arXiv subjects

Ishfaq Ahmad Malik

Publications and source records attributed to Ishfaq Ahmad Malik.

8 recordsLinked to original sources

arXiv2026

XEns-CKD: An Explainable Ensemble-Based Approach for Chronic Kidney Disease Stage Detection

Chronic kidney disease (CKD) is a silent disease. Its progression may not significantly hamper a person's daily routine. Human kidney function can be classified as normal or as one of the five stages of CKD. Early detection of the CKD stage can help patients understand the functional status of their kidneys and follow medical advice to slow CKD progression. In this paper, we propose XEns-CKD, a novel ensemble vision transformer-based scheme for CKD stage classification using ultrasound images. Three ViTs were trained on a private ultrasound image dataset using different training parameters. The performance of each ViT was evaluated using macro sensitivity, macro specificity, macro precision, macro F1-score, macro Youden index, the Matthews correlation coefficient (MCC), and macro balanced accuracy. The ensemble model achieved an overall classification accuracy of 86.36%. This work also emphasizes identifying and interpreting kidney regions affected by CKD progression. Explainable artificial intelligence techniques, including LIME, LRP, Attention-Min, and Attention-Max, were used to improve model transparency and clinical trust. An attention map combining the Attention-Min and Attention-Max results effectively identified and interpreted kidney regions affected during CKD progression from one stage to another. The attention map also highlighted the effects of CKD progression in these regions. Compared with existing methods, the proposed method classified the five CKD stages and normal kidney status with a 4% improvement in accuracy.

cs.CV↗

arXiv2025

CFL-SparseMed: Communication-Efficient Federated Learning for Medical Imaging with Top-k Sparse Updates

Secure and reliable medical image classification is crucial for effective patient treatment, but centralized models face challenges due to data and privacy concerns. Federated Learning (FL) enables privacy-preserving collaborations but struggles with heterogeneous, non-IID data and high communication costs, especially in large networks. We propose \textbf{CFL-SparseMed}, an FL approach that uses Top-k Sparsification to reduce communication overhead by transmitting only the top k gradients. This unified solution effectively addresses data heterogeneity while maintaining model accuracy. It enhances FL efficiency, preserves privacy, and improves diagnostic accuracy and patient care in non-IID medical imaging settings. The reproducibility source code is available on \href{https://github.com/Aniket2241/APK_contruct}{Github}.

eess.IV↗

arXiv2025

LIB-KD: Teaching Inductive Bias for Efficient Vision Transformer Distillation and Compression

With the rapid development of computer vision, Vision Transformers (ViTs) offer the tantalising prospect of unified information processing across visual and textual domains due to the lack of inherent inductive biases in ViTs. ViTs require enormous datasets for training. We introduce an innovative ensemble-based distillation approach that distils inductive bias from complementary lightweight teacher models to make their applications practical. Prior systems relied solely on convolution-based teaching. However, this method incorporates an ensemble of light teachers with different architectural tendencies, such as convolution and involution, to jointly instruct the student transformer. Because of these unique inductive biases, instructors can accumulate a wide range of knowledge, even from readily identifiable stored datasets, which leads to enhanced student performance. Our proposed framework LIB-KD also involves precomputing and keeping logits in advance, essentially the unnormalized predictions of the model. This optimisation can accelerate the distillation process by eliminating the need for repeated forward passes during knowledge distillation, significantly reducing the computational burden and enhancing efficiency.

cs.CV↗

arXiv2024

Optimizing Vision Transformers with Data-Free Knowledge Transfer

The groundbreaking performance of transformers in Natural Language Processing (NLP) tasks has led to their replacement of traditional Convolutional Neural Networks (CNNs), owing to the efficiency and accuracy achieved through the self-attention mechanism. This success has inspired researchers to explore the use of transformers in computer vision tasks to attain enhanced long-term semantic awareness. Vision transformers (ViTs) have excelled in various computer vision tasks due to their superior ability to capture long-distance dependencies using the self-attention mechanism. Contemporary ViTs like Data Efficient Transformers (DeiT) can effectively learn both global semantic information and local texture information from images, achieving performance comparable to traditional CNNs. However, their impressive performance comes with a high computational cost due to very large number of parameters, hindering their deployment on devices with limited resources like smartphones, cameras, drones etc. Additionally, ViTs require a large amount of data for training to achieve performance comparable to benchmark CNN models. Therefore, we identified two key challenges in deploying ViTs on smaller form factor devices: the high computational requirements of large models and the need for extensive training data. As a solution to these challenges, we propose compressing large ViT models using Knowledge Distillation (KD), which is implemented data-free to circumvent limitations related to data availability. Additionally, we conducted experiments on object detection within the same environment in addition to classification tasks. Based on our analysis, we found that datafree knowledge distillation is an effective method to overcome both issues, enabling the deployment of ViTs on less resourceconstrained devices.

cs.CV↗

arXiv2018

Boundary value problem for an infinite system of second order differential equations in $\ell_p$ spaces

In this paper the concept of measure of noncompactness is applied to prove the existence of solution for a boundary value problem for an infinite system of second order differential equations in $\ell_{p}$ space. We change the boundary value problem into an equivalent system of infinite integral equations and obtain the result for the system of integral equations, using Darbo type fixed point theorem. The result is applied to an example to illustrate the concept.

math.FA↗

arXiv2018

Measures of Noncompactness in $\bar{N}(p,q)$ Summable Sequence Spaces

In this paper we first define the $\bar{N}(p,q)$ summable sequence spaces and obtain some basic results related to these spaces. The necessary and sufficient conditions for infinite matrices $A$ to map these spaces on $X~~,~~X=c_0, c \text{ or } \ell_{\infty}$ is obtained and Hausdorff measure of noncompactness is then used to obtain the necessary and sufficient conditions for the compactness of linear operators defined on these spaces.

math.FA↗

arXiv2018

Measures of Noncompactness in $\left(\bar{N}_{Δ^{-}}^{q}\right)$ Summable Difference Sequence Spaces

In the given paper we first introduce $\bar{N}_{Δ^{-}}^{q}$ summable difference sequence spaces and prove some properties of these spaces. We then obtain the necessary and sufficient conditions for infinite matrices $A$ to map these sequence spaces on the spaces $c_0, c$ and $\ell_\infty$, the Hausdorff measure of noncompactness is then used to obtain the necessary and sufficient conditions for the compactness of the linear operators defined on these spaces.

math.FA↗

arXiv2018

I-convergent triple difference sequence spaces defined by a sequence of modulus function

The main objective of this paper is to introduce classes of $I$-convergent triple difference sequence spaces, $c_{0I}^{3}(Δ,\digamma)$, $c_{I}^{3}(Δ,\digamma)$, $\ell_{\infty I}^{3}(Δ,\digamma)$, $M_{I}^{3}(Δ,\digamma)$ and $M_{0I}^{3}(Δ,\digamma)$, by using sequence of modulii function $\digamma=(f_{pqr})$. We also study some algebraic and topological properties of these new sequence spaces.

math.FA↗