Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Face recognition biometric recognises human faces effectively where their performance is critically affected under deviating light effects. This work presents an efficient illumination invariant feature extraction technique using homomorphic filtering in integer wavelet transform (IWT) domain. The goal of this investigation is to subdue the low frequency components in small-scale extracted features with the simultaneous perpetuation of rugged texture components in face images. The technique exploits homomorphic filtering based illumination normalised (HFIN) images which are then utilised in analysing the low and high pass frequency coefficients. Furthermore, IWT-based multiscale features (MFIWT) over HFIN images are examined with orthogonal and biorthogonal wavelets. The HFIN-MFIWT features are hereafter mapped onto non-correlated lower dimensional subspace using eigenface mechanism. Significant facial features are then classified using K-nearest neighbour. The efficacy of HFIN-MFIWT approach is assessed on Yale, Yale B, CMU-PIE, and extended Yale B databases that evidently authenticate its effectiveness.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Gujarati script has a large number of characters with curvature shapes and complexities. The script can contain a variety of character sets like vowels, consonants, numerals, modifiers, conjunct characters, and other combinations of characters. Different forms of conjunct characters are possible but in this particular paper one of the forms of frequently used conjunct characters is considered for assessment point of view. This paper deals with the identification of typewritten and handwritten conjunct characters. There is no benchmark dataset available for Conjunct Gujarati characters; so, a train and test datasets are created using various features of characters such as a number of open edges, location of open edges in zone, pixels count on a constructed horizontal and vertical line. Artificial neural network is used for the classification of characters and a success rate of 99.4% and 94.1% is achieved in most of the typewritten and handwritten Gujarati conjunct characters respectively.

Due to non-invasive and easy to acquire procedure, electrocardiogram (ECG) is broadly adopted for extracting the correct heart health status of the subject (patient). In this paper, a method of analysing ECG signals (based on R-peak detection) recorded during different postures (i.e., sitting, standing and supine) has been presented. The results of this analysis are also compared with standard physioNet database (MIT-BIH arrhythmia database) for validation. Real time ECG signals in all three postures were acquired for 500 subjects, out of which signals of 17 subjects are used for analysis. For filtering these subjects' recordings, existing techniques need a higher order of analogue and digital filters. It increases the complexity of the system, which motivated us to use the combination of principal component analysis (PCA) and independent component analysis (ICA). This combination fulfils the need of higher order filters (HOFs). PCA is used for dimension reduction, whereas ICA is used for noise removal.

Parkinson's disease (PD) is a neurological disorder with more than six million people worldwide suffering from it. It is commonly diagnosed using clinical assessments and progression scale, which usually depends on the medical practitioner's expertise, and accuracy varies greatly between various examiners which also takes a long time to accurately diagnose. This paper proposes to develop a computer-aided diagnostic method to diagnose PD patients using MRI images of the brain, thus reducing cross-examiner variability and the time required to accurately differentiate between PD and control subjects. We have developed a CNN-based CAD system that classifies between PD and healthy patients, by utilising the differences between the Substantia Nigra region of the brain. The method proposed in this paper was successfully able to achieve an accuracy of 99.5%.