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Rehman, Atiq Ur
Publications (10 of 11) Show all publications
D'Cruze, R. S., Ahmed, M. U., Bengtsson, M., Rehman, A. U., Funk, P. & Sohlberg, R. (2024). A Case Study on Ontology Development for AI Based Decision Systems in Industry. In: Lecture Notes in Mechanical Engineering: . Paper presented at 7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, 13 June 2023 through 15 June 2023 (pp. 693-706). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>A Case Study on Ontology Development for AI Based Decision Systems in Industry
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2024 (English)In: Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH , 2024, p. 693-706Conference paper, Published paper (Refereed)
Abstract [en]

Ontology development plays a vital role as it provides a structured way to represent and organize knowledge. It has the potential to connect and integrate data from different sources, enabling a new class of AI-based services and systems such as decision support systems and recommender systems. However, in large manufacturing industries, the development of such ontology can be challenging. This paper presents a use case of an application ontology development based on machine breakdown work orders coming from a Computerized Maintenance Management System (CMMS). Here, the ontology is developed using a Knowledge Meta Process: Methodology for Ontology-based Knowledge Management. This ontology development methodology involves steps such as feasibility study, requirement specification, identifying relevant concepts and relationships, selecting appropriate ontology languages and tools, and evaluating the resulting ontology. Additionally, this ontology is developed using an iterative process and in close collaboration with domain experts, which can help to ensure that the resulting ontology is accurate, complete, and useful for the intended application. The developed ontology can be shared and reused across different AI systems within the organization, facilitating interoperability and collaboration between them. Overall, having a well-defined ontology is critical for enabling AI systems to effectively process and understand information.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2024
Keywords
Custom NER, Industrial AI, Machine failures prediction, Ontology development, Artificial intelligence, Decision support systems, Interoperability, Iterative methods, Knowledge management, AI systems, Case-studies, Decision systems, Failures prediction, Machine failure, Machine failure prediction, Ontology's, Ontology
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-65369 (URN)10.1007/978-3-031-39619-9_51 (DOI)2-s2.0-85181980940 (Scopus ID)9783031396182 (ISBN)
Conference
7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, 13 June 2023 through 15 June 2023
Available from: 2024-01-17 Created: 2024-01-17 Last updated: 2024-01-17Bibliographically approved
Javed, H., Muqeet, H. A., Javed, T., Rehman, A. U. & Sadiq, R. (2024). Ethical Frameworks for Machine Learning in Sensitive Healthcare Applications. IEEE Access, 12, 16233-16254
Open this publication in new window or tab >>Ethical Frameworks for Machine Learning in Sensitive Healthcare Applications
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2024 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 12, p. 16233-16254Article in journal (Refereed) Published
Abstract [en]

The application of Machine Learning (ML) in healthcare has opened unprecedented avenues for predictive analytics, diagnostics, and personalized medicine. However, the sensitivity of healthcare data and the ethical dilemmas associated with automated decision-making necessitate a rigorous ethical framework. This review paper aims to provide a comprehensive overview of the existing ethical frameworks that guide ML in healthcare and evaluates their adequacy in ad-dressing ethical challenges. Specifically, this article offers an in-depth examination of prevailing ethical constructs that oversee healthcare ML, spotlighting pivotal concerns: data protection, in-formed assent, equity, and patient autonomy. Various analytical approaches including quantitative metrics, statistical methods for bias detection, and qualitative thematic analyses are applied to address these challenges. Insights are further enriched through case studies of Clinical Decision Support Systems, Remote Patient Monitoring, and Telemedicine Applications. Each case is evaluated against existing ethical frameworks to identify limitations and gaps. Based on our com-prehensive review and evaluation, we propose actionable recommendations for evolving ethical guidelines. The paper concludes by summarizing key findings and underscoring the urgent need for robust ethical frameworks to guide ML applications in sensitive healthcare environments. Future work should focus on the development and empirical validation of new ethical frameworks that can adapt to emerging technologies and ethical dilemmas in healthcare ML.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2024
Keywords
Ethical frameworks, machine learning, healthcare applications, data privacy
National Category
Health Sciences
Identifiers
urn:nbn:se:mdh:diva-66038 (URN)10.1109/ACCESS.2023.3340884 (DOI)001155944200001 ()2-s2.0-85179835954 (Scopus ID)
Available from: 2024-02-14 Created: 2024-02-14 Last updated: 2024-02-14Bibliographically approved
Qadri, A. M., Hashmi, M. S., Raza, A., Zaidi, S. A. & Rehman, A. U. (2024). Heart failure survival prediction using novel transfer learning based probabilistic features. PeerJ Computer Science, 10, Article ID e1894.
Open this publication in new window or tab >>Heart failure survival prediction using novel transfer learning based probabilistic features
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2024 (English)In: PeerJ Computer Science, E-ISSN 2376-5992, Vol. 10, article id e1894Article in journal (Refereed) Published
Abstract [en]

Heart failure is a complex cardiovascular condition characterized by the heart's inability to pump blood effectively, leading to a cascade of physiological changes. Predicting survival in heart failure patients is crucial for optimizing patient care and resource allocation. This research aims to develop a robust survival prediction model for heart failure patients using advanced machine learning techniques. We analyzed data from 299 hospitalized heart failure patients, addressing the issue of imbalanced data with the Synthetic Minority Oversampling (SMOTE) method. Additionally, we proposed a novel transfer learning-based feature engineering approach that generates a new probabilistic feature set from patient data using ensemble trees. Nine fine-tuned machine learning models are built and compared to evaluate performance in patient survival prediction. Our novel transfer learning mechanism applied to the random forest model outperformed other models and state-of-the-art studies, achieving a remarkable accuracy of 0.975. All models underwent evaluation using 10-fold crossvalidation and tuning through hyperparameter optimization. The findings of this study have the potential to advance the field of cardiovascular medicine by providing more accurate and personalized prognostic assessments for individuals with heart failure.

Place, publisher, year, edition, pages
PEERJ INC, 2024
Keywords
Transfer learning, Machine learning, Heart failure, Feature engineering
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-66353 (URN)10.7717/peerj-cs.1894 (DOI)001182217200001 ()2-s2.0-85190872607 (Scopus ID)
Available from: 2024-04-03 Created: 2024-04-03 Last updated: 2024-05-08Bibliographically approved
Haq, M. U., Sethi, M. A. & Rehman, A. U. (2023). Capsule Network with Its Limitation, Modification, and Applications—A Survey. Machine Learning and Knowledge Extraction, 5(3), 891-921
Open this publication in new window or tab >>Capsule Network with Its Limitation, Modification, and Applications—A Survey
2023 (English)In: Machine Learning and Knowledge Extraction, ISSN 2504-4990, Vol. 5, no 3, p. 891-921Article in journal (Refereed) Published
Abstract [en]

Numerous advancements in various fields, including pattern recognition and image classification, have been made thanks to modern computer vision and machine learning methods. The capsule network is one of the advanced machine learning algorithms that encodes features based on their hierarchical relationships. Basically, a capsule network is a type of neural network that performs inverse graphics to represent the object in different parts and view the existing relationship between these parts, unlike CNNs, which lose most of the evidence related to spatial location and requires lots of training data. So, we present a comparative review of various capsule network architectures used in various applications. The paper’s main contribution is that it summarizes and explains the significant current published capsule network architectures with their advantages, limitations, modifications, and applications. 

Place, publisher, year, edition, pages
Multidisciplinary Digital Publishing Institute (MDPI), 2023
Keywords
capsule network, CNN, machine learning
National Category
Communication Systems
Identifiers
urn:nbn:se:mdh:diva-64511 (URN)10.3390/make5030047 (DOI)001073906900001 ()2-s2.0-85172813133 (Scopus ID)
Available from: 2023-10-11 Created: 2023-10-11 Last updated: 2023-10-18Bibliographically approved
Rehman, A. U., Ahmed, M. U. & Begum, S. (2023). Cognitive Digital Twin in Manufacturing: A Heuristic Optimization Approach. In: IFIP Advances in Information and Communication Technology: . Paper presented at IFIP Advances in Information and Communication Technology (pp. 441-453). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>Cognitive Digital Twin in Manufacturing: A Heuristic Optimization Approach
2023 (English)In: IFIP Advances in Information and Communication Technology, Springer Science and Business Media Deutschland GmbH , 2023, p. 441-453Conference paper, Published paper (Refereed)
Abstract [en]

Complex systems that link virtualization and simulation platforms with actual data from industrial processes are vital for the next generation of production. Digital twins are such systems that have several advantages, notably in manufacturing where they can boost productivity throughout the whole manufacturing life-cycle. Enterprises will be able to creatively, efficiently, and effectively leverage implicit information derived from the experience of current production processes, thanks to cognitive digital twins. The development of numerous technologies has made the digital twin notion more competent and sophisticated throughout time. This article proposes a heuristic approach for cognitive digital twin technology as the next development in a digital twin that will aid in the realization of the goal of Industry 4.0. In creating cognitive digital twins, this article suggests the use of a heuristic approach as a possible route to allowing cognitive functionalities. Here, heuristic optimization is proposed as a feature selection tool to enhance the cognitive capabilities of a digital twin throughout the product design phase of production. The proposed approach is validated using the use-case of Power Transfer Unit (PTU) production, which resulted in an improvement of 8.83% in classification accuracy to predict the faulty PTU in the assembly line. This leads to an improved throughput of the PTU assembly line and also saves the resources utilized by faulty PTUs.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2023
Keywords
Cognitive Digital Twins, Cyber-Physical Systems, Heuristic Optimization, Industrial Manufacturing, Assembly, Cognitive systems, Embedded systems, Energy transfer, Heuristic methods, Life cycle, Optimization, Product design, Simulation platform, Assembly line, Cognitive digital twin, Cybe-physical systems, Heuristics approaches, Optimization approach, Power transfer units, Virtualizations, Cyber Physical System
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-64546 (URN)10.1007/978-3-031-34107-6_35 (DOI)2-s2.0-85173568013 (Scopus ID)9783031341069 (ISBN)
Conference
IFIP Advances in Information and Communication Technology
Available from: 2023-10-24 Created: 2023-10-24 Last updated: 2023-10-24Bibliographically approved
Azeem, M., Malik, T. N., Muqeet, H. A., Hussain, M. M., Ali, A., Khan, B. & Rehman, A. U. (2023). Combined Economic Emission Dispatch in Presence of Renewable Energy Resources Using CISSA in a Smart Grid Environment. Electronics, 12(3), Article ID 715.
Open this publication in new window or tab >>Combined Economic Emission Dispatch in Presence of Renewable Energy Resources Using CISSA in a Smart Grid Environment
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2023 (English)In: Electronics, E-ISSN 2079-9292, Vol. 12, no 3, article id 715Article in journal (Refereed) Published
Abstract [en]

The geographically spatial and controlled distribution of fossil fuel resources, catastrophic global warming, and depletion of fossil fuel resources have forced us to integrate zero- or low-emissions energy resources, such as wind and solar, in the generation mix. These renewable energy resources are unexhausted, available around the globe, and free of cost. The advancement in wind and solar technologies has caused an appreciable decrease in installed the and global levelized costs of electricity via these sources. Therefore, the penetration of renewable energy resources in the generation mix can provide a promising solution to the above-mentioned problems. The aim of simultaneously reducing fuel consumption in terms of “Fuel Cost” and “Emission” in thermal power plants is called a combined economic emission dispatch problem. It is a combinatorial and multi-objective optimization problem. The solution of this problem is to allocate the load demand and losses on the committed units in such way that the overall costs of the generation and emission of thermal units are reduced, while the legal bounds (constraints) are met. It is a highly non-linear and complex optimization problem. The valve-point loading effect makes this problem non-convex. The addition of renewable energy resources (RERs) adds more complexities to this problem because they are intermittent. In this work, chaotic salp swarm algorithms (CISSA) are used to solve the combined economic emission dispatch problem. Chaos is used as an alternative to randomization for the tuning of the control variable to improve the trait of obtaining global extrema. Different test cases having different combinations of thermal, solar, and wind units are solved using the proposed algorithm. The results show the superiority of this study in comparison to the existent research results in terms of the cost of generation and emissions.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
chaos theory, combined economic dispatch, hazardous emissions, renewable energy resources, salp swarm algorithm, smart grid
National Category
Energy Systems
Identifiers
urn:nbn:se:mdh:diva-61957 (URN)10.3390/electronics12030715 (DOI)000929179500001 ()2-s2.0-85147828197 (Scopus ID)
Available from: 2023-02-22 Created: 2023-02-22 Last updated: 2023-03-15Bibliographically approved
Kabir, M. A., Rehman, A. U., Islam, M. M., Ali, N. & Baptista, M. L. (2023). Cross-Version Software Defect Prediction Considering Concept Drift and Chronological Splitting. Symmetry, 15(10), Article ID 1934.
Open this publication in new window or tab >>Cross-Version Software Defect Prediction Considering Concept Drift and Chronological Splitting
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2023 (English)In: Symmetry, E-ISSN 2073-8994, Vol. 15, no 10, article id 1934Article in journal (Refereed) Published
Abstract [en]

Concept drift (CD) refers to a phenomenon where the data distribution within datasets changes over time, and this can have adverse effects on the performance of prediction models in software engineering (SE), including those used for tasks like cost estimation and defect prediction. Detecting CD in SE datasets is difficult, but important, because it identifies the need for retraining prediction models and in turn improves their performance. If the concept drift is caused by symmetric changes in the data distribution, the model adaptation process might need to account for this symmetry to maintain accurate predictions. This paper explores the impact of CD within the context of cross-version defect prediction (CVDP), aiming to enhance the reliability of prediction performance and to make the data more symmetric. A concept drift detection (CDD) approach is further proposed to identify data distributions that change over software versions. The proposed CDD framework consists of three stages: (i) data pre-processing for CD detection; (ii) notification of CD by triggering one of the three flags (i.e., CD, warning, and control); and (iii) providing guidance on when to update an existing model. Several experiments on 30 versions of seven software projects reveal the value of the proposed CDD. Some of the key findings of the proposed work include: (i) An exponential increase in the error-rate across different software versions is associated with CD. (ii) A moving-window approach to train defect prediction models on chronologically ordered defect data results in better CD detection than using all historical data with a large effect size (Formula presented.).

Place, publisher, year, edition, pages
Multidisciplinary Digital Publishing Institute (MDPI), 2023
Keywords
chronological splitting, concept drift, cross-version defect prediction, software defect prediction
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-64695 (URN)10.3390/sym15101934 (DOI)001095251100001 ()2-s2.0-85175426760 (Scopus ID)
Available from: 2023-11-09 Created: 2023-11-09 Last updated: 2023-11-29Bibliographically approved
Khan, F., Yu, X., Yuan, Z. & Rehman, A. U. (2023). ECG classification using 1-D convolutional deep residual neural network. PLOS ONE, 18(4 April), Article ID e0284791.
Open this publication in new window or tab >>ECG classification using 1-D convolutional deep residual neural network
2023 (English)In: PLOS ONE, E-ISSN 1932-6203, Vol. 18, no 4 April, article id e0284791Article in journal (Refereed) Published
Abstract [en]

An electrocardiograph (ECG) is widely used in diagnosis and prediction of cardiovascular diseases (CVDs). The traditional ECG classification methods have complex signal processing phases that leads to expensive designs. This paper provides a deep learning (DL) based system that employs the convolutional neural networks (CNNs) for classification of ECG signals present in PhysioNet MIT-BIH Arrhythmia database. The proposed system implements 1-D convolutional deep residual neural network (ResNet) model that performs feature extraction by directly using the input heartbeats. We have used synthetic minority oversampling technique (SMOTE) that process class-imbalance problem in the training dataset and effectively classifies the five heartbeat types in the test dataset. The classifier's performance is evaluated with ten-fold cross validation (CV) using accuracy, precision, sensitivity, F1- score, and kappa. We have obtained an average accuracy of 98.63%, precision of 92.86%, sensitivity of 92.41%, and specificity of 99.06%. The average F1-score and Kappa obtained were 92.63% and 95.5% respectively. The study shows that proposed ResNet performs well with deep layers compared to other 1-D CNNs. 

Place, publisher, year, edition, pages
Public Library of Science, 2023
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-62488 (URN)10.1371/journal.pone.0284791 (DOI)000984483800022 ()37098024 (PubMedID)2-s2.0-85153899549 (Scopus ID)
Available from: 2023-05-10 Created: 2023-05-10 Last updated: 2023-05-31Bibliographically approved
Rehman, A. U., Belhaouari, S. B., Kabir, M. A. & Khan, A. (2023). On the Use of Deep Learning for Video Classification. Applied Sciences, 13(3), Article ID 2007.
Open this publication in new window or tab >>On the Use of Deep Learning for Video Classification
2023 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 13, no 3, article id 2007Article in journal (Refereed) Published
Abstract [en]

The video classification task has gained significant success in the recent years. Specifically, the topic has gained more attention after the emergence of deep learning models as a successful tool for automatically classifying videos. In recognition of the importance of the video classification task and to summarize the success of deep learning models for this task, this paper presents a very comprehensive and concise review on the topic. There are several existing reviews and survey papers related to video classification in the scientific literature. However, the existing review papers do not include the recent state-of-art works, and they also have some limitations. To provide an updated and concise review, this paper highlights the key findings based on the existing deep learning models. The key findings are also discussed in a way to provide future research directions. This review mainly focuses on the type of network architecture used, the evaluation criteria to measure the success, and the datasets used. To make the review self-contained, the emergence of deep learning methods towards automatic video classification and the state-of-art deep learning methods are well explained and summarized. Moreover, a clear insight of the newly developed deep learning architectures and the traditional approaches is provided. The critical challenges based on the benchmarks are highlighted for evaluating the technical progress of these methods. The paper also summarizes the benchmark datasets and the performance evaluation matrices for video classification. Based on the compact, complete, and concise review, the paper proposes new research directions to solve the challenging video classification problem.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
automatic video classification, deep learning, handcrafted features, video processing
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-61956 (URN)10.3390/app13032007 (DOI)000933763600001 ()2-s2.0-85147858873 (Scopus ID)
Available from: 2023-02-22 Created: 2023-02-22 Last updated: 2023-03-08Bibliographically approved
Bhatti, A., Arif, A., Khalid, W., Khan, B., Ali, A., Khalid, S. & Rehman, A. U. (2023). Recognition and Classification of Handwritten Urdu Numerals Using Deep Learning Techniques. Applied Sciences, 13(3), Article ID 1624.
Open this publication in new window or tab >>Recognition and Classification of Handwritten Urdu Numerals Using Deep Learning Techniques
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2023 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 13, no 3, article id 1624Article in journal (Refereed) Published
Abstract [en]

Urdu is a complex language as it is an amalgam of many South Asian and East Asian languages; hence, its character recognition is a huge and difficult task. It is a bidirectional language with its numerals written from left to right while script is written in opposite direction which induces complexities in the recognition process. This paper presents the recognition and classification of a novel Urdu numeral dataset using convolutional neural network (CNN) and its variants. We propose custom CNN model to extract features which are used by Softmax activation function and support vector machine (SVM) classifier. We compare it with GoogLeNet and the residual network (ResNet) in terms of performance. Our proposed CNN gives an accuracy of 98.41% with the Softmax classifier and 99.0% with the SVM classifier. For GoogLeNet, we achieve an accuracy of 95.61% and 96.4% on ResNet. Moreover, we develop datasets for handwritten Urdu numbers and numbers of Pakistani currency to incorporate real-life problems. Our models achieve best accuracies as compared to previous models in the literature for optical character recognition (OCR).

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
convolutional neural network, GoogLeNet, ResNet, SVM, urdu numeral recognition
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-61996 (URN)10.3390/app13031624 (DOI)000929237000001 ()2-s2.0-85148032356 (Scopus ID)
Available from: 2023-03-01 Created: 2023-03-01 Last updated: 2023-03-08Bibliographically approved
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