Browsing by Author "Dr. Muhammad Yousaf Bhatti"

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Annihilation of Bumps for Piecewise Defined External Input in a Two-Population Neural field Model
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Ayesha Laraib; CIIT/FA19-RMT-010/LHR; Dr. Muhammad Yousaf Bhatti; LHR TP 7392
In this Research Proposal, we have to analyze the Annihilation mechanism of persistent localized activity states (bumps) in response to transient Spatio-temporal external input with piecewise temporal function in two-population neural field model of the Wilson-Cowan type [1]. Blomquist et al.[2] explored the bump solutions of two- population neural field models with no external input. Two separate methodologies are utilized to investigate the stability of bumps, the Amari methodology and direct linearization process stability. It was shown both mathematically and numerically that Ammari and full linearized stability produces same results. Directly after, the same model was investigated by spatial dependent and Spatio- temporal external input, by Yousaf .et. al [3, 4]. Afzal. Z [5, 6] studied the bump solution of a two-population neural field model with a smooth $alpha$-type temporal function under the effect of transitory Spatio-temporal external input. For a detailed exploration of the effects of different parameters of external input, it was further classified into amplitude, spatial, and temporal parts. We are investigating the annihilation of localized activity states (bumps) in response to transient Spatio- temporal external input with piecewise temporal function in a two-population neural field model of the Wilson-Cowan type. In the 4th chapter, we will present the conclusion of our thesis. In the 5th chapter, we will present the references of our thesis.
Annihilation of Bumps in Two-Population Neural Field Model Under the Influence of Gaussian External Input
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Hafiz Shahzad Ali; FA18-RMT-019; LHR TP 6456; Dr. Muhammad Yousaf Bhatti
In this work, we have examined the impact of Gaussian-type spatio-temporal external input on annihilation of self-managed activity states (bumps) in a Wilson-Cowan type two- population neural field model. These activity states plays the role of the intellectual framework with a restricted limit that can hold information known as working memory in the prefrontal cortex. The effect of various parameters engaged with the external input on annihilation of these persistent activity states is researched in detail. This very choice of Gaussian-type spatio-temporal external input is quite near the nature as seen by Roth et al. [4]. In this work eraser mechanism is used to annihilate the spatially symmetric solutions. If there is an activity in the network, inhibitory external input with no excitatory part in the external input can kill the activity. In the whole of our investigation, the prominent thing is Gaussian-type function which yields quite better findings in spite of three sided (triangular function) used by Yousaf et al. [5]. It is additionally discovered that the relative inhibition time constant serves as a pivotal role in annihilation of the activity. Runge-Kutta fourth order method has been used for mathematical simulations of this work.
Emergence of Bumps for Piece Wise Defined External Input in a Two-Population Neural Field Model
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) ATTIQA CHAUDHARY; CIIT/FA19-RMT-016/LHR; Dr. Muhammad Yousaf Bhatti; LHR TP 7391
In this research, we have studied the appearance of bump solutions (localized activity states) in a two-population neural field model for the temporary external input that depends on space and time with piece wise define temporal part. The same model has been investigated by Blomquist et al [2] with no external input and later on by Yousaf et al with spatial and spatio temporal dependent external inputs [3,5]. Zeshan et al [4] investigated the same model for more realistic approach for temporal function (𝛼-type function) in the spatio external input. In present study, we explored the appearance of bump solutions (persistent localized activity states) for more detailed temporal part (piece wise defined) in the spatio external input of for the above said model. The effect of external input on appearance of bumps for different spatial, piece wise 𝛼-type temporal functions of external input is investigated and found that certain parameters play a key role in the generation of persistent activity states in the network e.g. 𝜏(relative inhibition time constant), 𝑇𝑒 (total duration), 𝐶𝑒(amplitude), 𝑡1(inclination peak time) and 𝑡2 (Peak time duration) of external input. It is found that the minimum values of the amplitude and active time to evoke the activity in the network is smaller than those observed in prior studies. These results show that the present choice of temporal functions in the external input is more effective and efficient
Laminar Flow in Porous Channels Through Transverse Magnetic Field by Using Optimal Homotopy Analysis Method
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Farooq Abid; FA18-RMT-063; LHR TP 6462; Dr. Muhammad Yousaf Bhatti
In this work, we have investigated the laminar flow in porous channels applying transverse magnetic field on it. We have reviewed the work of Suryaparkasarao et al.\cite{surya} in detailed. Suryaparkasarao et al. did this by reducing the order of the main equation to third order nonlinear differential equation which was derived after the simplification of Navier stokes equation and continuity equation. They have calculated the velocity components, pressure distribution and skin friction coefficients. Noreena et al. have solved same problem in two different ways. In the first part, they solved the forth order non-linear differential equation in a simplified way without reducing its order. In second part, they have employed Homotopy Perturbation method which is better approximation then regular perturbation method. We discuss the same problem with a different technique is known as Optimal Homotopy analysis method (OHAM). Finally, we compute the velocity components and compare these results .
Machine Learning Approaches in Medical Diagnosis
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Muhammad Bilal; CIIT/SP24-RMT-001/LHR; Dr. Muhammad Yousaf Bhatti; LHR TP 10065
Brain tumors are among the most life-threatening neurological conditions, characterized by abnormal and uncontrolled cell growth within brain tissues. Early identification of tumor type and stage is crucial for effective intervention, yet conventional diagnostic methods often struggle to detect tumors in their early or subtle stages. This study proposes advanced deep-learning models for multi-class classification of brain tumors and machine-learning approaches for predicting chemical properties of anti-cancer drugs to support new drugs development. A hybrid deep-learning model was developed for classifying three tumor categories glioma, Meningioma and pituitary tumors using MRI scans. the model was trained on the brain tumor MRI dataset and integrates ResNet50 and Efficient Net50 as its base ar-chitectures, combined through a novel triangular dense-layer fusion strategy to optimize multi-stage feature extraction. The proposed model achieved a test accuracy of 97%, recall of 97%, and an AUC score of 99%, demonstrating its effectiveness for early and accurate brain-tumor classification. In second phase of this research, machine-learning algorithms and topological indices were used to analyze molecular structure of thirteen anti-cancer drugs. Topological in-dices were computed using a python program, and true physicochemical properties were retrieved from the ChemSpider database via an automated script. QSPR and SHAP anal-ysis were performed to identify indices most predictive of each physicochemical property. Machine-learning models were then trained on these features to develop generalized pre-dictive models for key durg properties, providing a foundation for computationally assisted drug discovery. Overall, this thesis demonstrates the combined potential of deep learning for early tu-mor detection and machine-learning for anti-cancer drug analysis, ultimately contributing to improved diagnostic accuracy and future therapeutic development
Prediction of Lungs Cancer using Machine Learning Algorithms
(Library Information Services COMSATS University Islamabad Lahore Campus, 2025) Muhammad Hamza FA23-RMT-055; Dr. Muhammad Yousaf Bhatti; LHR TP 9875
The primary cause of death is lung cancer, primarily due to the uncontrolled growth of malignant tumors in the lungs that can spread to the body’s other organs, posing serious health risks. Smoking is a major contributing factor. Early detection is crucial to prevent this deadly disease. In order to detect lungs cancer early on, we want to develop deep learning and machine learning algorithms. Such a model would help physicians make informed diagnostic decisions and determine the appropriate level of diagnostic intensity for patients. This method has the potential to significantly reduce treatment costs by enabling physicians to tailor treatment plans based on precise predictions, thereby avoiding unnecessary and expensive procedures. Our aim is to establish a sustainable model that forecasts lung cancer affectively. Our findings indicate that ResNet-50 surpasses other models, achieving an accuracy rate of 76%. In comparison Support Vector Machine (SVM), Logistic Regression (LR), and EfficientNet-B0 achieved accuracies of 73%, 64%, and 55%, respectively. This research underscores the potential of leveraging computer technology and machine learning methods to increase the precision of lung cancer diagnoses from CT scans
Some Studies of Two-Population Neural Field Model
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2019) Zeeshan Afzal; CIIT/FA13-PMATH-009/LHR; Dr. Muhammad Yousaf Bhatti; LHR TP 5949
We investigate the appearance and disappearance of localized activity states, so called bumps in Wilson-Cowan type two population neural ﬁeld model under the inﬂuence of transient spatio-temporal external input with smooth α-type and piecewise-type temporal functions. This two-population model is composed of two coupled nonlinear differential equations derived for the dynamics of spatially localized populations of both excitatory and inhibitory model neurons. The model with no external input corresponds to atmost two bump pair solutions. Such a system can be interpreted as a minimal cortical model for short term working memory, that is the ability of the brain to actively hold stimulus-related information for some seconds in short term memory and discards once it becomes irrele- vant Initially, if there is no activity in the system, persistent activity state can be evoked by switching on a suitable transient excitatory external input. This activity remains stable even though external input is switched off. The effect of external input on appearance and disappearance of bumps for different spatial, smooth α-type and piecewise-type temporal functions of external input is investigated and found that certain parameters play a key role in the generation of persistent activity states in the network e.g. relative inhibition time constant, total duration and the amplitude of external input. It is found that the minimum values of the amplitude and active time to evoke the activity in the network is smaller than those observed in showing that the present choice of temporal functions in the external in- put is more effective and more close to natural behavior.