JOURNAL PAPER

Md. Julkar Nayeen Mahi; Sudipto Chaki; Shamim Ahmed; Milon Biswas; M. Shamim Kaiser; Mohammad Shahidul Islam..., A Review on VANET Research: Perspective of Recent Emerging Technologies, IEEE Access, 10, pp.65760-65783, 2022. doi: 10.1109/ACCESS.2022.3183605.

Recent technology has modeled VANET (vehicular adhoc network) communication well in terms of privileges to derive vehicular communication technologically to save time, energy, and money. Due to the increase in powerful technology in modern times, VANETs play a vital role in uplifting daily concerns across vehicles and vehicular identities. Hence, to tune VANETs to become compatible with traditional technologies and increase demand, VANETs require upgrading. The severity and frequency of unwanted occurrences have become a considerable concern for our day-to-day lives relating to vehicular position. Thus, verily updated methodologies or working procedures are needed for the future VANET interplay to eradicate such problems occurring through vehicular identities. This article outlines in technology related to VANETS, future developments, and coping issues by deriving comprehensive frameworks, workflow patterns, upgrading procedures including big data, fog computing, SDN (software defined networking), and SIoT (social Internet of Things). This article provides a high-level overview of a complete VANET upgrade solution to address future problem management issues under a range of acceptable scientific themes, indicators, and combinations.

Md. Zahid Hasan, Mohammad Shorif Uddin & Mohammad Shahidul Islam, An expert system for selecting optimal cloud-service provider, International Journal of Information Technology Springer, pp.1555–1563, 2021. doi: https://doi.org/10.1007/s41870-021-00822-8

Selection of the optimal cloud service provider is an important issue for business accessibility of cloud benefits as well as various costs and requirements. This paper aims to develop an expert system to rank the cloud service-providing companies and find the optimal one. For that purpose, existing ranking systems are first examined, and all the possible decision attributes are identified. Then, the generic feature extraction approach is employed to develop the basic ranking engine. The proposed expert system considers the decision parameters that have a high influence on the decision-making approach. Necessary datasets and domain knowledge are achieved from cloudharmony.com, cloudorado.com, and some popular cloud computing websites. The system experiments with an ample amount of data and the results are validated against the benchmark results that are collected using the nominal group technique from some domain experts. The performance of the proposed system is found superior to other ranking expert systems. For an easy understanding of the potential use of our expert system, a numeric study is provided in this paper.

M. Z. Hasan, S. Hossain, M. S. Uddin, and M. S. Islam, A Generic Feature Extraction Approach for Dealing with Multiple Attribute Decision Analysis Problems under Risk and Uncertainty, 10, 3, pp.5775-5783, 2020. doi: https://doi.org/10.48084/etasr.3537 M. Z. Hasan, S. Hossain, M. S. Uddin, and M. S. Islam, “A Generic Feature Extraction Approach for Dealing with Multiple Attribute Decision Analysis Problems under Risk and Uncertainty”, Eng. Technol. Appl. Sci. Res., vol. 10, no. 3, pp. 5775–5783, Jun. 2020.

This paper represents a generic feature extraction approach to handle multiple attribute decision analysis problems. For that purpose, available decision support frameworks are carefully studied and the basic types of attributes involved in the decision problems are identified. Based on this analysis, a generic decision support scheme is proposed that can deal with all sorts of attributes in order to deduce the optimal solution for any decision problem. The proposed framework is capable of handling multiple attributes throughout the process of providing a flawless solution for the decision problem under both risk and uncertainty. This paper provides detailed information about the sources of uncertainty in the decision-making process and proposes a sophisticated approach for capturing all sorts of uncertainties. In the proposed approach, a cross assessment of every attribute against the corresponding attribute of the other alternatives is conducted to extract the significant features of an attribute. The relative importance of every attribute is considered as a supporting knowledge representation parameter in order to optimize the attribute-assessment process. The final decision is made based on the numerical scores seized by the alternatives. The paper also represents a numerical study to demonstrate the potential applications of the proposed methodology.

Md Zahid Hasan, Shakhawat Hossain, Mohammad Shorif Uddin and Mohammad Shahidul Islam,, A Generic Approach for Weight Assignment to the Decision Making Parameters, International Journal of Advanced Computer Science and Applications(IJACSA), 10, 11, 1019. doi: 10.14569/IJACSA.2019.0101170

Weight assignment to the decision parameters is a crucial factor in the decision-making process. Any imprecision in weight assignment to the decision attributes may lead the whole decision-making process useless which ultimately mislead the decision-makers to find an optimal solution. Therefore, attributes’ weight allocation process should be flawless and rational, and should not be just assigning some random values to the attributes without a proper analysis of the attributes’ impact on the decision-making process. Unfortunately, there is no sophisticated mathematical framework for analyzing the attribute’s impact on the decision-making process and thus the weight allocation task is accomplished based on some human sensing factors. To fill this gap, present paper proposes a weight assignment framework that analyzes the impact of an attribute on the decision-making process and based on that, each attribute is evaluated with a justified numerical value. The proposed framework analyzes historical data to assess the importance of an attribute and organizes the decision problems in a hierarchical structure and uses different mathematical formulas to explicit weights at different levels. Weights of mid and higher-level attributes are calculated based on the weights of root-level attributes. The proposed methodology has been validated with diverse data. In addition, the paper presents some potential applications of the proposed weight allocation scheme.

Mohammad Shahidul Islam, Shamim Al Mamun, Zamshed Iqbal Chowdhury, M. Shamim Kaiser, Techno-financial analysis and design of on-board intelligent-assisting system for a hybrid solar–DEG-powered boat, International Journal of Energy and Environmental Engineering, 4, pp.361, 2016.

In this paper, we present a financial feasibility analysis and design of an on-board-assisting system for a hybrid solar–diesel-powered boat. The major components of this boat are solar panel, battery bank, speed and direction controller for dc motor, brushless dc motor, diesel engine generator (DEG), and intelligent-assisting system (IAS) to assist the sailor. A DEG is considered to maintain the ability to run the system even during night-time, cloudy, and partially cloudy days. The energy demand posed by the electrical system requires maintaining an appropriate balance between the energy sources-done by an on-board-processing element which facilitates the system to run automatically. The capacity of the battery bank is considered large enough to satisfy electrical power requirement of the system for a whole day during non-charging periods. The optimal configuration for the hybrid system is selected from HOMER simulation results, whereas a financial feasibility analysis of the proposed hybrid solar and DEG-powered boat is performed using a clean energy management software called RETScreen. The IAS also provides solutions to the common naval hazards, such as over-weight and inclement weather by employing depth of submergence detector and GSM network interface. Financial analysis reveals that the proposed system is financially feasible, while tests showed that IAS successfully provides navigational supports to sailors.

Mohammad Shahidul Islam, Md. Zahid Hasan, Mohammad Shorif Uddin, Ummay Salma, Learning To Classify Diabetes Disease Using Data Mining Techniques, 15, 1, 2017.

Data Classification and predictions are continuingto perform an utmost role in the area of data mining.Classification and clustering are two useful methods, which areused in various domains to address the challenge of accurateclassification. In this study, performances of classificationtechniques were observed in order to anticipate of classify thepatients of being diabetic or non-diabetic basing on somepreviously given data. We compared performances of LogisticRegression (LR), Naive Bayes, Decision Trees and reducedTreeBagger. Performances of given classification techniques werecontrasted based on classification of k-fold cross-validationmethod, accuracy and confusion matrix. TreeBegger was foundthe best technique to classify diabetic or non-diabetic in thisdataset due to its good classificatory performance.

Mohammad Shahidul Islam, Tarikuzzaman Emon and Tarin Kazi, Boosting Facial Expression Recognition in a Noisy Environment Using LDSP-Local Distinctive Star Pattern, International Journal of Computer Science Issues (IJCSI), 11, 4, pp.45-51, 2014.

This paper presents a local feature descriptor, the Local Distinctive Star Pattern (LDSP), for facial expression recognition. The feature is obtained from a local 3x3 pixels area by computing the directional edge response value. Each pixel is represented by two 4-bit binary patterns, which is named as LDSP feature for that pixel. Each face is divided into 81 equal sized blocks and histogram of LDSP codes from those blocks are concatenated to build the feature vector for classification. The recognition performance of our descriptor is tested on popular JAFFE dataset with Support vector Machine (SVM) as classifier. Extensive experimental results with prototype expressions show that proposed LDSP descriptor is superior to existing appearance-based feature descriptor in terms of classification accuracy on static image.

Mohammad Shahidul Islam, Gender Classification Using Gradient Direction Pattern, Science .International, 25, 4, pp.797-799, 2013.

A novel methodology for gender classification is presented in this paper. It extracts feature from local region of a face using gray color intensity difference. The facial area is divided into sub-regions and GDP histogram extracted from those regions are concatenated into a single vector to represent the face. The classification accuracy obtained by using support vector machine has outperformed all traditional feature descriptors for gender classification. It is evaluated on the images collected from FERET database and obtained very high accuracy. 

Mohammad Shahidul Islam and Surapong Auwatanamongkol, Facial Expression Recognition using Local Arc Pattern, Trends in Applied Sciences Research, 9, pp.113-120, 2014. doi: 10.3923/tasr.2014.113.120

The success of a good facial expression recognition system depends on the facial feature descriptor. Features extracted from local region are widely used for facial expression recognition due to their simplicity but the long feature vector length produces by them makes the overall system slow for recognition. This study presents a unique local facial feature descriptor, the Local Arc Pattern (LAP) for facial expression recognition. Feature is obtained from a local 5x5 pixels region by comparing the gray color intensity values surrounding the referenced pixel to formulate two separate binary patterns for the referenced pixel. Each face is divided into equal sized blocks and histograms of LAP codes from those blocks are concatenated to build the feature vector for classification. The recognition performance of proposed method was evaluated on popular Japanese Female Facial Expression dataset using Support Vector Machine as the classifier. Extensive experimental results with prototype expressions show that proposed feature descriptor outperforms several popular existing appearance-based feature descriptors in terms of classification accuracy.

Mohammad Shahidul IslamMohammad Shahidul Islam, Tarikuzzaman Emon, Md. Zahid Hasan, Local Appearance based Novel Facial Feature Extraction Method for Human Expression Recognition, International Journal of Computer Applications, 181, 35, pp.1-4, 2019.

A novel approach to extract the light invariant local feature for facial expression recognition is presented in this paper. It is robust in monotonic gray-scale changes caused by illumination variations. Proposed method is easy to perform and time effective. The local strength for a pixel is calculated by finding the decimal value of the neighboring of that pixel with the particular rank in term of its gray-scale value among all the nearest pixels. When eight neighboring pixels are considered, the gradient direction of the neighboring pixel with the mix of second minima and maxima of the gray scale intensity can capture more local details and yield the best performance for the facial expression recognition in our experiment. CK+ dataset is used in this experiment to find out the facial expression classification. The classification accuracy rate achieved is 92.1 ± 3.2%, which is not the best but easier to compute. The results show that the experimented feature extraction technique is fast, accurate and efficient for facial expression recognition.

Mohammad Shahidul Islam and Surapong Auwatanamongkol, Gradient Direction Pattern: A Gray-Scale Invariant Uniform Local Feature Representation for Facial Expression Recognition, The Journal of Applied Sciences, 13, 6, pp.837-845, 2013. doi: 10.3923/jas.2013.837.845

Local feature representations are widely used for facial expression recognition due to their simplicity and high accuracy rates achieved. However, local feature representations usually produce a long feature vector to represent a facial image and hence, require long processing time for training and recognition. To alleviate this problem, a simple gray-scale invariant local feature representation is proposed for facial expression recognition. The proposed local feature pattern at a pixel level, represented by a four-bit pattern, is derived based on the gradient directions of the gray color values of its neighboring pixels. A histogram of sixteen bins is required to count numbers of the patterns at the pixel level in a block. The histograms of all blocks in an image are concatenated to form the final local feature vector. To reduce the length of the local feature vector, a variance based feature selection method is used to select patterns that are more relevant and eight out of the sixteen possible patterns can be discarded without compromising the recognition rates. In addition, the result patterns become uniform. Experiments were performed on extended Cohn-Kanade and Japanese JAFFE datasets using Support Vector Machines as classifiers. The experimental results do show that the proposed feature representation is more effective than other local feature representations in terms of accuracy rates and processing time.

Mohammad Shahidul Islam, Robust Gender Classification Using LMnP-Local Minima Pattern, International Journal of Scientific & Engineering Research, 4, 11, 2013.

Gender classification is an important matter for Human Computer Interaction devices. A new methodology for gender classification is examined in this study where the facial feature is extracted from local region of a face using gray color intensity. The facial area is divided into eighty-one equal sized square sub-regions and Local Minima Pattern (LMnP) method is applied to each pixel. LMnP histograms extracted from those regions are concatenated into a single vector to represent that particular face. The classification accuracy obtained using Local Minima Pattern (LMnP) along with support vector machine as a classifier has outperformed all traditional feature descriptors for gender classification. It is evaluated on the images collected from popular FERET database

Mohammad Shahidul Islam and Surapong Auwatanamongkol, Facial Expression Recognition using Local Arc Pattern, Trends in Applied Sciences Research, 9, 2, pp.113-120, 2014. doi: 10.3923/tasr.2014.113.120

The success of a good facial expression recognition system depends on the facial feature descriptor. Features extracted from local region are widely used for facial expression recognition due to their simplicity but the long feature vector length produces by them makes the overall system slow for recognition. This study presents a unique local facial feature descriptor, the Local Arc Pattern (LAP) for facial expression recognition. Feature is obtained from a local 5x5 pixels region by comparing the gray color intensity values surrounding the referenced pixel to formulate two separate binary patterns for the referenced pixel. Each face is divided into equal sized blocks and histograms of LAP codes from those blocks are concatenated to build the feature vector for classification. The recognition performance of proposed method was evaluated on popular Japanese Female Facial Expression dataset using Support Vector Machine as the classifier. Extensive experimental results with prototype expressions show that proposed feature descriptor outperforms several popular existing appearance-based feature descriptors in terms of classification accuracy.

Mohammad Shahidul Islam and Surapong Auwatanamongkol, Uniform Local Active Forces: A Novel Gray-Scale Invariant Local Feature Representation for Facial Expression Recognition, Trends in Applied Sciences Research, 9, 2, pp.113-120, 2014. doi: 10.3923/tasr.2014.113.120

The success of a good facial expression recognition system depends on the facial feature descriptor. Features extracted from local region are widely used for facial expression recognition due to their simplicity but the long feature vector length produces by them makes the overall system slow for recognition. This study presents a unique local facial feature descriptor, the Local Arc Pattern (LAP) for facial expression recognition. Feature is obtained from a local 5x5 pixels region by comparing the gray color intensity values surrounding the referenced pixel to formulate two separate binary patterns for the referenced pixel. Each face is divided into equal sized blocks and histograms of LAP codes from those blocks are concatenated to build the feature vector for classification. The recognition performance of proposed method was evaluated on popular Japanese Female Facial Expression dataset using Support Vector Machine as the classifier. Extensive experimental results with prototype expressions show that proposed feature descriptor outperforms several popular existing appearance-based feature descriptors in terms of classification accuracy.

Mohammad Shahidul Islam, Local Gray Code Pattern (LGCP): A Robust Feature Descriptor for Facial Expression Recognition, International Journal of Science and Research, 2, 8, pp.413-419, 2013.

This paper presents a new local facial feature descriptor, Local Gray Code Pattern (LGCP), for facial expression recognition in contrast to widely adopted Local Binary pattern. Local Gray Code Pattern (LGCP) characterizes both the texture and contrast information of facial components. The LGCP descriptor is obtained using local gray color intensity differences from a local 3x3 pixels area weighted by their corresponding TF (term frequency). I have used extended Cohn-Kanade expression (CK+) dataset and Japanese Female Facial Expression (JAFFE) dataset with a Multiclass Support Vector Machine (LIBSVM) to evaluate proposed method. The proposed method is performed on six and seven basic expression classes in both person dependent and independent environment. According to extensive experimental results with prototypic expressions on static images, proposed method has achieved the highest recognition rate, as compared to other existing appearance-based feature descriptors LPQ, LBP, LBPU2, LBPRI, and LBPRIU2.

Mohammad Shahidul Islam, Local Gradient Pattern - A Novel Feature Representation for Facial Expression Recognition, Journal of Artificial Intelligence & Data Mining, 2, 1, pp.33-38,

Many researchers adopt Local Binary Pattern for pattern analysis. However, the long histogram created by Local Binary Pattern is not suitable for a large-scale facial database. This paper presents a simple facial pattern descriptor for facial expression recognition. Local pattern is computed based on local gradient flow from one side to another side through the center pixel in a 3x3 pixels region. The center pixel of that region is represented by two separate two-bit binary patterns, named as Local Gradient Pattern (LGP) for the pixel. LGP pattern is extracted from each pixel. Facial image is divided into 81 equal sized blocks and the histograms of local LGP features for all 81 blocks are concatenated to build the feature vector. Experimental results prove that the proposed technique along with Support Vector Machine is effective for facial expression recognition.

Mohammad Shahidul Islam. AUWATANAMONGKOL S., A Novel Feature Extraction Technique for Facial Expression Recognition, 10, 3, pp.9-14, 2013. doi: doi=10.1.1.301.7726

This paper presents a new technique to extract the light invariant local feature for facial expression recognition. It is not only robust to monotonic gray-scale changes caused by light variations but also very simple to perform which makes it possible for analyzing images in challenging real-time settings. The local feature for a pixel is computed by finding the direction of the neighboring of the pixel with the particular rank in term of its gray scale value among all the neighboring pixels. When eight neighboring pixels are considered, the direction of the neighboring pixel with the second minima of the gray scale intensity can yield the best performance for the facial expression recognition in our experiment. The facial expression classification in the experiment was performed using a support vector machine on CK+ dataset The average recognition rate achieved is 90.1 ± 3.8%, which is better than other previous local feature based methods for facial expression analysis. The experimental results do show that the proposed feature extraction technique is fast, accurate and efficient for facial expression recognition.

Mohammad Shahidul Islam, Analytical Analysis of Multimedia Mobile Networks, Journal of Telecommunications, 8, 1, 2011.

Cellular networks provide additional multimedia services, besides the traditional voice service. The introduction ofmultimedia services to cellular networks creates new problems for the design of such systems. We need to provide certain QoSfor the offered multimedia services. In multimedia mobile IP networks one may expect different bandwidth demands by differenttraffic classes. Also, different classes have different traffic parameters, such as new call intensity and call/session duration.Thispaper addresses problems that arise from the increasing demand of multimedia services and we derive an analytical model forsingle-class mobile networks

Mohammad Shahidul Islam, Analyze the Performance of Cellular IP Networks, Journal of Telecommunications, 5, 2, pp.11-17, 2011.

Mobile networks and the Internet are converging. This convergence challenges the QoS provisioning in such mobile IP networks. The future mobile Internet will include many portable devices connected to the global network. In order to achieve higher bandwidth for the users, the cell size will have to be limited. That leads to the creation of microcellular, or even picocellular environments, where the users move frequently among cells. This paper addresses problems that arise from the integration of mobile networks and the Internet, which are mainly due to user mobility. Also analyze the impact of handovers on different traffic types, such as CBR, VBR, as well as best-effort traffic.

Mohammad Shahidul Islam, Traffic Analysis of Wireless IP Network, Journal of Telecommunications, 5, 1, pp.33-37, 2010.

Wireless networks have crossed almost five billion subscribers worldwide with first, second and third generationmobile networks. The main service was voice and more recently modem-based low-rate and high rate data services. Becauseof the voice oriented traffic and circuit-switching technology, these networks are dimensioned and designed using the traditionaltraffic theory in telecommunications. Their design is based on high-cost centralized switching and signaling equipment and basestations as wireless access points. Another technology dominated the world in the wired communication networks is IPtechnology. The transparency of the Internet Protocol(IP) to all traffic types and low-cost switching equipment made it veryattractive to operators and customers. The 3G of mobile networks introduces wide spectrum and high data rates as well asdifferent types of circuit-switched and packet-based services. It provides IP connectivity besides the circuit switching. Futuregeneration mobile systems are expected to include heterogeneous access technologies, such as wireless LAN and 3G, as wellas end-to-end IP connectivity. The different type of traffic services and access technologies creates new possibilities for bothoperators and users. On the other hand, it raises new traffic and design issues. This paper provides traffic analysis and qualityof service(QoS) of wireless IP networks with multiple traffic classes

Md. Imran Hossain, Mohammad Shahidul Islam, The Future Aspects of Wireless Electricity Transmission, Journal of Telecommunications, 4, 2, pp.19-21, 2010.

This paper refers to that electricity transmission without using wire. To implement this concept, Medium is very essen-tial because electricity means flow of electron from one place to another. In the case of transmission there are some parameterswhich have to consider such as transmitter, receiver, medium or channel. So when we will be gone to think about wireless electrici-ty transmission we have to consider these terms. Process of transmission, occurring noise during transmission has discussed in this paper. Actually, Wireless electricity is not available in the world now. If it is implemented, real world will change dynamically, butthere are many problems behind it. To develop this think, System will be more costly than wired transmission and also biological impact is a major fact. Why can’t electricity be wireless? Here some reasons are showed. However, if all obstacles are overcome,will the electricity transmission be wireless? Yes, it is possible. We want to show that how it can be implemented? So potentially, itis possible but there are probably many challenges ahead. Today, it is still not viable but we never can tell, technology has alwaysdone the impossible

Md Zahid Hasan, Shakhawat Hossain, Mohammad Shorif Uddin and Mohammad Shahidul Islam,, A Generic Approach for Weight Assignment to the Decision Making Parameters, International Journal of Advanced Computer Science and Applications(IJACSA), 10, 11, 2019. doi: http://dx.doi.org/10.14569/IJACSA.2019.0101170

Weight assignment to the decision parameters is a crucial factor in the decision-making process. Any imprecision in weight assignment to the decision attributes may lead the whole decision-making process useless which ultimately mislead the decision-makers to find an optimal solution. Therefore, attributes’ weight allocation process should be flawless and rational, and should not be just assigning some random values to the attributes without a proper analysis of the attributes’ impact on the decision-making process. Unfortunately, there is no sophisticated mathematical framework for analyzing the attribute’s impact on the decision-making process and thus the weight allocation task is accomplished based on some human sensing factors. To fill this gap, present paper proposes a weight assignment framework that analyzes the impact of an attribute on the decision-making process and based on that, each attribute is evaluated with a justified numerical value. The proposed framework analyzes historical data to assess the importance of an attribute and organizes the decision problems in a hierarchical structure and uses different mathematical formulas to explicit weights at different levels. Weights of mid and higherlevel attributes are calculated based on the weights of root-level attributes. The proposed methodology has been validated with diverse data. In addition, the paper presents some potential applications of the proposed weight allocation scheme.

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CONFERENCE PAPER

Dr. Mohammad Shahidul Islam,

Abdur Rahman, Shanto Roy, M Shamim Kaiser, and Md. Shahidul Islam, “A lightweightmulti-tier S-MQTT framework to secure communication between low-end IoT de-vices“, International Conference on Networking, Systems and Security (NSysS - 2018).

 

Abstract:

Boosting Facial Expression Recognition Using LDGP - Local Distinctive Gradient Pattern,

Appearance based local feature methods are widely used for facial expression recognition because of their simplicity and high accuracy rates of recognition. However, the achieved accuracy rates and running time yet need to be improved. A new appearance based local feature method, called Local Distinctive Gradient Pattern (LDGP) is proposed in this paper. It derives two 4-bit local binary patterns from two different layers for a pixel by comparing the gray color intensity value of the pixel with its neighboring pixels in four distinct directions. Since each face image is divided into equal sized blocks, two histograms for the two 4-bit LDGP patterns of all pixels in each block can be constructed. The histograms of all blocks are then concatenated to build the feature vector for the given image. To evaluate the effectiveness of the proposed descriptor, experiments were conducted on the popular JAFFE dataset using Support Vector Machine (SVM) as the classifier. Extensive experimental results with seven prototype expressions show that proposed LDGP descriptor is superior to other appearance-based feature descriptors in terms of accuracy rates of recognition.

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BOOK CHAPTER

Md Zahid Hasan, Shakhawat Hossain, Mohammad Shorif Uddin and Mohammad Shahidul Islam,, An Optimized Pruning Technique for Handling Uncertainty in Decision-Making Process, Proceedings of International Joint Conference on Computational Intelligence, pp.467-475, 2020. doi: 10.1007/978-981-15-3607-6_37

It is important to understand the sources of uncertainties in the decision-making process as uncertainties very often lead a decision-maker or a decision support framework to an irrational and flawful solution for a decision problem. Decision scientists have been conducting researches to understand the sources of uncertainties in order to discover the possible equations for handling uncertainties associated with decision-making processes. This paper presents a detailed description of the researches conducted on uncertainties involved in decision-making parameters as well as decision-making processes. Based on the analysis of the research outcomes, the limitations of the available uncertainty handling methods are identified. To capture the limitations of the available decision support frameworks, an optimized pruning technique is proposed in this paper capable of handling uncertainties throughout the decision-making process. The proposed pruning technique analyses the impact of an attribute to find out the importance of that attribute in emerging an optimal solution for a certain decision problem. The attributes that have no impacts on decision assessment are directly pruned in the proposed methodology in which consequences remove the uncertainties associated with those attributes. The proposed system flattens the confidence degrees provided by the users to handle the uncertainties due to partial ignorance in the decision-making environment. The pruning method also focuses on minimizing decision-making calculations in order to lessen the complexity of the decision-making process. To demonstrate the practical implementation of the proposed methodology, a numerical study is presented in this paper.

Md Zahid Hasan, Shakhawat Hossain, Mohammad Shorif Uddin and Mohammad Shahidul Islam,, Sources and Impact of Uncertainty on Rule-Based Decision-Making Approaches, Proceedings of International Joint Conference on Computational Intelligence, pp.299-308, 2020. doi: 10.1007/978-981-15-3607-6_24

This paper provides a detailed description of the sources of uncertainties in rule-based decision-making approaches. An investigation into different rule-based decision support models under uncertainty is conducted. In addition, constructive criticism of available rule-based decision support frameworks in terms of uncertainty handling capacity is outlined. All the possible sources of uncertainties at different stages of the decision-making process are examined. The paper also represents the impacts of uncertainties on rule-based decision-making processes. The impacts of different types of uncertainties over the solution of a decision problem are evaluated in terms of its severity. Finally, a very straightforward direction to future research opportunities is provided through a deep analysis of the existing research gaps.

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