Attique Ur Rehman

@uskt.edu.pk

Lecturer in Department of Software Engineering
University of Sialkot

Attique Ur Rehman


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https://researchid.co/attiqueskt

EDUCATION

MS Software Engineering From NUST

RESEARCH INTERESTS

Ai in Medicine, Applied Machine Learning, Pure Software Engineering,
45

Scopus Publications

490

Scholar Citations

12

Scholar h-index

15

Scholar i10-index

Scopus Publications

  • Supply chain risk management and agility
    Mohsin Nasir Jat, Muhammad Shakeel Sadiq Jajja, Attique ur Rehman
    Encyclopedia in Operations Management, 2026
  • Analyzing and Predicting Alcohol or Non-Alcoholic Cocktails †
    Hifza Khan, Attique Ur Rehman, Anggun Fergina
    Engineering Proceedings, 2025
    Using a structured dataset, this study investigates the use of machine learning algorithms to analyze and forecast several properties of cocktails. Cocktails’ names, classifications, ingredients, alcoholic contents, glass types, and preparation guidelines are all included in the dataset. Based on the components, we created algorithms to categorize cocktails as either alcoholic or nonalcoholic, forecast their category, and suggest different kinds of glasses. The results give useful tools for customization in the beverage business, as well as information about cocktail trends.
  • COVID-19 Prediction Using Machine Learning †
    Ali Raza, Attique Ur Rehman, Imam Sanjaya
    Engineering Proceedings, 2025
    The COVID-19 virus caused unprecedented global disruption. There have been millions of cases and deaths reported worldwide. Accurate prediction of COVID-19 trends is crucial for effective decision-making, resource allocation, and policy formulation. ML has been shown to be an excellent method for projecting the virus’s growth and impact as it can analyze vast datasets, discover trends, and develop predictive models. This study examines the use of various machine learning techniques for the prediction of COVID-19 such as time series analysis, regression models, and classification techniques. This paper further addresses the problems and constraints of applying the ML model to this context and suggests possible enhancements for future forecasting endeavors. The overall intention of this work is to enlighten people as to how this ML-based method contributes to pandemic forecasting in terms of improvements in pandemic preparation and response schemes.
  • An ML Framework for the Early Detection and Prediction of Hypertension: Enhancing Diagnostic Accuracy †
    Muhammad Areeb, Attique Ur Rehman, Alun Sujjada
    Engineering Proceedings, 2025
    A major worldwide health problem, hypertension can result in serious consequences such as stroke, renal failure, and cardiovascular illnesses if it is not identified and treated promptly. Reducing death rates and facilitating prompt therapies need the early identification of hypertension. This research examines if there are ways ML could enhance early identification of hypertension. Therefore, hypertension is still considered a global public health problem, and one of the most important preventive goals is its timely and accurate diagnosis. Leveraging a 99.92% accuracy rate, the present study therefore proposes a novel ML framework that significantly dwarfs the currently documented best accuracy of 99.5%. This achievement of correctly identifying the essentiality of hypertension in establishing our recommended paradigm highlights the robustness and trustworthiness of the proposed actions to ensure timely treatment and enhance patients’ quality of life the largest amount.
  • Smart Cloud Architectures: The Combination of Machine Learning and Cloud Computing †
    Aqsa Asghar, Attique Ur Rehman, Rizwan Ayaz, Anang Suryana
    Engineering Proceedings, 2025
    Machine learning (ML) in cloud architectures is used to manage powerful servers that run distributed systems over the internet. ML predicts the workload and traffic from cloud consumers and allocates resources according to the demand. ML in cloud architectures is there to improve performance and increase availability to manage cloud computing resources. The combination of ML and cloud architectures balances the workload and ensures reliability. This research discusses cloud architectures that use ML to run different algorithms to predict the improvement in the cloud architectures by using a cloud computing resource dataset. The dataset is used with different classifiers with the same ML framework that is discussed in this paper; the ML framework has a sequence to provide the steps of the model training and testing and uses different techniques and methods for the better performance of the cloud architectures. The researchers used various ML techniques to create a model for predicting the workload. To enhance the model’s performance and flexibility, we used a regression-based dataset that was recently updated, which was used with different ML approaches to predict better performance in the cloud architectures. By using the Generalized Linear Model, we achieved the highest performance. The R2 value refers to the goodness of the model and its performance. Using cloud datasets and machine learning with cloud architectures enhances performance using the different techniques in this paper, resulting in a more generalizable model with overfitting risk. This study focuses on refining the execution of cloud architectures with the help of ML.
  • Evaluating the Role of Machine Learning in Migraine Detection and Classification †
    Irsa Imtiaz, Hamza Afzal, Attique Ur Rehman, Gina Purnama Insany
    Engineering Proceedings, 2025
    Migraine is a common neurological illness that has a major influence on the quality of life; yet, precise categorization and prediction remain difficult because of its complicated symptoms and multiple triggers. This work investigates the use of advanced machine learning (ML) algorithms to improve migraine diagnosis and prediction, drawing on a large dataset that includes clinical, lifestyle, and environmental aspects. Various machine learning models, such as ensemble methods, deep learning, and hybrid approaches, are tested to see how well they discriminate migraine from other headache conditions and predict migraine episodes. Feature selection approaches are used to identify the most important predictors, which improve model interpretability and performance. Experimental results show that the proposed machine learning framework outperforms established diagnostic methods in terms of classification accuracy, sensitivity, and specificity. The study demonstrates how ML-driven solutions may be used to manage migraines in a tailored way, helping medical practitioners with early diagnosis and intervention techniques. My suggested framework, NeuroVote(ensemble model), offers a remarkable 99.99% classification accuracy for migraines. Future studies will concentrate on optimizing models for clinical deployment and incorporating real-time data from wearable technology.
  • Revolutionizing Lung Cancer Detection: A High-Accuracy Machine Learning Framework for Early Diagnosis
    Tahir Muhammad Ali, Azka Mir, Attique Ur Rehman, Mamoona Humayun, Momina Shaheen, Rafeef Taresh Suliman Alshammari
    Biomed Research International, 2025
    Lung cancer is a deadly disease. According to a report of 2024, it is the primary reason for 1.82 million deaths. Given the high disease burden, early detection of lung cancer is crucial for improving survival rates and implementing effective strategies. This paper is aimed at conducting a systematic literature review and developing a highly accurate framework for predicting lung cancer effectively. Tollgate methodology has been used for systematic literature review, and quality assessment criteria were applied to select published articles relevant to the research questions. The paper investigates the effectiveness of machine learning in identifying patterns relevant to lung cancer prediction (Q1), examines the pros and cons of current predictive systems (Q2), compares the use of artificial intelligence in lung cancer prediction with traditional methods (Q3), and identifies key features that distinguish lung cancer from patient symptoms (Q4). Machine learning techniques were employed for the proposed framework. Two publicly available, distinct datasets containing clinical features were obtained. Then, the SelectKBest method was used for feature selection, and SMOTE was used to handle class imbalance. Our proposed framework includes a voting ensemble with random forest, support vector machine, and logistic regression with cross‐validation. The results indicate an accuracy of 99% and 92.5% for the first and second datasets, respectively. This study′s systematic literature review, based on four research questions and a machine learning model, exhibits high accuracy in predicting lung cancer.
  • A novel approach for the effective prediction of cardiovascular disease using applied artificial intelligence techniques
    Azka Mir, Attique Ur Rehman, Tahir Muhammad Ali, Sabeen Javaid, Maram Fahaad Almufareh, Mamoona Humayun, Momina Shaheen
    Esc Heart Failure, 2024
    AimsThe objective of this research is to develop an effective cardiovascular disease prediction framework using machine learning techniques and to achieve high accuracy for the prediction of cardiovascular disease.MethodsIn this paper, we have utilized machine learning algorithms to predict cardiovascular disease on the basis of symptoms such as chest pain, age and blood pressure. This study incorporated five distinct datasets: Heart UCI, Stroke, Heart Statlog, Framingham and Coronary Heart dataset obtained from online sources. For the implementation of the framework, RapidMiner tool was used. The three‐step approach includes pre‐processing of the dataset, applying feature selection method on pre‐processed dataset and then applying classification methods for prediction of results. We addressed missing values by replacing them with mean, and class imbalance was handled using sample bootstrapping. Various machine learning classifiers were applied out of which random forest with AdaBoost dataset using 10‐fold cross‐validation provided the high accuracy.ResultsThe proposed model provides the highest accuracy of 99.48% on Heart Statlog, 93.90% on Heart UCI, 96.25% on Stroke dataset, 86% on Framingham dataset and 78.36% on Coronary heart disease dataset, respectively.ConclusionsIn conclusion, the results of the study have shown remarkable potential of the proposed framework. By handling imbalance and missing values, a significantly accurate framework has been established that could effectively contribute to the prediction of cardiovascular disease at early stages.
  • Enhancing Database Security through AI-Based Intrusion Detection System
    Journal of Computing and Biomedical Informatics, 2024
  • Air Quality and Carbon Monoxide Monitoring Using IOT-based System
    Journal of Computing and Biomedical Informatics, 2024
  • AI and Sensing-Enhanced Irrigation through Cable Rail for Drought and fros Prone Regions in the Face of Climate Change
    Journal of Computing and Biomedical Informatics, 2024
  • An Integrated Framework for Effective Prediction of Chronic Kidney Disease
    Syed Muhammad Ali Aoun, Muneeba Javed, Attique Ur Rehman, Tahir Muhammad Ali, Farzeen Ashfaq
    Icetas 2024 9th IEEE International Conference on Engineering Technologies and Applied Sciences, 2024
  • A Machine Learning Framework for Effective Prediction of Breast Cancer
    Laiba Azeem, Zeba Nawaz, Attique Ur Rehman, Tahir Muhammad Ali, Sayan Kumar Ray
    Icetas 2024 9th IEEE International Conference on Engineering Technologies and Applied Sciences, 2024
  • Exploring Sleep Paralysis Phenomenon Through Machine Learning: An Analytical Study
    Samra Ishaq, Attique Ur Rehman, Tahir Muhammad Ali, Sabeen Javaid, Azka Mir
    Proceedings 2024 International Conference on Engineering and Computing Icect 2024, 2024
  • A Comprehensive Prediction Model for T20 and Test Match Outcomes Using Machine Learning
    Zoha Ahsan, Shahwaiz Ghumman, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali, Azka Mir
    Proceedings 2024 International Conference on Engineering and Computing Icect 2024, 2024
  • An Integrated Machine Learning Framework Based Liver Disease Diagnosis System
    Irsa Imtiaz, Ayesha Qaiser, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali, Azka Mir
    Proceedings 2024 International Conference on Engineering and Computing Icect 2024, 2024
  • An Intelligent Technique for Predicting Quality of Drinking Water
    Intizan Nadeem, Abdullah Yahya, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali, Azka Mir
    2024 International Conference on Decision Aid Sciences and Applications Dasa 2024, 2024
  • Leveraging Ensemble Learning for Dry Beans Classification
    Muhammad Jahanzaib, Qasim Zaheer, Attique Ur Rehman, Sabeen Javed, Tahir Muhammad Ali, Azka Mir
    Proceedings 2024 International Conference on Engineering and Computing Icect 2024, 2024
  • Type 2 Diabetes Mellitus Monitoring Through Non-invasive IoT-Based System
    Aamir Hussain, Attique ur Rehman, Altaf Hussain, Qimeng Li, Raffaele Gravina, Giancarlo Fortino
    Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering Lnicst, 2024
  • An Intelligent Technique for Effective Multi-Disease Prediction
    Ahsan Abdullah, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali, Azka Mir, Sayan Kumar Ray
    Icetas 2024 9th IEEE International Conference on Engineering Technologies and Applied Sciences, 2024
  • Predictive Modeling of Students' Stress Levels Using Machine Learning Algorithm
    Hassan Ali, M. Hamza Amin, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali, Azka Mir
    2024 International Conference on Emerging Trends in Networks and Computer Communications Etncc 2024 Proceedings, 2024
  • Enhancing Brain Stroke Risk Prediction with Multi-Algorithm Evaluation and Web Interface
    Muhammad Ahmed, Umer Liaquat, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali, Azka Mir
    Proceedings 2024 International Conference on Engineering and Computing Icect 2024, 2024
  • Optimizing Heart Failure Predictive Accuracy: An Effective Approach Using SMOTE Techniques
    Ahmed Baber, Faizan Ahmed, Attique Ur Rehman, Sabeen Javaid, Menwa Alshammeri, Azka Mir, Deepak Kumar
    2024 International Conference on Emerging Trends in Networks and Computer Communications Etncc 2024 Proceedings, 2024
  • An Intelligent Technique for the Effective Prediction of Parkinson Disease
    Sawera Tariq, Madiha Qadeer, Attique Ur Rehman, Sabeen Javed, Tahir Muhammad Ali, Azka Mir, Suresh Singh
    2024 International Conference on Emerging Trends in Networks and Computer Communications Etncc 2024 Proceedings, 2024
  • Hemochromatosis Pathogenesis and Its Association with Liver Disease: An Analysis Through Machine Learning
    Isra Imtiaz, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali, Azka Mir, Mehedi Masud, Deepak Kumar
    2024 International Conference on Emerging Trends in Networks and Computer Communications Etncc 2024 Proceedings, 2024
  • The Future of Differentiated Thyroid Cancer Recurrence Prediction Using a Machine Learning Framework Advancements, Challenges, and Prospects
    Irsa Imtiaz, Attique Ur Rehman, Sabeen Javaid, Tahir Mohammad Ali, Azka Mir, Mehedi Masud, Yadaiah Nirsanametla
    2024 International Conference on Emerging Trends in Networks and Computer Communications Etncc 2024 Proceedings, 2024
  • The Sophisticated Prognostication of Migraine Aura Using Machine Learning
    Samiullah, Abdul Rehman, Attique Ur Rehman, Sabeen Javaid, Tahir Mohammad Ali, Azka Mir, Yadaiah Nirsanametla
    2024 International Conference on Emerging Trends in Networks and Computer Communications Etncc 2024 Proceedings, 2024
  • An Applied Artificial Intelligence Technique for Early-Stage Alzheimer's Disease Prediction
    Hassan Ali, Hussain Imtiaz, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali, Menwa Alshammeri, Azka Mir, Himanshu Kumar
    2024 International Conference on Emerging Trends in Networks and Computer Communications Etncc 2024 Proceedings, 2024
  • A Machine Learning-Based Framework for Accurate and Early Diagnosis of Liver Diseases: A Comprehensive Study on Feature Selection, Data Imbalance, and Algorithmic Performance
    Attique Ur Rehman, Wasi Haider Butt, Tahir Muhammad Ali, Sabeen Javaid, Maram Fahaad Almufareh, Mamoona Humayun, Hameedur Rahman, Azka Mir, Momina Shaheen
    International Journal of Intelligent Systems, 2024
  • An Intelligent Technique for the Effective Prediction of Monkeypox Outbreak
    Azka Mir, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali
    3rd IEEE International Conference on Artificial Intelligence Icai 2023, 2023
  • An Integrated Machine Learning Framework for Effective Classification of Water
    Isha Aleem, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali
    2023 International Conference on Energy Power Environment Control and Computing Icepecc 2023 Proceedings, 2023
  • An Applied Artificial Intelligence Aided Technique for Effective Classification of Breast Cancer
    Mishal Waqar, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali, Ali Nawaz
    2023 International Conference on Energy Power Environment Control and Computing Icepecc 2023 Proceedings, 2023
  • A Sequential Machine Learning-cum-Attention Mechanism for Effective Segmentation of Brain Tumor
    Tahir Mohammad Ali, Ali Nawaz, Attique Ur Rehman, Rana Zeeshan Ahmad, Abdul Rehman Javed, Thippa Reddy Gadekallu, Chin-Ling Chen, Chih-Ming Wu
    Frontiers in Oncology, 2022
  • A Novel Model-Driven Approach for Visual Impaired People Assistance OPTIC ALLY
    Laiba Rana, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali
    Proceedings of 3rd International Conference on Latest Trends in Electrical Engineering and Computing Technologies Intellect 2022, 2022
  • An Applied Artificial Intelligence Technique For Early Prediction of Diabetes Disease
    Abdul Saboor, Attique Ur Rehman, Tahir Muhammad Ali, Sabeen Javaid, Ali Nawaz
    Proceedings of 3rd International Conference on Latest Trends in Electrical Engineering and Computing Technologies Intellect 2022, 2022
  • A Computer Aided Technique for Classification of Patients with Diabetes
    Faiza Mehreen, Attique Ur Rehman, Tahir Muhammad Ali, Sabeen Javaid, Ali Nawaz
    Proceedings of 3rd International Conference on Latest Trends in Electrical Engineering and Computing Technologies Intellect 2022, 2022
  • An Application of Artificial Intelligence for an Early and Effective Prediction of Heart Failure
    Muhammad Owais Butt, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali, Ali Nawaz
    Proceedings of 3rd International Conference on Latest Trends in Electrical Engineering and Computing Technologies Intellect 2022, 2022
  • An Integrated Machine Learning Framework for Classification of Cirrhosis, Fibrosis, and Hepatitis
    Sibgha Islam, Attique Ur Rehman, Sabeen Javaid, Tahir Muhammad Ali, Ali Nawaz
    Proceedings of 3rd International Conference on Latest Trends in Electrical Engineering and Computing Technologies Intellect 2022, 2022
  • An Ensemble Model for Software Defect Prediction
    Amad Rizwan Ali, Attique Ur Rehman, Ali Nawaz, Tahir Muhammad Ali, Muhammad Abbas
    2022 2nd International Conference on Digital Futures and Transformative Technologies Icodt2 2022, 2022
  • An Integrated Framework for Sentiment Analysis of Giant Cold Drinks Companies
    Tahir Mohammad Ali, Ali Nawaz, Attique Ur Rehman
    4th International Conference on Innovative Computing Icic 2021, 2021
  • VGG-UNET for Brain Tumor Segmentation and Ensemble Model for Survival Prediction
    Ali Nawaz, Usman Akram, Anum Abdul Salam, Amad Rizwan Ali, Attique Ur Rehman, Jahan Zeb
    2021 International Conference on Robotics and Automation in Industry Icrai 2021, 2021
  • A Novel Model Driven Approach for Trajectory-Based Technique of Autonomous Vehicle Route Prediction
    Ali Nawaz, Attique Ur Rehman, Tahir Mohammad Ali, Farooque Azam, Yawar Rasheed, Muhammad Waseem Anwar
    ICET 2021 16th International Conference on Emerging Technologies 2021 Proceedings, 2021
  • A Comprehensive Literature Review of Application of Artificial Intelligence in Functional Magnetic Resonance Imaging for Disease Diagnosis
    Ali Nawaz, Attique Ur Rehman, Tahir Mohammad Ali, Zara Hayat, Aqsa Rahim, Uzair Khaleeq Uz Zaman, Amad Rizwan Ali
    Applied Artificial Intelligence, 2021
  • A Comparative Study of Agile Methods, Testing Challenges, Solutions Tool Support
    Attique Ur Rehman, Ali Nawaz, Mohammad Tahir Ali, Muhammad Abbas
    2020 14th International Conference on Open Source Systems and Technologies Icosst 2020 Proceedings, 2020

RECENT SCHOLAR PUBLICATIONS

  • An ML Framework for the Early Detection and Prediction of Hypertension: Enhancing Diagnostic Accuracy
    M Areeb, AU Rehman, A Sujjada
    Engineering Proceedings 107 (1), 18 , 2025
    2025
  • Revolutionizing Lung Cancer Detection: A High‐Accuracy Machine Learning Framework for Early Diagnosis
    TM Ali, A Mir, AU Rehman, M Humayun, M Shaheen, RTS Alshammari
    BioMed Research International 2025 (1), 9961773 , 2025
    2025
    Citations: 4
  • An Intelligent Technique for Predicting Quality of Drinking Water
    I Nadeem, A Yahya, AU Rehman, S Javaid, TM Ali, A Mir
    2024 International Conference on Decision Aid Sciences and Applications … , 2024
    2024
  • A novel approach for the effective prediction of cardiovascular disease using applied artificial intelligence techniques
    A Mir, A Ur Rehman, TM Ali, S Javaid, MF Almufareh, M Humayun, ...
    ESC heart failure 11 (6), 3742-3756 , 2024
    2024
    Citations: 28
  • An Applied Artificial Intelligence Technique for Early-Stage Alzheimer's Disease Prediction
    H Ali, H Imtiaz, AU Rehman, S Javaid, TM Ali, M Alshammeri, A Mir, ...
    2024 International Conference on Emerging Trends in Networks and Computer … , 2024
    2024
    Citations: 4
  • The future of differentiated thyroid cancer recurrence prediction using a machine learning framework advancements, challenges, and prospects
    I Imtiaz, AU Rehman, S Javaid, TM Ali, A Mir, M Masud, Y Nirsanametla
    2024 International Conference on Emerging Trends in Networks and Computer … , 2024
    2024
    Citations: 9
  • Hemochromatosis Pathogenesis and Its Association with Liver Disease: An Analysis Through Machine Learning
    I Imtiaz, AU Rehman, S Javaid, TM Ali, A Mir, M Masud, D Kumar
    2024 International Conference on Emerging Trends in Networks and Computer … , 2024
    2024
    Citations: 1
  • An Intelligent Technique for the Effective Prediction of Parkinson Disease
    S Tariq, M Qadeer, AU Rehman, S Javed, TM Ali, A Mir, S Singh
    2024 International Conference on Emerging Trends in Networks and Computer … , 2024
    2024
  • Beyond Glucose Levels: A Machine Learning Perspective on Type 2 Diabetes Prediction
    A Aslam, A Ashraf, AU Rehman, S Javaid, AA Khan, A Mir, D Kumar
    2024 International Conference on Emerging Trends in Networks and Computer … , 2024
    2024
    Citations: 1
  • Predictive Modeling of students' stress levels using machine learning algorithm
    H Ali, MH Amin, AU Rehman, S Javaid, TM Ali, A Mir
    2024 International Conference on Emerging Trends in Networks and Computer … , 2024
    2024
    Citations: 6
  • The Sophisticated Prognostication of Migraine Aura Using Machine Learning
    A Rehman, AU Rehman, S Javaid, TM Ali, A Mir, Y Nirsanametla
    2024 International Conference on Emerging Trends in Networks and Computer … , 2024
    2024
    Citations: 2
  • Optimizing Heart Failure Predictive Accuracy: An Effective Approach Using SMOTE Techniques
    A Baber, F Ahmed, AU Rehman, S Javaid, M Alshammeri, A Mir, D Kumar
    2024 International Conference on Emerging Trends in Networks and Computer … , 2024
    2024
    Citations: 1
  • Enhancing Brain Stroke Risk Prediction with Multi-Algorithm Evaluation and Web Interface
    M Ahmed, U Liaquat, AU Rehman, S Javaid, TM Ali, A Mir
    2024 International Conference on Engineering & Computing Technologies (ICECT … , 2024
    2024
    Citations: 5
  • Exploring sleep paralysis phenomenon through machine learning: An analytical study
    S Ishaq, AU Rehman, TM Ali, S Javaid, A Mir
    2024 International conference on engineering & computing technologies (ICECT … , 2024
    2024
    Citations: 4
  • A comprehensive prediction model for T20 and test match outcomes using machine learning
    Z Ahsan, S Ghumman, AU Rehman, S Javaid, TM Ali, A Mir
    2024 International Conference on Engineering & Computing Technologies (ICECT … , 2024
    2024
    Citations: 4
  • Leveraging Ensemble Learning for Dry Beans Classification
    M Jahanzaib, Q Zaheer, AU Rehman, S Javed, TM Ali, A Mir
    2024 International Conference on Engineering & Computing Technologies (ICECT … , 2024
    2024
    Citations: 2
  • An Integrated Machine Learning Framework Based Liver Disease Diagnosis System
    I Imtiaz, A Qaiser, AU Rehman, S Javaid, TM Ali, A Mir
    2024 International Conference on Engineering & Computing Technologies (ICECT … , 2024
    2024
    Citations: 5
  • A machine learning‐based framework for accurate and early diagnosis of liver diseases: A comprehensive study on feature selection, data imbalance, and algorithmic performance
    AU Rehman, WH Butt, TM Ali, S Javaid, MF Almufareh, M Humayun, ...
    International Journal of Intelligent Systems 2024 (1), 6111312 , 2024
    2024
    Citations: 41
  • An applied artificial intelligence aided technique for effective classification of breast cancer
    M Waqar, AU Rehman, S Javaid, TM Ali, A Nawaz
    2023 International Conference on Energy, Power, Environment, Control, and … , 2023
    2023
    Citations: 13
  • An integrated machine learning framework for effective classification of water
    I Aleem, AU Rehman, S Javaid, TM Ali
    2023 International Conference on Energy, Power, Environment, Control, and … , 2023
    2023
    Citations: 9

MOST CITED SCHOLAR PUBLICATIONS

  • A sequential machine learning-cum-attention mechanism for effective segmentation of brain tumor
    TM Ali, A Nawaz, A Ur Rehman, RZ Ahmad, AR Javed, TR Gadekallu, ...
    Frontiers in Oncology 12, 873268 , 2022
    2022
    Citations: 87
  • VGG-UNET for brain tumor segmentation and ensemble model for survival prediction
    A Nawaz, U Akram, AA Salam, AR Ali, AU Rehman, J Zeb
    2021 International Conference on Robotics and Automation in Industry (ICRAI … , 2021
    2021
    Citations: 47
  • A machine learning‐based framework for accurate and early diagnosis of liver diseases: A comprehensive study on feature selection, data imbalance, and algorithmic performance
    AU Rehman, WH Butt, TM Ali, S Javaid, MF Almufareh, M Humayun, ...
    International Journal of Intelligent Systems 2024 (1), 6111312 , 2024
    2024
    Citations: 41
  • A systematic literature review on phishing and anti-phishing techniques
    A Arshad, AU Rehman, S Javaid, TM Ali, JA Sheikh, M Azeem
    arXiv preprint arXiv:2104.01255 , 2021
    2021
    Citations: 41
  • A comparative study of agile methods, testing challenges, solutions & tool support
    AU Rehman, A Nawaz, MT Ali, M Abbas
    2020 14th International Conference on Open Source Systems and Technologies … , 2020
    2020
    Citations: 29
  • A novel approach for the effective prediction of cardiovascular disease using applied artificial intelligence techniques
    A Mir, A Ur Rehman, TM Ali, S Javaid, MF Almufareh, M Humayun, ...
    ESC heart failure 11 (6), 3742-3756 , 2024
    2024
    Citations: 28
  • An integrated machine learning framework for classification of cirrhosis, fibrosis, and hepatitis
    S Islam, AU Rehman, S Javaid, TM Ali, A Nawaz
    2022 Third International Conference on Latest trends in Electrical … , 2022
    2022
    Citations: 20
  • An application of artificial intelligence for an early and effective prediction of heart failure
    MO Butt, AU Rehman, S Javaid, TM Ali, A Nawaz
    2022 Third International Conference on Latest trends in Electrical … , 2022
    2022
    Citations: 16
  • Role of Project Management in Virtual Teams Success
    AU Rehman, A Nawaz, M Abbas, TM Ali
    iKSP Journal of Computer Science and Engineering (iJCSE) 1 (2), 32-42 , 2020
    2020
    Citations: 16
  • An intelligent technique for the effective prediction of monkeypox outbreak
    A Mir, AU Rehman, S Javaid, TM Ali
    2023 3rd International Conference on Artificial Intelligence (ICAI), 220-226 , 2023
    2023
    Citations: 15
  • An ensemble model for software defect prediction
    AR Ali, AU Rehman, A Nawaz, TM Ali, M Abbas
    2022 2nd International conference on digital futures and transformative … , 2022
    2022
    Citations: 14
  • An applied artificial intelligence aided technique for effective classification of breast cancer
    M Waqar, AU Rehman, S Javaid, TM Ali, A Nawaz
    2023 International Conference on Energy, Power, Environment, Control, and … , 2023
    2023
    Citations: 13
  • A comprehensive literature review of application of artificial intelligence in functional magnetic resonance imaging for disease diagnosis
    A Nawaz, AU Rehman, TM Ali, Z Hayat, A Rahim, UK Uz Zaman, AR Ali
    Applied Artificial Intelligence 35 (15), 1420-1438 , 2021
    2021
    Citations: 12
  • An applied artificial intelligence technique for early prediction of diabetes disease
    A Saboor, AU Rehman, TM Ali, S Javaid, A Nawaz
    2022 Third International Conference on Latest trends in Electrical … , 2022
    2022
    Citations: 11
  • A novel multiple ensemble learning models based on different datasets for software defect prediction
    A Nawaz, AU Rehman, M Abbas
    arXiv preprint arXiv:2008.13114 , 2020
    2020
    Citations: 11
  • The future of differentiated thyroid cancer recurrence prediction using a machine learning framework advancements, challenges, and prospects
    I Imtiaz, AU Rehman, S Javaid, TM Ali, A Mir, M Masud, Y Nirsanametla
    2024 International Conference on Emerging Trends in Networks and Computer … , 2024
    2024
    Citations: 9
  • An integrated machine learning framework for effective classification of water
    I Aleem, AU Rehman, S Javaid, TM Ali
    2023 International Conference on Energy, Power, Environment, Control, and … , 2023
    2023
    Citations: 9
  • Complementary effects of organic manures on the agronomic traits of spring maize
    R Ahmad, DE Habib, A Ur-rehman
    Crop and Environment 3 (1-2), 28-31 , 2012
    2012
    Citations: 9
  • A computer aided technique for classification of patients with diabetes
    F Mehreen, AU Rehman, TM Ali, S Javaid, A Nawaz
    2022 Third International Conference on Latest trends in Electrical … , 2022
    2022
    Citations: 7
  • A novel model-driven approach for visual impaired people assistance optic ally
    L Rana, AU Rehman, S Javaid, TM Ali
    2022 Third International Conference on Latest trends in Electrical … , 2022
    2022
    Citations: 7