Performance Evaluation of Online Machine Learning Models Based on Cyclic Dynamic and Feature-Adaptive Time Series

[1]  Wei Liu,et al.  Multimodal Emotion Recognition Using Deep Neural Networks , 2017, ICONIP.

[2]  Zhijun Zhang,et al.  Adaptive incremental learning of image semantics with application to social robot , 2016, Neurocomputing.

[3]  Yaser Jararweh,et al.  Blockchain-Enhanced Data Sharing With Traceable and Direct Revocation in IIoT , 2021, IEEE Transactions on Industrial Informatics.

[4]  Shaogang Gong,et al.  Incremental Activity Modeling in Multiple Disjoint Cameras , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Salvatore J. Stolfo,et al.  A framework for constructing features and models for intrusion detection systems , 2000, TSEC.

[6]  Amit K. Roy-Chowdhury,et al.  Incremental learning of human activity models from videos , 2016, Comput. Vis. Image Underst..

[7]  Yiqiang Chen,et al.  Feature Adaptive Online Sequential Extreme Learning Machine for lifelong indoor localization , 2014, Neural Computing and Applications.

[8]  Ahmed Al-Saffar,et al.  Feature Adaptive and Cyclic Dynamic Learning Based on Infinite Term Memory Extreme Learning Machine , 2019, Applied Sciences.

[9]  Azizi Abdullah,et al.  Improved CAMshift Based on Supervised Learning , 2012, RiTA.

[10]  Guoliang Yang,et al.  Robust Visual Tracking via Incremental Subspace Learning and Local Sparse Representation , 2018 .

[11]  Gregory D. Hager,et al.  An incremental approach to learning generalizable robot tasks from human demonstration , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[12]  Gürsel Serpen,et al.  Why machine learning algorithms fail in misuse detection on KDD intrusion detection data set , 2004, Intell. Data Anal..

[13]  Shahrul Azman Mohd. Noah,et al.  Fish recognition based on the combination between robust feature selection, image segmentation and geometrical parameter techniques using Artificial Neural Network and Decision Tree , 2009, ArXiv.

[14]  Mohamed Akil,et al.  Fully Automatic Brain Tumor Segmentation using End-To-End Incremental Deep Neural Networks in MRI images , 2018, Comput. Methods Programs Biomed..

[15]  Qin Qin,et al.  Robust visual tracking based on incremental discriminative projective non-negative matrix factorization , 2015, Neurocomputing.

[16]  Jonathan P. How,et al.  Human aware UAS path planning in urban environments using nonstationary MDPs , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[17]  Qin Qin,et al.  Moving object detection based on incremental learning low rank representation and spatial constraint , 2015, Neurocomputing.

[18]  Matteo Saveriano,et al.  Incremental kinesthetic teaching of end-effector and null-space motion primitives , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[19]  Jun Zhou,et al.  An incremental structured part model for object recognition , 2015, Neurocomputing.

[20]  Bala Srinivasan,et al.  Adaptive mobile activity recognition system with evolving data streams , 2015, Neurocomputing.

[21]  Yusheng Ji,et al.  Computation Offloading in Beyond 5G Networks: A Distributed Learning Framework and Applications , 2020, IEEE Wireless Communications.

[22]  Rosilah Hassan,et al.  A performance study of various mobility speed on AODV routing protocol in homogeneous and heterogeneous MANET , 2011, The 17th Asia Pacific Conference on Communications.

[23]  Min Liu,et al.  An incremental extreme learning machine for online sequential learning problems , 2014, Neurocomputing.

[24]  Mamoun Alazab,et al.  Deep Learning-Based Traffic Safety Solution for a Mixture of Autonomous and Manual Vehicles in a 5G-Enabled Intelligent Transportation System , 2021, IEEE Transactions on Intelligent Transportation Systems.

[25]  Tim Fingscheidt,et al.  Towards Corner Case Detection for Autonomous Driving , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).

[26]  Adolfo Martínez Usó,et al.  UJIIndoorLoc: A new multi-building and multi-floor database for WLAN fingerprint-based indoor localization problems , 2014, 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[27]  Reza Firsandaya Malik,et al.  Knowledge Preserving OSELM Model for Wi-Fi-Based Indoor Localization , 2019, Sensors.

[28]  Narasimhan Sundararajan,et al.  A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks , 2006, IEEE Transactions on Neural Networks.

[29]  Md Jan Nordin,et al.  SIFT based monocular SLAM with multi-clouds features for indoor navigation , 2010, TENCON 2010 - 2010 IEEE Region 10 Conference.

[30]  Mahardhika Pratama,et al.  An Incremental Type-2 Meta-Cognitive Extreme Learning Machine , 2017, IEEE Transactions on Cybernetics.

[31]  Narasimhan Sundararajan,et al.  On-Line Sequential Extreme Learning Machine , 2005, Computational Intelligence.

[32]  Gautam Srivastava,et al.  Deep Learning-Embedded Social Internet of Things for Ambiguity-Aware Social Recommendations , 2022, IEEE Transactions on Network Science and Engineering.

[33]  Shahnorbanun Sahran,et al.  Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages , 2013 .

[34]  Mohd Asyraf Zulkifley,et al.  Kalman Filter-Based Aggressive Behaviour Detection for Indoor Environment , 2016 .

[35]  Francesc J. Ferri,et al.  Incremental Generalized Discriminative Common Vectors for Image Classification , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[36]  Bernhard Pfahringer,et al.  Winning the KDD99 classification cup: bagged boosting , 2000, SKDD.

[37]  Salwani Abdullah,et al.  An Analysis of the KDD99 and UNSW-NB15 Datasets for the Intrusion Detection System , 2020, Symmetry.

[38]  Yazan Aljeroudi,et al.  Infinite-Term Memory Classifier for Wi-Fi Localization Based on Dynamic Wi-Fi Simulator , 2018, IEEE Access.

[39]  Atilla Özgür,et al.  A review of KDD99 dataset usage in intrusion detection and machine learning between 2010 and 2015 , 2016, PeerJ Prepr..

[40]  Athanasios V. Vasilakos,et al.  Machine learning on big data: Opportunities and challenges , 2017, Neurocomputing.

[41]  Yusheng Ji,et al.  Intelligent Post-Disaster Networking by Exploiting Crowd Big Data , 2020, IEEE Network.

[42]  Xin Pan,et al.  ARIEL: automatic wi-fi based room fingerprinting for indoor localization , 2012, UbiComp.

[43]  Sinan Kalkan,et al.  CINet: A Learning Based Approach to Incremental Context Modeling in Robots , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[44]  Zhi Liu,et al.  Mining Mobile Intelligence for Wireless Systems: A Deep Neural Network Approach , 2020, IEEE Computational Intelligence Magazine.

[45]  Keping Yu,et al.  Robust Spammer Detection Using Collaborative Neural Network in Internet-of-Things Applications , 2021, IEEE Internet of Things Journal.

[46]  Jie Li,et al.  AF-DCGAN: Amplitude Feature Deep Convolutional GAN for Fingerprint Construction in Indoor Localization Systems , 2018, IEEE Transactions on Emerging Topics in Computational Intelligence.

[47]  Sabir Hossain,et al.  Driverless Car: Autonomous Driving Using Deep Reinforcement Learning in Urban Environment , 2018, 2018 15th International Conference on Ubiquitous Robots (UR).