signals and offering clear visualizations of signal power and time-frequency dynamics. This system represents a novel approach to real-time spectrum monitoring by combining spectral and signal data analysis. Future enhancements will refine algorithm sensitivity and adapt the system for specific monitoring scenarios. © 2024 IEEE.

关键词： Network security

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Applications and Prospects of Neural Networks in Fault Detection: A Visualization analysis Based on CiteSpace 5

Applications and Prospects of Neural Networks in Fault Detec...

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5th IEEE international conference on pattern Recognition and Machine Learning, PRML 2024

作者： Wu, Kechen Lu, Xingcheng Chen, Yixuan Nanjing University of Finance and Economics Nanjing China

ISBN: (纸本)9798350355925

With the rapid development of industrial automation and intelligence, fault detection technology has become key to ensuring system stability and safety. Neural networks, as outstanding data processing and pattern recognition tools, have demonstrated extensive application potential in the field of fault detection. This study utilizes CiteSpace visualization software to conduct a thorough analysis of 2,211 papers on neural networks and fault detection in the Web of Science database from 2020 to 2024, aiming to reveal the research dynamics and development trends in this field. The analysis covered indicators such as authors, research institutions and research direction identifying major research hotspots including process monitoring, rolling element bearing monitoring, structural health monitoring, gas turbine performance assessment, and power system stability. The results indicate that advanced deep learning techniques, the integrated application of digital twins, and the development of the industrial Internet of Things are driving fault detection towards greater intelligence. Future research is expected to focus more on algorithm optimization, model interpretability, and the enhancement of real-time monitoring technologies, while also emphasizing the importance of security and privacy protection. © 2024 IEEE.

关键词： Structural health monitoring

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