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Tiwari, Virendra Kumar
- A Comparison of Methods for Internet Traffic Sharing in Computer Network
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Authors
Affiliations
1 Department of Mathematics and Statistics, Sagar University, Sagar-470003, M.P., IN
2 Department of Computer Science & Applications, Sagar University, Sagar-470003, M.P., IN
3 Department of Physics & Electronics, Sagar University, Sagar-470003, M.P., IN
1 Department of Mathematics and Statistics, Sagar University, Sagar-470003, M.P., IN
2 Department of Computer Science & Applications, Sagar University, Sagar-470003, M.P., IN
3 Department of Physics & Electronics, Sagar University, Sagar-470003, M.P., IN
Source
International Journal of Advanced Networking and Applications, Vol 1, No 3 (2009), Pagination: 164-169Abstract
Naldi presented a Markov chain model based analysis for the user’s behaviour in a simple scenario of two competitors. The model is applied to predict influence of both parameters (blocking probability and initial preference) on the traffic distribution between the operators. It is also shown that smaller blocking competitors can be benefited from call-by-call basis assumption. In this paper this criteria of Call-by-call attempt is converted into two call attempts and new mathematical results are derived. A comparative study between call-attempts is made with Naldi expressions. It is found that, by two-call attempt model, the operator gains more traffic than one-call attempt.Keywords
Blocking Probability, Call-by-call Basis, Internet Service Provider [Operators], Internet Access, Internet Traffic, Markov Chain Model, Network Congestion, Quality of Service (QOS), Transition Probability Matrix, Users Behavior.- Classification of Motor Imaginary in EEG using feature Optimization and Machine Learning
Abstract Views :41 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Application, Lakshmi Narain College of Technology (MCA), Bhopal, MP-462022, IN
2 Department of Computer Science Engineering, LNCT University, Bhopal, MP-462022, IN
1 Department of Computer Application, Lakshmi Narain College of Technology (MCA), Bhopal, MP-462022, IN
2 Department of Computer Science Engineering, LNCT University, Bhopal, MP-462022, IN
Source
International Journal of Advanced Networking and Applications, Vol 15, No 2 (2023), Pagination: 5887-5891Abstract
Motor image Critical disease diagnosis relies heavily on EEG classification. The complexity of the motor imagery EEG data hindered accurate classification. The motor imagery EEG classification rate is increased using the feature Optimization procedure. A deep neural network-based classifier for motor imagery EEG classification was proposed in this paper. The design deep neural network is a three-layer neural network model that incorporates the teacher learning-based optimization and feature optimization technique. The EEG data's noise and artefacts are reduced by a teacher learning-based optimization technique, which also enhances the input vectors for DNN. The suggested algorithm has been tested on datasets from the third and fourth BCI competitions and has been simulated in MATLAB environments. According to the evaluation's findings, the suggested algorithm compresses the current motor imagery EEG categorization technique quite effectively.Keywords
Motor Imagery (MI) EEG, Bayesian Feature Extraction, TLBO, DNN, Wavelet Transform, MATLAB, DWT, Optimization, BCI.References
- . Han, Chang-Hee, Klaus-Robert Müller, and Han- Jeong Hwang. "Enhanced Performance of a Brain Switch by Simultaneous Use of EEG and NIRS Data for Asynchronous Brain-Computer Interface." IEEE Transactions on Neural Systems and Rehabilitation Engineering 28, no. 10 (2020): 2102-2112.
- . Vasiljevic, Gabriel Alves Mendes, and Leonardo Cunha de Miranda. "Brain–computer interface games based on consumer-grade EEG Devices: A systematic literature review." International Journal of Human– Computer Interaction 36, no. 2 (2020): 105-142.
- . Abdulwahab, Samaa S., Hussain K. Khleaf, Manal H. Jassim, and S. Abdulwahab. "A Systematic Review of Brain-Computer Interface Based EEG." Iraqi J. Electr. Electron. Eng 16, no. 2 (2020): 1-10.
- . Sun, Zhe, Zihao Huang, Feng Duan, and Yu Liu. "A novel multimodal approach for hybrid brain– computer interface." IEEE Access 8 (2020): 89909- 89918.
- . Bhise, Pratibha R., Sonali B. Kulkarni, and Talal A. Aldhaheri. "Brain computer interface-based EEG for emotion recognition system: A systematic review." In 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp. 327-334. IEEE, 2020.
- . Fahimi, Fatemeh, Strahinja Dosen, Kai Keng Ang, Natalie Mrachacz-Kersting, and Cuntai Guan. "Generative adversarial networks-based data augmentation for brain-computer interface." IEEE transactions on neural networks and learning systems (2020).
- . Al-Nuaimi, Fatima Ali, Rauda Jasem Al-Nuaimi, Sara Saaed Al-Dhaheri, Sofia Ouhbi, and Abdelkader Nasreddine Belkacem. "Mind drone chasing using EEG-based Brain Computer Interface." In 2020 16th International Conference on Intelligent Environments (IE), pp. 74-79. IEEE, 2020.
- . Ieracitano, Cosimo, Nadia Mammone, Amir Hussain, and Francesco Carlo Morabito. "A novel explainable machine learning approach for EEG-based braincomputer interface systems." Neural Computing and Applications (2021): 1-14.
- . Wang, Li, Weijian Huang, Zhao Yang, and Chun Zhang. "Temporal-spatial-frequency depth extraction of brain-computer interface based on mental tasks." Biomedical Signal Processing and Control 58 (2020): 101845.
- . Kant, Piyush, Shahedul Haque Laskar, Jupitara Hazarika, and Rupesh Mahamune. "CWT Based transfer learning for motor imagery classification for brain computer interfaces." Journal of Neuroscience Methods 345 (2020): 108886.