| 1. |
- Azeem, Muhammad, et al.
(author)
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Combined Economic Emission Dispatch in Presence of Renewable Energy Resources Using CISSA in a Smart Grid Environment
- 2023
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In: Electronics. - : MDPI. - 2079-9292. ; 12:3
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Journal article (peer-reviewed)abstract
- 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.
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| 2. |
- Bhatti, A., et al.
(author)
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Recognition and Classification of Handwritten Urdu Numerals Using Deep Learning Techniques
- 2023
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In: Applied Sciences. - : MDPI. - 2076-3417. ; 13:3
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Journal article (peer-reviewed)abstract
- 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).
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| 3. |
- Das, Sushil K., et al.
(author)
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Calibration, validation and uncertainty analysis of a SWAT water quality model
- 2024
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In: Applied water science. - : Springer Nature. - 2190-5487 .- 2190-5495. ; 14:4
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Journal article (peer-reviewed)abstract
- Sediment and nutrient pollution in water bodies is threatening human health and the ecosystem, due to rapid land use changes and improper agricultural practices. The impact of the nonpoint source pollution needs to be evaluated for the sustainable use of water resources. An ideal tool like the soil and water assessment tool (SWAT) can assess the impact of pollutant loads on the drainage area, which could be beneficial for developing a water quality management model. This study aims to evaluate the SWAT model’s multi-objective and multivariable calibration, validation, and uncertainty analysis at three different sites of the Yarra River drainage area in Victoria, Australia. The drainage area is split into 51 subdrainage areas in the SWAT model. The model is calibrated and validated for streamflow from 1990 to 2008 and sediment and nutrients from 1998 to 2008. The results show that most of the monthly and annual calibration and validation for streamflow, nutrients, and sediment at the three selected sites are found with Nash–Sutcliffe efficiency values greater than 0.50. Furthermore, the uncertainty analysis of the model shows satisfactory results where the p-factor value is reliable by considering 95% prediction uncertainty and the d-factor value is close to zero. The model's results indicate that the model performs well in the river's watershed, which helps construct a water quality management model. Finally, the model application in the cost-effective management of water quality might reduce pollution in water bodies due to land use and agricultural activities, which would be beneficial to water management managers.
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| 4. |
- D'Cruze, Ricky Stanley, et al.
(author)
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A Case Study on Ontology Development for AI Based Decision Systems in Industry
- 2024
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In: Lecture Notes in Mechanical Engineering. - : Springer Science and Business Media Deutschland GmbH. - 9783031396182 ; , s. 693-706
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Conference paper (peer-reviewed)abstract
- 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.
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| 5. |
- Haq, M. U., et al.
(author)
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Capsule Network with Its Limitation, Modification, and Applications—A Survey
- 2023
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In: Machine Learning and Knowledge Extraction. - : Multidisciplinary Digital Publishing Institute (MDPI). - 2504-4990. ; 5:3, s. 891-921
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Journal article (peer-reviewed)abstract
- 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.
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| 6. |
- Jaffari, G. Hassnain, et al.
(author)
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Extrinsic contributions to the dielectric response in sintered BaTiO3 nanostructures in paraelectric and ferroelectric regimes
- 2017
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In: Physica. B, Condensed matter. - : ELSEVIER SCIENCE BV. - 0921-4526 .- 1873-2135. ; 525, s. 70-77
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Journal article (peer-reviewed)abstract
- Post sintering studies of BaTiO3 (BTO) nanoparticles are presented in detail. Bulk nanostructures were prepared via three different compaction processes, namely, uniaxial cold pressing (UCP), Cold Isostatic Pressing (CIP) and Spark Plasma Sintering (SPS). Effect of compaction technique on microstructures have been investigated and correlated with electrical response for each sample. In addition to the transport properties, temperature and frequency dependent dielectric response of variously sintered samples and bulk counterpart was recorded. Several aspects have been identified that are essential to be taken into account in order to completely understand physical processes. Drastically distinct features were observed in paraelectric (PE) regime well above ferroelectric (FE)-PE transition temperature. These features include intra grain conduction with a reduction in the magnitude of PE to FE peak dielectric constant magnitude. Role of strain, grain boundary conduction associated with observation of Maxwell Wagner relaxation and hopping conduction in dielectric and ferroelectric response have been observed and discussed. Densification with presence of oxygen vacancies, significantly enhances conductivity associated with the hopping of the carriers, in turn deteriorated ferroelectric response.
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| 7. |
- Javed, Haseeb, et al.
(author)
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Ethical Frameworks for Machine Learning in Sensitive Healthcare Applications
- 2024
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In: IEEE Access. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2169-3536. ; 12, s. 16233-16254
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Journal article (peer-reviewed)abstract
- 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.
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| 8. |
- Kabir, Md Alamgir, et al.
(author)
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CODE : A Moving-Window-Based Framework for Detecting Concept Drift in Software Defect Prediction
- 2022
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In: Symmetry. - : MDPI AG. - 2073-8994. ; 14:12
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Journal article (peer-reviewed)abstract
- Concept drift (CD) refers to data distributions that may vary after a minimum stable period. CD negatively influences models’ performance of software defect prediction (SDP) trained on past datasets when applied to the new datasets. Based on previous studies of SDP, it is confirmed that the accuracy of prediction models is negatively affected due to changes in data distributions. Moreover, cross-version (CV) defect data are naturally asymmetric due to the nature of their class imbalance. In this paper, a moving window-based concept-drift detection (CODE) framework is proposed to detect CD in chronologically asymmetric defective datasets and to investigate the feasibility of alleviating CD from the data. The proposed CODE framework consists of four steps, in which the first pre-processes the defect datasets and forms CV chronological data, the second constructs the CV defect models, the third calculates the test statistics, and the fourth provides a hypothesis-test-based CD detection method. In prior studies of SDP, it is observed that in an effort to make the data more symmetric, class-rebalancing techniques are utilized, and this improves the prediction performance of the models. The ability of the CODE framework is demonstrated by conducting experiments on 36 versions of 10 software projects. Some of the key findings are: (1) Up to 50% of the chronological-defect datasets are drift-prone while applying the most popular classifiers used from the SDP literature. (2) The class-rebalancing techniques had a positive impact on the prediction performance for CVDP by correctly classifying the CV defective modules and detected CD by up to 31% on the resampled datasets.
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| 9. |
- Kabir, Md Alamgir, et al.
(author)
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Cross-Version Software Defect Prediction Considering Concept Drift and Chronological Splitting
- 2023
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In: Symmetry. - : Multidisciplinary Digital Publishing Institute (MDPI). - 2073-8994. ; 15:10
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Journal article (peer-reviewed)abstract
- 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.).
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| 10. |
- Khan, F., et al.
(author)
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ECG classification using 1-D convolutional deep residual neural network
- 2023
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In: PLOS ONE. - : Public Library of Science. - 1932-6203. ; 18:4 April
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Journal article (peer-reviewed)abstract
- 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.
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