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All Outputs (35)

Dynamic risk stratification using Markov chain modelling in patients with chronic heart failure (2022)
Journal Article
Kazmi, S., Kambhampati, C., Cleland, J., Cuthbert, J., Kazmi, K. S., Pellicori, P., …Clark, A. L. (2022). Dynamic risk stratification using Markov chain modelling in patients with chronic heart failure. ESC Heart Failure, https://doi.org/10.1002/ehf2.14028

Aims: Risk changes with the progression of disease and the impact of treatment. We developed a dynamic risk stratification Markov chain model using artificial intelligence in patients with chronic heart failure (CHF). Methods and results: We describe... Read More about Dynamic risk stratification using Markov chain modelling in patients with chronic heart failure.

Locally fitting hyperplanes to high-dimensional data (2022)
Journal Article
Hou, M., & Kambhampati, C. (2022). Locally fitting hyperplanes to high-dimensional data. Neural Computing and Applications, 34(11), 8885-8896. https://doi.org/10.1007/s00521-022-06909-y

Problems such as data compression, pattern recognition and artificial intelligence often deal with a large data sample as observations of an unknown object. An effective method is proposed to fit hyperplanes to data points in each hypercubic subregio... Read More about Locally fitting hyperplanes to high-dimensional data.

Addressing Optimisation Challenges for Datasets with Many Variables, Using Genetic Algorithms to Implement Feature Selection (2022)
Journal Article
Gordon, N., Kambhampati, C., & Alabad, A. (2022). Addressing Optimisation Challenges for Datasets with Many Variables, Using Genetic Algorithms to Implement Feature Selection. AI, Computer Science and Robotics Technology, 1, 1-21. https://doi.org/10.5772/acrt.01

This article provides an optimisation method using a Genetic Algorithm approach to apply feature selection techniques for large data sets to improve accuracy. This is achieved through improved classification, a reduced number of features, and further... Read More about Addressing Optimisation Challenges for Datasets with Many Variables, Using Genetic Algorithms to Implement Feature Selection.

Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease (2020)
Journal Article
Alabed, A., Kambhampati, C., & Gordon, N. (in press). Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease. Advances in Intelligent Systems and Computing, 1229 AISC, 531-543. https://doi.org/10.1007/978-3-030-52246-9_38

A great wealth of information is hidden in clinical datasets, which could be analyzed to support decision-making processes or to better diagnose patients. Feature selection is one of the data pre-processing that selects a set of input features by rem... Read More about Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease.

Ionic Imbalances and Coupling in Synchronization of Responses in Neurons (2019)
Journal Article
Sadegh-Zadeh, S., Kambhampati, C., & Davis, D. N. (2019). Ionic Imbalances and Coupling in Synchronization of Responses in Neurons. J — Multidisciplinary Scientific Journal, 2(1), 17-40. https://doi.org/10.3390/j2010003

Most neurodegenerative diseases (NDD) are a result of changes in the chemical composition of neurons. For example, Alzheimer's disease (AD) is the product of Aβ peptide deposition which results in changes in the ion concentration. These changes in io... Read More about Ionic Imbalances and Coupling in Synchronization of Responses in Neurons.

Issues in the mining of heart failure datasets (2014)
Journal Article
Poolsawad, N., Moore, L., Kambhampati, C., & Cleland, J. G. (2014). Issues in the mining of heart failure datasets. International Journal of Automation and Computing, 11(2), 162-179. https://doi.org/10.1007/s11633-014-0778-5

This paper investigates the characteristics of a clinical dataset using a combination of feature selection and classification methods to handle missing values and understand the underlying statistical characteristics of a typical clinical dataset. Ty... Read More about Issues in the mining of heart failure datasets.

A comparative study of missing value imputation with multiclass classification for clinical heart failure data (2012)
Conference Proceeding
Zhang, Y., Kambhampati, C., Davis, D. N., Goode, K., & Cleland, J. G. F. (2012). A comparative study of missing value imputation with multiclass classification for clinical heart failure data. In Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on (2840-2844). https://doi.org/10.1109/fskd.2012.6233805

Clinical data often contains missing values. Imputation is one of the best known schemes to overcome the drawbacks associated with missing values in data mining tasks. In this work, we compared several imputation methods and analyzed their performanc... Read More about A comparative study of missing value imputation with multiclass classification for clinical heart failure data.

A numerical model for Hodgkin-Huxley neural stimulus reconstruction (2011)
Journal Article
Kambhampati, C., & Sarangdhar, M. (2011). A numerical model for Hodgkin-Huxley neural stimulus reconstruction. Iaeng International Journal of Computer Science, 38(1), 89--94

The information about a neural activity is encoded in a neural response and usually the underlying stimulus that triggers the activity is unknown. This paper presents a numerical solution to reconstruct stimuli from Hodgkin-Huxley neural responses wh... Read More about A numerical model for Hodgkin-Huxley neural stimulus reconstruction.

Dysphonia measures in parkinson's disease and their use in prediction of its progression (2010)
Conference Proceeding
Kambhampati, C., Sarangdhar, M., & Poolsawad, N. (2010). Dysphonia measures in parkinson's disease and their use in prediction of its progression.

Parkinson's Disease (PD) is a neurodegenerative disorder that impairs the motor skills, speech and general muscle coordination. The progression of PD is assessed using a clinically defined rating scale known as Unified Parkinson's Disease Rating Scal... Read More about Dysphonia measures in parkinson's disease and their use in prediction of its progression.

Stimulus reconstruction from a Hodgkin-Huxley neural response: A numerical solution (2010)
Conference Proceeding
Sarangdhar, M., & Kambhampati, C. (2010). Stimulus reconstruction from a Hodgkin-Huxley neural response: A numerical solution. In Proceedings of the World Congress on Engineering (627 - 632)

Neural responses are the fundamental expressions of any neural activity. Information carried by a neural response is determined by the nature of a neural activity. In majority of cases the underlying stimulus that triggers it remains largely... Read More about Stimulus reconstruction from a Hodgkin-Huxley neural response: A numerical solution.

Spiking neurons and synaptic stimuli: Neural response comparison using coincidence-factor (2009)
Book Chapter
Sarangdhar, M., & Kambhampati, C. (2009). Spiking neurons and synaptic stimuli: Neural response comparison using coincidence-factor. In S. Ao, & L. Gelman (Eds.), Lecture Notes in Electrical Engineering; Advances in Electrical Engineering and Computational Science (681-692). Springer Verlag. https://doi.org/10.1007/978-90-481-2311-7_58

In this chapter, neural responses are generated by changing the Inter-Spike-Interval (ISI) of the stimulus. These responses are subsequently compared and a coincidence factor is obtained. Coincidence-factor, a measure of similarity, is expected to ge... Read More about Spiking neurons and synaptic stimuli: Neural response comparison using coincidence-factor.

Spiking neurons and synaptic stimuli : determining the fidelity of coincidence-factor in neural response comparison (2008)
Journal Article
Kambhampati, C., & Sarangdhar, M. (2008). Spiking neurons and synaptic stimuli : determining the fidelity of coincidence-factor in neural response comparison. Engineering Letters International Association of Engineers, 16(4), 512-517

Similarity between two spike trains is generally estimated using a ‘coincidence factor’. This factor relies on counting coincidences of firing-times for spikes in a given time window. However, in cases where there are significant fluctuations in memb... Read More about Spiking neurons and synaptic stimuli : determining the fidelity of coincidence-factor in neural response comparison.

Spiking neurons: Is coincidence-factor enough for comparing responses with fluctuating membrane voltage? (2008)
Conference Proceeding
Sarangdhar, M., & Kambhampati, C. (2008). Spiking neurons: Is coincidence-factor enough for comparing responses with fluctuating membrane voltage?. In Proceedings of the World Congress on Engineering (1640 - 1645)

Similarity between two spike trains is generally estimated using a ‘coincidence factor’. This factor relies on counting coincidences of firing-times for spikes in a given time window. However, in cases where there are significant fluctuations in memb... Read More about Spiking neurons: Is coincidence-factor enough for comparing responses with fluctuating membrane voltage?.

Robust FDI for FTC coordination in a distributed network system (2008)
Journal Article
Klinkhieo, S., Patton, R. J., & Kambhampati, C. (2008). Robust FDI for FTC coordination in a distributed network system. IFAC Proceedings Volumes/ International Federation of Automatic Control, 41(2), 13551-13556. https://doi.org/10.3182/20080706-5-KR-1001.0468

This paper focuses on the development of a suitable Fault Detection and Isolation (FDI) strategy for application to a system of inter-connected and distributed systems, as a basis for a fault-tolerant Network Control System (NCS) problem. The work fo... Read More about Robust FDI for FTC coordination in a distributed network system.

Stable quantum filters with scattering phenomena (2008)
Journal Article
Ahamed, W. U., & Kambhampati, C. (2008). Stable quantum filters with scattering phenomena. International Journal of Automation and Computing, 5(2), 132-137. https://doi.org/10.1007/s11633-008-0132-x

Quantum neural network filters for signal processing have received a lot of interest in the recent past. The implementations of these filters had a number of design parameters that led to numerical inefficiencies. At the same time the solution proced... Read More about Stable quantum filters with scattering phenomena.