Muhammad Imran Malik

Airedale General Hospital

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Sheraz Ahmed

German Research Centre for Artificial Intelligence

Andreas Dengel

German Research Centre for Artificial Intelligence

Marcus Liwicki

Luleå University of Technology

Faisal Shafait

National University of Sciences and Technology

Adnan Malik

Mountain Vista Medical Center

Deepa Joseph

All India Institute of Medical Sciences Rishikesh

Muhammad Naseer Bajwa

University of Kaiserslautern

Shoaib Ahmed Siddiqui

German Research Centre for Artificial Intelligence

Deepthi Valiyaveettil

Nizam's Institute of Medical Sciences

Mahum Nadeem

Cleveland Clinic

Cooperative Institutions

University of Kaiserslautern

Loyola University Medical Center

German Research Centre for Artificial Intelligence

National University of Sciences and Technology

Nizam's Institute of Medical Sciences

All India Institute of Medical Sciences

DR. B.R.A. Institute Rotary Cancer Hospital

Loyola University Chicago

COMSATS University Islamabad

Aga Khan University Hospital

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Effects of curcumin in patients with non-alcoholic fatty liver disease: A systematic review and meta-analysis

Canadian Liver Journal (2024)

Adnan Malik Muhammad Imran Malik

Curcumin is an anti-inflammatory that is proposed to have a positive impact on patients with non-alcoholic fatty liver disease (NAFLD). We aim to assess the effects of curcumin in patients with NAFLD.

10.3138/canlivj-2023-0022

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IDENTIFICATION OF POTENTIAL ANTIBIOTIC TARGETS IN THE PROTEOME OF MULTI-DRUG RESISTANT EISENBERGIELLA TAYI

Journal of medical & health sciences review. (2025)

Muhammad Imran Malik Rida Batool Faisal Abbas Kausar Perveen Muhammad Yousuf Joiya

Eisenbergiella tayi is a pathogen that affects the oral cavity, gastrointestinal tract, skin, and vagina, and shows some resistance to existing antibiotics. Identifying new antibiotic targets through computational methods could expedite the process. At the same time, there are numerous opportunities to develop new antibiotics to address infections caused by this pathogen. In this study, the proteome of E. tayi was progressively reduced to pinpoint potential antibiotic targets. The main goals were to identify proteins that are non-redundant, unique to the pathogen, essential, located in the cytoplasm, and associated with virulence and resistance. The druggability of these proteins was assessed using the BLASTp tool from the DrugBank database against FDA-approved drugs. The study found that the core proteome of the pathogen consists of 6,044 proteins. Of these, 2,598 were identified as non-homologous to human proteins, and 1,169 were deemed essential to the pathogen. Sub-cellular localization revealed that 594 proteins are cytoplasmic, with 76 being selected as virulent. Metabolic pathway analysis linked 32 proteins to unique pathogen-specific pathways and identified six as druggable. Further analysis highlighted the “argD” protein as both resistant and a promising target for future drug development. These results could lay the groundwork for creating new antibiotics to combat E. tayi infections.

Identification

Proteome

10.62019/bt18cq46

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Engaging Europe’s Muslims

ISIM Review (2008)

Muhammad Imran Malik

Debates on the role of Muslim citizens in Europe question how European liberal democracies should engage with non-liberal ideas. Until now the debate has focused on how Muslim groups and individuals should behave. The author seeks ways to redefine liberal democracy in order to make it more inclusive of those who may hold illiberal views, Muslim or otherwise.

Liberal democracy

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From Terminology to Evaluation: Performance Assessment of Automatic Signature Verification Systems

International Conference on Frontiers in Handwriting Recognition (2012)

Muhammad Imran Malik Marcus Liwicki

This paper is an effort towards the development of a shared conceptualization regarding automatic signature verification systems. The requirements of both communities, Pattern Recognition and Forensic Handwriting Examiners, are explicitly focused. This is required because an increasing gap regarding evaluation of automatic verification systems is observed in the recent past. The paper addresses three major areas. First, it highlights how signature verification is taken differently in the above mentioned communities and why this gap is increasing. Various factors that widen this gap are discussed with reference to some of the recent signature verification studies and probable solutions are suggested. Second, it discusses the state-of-the-art evaluation and its problems as seen by FHEs. The real evaluation issues faced by FHEs, when trying to incorporate automatic signature verification systems in their routine casework, are presented. Third, it reports a standardized evaluation scheme capable of fulfilling the requirements of both PR researchers and FHEs.

Signature (topology)

Handwriting

Handwriting Recognition

Functional verification

10.1109/icfhr.2012.205

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Citations (28)

Histopathological features of idiopathic portal hypertension: A systematic review and meta-analysis

Science Progress (2024)

Adnan Malik Sohira Malik Ahsan Farooq Muhammad Imran Malik Sadia Javaid

Portal hypertension (PH) is a clinically significant entity that could present with life-threatening gastrointestinal bleeding. Cirrhosis is the most common cause of PH, with well-documented histopathology and etiology. However, in idiopathic portal hypertension (IPH), no single histopathologic finding is associated with PH. Our systematic review aims to identify and summarize the prevalence of the common histological findings of IPH.

Nodular regenerative hyperplasia

Histopathology

10.1177/00368504241264996

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FFD: Figure and Formula Detection from Document Images

Junaid Younas Syed Tahseen Raza Rizvi Muhammad Imran Malik Faisal Shafait Paul Lukowicz

In this work, we present a novel and generic approach, Figure and Formula Detector (FFD) to detect the formulas and figures from document images. Our proposed method employs traditional computer vision approaches in addition to deep models. We transform input images by applying connected component analysis (CC), distance transform, and colour transform, which are stacked together to generate an input image for the network. The best results produced by FFD for figure and formula detection are with F1-score of 0.906 and 0.905, respectively. We also propose a new dataset for figures and formulas detection to aid future research in this direction. The obtained results advocate that enhancing the input representation can simplify the subsequent optimization problem resulting in significant gains over their conventional counterparts.

Representation

10.1109/dicta47822.2019.8945972

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Endmember Analysis of Overlapping Handwritten Text in Hyperspectral Document Images

IEEE Access (2024)

Hasan Irtaza Mirza Saad Bin Ahmed Roberto Solis-Oba Muhammad Imran Malik

Hyperspectral Imaging (HSI) uses large portions of the electromagnetic spectrum to obtain information from images that would be very difficult to obtain otherwise. An important task in forensic analysis of documents is to extract signatures for authentication. Signatures in documents often overlap with other parts of a document such as typed text or stamps and hence it is difficult to retrieve them. In this work we present a novel algorithm for recovering signatures from hyperspectral images of documents where signatures overlap typed text, seals, stamps, or other images. We used pure pixel index approaches to analyze the hyper-pixels corresponding to regions where overlaps occur and spectral classification methods to analyze the extracted channel’s information and to separate the overlapping signatures. For validation, we used structural similarity index approach to validate the extracted signatures from the original dataset. Our experimental results highlights the capability of proposed algorithm to recover signature with great precision even from documents having significant overlap. This provides a baseline with potential of extracting signatures using HSI images in forensic analysis.

Endmember

Signature (topology)

Spectral signature

Similarity (geometry)

10.1109/access.2024.3349951

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Banking Developments in Pakistan: A Journey from Conventional to Islamic Banking

Ashfaq Ahmad Muhammad Imran Malik Asad Afzal Humayoun

This study reflects a historical background of Pakistani banking sector since its independence on August 14, 1947 from British rule. It indicates the journey of Pakistani banking sector from the establishment of SBP on July 1, 1948 as central bank. During 1950s and 1960s banking sector got expansion due to development projects. In 1974, banks operating in Pakistan got nationalized and came under the direct control of the Govt. of Pakistan. The Govt. of Pakistan is required to eliminate interest based transactions from the country according to its constitutions (i.e.1956, 1962 and 1973). In 1992, Govt. started privatization process of the financial sector especially banking sector. However, the inception of 21 st century came with Islamic banking practices across the globe to facilitate the different segments of the economy. Islamic banking practices in Pakistan proved a successful experience due to growth and expansion of the banking sector. This study reflects a picture of Pakistani banking sector since its creation. It enables the readers, academician and bankers to have a look about banking developments in Pakistan.

Islamic Banking

Globe

Independence

Commercial banking

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Citations (28)

Computer-Aided Diagnosis of Skin Diseases Using Deep Neural Networks

Applied Sciences (2020)

Muhammad Naseer Bajwa Kaoru Muta Muhammad Imran Malik Shoaib Ahmed Siddiqui Stephan A. Braun

Propensity of skin diseases to manifest in a variety of forms, lack and maldistribution of qualified dermatologists, and exigency of timely and accurate diagnosis call for automated Computer-Aided Diagnosis (CAD). This study aims at extending previous works on CAD for dermatology by exploring the potential of Deep Learning to classify hundreds of skin diseases, improving classification performance, and utilizing disease taxonomy. We trained state-of-the-art Deep Neural Networks on two of the largest publicly available skin image datasets, namely DermNet and ISIC Archive, and also leveraged disease taxonomy, where available, to improve classification performance of these models. On DermNet we establish new state-of-the-art with 80% accuracy and 98% Area Under the Curve (AUC) for classification of 23 diseases. We also set precedence for classifying all 622 unique sub-classes in this dataset and achieved 67% accuracy and 98% AUC. On ISIC Archive we classified all 7 diseases with 93% average accuracy and 99% AUC. This study shows that Deep Learning has great potential to classify a vast array of skin diseases with near-human accuracy and far better reproducibility. It can have a promising role in practical real-time skin disease diagnosis by assisting physicians in large-scale screening using clinical or dermoscopic images.

Deep Neural Networks

Computer-Aided Diagnosis

Computer-aided

10.3390/app10072488

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BIVENTRICULAR PACING VS LEFT BUNDLE BRANCH AREA PACING FOR CARDIAC RESYCHRONIZATION IN PATIENTS WITH HEART FAILURE: A META-ANALYSIS.

Journal of the American College of Cardiology (2023)

Mark Mujer Muhammad Imran Malik Besher Kashlan Nicole Chahine Manel Boumegouas

Cardiac Pacing

10.1016/s0735-1097(23)00686-1

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