arXiv subjects

Muhammad Azeem

Publications and source records attributed to Muhammad Azeem.

7 recordsLinked to original sources

arXiv2026

Incidence-based Combinatorial Geometry on Cell Complexes

Combinatorial Mesh Calculus (CMC) formulates conservation laws directly on cell complexes using combinatorial differential forms and their cochain representations. This note develops an incidence-based geometric extension of that framework for directional quantities and local geometric structure on explicit cellular organisation. The central construction is a family of local fibres generated intrinsically by the incidence structure of the complex. Each vertex carries a vector space spanned by its incident edge directions, providing a combinatorial analogue of a tangent space whose dimension reflects local topology. These fibres define local coefficient spaces for incidence-based vector-, covector- and endomorphism-valued cochains. Directed transport maps compare states attached to neighbouring fibres, while a canonical solder form relates fibre directions to the underlying cell-complex structure. Together they give rise to finite combinatorial analogues of transport, torsion, curvature and metric structure. Evaluation cup products provide pairings between kinematic and force-like quantities, while bundle Hodge operators induced by the fibre metric relate vector- and covector-valued cochains, and weighted covariant incidence operators define degree-raising transport-corrected operations on incidence cochains. The framework does not assume a smooth manifold, fixed-rank bundle, cellular sheaf or local system. Instead, geometric structure is generated directly from the organisation represented by the cell complex. The resulting theory establishes a combinatorial scaffold for geometry on explicit cellular organisation and identifies the principal mathematical questions required for its further development, including admissible transport classes, metric compatibility, Cartan-type structure equations and locality-dependent algebraic structures.

math.GM↗

arXiv2026

Geometry-Aware Superpixel Graph Transformer with Metadata for Skin Lesion Classification

Automated skin cancer classification from dermoscopic images remains challenging due to heterogeneous lesion structure, strong intra-class variability, and subtle visual differences between benign and malignant cases. Existing CNN/ViT pipelines typically rely on global or patch-level features and often combine patient metadata via late fusion, which limits spatially grounded multimodal reasoning. We present a novel region-based graph learning framework that explicitly models lesions as graphs of spatially coherent superpixel regions represented as frozen CNN features. To capture fine-grained lesion arrangements, we encode inter-regional geometry as edge attributes and introduce a dedicated metadata context node connected to all regions, providing structured integration of demographic/clinical variables within the same relational space. Node representations are updated using our edge-aware graph transformer followed by attention-driven propagation, and a final graph-level embedding for benign-malignant classification. Experiments on four public benchmarks demonstrate that explicit region-level relational modeling and graph-native multimodal fusion yield consistent gains over the state-of-the-art. Consequently, we establish a new graph-centric perspective in which CNN features are modeled as relational nodes and improved through contextual integration, yielding more expressive and robust classifications.

cs.CV↗

arXiv2022

Exotic Compact Objects with Two Dark Matter Fluids

The generic properties of compact objects made of two different fluids of dark matter are studied in a scale invariant approach. We investigate compact objects with a core-shell structure, where the two fluids are separated, and with mixed dark matter components, where both dark matter fluids are immersed within each other. The constellations considered are combinations of incompressible fluids, free and interacting Fermi gases, and equations of state with a vacuum term, i.e. self-bound dark matter. We find novel features in the mass-radius relations for combined dark matter compact objects which distinguishes them from compact objects with a single dark matter fluid and compact stars made of ordinary baryonic matter, as white dwarfs, neutron stars and quark stars. The maximum compactness of certain combined dark matter stars can reach values up to the causality limit for compact stars but not beyond that limit if causality of the dark matter fluids is ensured.

gr-qc↗

arXiv2021

Neural Networks for Infectious Diseases Detection: Prospects and Challenges

Artificial neural network (ANN) ability to learn, correct errors, and transform a large amount of raw data into useful medical decisions for treatment and care have increased its popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients' healthcare decisions and efficient disease diagnosis. We thoroughly review different types of ANNs presented in the existing literature that advanced ANNs adaptation for complex applications. Moreover, we also investigate ANN's advances for various disease diagnoses and treatments such as viral, skin, cancer, and COVID-19. Furthermore, we propose a novel deep Convolutional Neural Network (CNN) model called ConXNet for improving the detection accuracy of COVID-19 disease. ConXNet is trained and tested using different datasets, and it achieves more than 97% detection accuracy and precision, which is significantly better than existing models. Finally, we highlight future research directions and challenges such as complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.

cs.LG↗

arXiv2021

A Systematic Literature Review on Phishing and Anti-Phishing Techniques

Phishing is the number one threat in the world of internet. Phishing attacks are from decades and with each passing year it is becoming a major problem for internet users as attackers are coming with unique and creative ideas to breach the security. In this paper, different types of phishing and anti-phishing techniques are presented. For this purpose, the Systematic Literature Review(SLR) approach is followed to critically define the proposed research questions. At first 80 articles were extracted from different repositories. These articles were then filtered out using Tollgate Approach to find out different types of phishing and anti-phishing techniques. Research study evaluated that spear phishing, Email Spoofing, Email Manipulation and phone phishing are the most commonly used phishing techniques. On the other hand, according to the SLR, machine learning approaches have the highest accuracy of preventing and detecting phishing attacks among all other anti-phishing approaches.

cs.CR↗

arXiv2019

Role of electrostatic potential energy in carbon nanotube augmented cement paste matrix

The empirical data in conjunction with the quantum mechanical calculations show that the strength enhancement in the cement-carbon nanotubes (CNTs) composites is the courtesy of electrostatic potential energy. This is contrary to the general belief that the CNTs form bridges between the adjacent grains to slow down the breaking process. The yield point for the cement paste is improved up to 25% when prepared with 0.2 % by weight of various types of CNTs. A significant strength enhancement is observed with carboxyl functionalized (COOH) CNTs compared to other types. Further, an increase in the concentration of CNTs up to 0.4 wt% has a negative effect on the strength of the matrix. The electrostatic potential energy is mapped by using density functional theory (DFT) with {\omega}B97X-D functional. At lower concentration of CNTs, ion-dipole interaction in the cement paste and the CNTs creates a very strong long range intermolecular force. Due to the increased entropy resulting from the exothermic hydration process, these forces augment the strength of the cement paste.

physics.app-ph↗

arXiv2016

AZ Model for Software Development

Know a days Computer system become essential and it is most commonly used in every field of life. The computer saves time and use to solve complex and extensive problem quickly in an efficient way. For this purpose software programs are develop to facilitate the works for administrator, offices, banks etc. so Quality is the most important factor as it mostly defines CUSTOMER SATISFACTION which directly related to success of the project so there are many approaches (methodologies) have been developed for this purpose occasionally. The main study of this paper is to propose a new methodology for the development of the software which focuses on the quality improvement of all kind of product. This study will also discuss the features and limitation of the traditional methodologies like waterfall iterative spiral RUP and Agile and show how the new innovative methodology is better than previous one.

cs.SE↗