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International journal of simulation: systems, science & technology最新文献

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AI Classification of Respiratory Illness Through Vocal Biomarkers and a Bespoke Articulatory Speech Protocol 通过声乐生物标志物和定制发音语音协议对呼吸系统疾病进行人工智能分类
Pub Date : 2024-05-05 DOI: 10.5013/ijssst.a.25.01.13
Tim Bashford
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引用次数: 0
Chaotic Attractor Generated by Combining Chua Attractor with Another Circuit 将 Chua 吸引器与另一个电路相结合产生的混沌吸引器
Pub Date : 2024-05-05 DOI: 10.5013/ijssst.a.25.01.05
Kaouther Selmi
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引用次数: 0
Sentiment Clustering - A Hybrid Approach for Insider Threat Detection 情感聚类--内部威胁检测的混合方法
Pub Date : 2024-05-05 DOI: 10.5013/ijssst.a.25.01.04
Rawabi Alqahtani, H. Farooq, Ahmad Khoraidly
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引用次数: 0
Enhancing Cloud Computing Efficiency: Fuzzy Based Task Classification for Better Resource Management 提高云计算效率:基于模糊的任务分类,实现更好的资源管理
Pub Date : 2024-05-05 DOI: 10.5013/ijssst.a.25.01.07
Mubarak Banisakher, Marcie Yordy
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引用次数: 0
The Efficiency of Artificial Recurrent Neural Network (RNN) in Predicting Academic Performance for Students 人工循环神经网络(RNN)在预测学生学业成绩方面的效率
Pub Date : 2024-05-05 DOI: 10.5013/ijssst.a.25.01.02
A. Alsulaimani
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引用次数: 0
Leveraging the Double-Slit Experiment to Explore New Horizons in Quantum Computation 利用双光实验探索量子计算的新视野
Pub Date : 2024-05-05 DOI: 10.5013/ijssst.a.25.01.12
Pravir Malik
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引用次数: 0
Understanding the Interplay Between Trust, Reliability, and Human Factors in the Age of Generative AI 了解生成式人工智能时代信任、可靠性和人为因素之间的相互作用
Pub Date : 2024-05-05 DOI: 10.5013/ijssst.a.25.01.10
Simon Thorne
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引用次数: 0
A Loss Landscape Perspective and Simulations for Imaging Inverse Problems Based on AI and Neuron Network Training Method 基于人工智能和神经元网络训练方法的损失景观视角和成像逆问题模拟
Pub Date : 2024-05-05 DOI: 10.5013/ijssst.a.25.01.06
Mingyong Zhou
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引用次数: 0
Developing a Tool for Modeling and Simulation of Discrete Systems Using Iterative Approach 利用迭代法开发离散系统建模和仿真工具
Pub Date : 2024-05-05 DOI: 10.5013/ijssst.a.25.01.11
R. Davidrajuh
- General-purpose Petri Net Simulator (GPenSIM) is a new tool for modelling and simulating discrete systems. GPenSIM was developed using the Iterative Approach, which divides the software development life-cycle into smaller tasks. In each iteration, we add more functionality. However, GPenSIM was created to model industrial applications and academic purposes (teaching and research). Hence, in each iteration, some industrial, real-life, and large discrete systems are modelled, and the experiences received from these projects are also incorporated into the tool development. This paper follows the development of GPenSIM through three major iterations and explains the importance and lessons learned during these iterations.
- 通用 Petri 网模拟器(GPenSIM)是一种用于离散系统建模和模拟的新工具。GPenSIM 采用迭代法开发,该方法将软件开发生命周期划分为多个较小的任务。在每次迭代中,我们都会增加更多功能。然而,GPenSIM 是为工业应用和学术目的(教学和研究)而创建的模型。因此,在每次迭代中,我们都会对一些工业、现实生活和大型离散系统进行建模,并将从这些项目中获得的经验融入到工具开发中。本文将介绍 GPenSIM 的三次主要迭代开发过程,并解释这些迭代的重要性和经验教训。
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引用次数: 0
AI Efficacy in Sparse Data Environments: Exploring Approximate Knowledge Interpolation for Practical Applications 稀疏数据环境中的人工智能效能:探索近似知识插值的实际应用
Pub Date : 2024-05-05 DOI: 10.5013/ijssst.a.25.01.09
Qiang Shen
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引用次数: 0
期刊
International journal of simulation: systems, science & technology
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