Institutional Repository

Scholar@UOC is the primary academic repository of the University of Calicut.

This repository is aimed to collect, preserve and distribute the research output of the members of our University. This is an open access system hosted and managed by the University Library.

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Recent Submissions

Alzheimer’s disease diagnosis and risk prediction using advanced machine learning models
(Sullamussalam Science College, University of Calicut., 2025) Haulath, K.; Mohamed Basheer K.P
Alzheimer's Disease (AD), which impairs memory, cognition, and behavior, is a progressive Neurodegenerative Disorder (ND). Accounting for 60-80% of dementia cases, AD advances from mild memory loss to complete loss of interaction with the environment. As there is no cure for AD, early detection is vital to mitigate disease progression. Currently, Machine Learning (ML) and Deep Learning (DL) have exhibited promise in neuroimaging-based AD prediction by employing approaches, namely Lasso Regression (LR), Convolutional Neural Networks (CNN), Support Vector Machine (SVM), and Deep Neural Networks (DNN). Nevertheless, these models face limitations, comprising overfitting, high error rates, and limited small dataset performance. To address these challenges, a two-part framework is introduced in this study. The AD Neuroimaging Initiative (ADNI) MRI dataset is used by the first implementation to enhance prediction and classification accuracy through the GELU Swish Radial Basis Function Network (GS-RBFN). MRI images undergo preprocessing steps, such as normalization, skull stripping, and spatial smoothing, followed by essential brain tissue segmentation with Brownian Log Scaling Archimedes Optimization-centric Watershed Segmentation (BLSAOWS) and Feature Selection (FS) using the Base Switch Rule Infimum and Supremum-based Rock Hyrax Swarm Optimization (BSRISRHSO) approach. AD classification is further performed by the GS-RBFN classifier. GS-RBFN (Gaussian—Swish Radial Basis Function Network) and TT Self-Weighted Deep-AD3-Net—for early detection and staging of AD. The proposed models employ novel optimization algorithms (BLSAOWS and BSRISRHSO) for improved segmentation and feature selection from MRI datasets. The performance analysis of GS-RBFN model learns the features efficiently, it achieves superior accuracy (98.45%), precision (98.44%), F-measure (98.44%), Sensitivity (98.44%), Recall (98.45%), and specificity (98.45%). In the second implementation, AD risk scoring and stage prediction are focused on using The AD Prediction Of Longitudinal Evolution (TADPOLE) dataset. This methodology involves ranking critical variables, Risk Score (RS) calculation, and brain shrinkage analysis. Essential variables are selected by the Recursive Hypothesis-Creation Algorithm (RHCA), with the True True Self-Weighting Mechanism (TT-SWM) calculating RS. By using the Queue-Boltzmann-ConstantSphere (QBCS) technique, brain shrinkage in the hippocampus is measured, whereas Gray-Level Co-occurrence Matrix (GLCM) features facilitate staging through the Deep-AD3-Net classifier. Experimental evaluation using the TADPOLE dataset achieved an accuracy of 98.45%, sensitivity of 97.82%, and AUC of 0.981, outperforming recent state-of-the-art models. The frameworks demonstrate robustness and clinical potential for early intervention and treatment planning. The study advances existing AD diagnostic approaches by improving interpretability, computational efficiency, and predictive reliability. Keywords: Alzheimer’s disease, Data augmentation, Brownian Log Scaling Archimedes Optimization-based Watershed Segmentation (BLSAOWS), Base Switch Rule Infmum and Supremum-based Rock Hyrax Swarm Optimization (BSRISRHSO), GELU and SWISH-based Radial Basis Function Network (GSRBFN), Recursive Hypothesis-Creation Algorithm (RHCA), Phylogenetic Method (PM), one Gray-Level Co-occurrence Matrix (GLCM), Gray-Level Co-occurrence Matrix (GLCM Genetic Algorithm (GA), Deep-AD3-Net classifier.
Writing the City: Exploring Urban Space in the Narratives of Select Indian Women Writers
(English Department, Vimala College (Autonomous), Thrissur, 2025) Keerthy Sophiya Ponnachan; Joycee, O. J.; Sijo Varghese C
Mathematical Modelling of Biological Variations due to Application of Nanofluids in Body Fluids
(Research and Postgraduate Department of Mathematics St. Thomas’ College, Thrissur., 2022) Sujesh A. S.; Alphonsa Mathew
A substance capable of flowing is termed as a fluid. Fluids are of two types, namely liquids and gases. The study of fluid's behaviour at rest (termed fluid statics) and in motion (termed fluid dynamics) is combinedly known as fluid mechanics. The fluid produced and circulated within the human body or secreted outside the human body is known as body fluid. Blood, saliva, urine, tears, sweat, and breast milk are a few examples of body fluid. Water is the basis of all body fluids and the human body is composed of about 60% of water.Nano fluid is a colloidal mixture in which a base fluid (water, oil, ethylene glycol, etc.) is mixed with nanometer-sized particles (metals, carbides, oxides or carbon nano tubes). Fluids constituting two nanometer-sized particles are termed hybrid nano fluids. Nano fluids tend to upgrade and stabilize the thermal properties of the fluid which marked a revolution in the field of fluid dynamics. The description of a system using mathematical concepts and language is known as mathematical model and the process of developing a mathematical model is known as mathematical modelling. Mathematical models finds its use in natural sciences, engineering disciplines and social sciences. A mathematical model helps to explain a system and to study the effects of different components and also to make predictions about its behaviour. The thesis entitled Mathematical Modelling of Biological Variations due to Application of Nano fluids in Body Fluids has been arranged into 12 chapters. Chapter 1 introduces the basic concepts, preliminaries and definitions to the reader. An extensive review of related literature has been presented in Chapter 2. Owing to the practical applications (like biomedical imaging, hyperthermia, pharmaceuticals, biosensors, medical instruments, bio-chromatography, microchip pump, thermostatic, biomedical science, targeted drug delivery, and cancer therapy), nine fluid flow problems are modeled and investigated in this thesis. In Chapter 3, the bio convective stagnation point flow involving carbon nano tubes along a lengthening sheet subject to induced magnetic field and multiple stratification effects is investigated. The dynamics of water conveying single-wall viicarbon nano tubes (SWCNTs) and magnetite nano particles on the bio convective stagnation-point ow along a stretching sheet subject to chemical reaction, viscous dissipation, induced magnetic field, and stratification effects is investigated in Chapter 4. Non-spherical nanoparticles have gained popularity for their ability in changing the thermo physical properties of a nano fluid. Chapter 5 elucidates the significance of multiple slip and nanoparticle shape on stagnation point flow of blood-based silver nano fluid considering chemical reaction, induced magnetic field, thermal radiation, nano particle shape and linear heat source. The numerical study on the stratification effects of bio convective electroencephalographic (EMHD) flow past a stretching sheet using water-based CNT has been presented in Chapter 6. The focal concern of Chapter 7 is to numerically scrutinize the consequences of multiple slip, linear radiation and chemically reactive species on MHD convective Carreau nanoliquid flow over an elongating cylinder. Moreover, statistical scrutiny on the impact of Hartmann number, thermal radiation and thermal slip parameter over heat transfer rate employing Response Surface Methodology (RSM) and sensitivity analysis is also performed. The nanomaterial flow of Chapter 8 has been modeled using the modified Buongiorno nano fluid model. The impact of the stratification constraints and magnetic field are also accounted. Further, the influence of magnetic field parameter, thermal stratification parameter, volume fraction of magnetite nanoparticles, and velocity ratio parameter on the heat transfer rate has been scrutinized statistically using a five-level four-factor response surface optimized model. In Chapter 9, the dynamics of the T iO2 − H2 O nano material over a non linearly stretched surface and modeled using modified Buongiorno model is investigated. Experimentally derived correlations of the thermal conductivity and dynamic viscosity of the nano material are utilized.The hydro-magnetic bio convective flow of a nano material over a lengthening surface is investigated in Chapter 10. Realistic nano material modelling is achieved by incorporating passive control of the nano particles at the boundary. The impact of the Newtonian heating and Stefan blowing constraints are also accounted. The sensitivity of heat transport rate is also computed. Chapter 11 numerically elucidates the dynamics of elector-magneto hydrodynamic flow of blood-gold nano material over a non linearly stretching surface utilizing the Casson model. The impact of second-order hydrodynamic-slip, nano particle radius, first-order thermal-slip, inter-particle spacing and non-uniform heat source are also accounted. Lastly, Chapter 12 presents the concluding remarks of the thesis and proposals for future work.
Electrochemical studies on N, S, O donor heterocycles as corrosion protection agents for commercial metals in different environments
(Department of Chemistry, University of Calicut, 2015) Revathi Mohan; Abraham Joseph
Study on minification processes
(Department of Statistics, University of Calicut, 2007) Krishnarani,S. D.; Jayakumar, K.