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All Outputs (37)

Disease progression in chronic heart failure is linear: Insights from multistate modelling (2024)
Journal Article
Kazmi, S., Kambhampati, C., Rigby, A. S., Cleland, J. G. F., Kazmi, K. S., Cuthbert, J., Pellicori, P., & Clark, A. L. (online). Disease progression in chronic heart failure is linear: Insights from multistate modelling. European journal of heart failure, https://doi.org/10.1002/ejhf.3400

Aims: Understanding the pattern of disease progression in chronic heart failure (HF) may inform patient care and healthcare system design. We used a four-state Markov model to describe the disease trajectory of patients with HF. Methods and results:... Read More about Disease progression in chronic heart failure is linear: Insights from multistate modelling.

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.

Ionic Imbalances and Coupling in Synchronization of Responses in Neurons (2019)
Journal Article
Sadegh-Zadeh, S.-A., 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)
Presentation / Conference Contribution
Zhang, Y., Kambhampati, C., Davis, D. N., Goode, K., & Cleland, J. G. F. A comparative study of missing value imputation with multiclass classification for clinical heart failure data. Presented at 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery

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)
Presentation / Conference Contribution
Kambhampati, C., Sarangdhar, M., & Poolsawad, N. (2010, October). Dysphonia measures in parkinson's disease and their use in prediction of its progression. Presented at International Conference on Knowledge Engineering and Ontology Development, Valencia, Spain

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)
Presentation / Conference Contribution
Sarangdhar, M., & Kambhampati, C. (2010, June). Stimulus reconstruction from a Hodgkin-Huxley neural response: A numerical solution. Presented at World Congress on Engineering 2010

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.-I. 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.

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.

Spiking neurons: Is coincidence-factor enough for comparing responses with fluctuating membrane voltage? (2008)
Presentation / Conference Contribution
Sarangdhar, M., & Kambhampati, C. Spiking neurons: Is coincidence-factor enough for comparing responses with fluctuating membrane voltage?

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?.

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.

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.

Autonomous clustering using rough set theory (2008)
Journal Article
Bean, C., & Kambhampati, C. (2008). Autonomous clustering using rough set theory. International Journal of Automation and Computing, 5(1), 90-102. https://doi.org/10.1007/s11633-008-0090-3

This paper proposes a clustering technique that minimizes the need for subjective human intervention and is based on elements of rough set theory (RST). The proposed algorithm is unified in its approach to clustering and makes use of both local and g... Read More about Autonomous clustering using rough set theory.