Adv. Artif. Intell.最新文献_第7页

Reducing Excessive Amounts of Data: Multiple Web Queries for Generation of Pun Candidates 减少过多的数据量:生成双关语候选词的多个Web查询

Adv. Artif. Intell. Pub Date : 2011-01-01 DOI: 10.1155/2011/107310

Pawel Dybala, M. Ptaszynski, Kohichi Sayama

引用次数: 5

Generalization of the Self-Shrinking Generator in the Galois Field GF(pn) 伽罗瓦场GF(pn)中自收缩发生器的推广

Adv. Artif. Intell. Pub Date : 2011-01-01 DOI: 10.1155/2011/464971

Antoniya Tasheva, Zhaneta Tasheva, A. Milev

引用次数: 15

NEST: A Compositional Approach to Rule-Based and Case-Based Reasoning NEST:基于规则和基于案例推理的组合方法

Adv. Artif. Intell. Pub Date : 2011-01-01 DOI: 10.1155/2011/374250

P. Berka

引用次数: 14

Convergence Time Analysis of Particle Swarm Optimization Based on Particle Interaction 基于粒子相互作用的粒子群优化收敛时间分析

Adv. Artif. Intell. Pub Date : 2011-01-01 DOI: 10.1155/2011/204750

Chao-Hong Chen, Ying-ping Chen

引用次数: 12

Development of Artificial Neural-Network-Based Models for the Simulation of Spring Discharge 基于人工神经网络的弹簧放电仿真模型的建立

Adv. Artif. Intell. Pub Date : 2011-01-01 DOI: 10.1155/2011/686258

M. M. Raju, R. Srivastava, D. Bisht, H. Sharma, Anil Kumar

{"title":"Development of Artificial Neural-Network-Based Models for the Simulation of Spring Discharge","authors":"M. M. Raju, R. Srivastava, D. Bisht, H. Sharma, Anil Kumar","doi":"10.1155/2011/686258","DOIUrl":"https://doi.org/10.1155/2011/686258","url":null,"abstract":"The present study demonstrates the application of artificial neural networks (ANNs) in predicting the weekly spring discharge. The study was based on the weekly spring discharge from a spring located near Ranichauri in Tehri Garhwal district of Uttarakhand, India. Five models were developed for predicting the spring discharge based on a weekly interval using rainfall, evaporation, temperature with a specified lag time. All models were developed both with one and two hidden layers. Each model was developed with many trials by selecting different network architectures and different number of hidden neurons; finally a best predicting model presented against each developed model. The models were trained with three different algorithms, that is, quick-propagation algorithm, batch backpropagation algorithm, and Levenberg-Marquardt algorithm using weekly data from 1999 to 2005. A best model for the simulation was selected from the three presented algorithms using the statistical criteria such as correlation coefficient (R), determination coefficient, orNash Sutcliff's efficiency (DC). Finally, optimized number of neurons were considered for the best model. Training and testing results revealed that the models were predicting the weekly spring discharge satisfactorily. Based on these criteria, ANN-based model results in better agreement for the computation of spring discharge. LMR models were also developed in the study, and they also gave good results, but, when compared with the ANN methodology, ANN resulted in better optimized values.","PeriodicalId":7253,"journal":{"name":"Adv. Artif. Intell.","volume":"8 1","pages":"686258:1-686258:11"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75106112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 26

Tuning Expert Systems for Cost-Sensitive Decisions 调整专家系统的成本敏感决策

Adv. Artif. Intell. Pub Date : 2011-01-01 DOI: 10.1155/2011/587285

Atish P. Sinha, Huimin Zhao

引用次数: 3

Quo Vadis, Artificial Intelligence? Quo Vadis，人工智能?

Adv. Artif. Intell. Pub Date : 2010-02-24 DOI: 10.1155/2010/629869

D. Berrar, N. Sato, A. Schuster

{"title":"Quo Vadis, Artificial Intelligence?","authors":"D. Berrar, N. Sato, A. Schuster","doi":"10.1155/2010/629869","DOIUrl":"https://doi.org/10.1155/2010/629869","url":null,"abstract":"Since its conception in the mid 1950s, artificial intelligence with its great ambition to understand and emulate intelligence in natural and artificial environments alike is now a truly multidisciplinary field that reaches out and is inspired by a great diversity of other fields. Rapid advances in research and technology in various fields have created environments into which artificial intelligence could embed itself naturally and comfortably. Neuroscience with its desire to understand nervous systems of biological organisms and systems biology with its longing to comprehend, holistically, the multitude of complex interactions in biological systems are two such fields. They target ideals artificial intelligence has dreamt about for a long time including the computer simulation of an entire biological brain or the creation of new life forms from manipulations of cellular and genetic information in the laboratory. The scope for artificial intelligence in neuroscience and systems biology is extremely wide. This article investigates the standing of artificial intelligence in relation to neuroscience and systems biology and provides an outlook at new and exciting challenges for artificial intelligence in these fields. These challenges include, but are not necessarily limited to, the ability to learn from other projects and to be inventive, to understand the potential and exploit novel computing paradigms and environments, to specify and adhere to stringent standards and robust statistical frameworks, to be integrative, and to embrace openness principles.","PeriodicalId":7253,"journal":{"name":"Adv. Artif. Intell.","volume":"66 1","pages":"629869:1-629869:12"},"PeriodicalIF":0.0,"publicationDate":"2010-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80101445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 65

Evaluation of Data Quality and Drought Monitoring Capability of FY-3A MERSI Data FY-3A MERSI数据质量与干旱监测能力评价

Adv. Artif. Intell. Pub Date : 2010-02-23 DOI: 10.1155/2010/124816

D. Xiang, Liangming Liu, Qiao Wang, Na Yang, T. Han

引用次数: 3

Investigating the Underlying Intelligence Mechanisms of the Biological Olfactory System 研究生物嗅觉系统的潜在智能机制

Adv. Artif. Intell. Pub Date : 2010-02-23 DOI: 10.1155/2010/478107

Y. Makino, M. Yano

引用次数: 5

Bootstrap Learning and Visual Processing Management on Mobile Robots 移动机器人的自举学习与视觉处理管理

Adv. Artif. Intell. Pub Date : 2010-02-09 DOI: 10.1155/2010/765876

M. Sridharan

{"title":"Bootstrap Learning and Visual Processing Management on Mobile Robots","authors":"M. Sridharan","doi":"10.1155/2010/765876","DOIUrl":"https://doi.org/10.1155/2010/765876","url":null,"abstract":"A central goal of robotics and AI is to enable a team of robots to operate autonomously in the real world and collaborate with humans over an extended period of time. Though developments in sensor technology have resulted in the deployment of robots in specific applications the ability to accurately sense and interact with the environment is still missing. Key challenges to the widespread deployment of robots include the ability to learn models of environmental features based on sensory inputs, bootstrap off of the learned models to detect and adapt to environmental changes, and autonomously tailor the sensory processing to the task at hand. This paper summarizes a comprehensive effort towards such bootstrap learning, adaptation, and processing management using visual input. We describe probabilistic algorithms that enable a mobile robot to autonomously plan its actions to learn models of color distributions and illuminations. The learned models are used to detect and adapt to illumination changes. Furthermore, we describe a probabilistic sequential decision-making approach that autonomously tailors the visual processing to the task at hand. All algorithms are fully implemented and tested on robot platforms in dynamic environments.","PeriodicalId":7253,"journal":{"name":"Adv. Artif. Intell.","volume":"12 1","pages":"765876:1-765876:20"},"PeriodicalIF":0.0,"publicationDate":"2010-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88427145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2