Papers by Mali Abdollahian
Rairo-operations Research, Jul 1, 2021
In this study, a novel algorithm is developed to solve the multi-level multiobjective fractional ... more In this study, a novel algorithm is developed to solve the multi-level multiobjective fractional programming problems, using the idea of a neutrosophic fuzzy set. The coefficients in each objective functions is assumed to be rough intervals. Furthermore, the objective functions are transformed into two sub-problems based on lower and upper approximation intervals. The marginal evaluation of predetermined neutrosophic fuzzy goals for all objective functions at each level is achieved by different membership functions, such as truth, indeterminacy/neutral, and falsity degrees in neutrosophic uncertainty. In addition, the neutrosophic fuzzy goal programming algorithm is proposed to attain the highest degrees of each marginal evaluation goals by reducing their deviational variables and consequently obtain the optimal solution for all the decision-makers at all levels. To verify and validate the proposed neutrosophic fuzzy goal programming techniques, a numerical example is adressed in a hierarchical decision-making environment along with the conclusions.
Mathematics, Jul 13, 2022
Journal of Computational Methods in Sciences and Engineering, Apr 19, 2007
Sustainability
The brick kiln industry is one of the largest and most highly unregulated industrial sectors in d... more The brick kiln industry is one of the largest and most highly unregulated industrial sectors in developing countries. Most of the kilns use low-quality coal as primary fuel along with small quantities of bagasse, rice husk, and wooden chips. As a result of inefficient methods of combustion in conventional brick kilns, such as fixed chimney Bull’s trench kilns (FCBTKs), harmful pollutants are emitted in high quantities, which ultimately deteriorate the environment and are widely in operation in Pakistan. The most prominent harmful pollutants include carbon dioxide (CO2), carbon monoxide (CO), sulphur dioxide (SO2), black carbon (BC), and particulate matter less than 2.5 microns (PM2.5). Over the years, new technologies have been adopted by developed countries for the reduction of environmental burdens. One of these technologies is induced draught zigzag kilns (IDZKs), or zigzag kilns (ZZKs), technology, which effectively improves the combustion across the path of bricks stacked in a ...
PLOS ONE
The rising incidence of type 1 diabetes (T1D) among children is an increasing concern globally. A... more The rising incidence of type 1 diabetes (T1D) among children is an increasing concern globally. A reliable estimate of the age at onset of T1D in children would facilitate intervention plans for medical practitioners to reduce the problems with delayed diagnosis of T1D. This paper has utilised Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and Random Forest (RF) to model and predict the age at onset of T1D in children in Saudi Arabia (S.A.) which is ranked as the 7th for the highest number of T1D and 5th in the world for the incidence rate of T1D. De-identified data between (2010-2020) from three cities in S.A. were used to model and predict the age at onset of T1D. The best subset model selection criteria, coefficient of determination, and diagnostic tests were deployed to select the most significant variables. The efficacy of models for predicting the age at onset was assessed using multi-prediction accuracy measures. The average age at onset of T1D is 6.2 years...
Energies, 2021
In this paper, we proposed a home energy management system (HEMS) that includes photovoltaic (PV)... more In this paper, we proposed a home energy management system (HEMS) that includes photovoltaic (PV), electric vehicle (EV), and energy storage systems (ESS). The proposed HEMS fully utilizes the PV power in operating domestic appliances and charging EV/ESS. The surplus power is fed back to the grid to achieve economic benefits. A novel charging and discharging scheme of EV/ESS is presented to minimize the energy cost, control the maximum load demand, increase the battery life, and satisfy the user’s-traveling needs. The EV/ESS charges during low pricing periods and discharges in high pricing periods. In the proposed method, a multi-objective problem is formulated, which simultaneously minimizes the energy cost, peak to average ratio (PAR), and customer dissatisfaction. The multi-objective optimization is solved using binary particle swarm optimization (BPSO). The results clearly show that it minimizes the operating cost from 402.89 cents to 191.46 cents, so that a reduction of 52.47% ...
6th Internat. Conf. on Fuzzy Theory & Technology/with 3rd Joint Computer Information Sciences Conf., 1998
International Journal of Mathematics in Operational Research, 2012
In this paper, we propose a hybrid fuzzy logic-genetic algorithm for optimising a non-linear prob... more In this paper, we propose a hybrid fuzzy logic-genetic algorithm for optimising a non-linear problem related to pressure vessel design. The fuzzy non-linear program that we obtain is solved using a Genetic Algorithm (GA), and a simulation was used for generating the initial population. The efficiency of some of the design optimisation algorithms are compared with the proposed approach of this study, based on the Data Envelopment Analysis (DEA). The results of the DEA reveal that the proposed method is superior. This is the first study that integrates GA and Fuzzy logic for optimising the pressure vessel design problems.
IEEE transactions on green communications and networking, Mar 1, 2022
Smart grid is one of the major geo-distributed Internet of Things (IoT) networks. To support diff... more Smart grid is one of the major geo-distributed Internet of Things (IoT) networks. To support different functionalities of the smart grid such as continuous monitoring, the grid generates massive volumes of data. In a centralized system, majority of control decisions are accomplished at the cloud tier. This generates several drawbacks including limited bandwidth, privacy leakage, data confidentiality and integrity risk, and a single point of failure. Therefore, edge- computing paradigm is one of the possible solutions to avoid the drawbacks and make the system more trustworthy. This paper proposes an edge intelligence-based monitoring paradigm that can use data at the thing tier to monitor large variance shifts of control variables. We propose a Multivariate Exponentially Weighted Moving Variance (MEWMV) chart and a hybrid of wrapper and filter techniques to monitor the variables. The proposed approach can identify variables responsible for the out-of-control signals while considering the correlation among variables. This enables the grid to offload the decision task to the edge tier thus avoiding the latency, risks of data integrity and providing faster monitoring facilities. A case study on the smart grid indicates that the proposed hybrid can identify variables responsible for the out-of-control signals more accurately than existing approaches.
IEEE Transactions on Green Communications and Networking
Smart grid is one of the major geo-distributed Internet of Things (IoT) networks. To support diff... more Smart grid is one of the major geo-distributed Internet of Things (IoT) networks. To support different functionalities of the smart grid such as continuous monitoring, the grid generates massive volumes of data. In a centralized system, majority of control decisions are accomplished at the cloud tier. This generates several drawbacks including limited bandwidth, privacy leakage, data confidentiality and integrity risk, and a single point of failure. Therefore, edge- computing paradigm is one of the possible solutions to avoid the drawbacks and make the system more trustworthy. This paper proposes an edge intelligence-based monitoring paradigm that can use data at the thing tier to monitor large variance shifts of control variables. We propose a Multivariate Exponentially Weighted Moving Variance (MEWMV) chart and a hybrid of wrapper and filter techniques to monitor the variables. The proposed approach can identify variables responsible for the out-of-control signals while considering the correlation among variables. This enables the grid to offload the decision task to the edge tier thus avoiding the latency, risks of data integrity and providing faster monitoring facilities. A case study on the smart grid indicates that the proposed hybrid can identify variables responsible for the out-of-control signals more accurately than existing approaches.
International Journal of Production Research, Jan 11, 2017
Multivariate monitoring of industrial or clinical procedures often involves more than three corre... more Multivariate monitoring of industrial or clinical procedures often involves more than three correlated quality characteristics and the status of the process is judged using a sample of size one. Majority of existing control charts for monitoring process variability for individual observations are capable of monitoring up to three characteristics. One of the hurdles in designing optimal control charts for large dimension data is the enormous computing resources and time that is required by simulation algorithm to estimate the charts parameters. This paper proposes a novel algorithm based on Parallelised Monte Carlo simulation to improve the ability of the Multivariate Exponentially Weighted Mean Squared Deviation and Multivariate Exponentially Weighted Moving Variance charts to monitor process variability for high dimensions in a computationally efficient way. Different techniques have been deployed to reduce computing space and execution time. The optimal control limits (L) to detect small, medium and large shifts in the covariance matrix of up to 15 characteristics are provided. Furthermore, utilising the large number of optimal L values generated by the algorithm enabled authors to develop exponential decay functions to predict L values. This eliminates the need for further execution of the parallelised Monte Carlo simulation.
International Journal of General Medicine, Apr 1, 2022
Percentile reference of babies' birth weight is an effective reference tool for early detection o... more Percentile reference of babies' birth weight is an effective reference tool for early detection of the risk of neonatal morbidity and impaired growth. However, the lack of minimum local and national perinatal data makes its development in Indonesia difficult. This study aims to develop a local birth weight percentile reference for babies based on gestational age and sex by utilizing local data in South Kalimantan Province which is one of the provinces with the highest neonatal mortality rate in Indonesia. Patients and Methods: All single live newborns who were born and were recorded in 20 primary healthcare centers, between 1 June 2016 and 30 June 2017, were included in the study. Birth weight percentiles of infants were calculated using the weighted average method. The study focused on neonates born with gestational age from 36 to 40 weeks. Results: A local birth weight reference for babies has been developed. According to our local reference, the proportion of male newborns with a birth weight < 10th percentile was higher (7.0%) than the existing Indonesian (4.2-4.3%) and international references (3.3-6.2%). Similarly, the proportion of female newborns with a birth weight <10th percentile was higher (6.5%) than the existing Indonesian references (3.6-4.4%) and the global reference (5.8%) but lower than the Intergrowth 21st project (7.2%). The differences suggest that relative birth weight will likely be underestimated (overestimated) if other percentile references are used for the local population. Conclusion: A local birth weight percentile reference for babies in South Kalimantan Province based on gestational age (36-40 weeks) and sex has been developed. Access to the local data, as baseline information, will allow the compilation and comparison of pregnancy-related outcomes across provinces in Indonesia. Consequently, reliable national perinatal data can be strengthened to establish the national references for newborns' anthropometric measurements.
PLOS ONE
The increasing incidence of type 1 diabetes (T1D) in children is a growing global concern. It is ... more The increasing incidence of type 1 diabetes (T1D) in children is a growing global concern. It is known that genetic and environmental factors contribute to childhood T1D. An optimal model to predict the development of T1D in children using Key Performance Indicators (KPIs) would aid medical practitioners in developing intervention plans. This paper for the first time has built a model to predict the risk of developing T1D and identify its significant KPIs in children aged (0-14) in Saudi Arabia. Machine learning methods, namely Logistic Regression, Random Forest, Support Vector Machine, Naive Bayes, and Artificial Neural Network have been utilised and compared for their relative performance. Analyses were performed in a population-based case-control study from three Saudi Arabian regions. The dataset (n = 1,142) contained demographic and socioeconomic status, genetic and disease history, nutrition history, obstetric history, and maternal characteristics. The comparison between case ...
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Papers by Mali Abdollahian