ORCID Profile
0000-0002-2229-6717
Current Organisation
University of Engineering and Technology
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 24-07-2018
Publisher: MDPI AG
Date: 25-07-2022
Abstract: Tea (
Publisher: Wiley
Date: 08-03-2022
DOI: 10.1002/OCA.2877
Abstract: The purpose of this article is twofold. We first develop the first‐order necessary optimality conditions for a general constrained fractional optimal control problem using calculus of variation. These conditions are of the form of fractional ordinary differential equations which reduce to the conventional Euler–Lagrange equations when the dynamical system becomes a first‐order one. Then, we prove, via the optimality conditions established, that a popular penalty method used for conventional constrained optimal control and optimization is an “exact” penalty method for the fractional control problem by showing that an optimal solution to the penalized problem satisfies the optimality conditions of the original one when the penalty parameter is sufficiently large. An ex le is also provided to verify the necessary optimality conditions.
Publisher: Elsevier BV
Date: 2022
Publisher: Elsevier BV
Date: 07-2021
Publisher: Springer Science and Business Media LLC
Date: 10-09-2022
Publisher: Springer Science and Business Media LLC
Date: 30-08-2021
Publisher: Elsevier BV
Date: 12-2020
Publisher: Elsevier BV
Date: 10-2023
Publisher: Institute of Advanced Engineering and Science
Date: 03-2022
DOI: 10.11591/IJEECS.V25.I3.PP1599-1606
Abstract: span Driving is a complex task that involves interacting adequately with the vehicle and the environmental changes simultaneously. Drivers' health is an essential factor in determining performance outcomes and enhancing road safety. It is a known reality that drivers with sudden health complications are most likely to be involved in road accidents and suffer several injuries. Besides that, drunk driving is another aspect of a significant public health issue, where drivers under the influence of alcohol show a clear vision loss and vehicle control. The internet of things (IoT) is a trendsetting advancement in which all sensor data can be collected in the cloud. In this paper, an active monitoring tool is developed to record the driver's heart rate if these readings reach vital values while on the move. Additionally, the tool monitors the driver's alcohol concentration, and if it rises beyond a certain threshold, an alarm is sent to the designated emergency contact. The tool has been tested and has been found to work satisfactorily. /span
Publisher: Hindawi Limited
Date: 23-08-2022
DOI: 10.1155/2022/2697303
Abstract: A decentralized power grid is a modern system that implements demand response without requiring major infrastructure changes. In decentralization, the consumers regulate their electricity demand autonomously based on the grid frequency. With cheap equipment (i.e., smart meters), the grid frequency can be easily measured anywhere. Electrical grids need to be stable to balance electricity supply and demand to ensure economically and dynamically viable grid operation. The volumes of electricity consumed roduced (p) by each grid participant, cost-sensitivity (g), and grid participants’ response times (tau) to changing grid conditions affect the stability of the grid. Renewable energy resources are volatile on varying time scales. Due to the volatile nature of these renewable energy resources, there are more frequent fluctuations in decentralized grids integrating renewable energy resources. The decentralized grid is designed by linking real-time electricity rates to the grid frequency over a few seconds to provide demand-side control. In this study, a model has been proposed to predict the stability of a decentralized power grid. The simulated data obtained from the online machine learning repository has been employed. Data normalization has been employed to reduce the biased behavior among attributes. Various data level res ling techniques have been used to address the issue of data imbalance. The results showed that a balanced dataset outperformed an imbalanced dataset regarding classifiers’ performance. It has also been observed that overs ling techniques proved better than unders ling techniques and imbalanced datasets. Overall, the XGBoost algorithm outperformed all other machine learning algorithms based on performance. XGBoost has been given an accuracy of 94.7%, but while combining XGBoost with random overs ling, its accuracy prediction has been improved to 96.8%. This model can better predict frequency fluctuations in decentralized power grids and the volatile nature of renewable energy resources resulting in better utilization. This prediction may contribute to the stability of a decentralized power grid for better distribution and management of electricity.
Publisher: MDPI AG
Date: 31-08-2022
DOI: 10.3390/DIAGNOSTICS12092115
Abstract: Efficient skin cancer detection using images is a challenging task in the healthcare domain. In today’s medical practices, skin cancer detection is a time-consuming procedure that may lead to a patient’s death in later stages. The diagnosis of skin cancer at an earlier stage is crucial for the success rate of complete cure. The efficient detection of skin cancer is a challenging task. Therefore, the numbers of skilful dermatologists around the globe are not enough to deal with today’s healthcare. The huge difference between data from various healthcare sector classes leads to data imbalance problems. Due to data imbalance issues, deep learning models are often trained on one class more than others. This study proposes a novel deep learning-based skin cancer detector using an imbalanced dataset. Data augmentation was used to balance various skin cancer classes to overcome the data imbalance. The Skin Cancer MNIST: HAM10000 dataset was employed, which consists of seven classes of skin lesions. Deep learning models are widely used in disease diagnosis through images. Deep learning-based models (AlexNet, InceptionV3, and RegNetY-320) were employed to classify skin cancer. The proposed framework was also tuned with various combinations of hyperparameters. The results show that RegNetY-320 outperformed InceptionV3 and AlexNet in terms of the accuracy, F1-score, and receiver operating characteristic (ROC) curve both on the imbalanced and balanced datasets. The performance of the proposed framework was better than that of conventional methods. The accuracy, F1-score, and ROC curve value obtained with the proposed framework were 91%, 88.1%, and 0.95, which were significantly better than those of the state-of-the-art method, which achieved 85%, 69.3%, and 0.90, respectively. Our proposed framework may assist in disease identification, which could save lives, reduce unnecessary biopsies, and reduce costs for patients, dermatologists, and healthcare professionals.
Publisher: MDPI AG
Date: 07-10-2021
DOI: 10.3390/APP11199296
Abstract: Lack of education is a major concern in underdeveloped countries because it leads to poor human and economic development. The level of education in public institutions varies across all regions around the globe. Current disparities in access to education worldwide are mostly due to systemic regional differences and the distribution of resources. Previous research focused on evaluating students’ academic performance, but less has been done to measure the performance of educational institutions. Key performance indicators for the evaluation of institutional performance differ from student performance indicators. There is a dire need to evaluate educational institutions’ performance based on their disparities and academic results on a large scale. This study proposes a model to measure institutional performance based on key performance indicators through data mining techniques. Various feature selection methods were used to extract the key performance indicators. Several machine learning models, namely, J48 decision tree, support vector machines, random forest, rotation forest, and artificial neural networks were employed to build an efficient model. The results of the study were based on different factors, i.e., the number of schools in a specific region, teachers, school locations, enrolment, and availability of necessary facilities that contribute to school performance. It was also observed that urban regions performed well compared to rural regions due to the improved availability of educational facilities and resources. The results showed that artificial neural networks outperformed other models and achieved an accuracy of 82.9% when the relief-F based feature selection method was used. This study will help support efforts in governance for performance monitoring, policy formulation, target-setting, evaluation, and reform to address the issues and challenges in education worldwide.
Publisher: Oxford University Press (OUP)
Date: 17-06-2020
Abstract: The area of corporate bankruptcy prediction attains high economic importance, as it affects many stakeholders. The prediction of corporate bankruptcy has been extensively studied in economics, accounting and decision sciences over the past two decades. The corporate bankruptcy prediction has been a matter of talk among academic literature and professional researchers throughout the world. Different traditional approaches were suggested based on hypothesis testing and statistical modeling. Therefore, the primary purpose of the research is to come up with a model that can estimate the probability of corporate bankruptcy by evaluating its occurrence of failure using different machine learning models. As the dataset was not well prepared and contains missing values, various data mining and data pre-processing techniques were utilized for data preparation. Within this research, the task of resolving the issues induced by the imbalance between the two classes is approached by applying different data balancing techniques. We address the problem of imbalanced data with the random unders ling and Synthetic Minority Over S ling Technique (SMOTE). We used five machine learning models (support vector machine, J48 decision tree, Logistic model tree, random forest and decision forest) to predict corporate bankruptcy earlier to the occurrence. We use data from 2009 to 2013 on Poland manufacturing corporates and selected the 64 financial indicators to be broken down. The main finding of the study is a significant improvement in predictive accuracy using machine learning techniques. We also include other economic indicators ratios, along with Altman’s Z-score variables related to profitability, liquidity, leverage and solvency (short/long term) to propose an efficient model. Machine learning models give better results while balancing the data through SMOTE as compared to random unders ling. The machine learning technique related to decision forest led to 99% accuracy, whereas support vector machine (SVM), J48 decision tree, Logistic Model Tree (LMT) and Random Forest (RF) led to 92%, 92.3%, 93.8% and 98.7% accuracy, respectively, with all predictive financial indicators. We find that the decision forest outperforms the other techniques and previous techniques discussed in the literature. The proposed method is also deployed on the web to assist regulators, investors, creditors and scholars to predict corporate bankruptcy.
Publisher: IEEE
Date: 06-2020
Publisher: ACM
Date: 16-01-2021
Publisher: MDPI AG
Date: 12-12-2022
DOI: 10.3390/DIAGNOSTICS12123138
Abstract: Schistosomiasis is a neglected tropical disease that continues to be a leading cause of illness and mortality around the globe. The causing parasites are affixed to the skin through defiled water and enter the human body. Failure to diagnose Schistosomiasis can result in various medical complications, such as ascites, portal hypertension, esophageal varices, splenomegaly, and growth retardation. Early prediction and identification of risk factors may aid in treating disease before it becomes incurable. We aimed to create a framework by incorporating the most significant features to predict Schistosomiasis using machine learning techniques. A dataset of advanced Schistosomiasis has been employed containing recovery and death cases. A total data of 4316 in iduals containing recovery and death cases were included in this research. The dataset contains demographics, socioeconomic, and clinical factors with lab reports. Data preprocessing techniques (missing values imputation, outlier removal, data normalisation, and data transformation) have also been employed for better results. Feature selection techniques, including correlation-based feature selection, Information gain, gain ratio, ReliefF, and OneR, have been utilised to minimise a large number of features. Data res ling algorithms, including Random unders ling, Random overs ling, Cluster Centroid, Near miss, and SMOTE, are applied to address the data imbalance problem. We applied four machine learning algorithms to construct the model: Gradient Boosting, Light Gradient Boosting, Extreme Gradient Boosting and CatBoost. The performance of the proposed framework has been evaluated based on Accuracy, Precision, Recall and F1-Score. The results of our proposed framework stated that the CatBoost model showed the best performance with the highest accuracy of (87.1%) compared with Gradient Boosting (86%), Light Gradient Boosting (86.7%) and Extreme Gradient Boosting (86.9%). Our proposed framework will assist doctors and healthcare professionals in the early diagnosis of Schistosomiasis.
Publisher: Elsevier BV
Date: 05-2019
Publisher: IEEE
Date: 04-11-2020
Publisher: MDPI AG
Date: 04-11-2021
DOI: 10.3390/APP112110353
Abstract: The contemporary innovations in financial technology (fintech) serve society with an environmentally friendly atmosphere. Fintech covers an enormous range of activities from data security to financial service deliverables that enable the companies to automate their existing business structure and introduce innovative products and services. Therefore, there is an increasing demand for scholars and professionals to identify the future trends and directions of the topic. This is why the present study conducted a bibliometric analysis in social, environmental, and computer sciences fields to analyse the implementation of environment-friendly computer applications to benefit societal growth and well-being. We have used the ‘bibliometrix 3.0’ package of the r-program to analyse the core aspects of fintech systematically. The study suggests that ‘ACM International Conference Proceedings’ is the core source of published fintech literature. China leads in both multiple and single country production of fintech publications. Bina Nusantara University is the most relevant affiliation. Arner and Buckley provide impactful fintech literature. In the conceptual framework, we analyse relationships between different topics of fintech and address dynamic research streams and themes. These research streams and themes highlight the future directions and core topics of fintech. The study deploys a co-occurrence network to differentiate the entire fintech literature into three research streams. These research streams are related to ‘cryptocurrencies, smart contracts, financial technology’, ‘financial industry stability, service, innovation, regulatory technology (regtech)’, and ‘machine learning and deep learning innovations’. The study deploys a thematic map to identify basic, emerging, dropping, isolated, and motor themes based on centrality and density. These various themes and streams are designed to lead the researchers, academicians, policymakers, and practitioners to narrow, distinctive, and significant topics.
Publisher: Elsevier BV
Date: 02-2023
Publisher: Elsevier BV
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 23-10-2021
Publisher: Elsevier BV
Date: 09-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Society for Industrial & Applied Mathematics (SIAM)
Date: 2018
DOI: 10.1137/16M1070530
Publisher: Elsevier BV
Date: 08-2017
Publisher: Springer International Publishing
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Peertechz Publications Private Limited
Date: 15-09-2020
Publisher: International Association of Online Engineering (IAOE)
Date: 12-02-2021
DOI: 10.3991/IJET.V16I03.18851
Abstract: In our work, we have presented two widely used recommendation systems. We have presented a context-aware recommender system to filter the items associated with user’s interests coupled with a context-based recommender system to prescribe those items. In this study, context-aware recommender systems perceive the user’s location, time, and company. The context-based recommender system retrieves patterns from World Wide Web-based on the user’s past interactions and provides future news recommendations. We have presented different techniques to support media recommendations for smartphones, to create a framework for context-aware, to filter E-learning content, and to deliver convenient news to the user. To achieve this goal, we have used content-based, collaborative filtering, a hybrid recommender system, and implemented a Web ontology language (OWL). We have also used the Resource Description Framework (RDF), JAVA, machine learning, semantic mapping rules, and natural ontology languages that suggest user items related to the search. In our work, we have used E-paper to provide users with the required news. After applying the semantic reasoning approach, we have concluded that by some means, this approach works similarly as a content-based recommender system since by taking the gain of a semantic approach, we can also recommend items according to the user’s interests. In a content-based recommender system, the system provides additional options or results that rely on the user’s ratings, appraisals, and interests.
Publisher: Oxford University Press (OUP)
Date: 07-06-2022
Abstract: Disease diagnosis is an exciting task due to many associated factors. Inaccuracy in the measurement of a patient’s symptoms and the medical expert’s expertise has some limitations capacity to articulate cause affects the diagnosis process when several connected variables contribute to uncertainty in the diagnosis process. In this case, a decision support system that can assist clinicians in developing a more accurate diagnosis has a lot of potentials. This work aims to deploy a fuzzy inference-based decision support system to diagnose various diseases. Our suggested method distinguishes new cases based on illness symptoms. Distinguishing symptomatic disorders becomes a time-consuming task in most cases. It is critical to design a system that can accurately track symptoms to identify diseases using a fuzzy inference system (FIS). Different coefficients were used to predict and compute the severity of the predicted diseases for each sign of disease. This study aims to differentiate and diagnose COVID-19, typhoid, malaria and pneumonia. The FIS approach was utilized in this study to determine the condition correlating with input symptoms. The FIS method demonstrates that afflictive illness can be diagnosed based on the symptoms. Our decision support system’s findings showed that FIS might be used to identify a variety of ailments. Doctors, patients, medical practitioners and other healthcare professionals could benefit from our suggested decision support system for better diagnosis and treatment.
Publisher: Oxford University Press (OUP)
Date: 11-05-2021
Abstract: In today’s world, lung cancer is a significant health burden, and it is one of the most leading causes of death. A leading type of lung cancer is malignant mesothelioma (MM). Most of the MM patients do not show any symptoms. Etiology plays a vital factor in the diagnosis of any disease. Positron emission tomography (PET), magnetic resonance imaging (MRI), biopsies, X-rays and blood tests are essential but costly and invasive MM risk factor identification methods. In this work, we mainly focused on the exploration of the MM risk factors. The identification of mesothelioma symptoms was carried out by utilizing the data of mesothelioma patients. However, the dataset was comprised of both healthy and mesothelioma patients. The dataset is prone to a class imbalance problem in which the number of MM patients significantly less than healthy in iduals. To overcome the class imbalance problem, the synthetic minority overs ling technique has been utilized. The association rule mining-based Apriori algorithm has been applied to a preprocessed dataset. Before using the Apriori algorithm, both duplicate and irrelevant attributes were removed. Moreover, the numerical attributes were also classified into nominal attributes and the association rules were generated in the dataset. Our results show that erythrocyte sedimentation rate, asbestos exposure and its duration time, and pleural and serum lactic dehydrogenase ratio are major risk factors of MM. The severe stages of MM can be avoided by earlier identification of risk factors of the disease. The failure of identification of risk factors can lead to increased risk of multiple medical conditions, including cardiovascular diseases, mental distress, diabetes and anemia.
No related grants have been discovered for Chongyang Liu.