Because of the stochastic nature of traffic requirement matrix, it is very difficult to get the optimal traffic distribution to minimize the delay even with adaptive routing protocol in a fixed connection network where capacity already defined for each link. Hence there is a requirement to define such a method, which could generate the optimal solution very quickly and efficiently. This paper presenting a new concept to provide the adaptive optimal traffic distribution for dynamic condition of traffic matrix using nature based intelligence methods. With the defined load and fixed capacity of links, average delay for packet has minimized with various variations of evolutionary programming and particle swarm optimization. Comparative study has given over their performance in terms of converging speed. Universal approximation capability, the key feature of feed forward neural network has applied to predict the flow distribution on each link to minimize the average delay for a total load available at present on the network. For any variation in the total load, the new flow distribution can be generated by neural network immediately, which could generate minimum delay in the network. With the inclusion of this information, performance of routing protocol will be improved very much.
An Enhanced Analysis of Traffic Intelligence in Smart Cities Using Sustainable Deep Radial Function
Al-Kitab University（阿尔基塔布大学）；Annai Velankanni College of Engineering（安奈维兰卡尼工程学院）；Saveetha School of Engineering（萨维塔工程学院）；Sri Eshwar College of Engineering（斯里埃什瓦尔工程学院）；Northern Technical University（北方技术大学）；University of Anbar（安巴尔大学）
Bio-inspired AI: Integrating Biological Complexity into Artificial Intelligence
McGovern Institute for Brain Research, MIT（麻省理工学院麦戈文脑科学研究所）；Allen Discovery Center at Tufts University（塔夫茨大学艾伦发现中心）；Center for Brains, Minds, and Machines, MIT（麻省理工学院脑、心智与机器中心）；Wyss Institute for Biologically Inspired Engineering, Harvard University（哈佛大学威斯生物启发工程研究所）
Brain-Inspired Hardware for Artificial Intelligence: Accelerated Learning in a Physical-Model Spiking Neural Network
Kirchhoff Institute for Physics, Heidelberg University（海德堡大学基尔霍夫物理研究所）；Department of Physiology, University of Bern（伯尔尼大学生理学系）；University of Bern（伯尔尼大学）；Heidelberg University（海德堡大学）；Kirchhoff Institute for Physics（基尔霍夫物理研究所）
Recent Advances and New Frontiers in Spiking Neural Networks
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
Brain-inspired Computational Intelligence via Predictive Coding
VERSES AI Research Lab（VERSES AI研究实验室）；Institute of Logic and Computation（逻辑与计算研究所）；University of South Florida（佛罗里达州立大学）；Sussex AI Group（Sussex人工智能小组）；Department of Computer Science（计算机科学系）；University of Oxford（牛津大学）；Paul G. Allen School of Computer Science and Engineering（保罗·G·阿伦计算机科学与工程学院）；University of Washington（华盛顿大学）；The Wellcome Centre for Human Neuroimaging（Wellcome人类神经成像中心）；Institute of Neurology University College London（神经学研究所伦敦大学学院）；Rochester Institute of Technology（罗切斯特理工学院）
Brain-inspired AI for Edge Intelligence: a systematic review
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））；Cloud Computing and Distributed Systems (CLOUDS) Lab, School of Computing and Information Systems, University of Melbourne（墨尔本大学计算与信息系统学院云计算与分布式系统实验室）；College of Mathematics and Computer Science, Shantou University（汕头大学数学与计算机学院）
