Search Results for author: Muhammad Imran Malik

Found 12 papers, 2 papers with code

A Perspective Analysis of Handwritten Signature Technology

no code implementations22 May 2024 Moises Diaz, Miguel A. Ferrer, Donato Impedovo, Muhammad Imran Malik, Giuseppe Pirlo, Rejean Plamondon

Ever since, and over the last 10 years, the application of handwritten signature technology has strongly evolved, and much research has focused on the possibility of applying systems based on handwritten signature analysis and processing to a multitude of new fields.

Few-Shot Learning for Biometric Verification

no code implementations12 Nov 2022 Saad Bin Ahmed, Umaid M. Zaffar, Marium Aslam, Muhammad Imran Malik

In machine learning applications, it is common practice to feed as much information as possible.

Few-Shot Learning

KENN: Enhancing Deep Neural Networks by Leveraging Knowledge for Time Series Forecasting

no code implementations8 Feb 2022 Muhammad Ali Chattha, Ludger van Elst, Muhammad Imran Malik, Andreas Dengel, Sheraz Ahmed

End-to-end data-driven machine learning methods often have exuberant requirements in terms of quality and quantity of training data which are often impractical to fulfill in real-world applications.

Anomaly Detection Time Series +1

ExAID: A Multimodal Explanation Framework for Computer-Aided Diagnosis of Skin Lesions

no code implementations4 Jan 2022 Adriano Lucieri, Muhammad Naseer Bajwa, Stephan Alexander Braun, Muhammad Imran Malik, Andreas Dengel, Sheraz Ahmed

This work presents ExAID (Explainable AI for Dermatology), a novel framework for biomedical image analysis, providing multi-modal concept-based explanations consisting of easy-to-understand textual explanations supplemented by visual maps justifying the predictions.

Decision Making

Combining Fine- and Coarse-Grained Classifiers for Diabetic Retinopathy Detection

no code implementations28 May 2020 Muhammad Naseer Bajwa, Yoshinobu Taniguchi, Muhammad Imran Malik, Wolfgang Neumeier, Andreas Dengel, Sheraz Ahmed

Visual artefacts of early diabetic retinopathy in retinal fundus images are usually small in size, inconspicuous, and scattered all over retina.

Diabetic Retinopathy Detection

G1020: A Benchmark Retinal Fundus Image Dataset for Computer-Aided Glaucoma Detection

2 code implementations28 May 2020 Muhammad Naseer Bajwa, Gur Amrit Pal Singh, Wolfgang Neumeier, Muhammad Imran Malik, Andreas Dengel, Sheraz Ahmed

Scarcity of large publicly available retinal fundus image datasets for automated glaucoma detection has been the bottleneck for successful application of artificial intelligence towards practical Computer-Aided Diagnosis (CAD).

Optic Cup Segmentation

On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors

no code implementations5 May 2020 Adriano Lucieri, Muhammad Naseer Bajwa, Stephan Alexander Braun, Muhammad Imran Malik, Andreas Dengel, Sheraz Ahmed

This work aims at elucidating a deep learning based medical image classifier by verifying that the model learns and utilizes similar disease-related concepts as described and employed by dermatologists.

Decision Making Image Classification +1

A Robust and Precise ConvNet for small non-coding RNA classification (RPC-snRC)

no code implementations23 Dec 2019 Muhammad Nabeel Asima, Muhammad Imran Malik, Andreas Dengela, Sheraz Ahmed

In order to assess the effectiveness of deeper architectures for small non-coding RNA classification, we also adapted two ResNet architectures having different number of layers.

Classification General Classification

A Robust Hybrid Approach for Textual Document Classification

1 code implementation12 Sep 2019 Muhammad Nabeel Asim, Muhammad Usman Ghani Khan, Muhammad Imran Malik, Andreas Dengel, Sheraz Ahmed

Evaluation results reveal that the proposed methodology outperforms the state-of-the-art of both the (traditional) machine learning and deep learning based text document classification methodologies with a significant margin of 7. 7% on 20 Newsgroups and 6. 6% on BBC news datasets.

BIG-bench Machine Learning Classification +5

KINN: Incorporating Expert Knowledge in Neural Networks

no code implementations15 Feb 2019 Muhammad Ali Chattha, Shoaib Ahmed Siddiqui, Muhammad Imran Malik, Ludger van Elst, Andreas Dengel, Sheraz Ahmed

The promise of ANNs to automatically discover and extract useful features/patterns from data without dwelling on domain expertise although seems highly promising but comes at the cost of high reliance on large amount of accurately labeled data, which is often hard to acquire and formulate especially in time-series domains like anomaly detection, natural disaster management, predictive maintenance and healthcare.

Anomaly Detection Management +1

A Generic Method for Automatic Ground Truth Generation of Camera-captured Documents

no code implementations4 May 2016 Sheraz Ahmed, Muhammad Imran Malik, Muhammad Zeshan Afzal, Koichi Kise, Masakazu Iwamura, Andreas Dengel, Marcus Liwicki

The method is generic, language independent and can be used for generation of labeled documents datasets (both scanned and cameracaptured) in any cursive and non-cursive language, e. g., English, Russian, Arabic, Urdu, etc.

Optical Character Recognition (OCR)

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