Dr Chandrasekhar Kambhampati's Outputs (31)

A LDA-Based Social Media Data Mining Framework for Plastic Circular Economy (2024)
Journal Article
Xue, Y., Kambhampati, C., Cheng, Y., Mishra, N., Wulandhari, N., & Deutz, P. (2024). A LDA-Based Social Media Data Mining Framework for Plastic Circular Economy. International Journal of Computational Intelligence Systems, 17(1), Article 8. https://doi.org/10.1007/s44196-023-00375-7

The mass production of plastic waste has caused an urgent worldwide public health crisis. Although government policies and industrial innovation are the driving forces to meet this challenge, trying to understand public attitudes may improve the effi... Read More about A LDA-Based Social Media Data Mining Framework for Plastic Circular Economy.

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)
Presentation / Conference Contribution
Alabed, A., Kambhampati, C., & Gordon, N. Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease. Presented at Computing 2020, London

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.-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.

An evaluation framework for mobile health education software (2015)
Presentation / Conference Contribution
Aljaber, T., Gordon, N., Kambhampati, C., & Brayshaw, M. (2015, July). An evaluation framework for mobile health education software. Presented at 2015 Science and Information Conference (SAI), London

© 2015 IEEE. Mobile applications in general, and mobile applications for health education in particular, are commonly used to support patients, health professionals and other stakeholders. A critical evaluation framework is needed to ensure the usabi... Read More about An evaluation framework for mobile health education software.

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.

Use of a propositional rule learner for prognosis of mortality rates in heart failure patients (2014)
Journal Article
Bohacik, J., Kambhampati, C., Davis, D. N., & Cleland, J. G. F. (2014). Use of a propositional rule learner for prognosis of mortality rates in heart failure patients. Journal of information technologies, 7(1),

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.

Chaotic oscillations in hodgkin-huxley neural dynamics: Stimulus reconstruction and neural dynamics retrieval (2011)
Book Chapter
Sarangdhar, M., & Kambhampati, C. (2011). Chaotic oscillations in hodgkin-huxley neural dynamics: Stimulus reconstruction and neural dynamics retrieval. In Electrical engineering and applied computing (675-686). Springer. https://doi.org/10.1007/978-94-007-1192-1_53

Quantification of similarity using amplitudes and firing times of a Hodgkin-Huxley neural response (2011)
Book Chapter
Sarangdhar, M., & Kambhampati, C. (2011). Quantification of similarity using amplitudes and firing times of a Hodgkin-Huxley neural response. In Electrical Engineering and Applied Computing; Lecture Notes in Electrical Engineering (687-698). Springer Verlag. https://doi.org/10.1007/978-94-007-1192-1_54

Chaotic oscillations in a Hodgkin-Huxley neuron - Quantifying similarity estimation of neural responses (2010)
Presentation / Conference Contribution
Sarangdhar, M., & Kambhampati, C. (2010, June). Chaotic oscillations in a Hodgkin-Huxley neuron - Quantifying similarity estimation of neural responses. Presented at WCE 2010 - World Congress on Engineering 2010, London; United Kingdom

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.

Networked control systems tolerant to faults (2009)
Journal Article
Casavola, A., & Kambhampati, C. (2009). Networked control systems tolerant to faults. International journal of adaptive control and signal processing, 23(8), 679-681. https://doi.org/10.1002/acs.1115

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.