List of Titles

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A comprehensive spectrum trading scheme based on market competition, reputation and buyer specific requirements

- Hassan, Rakib, Karmakar, Gour, Kamruzzaman, Joarder, Srinivasan, Bala

Authors: Hassan, Rakib , Karmakar, Gour , Kamruzzaman, Joarder , Srinivasan, Bala
Date: 2015
Type: Text , Journal article
Relation: Computer Networks Vol. 84, no. (2015), p. 17-31
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Description: In the exclusive-use model of spectrum trading, cognitive radio devices or secondary users can buy spectrum resources from licensed users or primary users for a short or long period of time. Considering such spectrum access, a trading model is introduced where a buyer can select a set of candidate sellers based on their reputation and their offers in fulfilling its requirements, namely, offered signal quality, contract duration, coverage and bandwidth. Similarly, a seller can assess a buyer as a potential trading partner considering the buyer's reliability, which the seller can derive from the buyer's reputation and financial profile. In our scheme, seller reputation or buyer reliability can be either obtained from a reputation brokerage service, if one exists, or calculated using our model. Since in a competitive market, the price of a seller depends on that of other sellers, game theory is used to model the competition among multiple sellers. An optimization technique is used by a buyer to select the best seller(s) and optimize purchase to maximize its utility. This may result in buying from multiple sellers of certain amount of bandwidth from each, depending on price and meeting requirements and budget constraints. Stability of the model is analyzed and performance evaluation shows that it benefits sellers and buyers in terms of profit and throughput, respectively. © 2015 Elsevier B.V. All rights reserved.

A contender-aware backoff algorithm for CSMA based MAC protocol for wireless sensor network

- Miraz Al-Mamun, Miraz, Karmakar, Gour, Kamruzzaman, Joarder

Authors: Miraz Al-Mamun, Miraz , Karmakar, Gour , Kamruzzaman, Joarder
Date: 2010
Type: Text , Conference proceedings
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Description: Existing contention based nonpersistent medium access control protocols in Wireless Sensor Network (WSN) do not perform well in high contention. Their performances are affected by occurrence of collision due to uniform probability distribution in choosing Time Slot (TS) during backoff period. To address this issue nonuniform probability distribution was proposed. However success rate still drops for higher number of contenders. In this paper we propose CSMA/s (Collision Sense Multiple Access /per Slot based), a new approach in nonuniform contender-aware probability distribution for choosing TS in the backoff period. Rather than taking a premeditated fixed value for contender population size, our proposed scheme embeds neighborhood population size into its bedrock to automatically converge to the actual number of contenders which enables the contender to adaptively choose TS in the backoff period for reducing collision. This method produces better success rate and lower latency for even very high number of contenders.

A data mining approach for machine fault diagnosis based on associated frequency patterns

- Rashid, Md. Mamunur, Amar, Muhammad, Gondal, Iqbal, Kamruzzaman, Joarder

Authors: Rashid, Md. Mamunur , Amar, Muhammad , Gondal, Iqbal , Kamruzzaman, Joarder
Date: 2016
Type: Text , Journal article
Relation: Applied Intelligence Vol. 45, no. 3 (2016), p. 638-651
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Description: Bearings play a crucial role in rotational machines and their failure is one of the foremost causes of breakdowns in rotary machinery. Their functionality is directly relevant to the operational performance, service life and efficiency of these machines. Therefore, bearing fault identification is very significant. The accuracy of fault or anomaly detection by the current techniques is not adequate. We propose a data mining-based framework for fault identification and anomaly detection from machine vibration data. In this framework, to capture the useful knowledge from the vibration data stream (VDS), we first pre-process the data using Fast Fourier Transform (FFT) to extract the frequency signature and then build a compact tree called SAFP-tree (sliding window associated frequency pattern tree), and propose a mining algorithm called SAFP. Our SAFP algorithm can mine associated frequency patterns (i.e., fault frequency signatures) in the current window of VDS and use them to identify faults in the bearing data. Finally, SAFP is further enhanced to SAFP-AD for anomaly detection by determining the normal behavior measure (NBM) from the extracted frequency patterns. The results show that our technique is very efficient in identifying faults and detecting anomalies over VDS and can be used for remote machine health diagnosis. Â© 2016, Springer Science+Business Media New York.

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A dynamic content distribution scheme for decentralized sharing in tourist hotspots

- Kaisar, Shahriar, Kamruzzaman, Joarder, Karmakar, Gour

Authors: Kaisar, Shahriar , Kamruzzaman, Joarder , Karmakar, Gour
Date: 2019
Type: Text , Journal article
Relation: Journal of Network and Computer Applications Vol. 129, no. (2019), p. 9-24
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Description: Decentralized content sharing (DCS) is emerging as a suitable platform for smart mobile device users to generate and share contents seamlessly without the requirement of a centralized server. This feature is particularly important for places that lack Internet coverage such as tourist attractions where users can form an ad-hoc network and communicate opportunistically to share contents. Existing DCS approaches when applied for such type of places suffer from low delivery success rate and high latency. Although a handful of recent approaches have specifically targeted improvement of content delivery service in tourist spot like scenario, these and other DCS approaches do not focus on contents’ demand and supply which vary considerably due to visitor in-and-out flow and occurrence of influencing events. This is further compounded by the lack of any content distribution (replication) scheme. The content delivery service will be improved if contents can be proactively distributed in strategic positions based on dynamic demand and supply and medium access contention. In this paper, we propose a dynamic content distribution scheme (DCDS) considering these practical issues for sharing contents in tourist attractions. Simulation results show that the proposed approach significantly improves (7

A framework for collaborative multi class heterogeneous wireless sensor networks

- Azad, Arman, Kamruzzaman, Joarder

Authors: Azad, Arman , Kamruzzaman, Joarder
Date: 2008
Type: Text , Conference paper
Relation: Proceedings of the 2008 IEEE International Conference on Communications p. 1-6
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Description: For many applications, simultaneous sensing of a number of parameters is crucial that leads to the deployment of multiple classes of sensors having different initial energy, data generation rate and deployment density within the vicinity of a cluster as opposed to identical sensors assumed in the existing heterogeneous sensor networks. For data transmission to cluster head, such networks use single hop, multi hop and their hybrid as intra-cluster transmission policy which suffer highly from non-uniform energy usage among sensors, thereby reducing the lifetime drastically leaving considerable amount of energy in many nodes. In this paper, we propose a framework for multi-class heterogeneous sensor networks where incoming traffic is relayed towards cluster head in collaboration among multiple classes of sensors considering their heterogeneity. We also propose two transmission policies for this framework considering generic polygonal cluster and limited transmission range for individual sensors. Performance analysis shows substantial improvement of overall lifetime by the collaborative framework of multi-class sensors. Our proposed transmission policies further improve the lifetime over existing multi hop and hybrid communications through better distribution of energy usage among sensors.kam

A HMM-based adaptive fuzzy inference system for stock market forecasting

- Hassan, Md Rafiul, Ramamohanarao, Kotagiri, Kamruzzaman, Joarder, Rahman, Mustafizur, Hossain, Maruf

Authors: Hassan, Md Rafiul , Ramamohanarao, Kotagiri , Kamruzzaman, Joarder , Rahman, Mustafizur , Hossain, Maruf
Date: 2013
Type: Text , Journal article
Relation: Neurocomputing Vol. 104, no. (2013), p. 10-25
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Description: In this paper, we propose a new type of adaptive fuzzy inference system with a view to achieve improved performance for forecasting nonlinear time series data by dynamically adapting the fuzzy rules with arrival of new data. The structure of the fuzzy model utilized in the proposed system is developed based on the log-likelihood value of each data vector generated by a trained Hidden Markov Model. As part of its adaptation process, our system checks and computes the parameter values and generates new fuzzy rules as required, in response to new observations for obtaining better performance. In addition, it can also identify the most appropriate fuzzy rule in the system that covers the new data; and thus requires to adapt the parameters of the corresponding rule only, while keeping the rest of the model unchanged. This intelligent adaptive behavior enables our adaptive fuzzy inference system (FIS) to outperform standard FISs. We evaluate the performance of the proposed approach for forecasting stock price indices. The experimental results demonstrate that our approach can predict a number of stock indices, e.g., Dow Jones Industrial (DJI) index, NASDAQ index, Standard and Poor500 (S&P500) index and few other indices from UK (FTSE100), Germany (DAX) , Australia (AORD) and Japan (NIKKEI) stock markets, accurately compared with other existing computational and statistical methods.

A hybrid of multiobjective evolutionary algorithm and HMM-Fuzzy model for time series prediction

- Hassan, Md Rafiul, Nath, Gupta, Kirley, Michael, Kamruzzaman, Joarder

Authors: Hassan, Md Rafiul , Nath, Gupta , Kirley, Michael , Kamruzzaman, Joarder
Date: 2012
Type: Text , Journal article
Relation: Neurocomputing Vol. 81, no. April (2012), p. 1-11
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Description: In this paper, we introduce a new hybrid of Hidden Markov Model (HMM), Fuzzy Logic and multiobjective Evolutionary Algorithm (EA) for building a fuzzy model to predict non-linear time series data. In this hybrid approach, the HMM's log-likelihood score for each data pattern is used to rank the data and fuzzy rules are generated using the ranked data. We use multiobjective EA to find a range of trade-off solutions between the number of fuzzy rules and the prediction accuracy. The model is tested on a number of benchmark and more recent financial time series data. The experimental results clearly demonstrate that our model is able to generate a reduced number of fuzzy rules with similar (and in some cases better) performance compared with typical data driven fuzzy models reported in the literature.

A machine learning approach for prediction of pregnancy outcome following IVF treatment

- Hassan, Md Rafiul, Al-Insaif, Sadiq, Hossain, Muhammad, Kamruzzaman, Joarder

Authors: Hassan, Md Rafiul , Al-Insaif, Sadiq , Hossain, Muhammad , Kamruzzaman, Joarder
Date: 2020
Type: Text , Journal article
Relation: Neural Computing and Applications Vol. 32, no. 7 (2020), p. 2283-2297
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Description: Infertility affects one out of seven couples around the world. Therefore, the best possible management of the in vitro fertilization (IVF) treatment and patient advice is crucial for both patients and medical practitioners. The ultimate concern of the patients is the success of an IVF procedure, which depends on a number of influencing attributes. Without any automated tool, it is hard for the practitioners to assess any influencing trend of the attributes and factors that might lead to a successful IVF pregnancy. This paper proposes a hill climbing feature (attribute) selection algorithm coupled with automated classification using machine learning techniques with the aim to analyze and predict IVF pregnancy in greater accuracy. Using 25 attributes, we assessed the prediction ability of IVF pregnancy success for five different machine learning models, namely multilayer perceptron (MLP), support vector machines (SVM), C4.5, classification and regression trees (CART) and random forest (RF). The prediction ability was measured in terms of widely used performance metrics, namely accuracy rate, F-measure and AUC. Feature selection algorithm reduced the number of most influential attributes to nineteen for MLP, sixteen for RF, seventeen for SVM, twelve for C4.5 and eight for CART. Overall, the most influential attributes identified are: ‘age’, ‘indication’ of fertility factor, ‘Antral Follicle Counts (AFC)’, ‘NbreM2’, ‘method of sperm collection’, ‘Chamotte’, ‘Fertilization rate in vitro’, ‘Follicles on day 14’ and ‘Embryo transfer day.’ The machine learning models trained with the selected set of features significantly improved the prediction accuracy of IVF pregnancy success to a level considerably higher than those reported in the current literature. © 2018, The Natural Computing Applications Forum.

A mapreduce based technique for mining behavioral patterns from sensor data

- Rashid, Md. Mamunur, Gondal, Iqbal, Kamruzzaman, Joarder

Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
Date: 2015
Type: Text , Conference paper
Relation: 22nd International Conference on Neural Information Processing, ICONIP 2015; Istanbul, Turkey; 9th-12th November 2015 Vol. 9492, p. 145-153
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Description: WSNs generate a large amount of data in the form of streams, and temporal regularity in occurrence behavior is considered as an important measure for assessing the importance of patterns in WSN data. A frequent sensor pattern that occurs after regular intervals in WSNs is called regularly frequent sensor patterns (RFSPs). Existing RFSPs techniques assume that the data structure of the mining task is small enough to fit in the main memory of a processor. However, given the emergence of the Internet of Things (IoT), WSNs in future will generate huge volume of data, which means such an assumption does not hold any longer. To overcome this, a distributed solution using MapReduce model has not yet been explored extensively. Since MapReduce is becoming the de-facto model for computation on large data, an efficient RFSPs mining algorithm on this model is likely to provide a highly effective solution. In this work, we propose a regularly frequent sensor patterns mining algorithm called RFSP-H which uses MapReduce based framework. Extensive performance analyses show that our technique is significantly time efficient in finding regularly frequent sensor patterns. © Springer International Publishing Switzerland 2015.

A modified immune network optimization algorithm

- Hong, Lu, Kamruzzaman, Joarder

Authors: Hong, Lu , Kamruzzaman, Joarder
Date: 2014
Type: Text , Journal article
Relation: IAENG International Journal of Computer Science Vol. 41, no. 4 (2014), p. 231-236
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Description: This study proposes a modified artificial immune network algorithm for function optimization problems based on idiotypic immune network theory. A hyper-cubic mutation operator was introduced to reduce the heavy computational cost of the traditional opt-AINet algorithm. Moreover, the new symmetrical mutation can effectively improve local search. To maintain population diversity, we also devised an immune selection mechanism based on density and fitness. The global convergence of the algorithm was deduced through the method of pure probability and iterative formula. Simulation results of benchmark function optimization show that the modified algorithm converges more effectively than other immune network algorithms.

A new convergence rate estimation of general artificial immune algorithm

- Hong, Lu, Kamruzzaman, Joarder

Authors: Hong, Lu , Kamruzzaman, Joarder
Date: 2015
Type: Text , Journal article
Relation: Journal of Intelligent and Fuzzy Systems Vol. 28, no. 6 (2015), p. 2793-2800
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Description: Artificial immune algorithm has been used widely and successfully in many computational optimization areas, but the theoretical research exploring the convergence rate characteristics of artificial immune algorithm is yet inadequate. In this paper, instead of the traditional eigenvalue estimation of state transition matrix, stochastic processes theory is introduced to study the convergence rate of general artificial immune algorithm. The method begins by analyzing the necessary condition for convergence of artificial immune algorithm and takes it as the sufficient condition for a class of general artificial immune algorithm. Through the definition of Markov chain convergence rate, a probability strong convergence rate estimation method of general artificial immune algorithm is proposed. This method is judged by the final convergence of the best antibody, which overcomes the conservative defect of traditional estimation methods. The simulation results show the correctness of the proposed estimation method, and the estimation method can be used to judge the convergence and convergence rate of a class of artificial immune algorithms. This research has a certain theoretical reference value to optimize the convergence rate in the practical application of artificial immune algorithm.

A novel algorithm for mining behavioral patterns from wireless sensor networks

- Rashid, Md. Mamunur, Gondal, Iqbal, Kamruzzaman, Joarder

Authors: Rashid, Md. Mamunur , Gondal, Iqbal , Kamruzzaman, Joarder
Date: 2014
Type: Text , Conference paper
Relation: 2014 International Joint Conference on Neural Networks, IJCNN 2014; Beijing, China; 6th-11th July 2014 p. 1-7
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Description: Due to recent advances in wireless sensor networks (WSNs) and their ability to generate huge amount of data in the form of streams, knowledge discovery techniques have received a great deal of attention to extract useful knowledge regarding the underlying network. Traditionally sensor association rules measure occurrence frequency of patterns. However, these rules often generate a huge number of rules, most of which are non-informative or fail to reflect the true correlation among data objects. In this paper, we propose a new type of sensor behavioral pattern called associated sensor patterns that captures association-like co-occurrences and the strong temporal correlations implied by such co-occurrences in the sensor data. We also propose a novel tree structure called as associated sensor pattern tree (ASPT) and a mining algorithm, associated sensor pattern (ASP) which facilitates frequent pattern (FP) growth-based technique to generate all associated sensor patterns from WSN data with only one scan over the sensor database. Extensive performance study shows that our algorithm is very efficient in finding associated sensor patterns than the existing significant algorithms.

A novel dataset for baby broccoli identification by using YOLOv8 model

- Mohamed, Rizan, Appuhamillage, Gayan, Kamruzzaman, Joarder, Nguyen, Linh

Authors: Mohamed, Rizan , Appuhamillage, Gayan , Kamruzzaman, Joarder , Nguyen, Linh
Date: 2024
Type: Text , Conference paper
Relation: 33rd International Symposium on Industrial Electronics, ISIE 2024, Ulsan, South Korea, 18-21 June 2024, IEEE International Symposium on Industrial Electronics, 2024 33rd International Symposium on Industrial Electronics (ISIE) Proceedings
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Description: As global population increases, the need for agricultural automation becomes crucial for a stable food supply. An autonomous mechanism for picking baby broccoli could economically benefit farmers and society. To date, efforts to develop an automated baby broccoli harvesting solution have been absent. A key step in automation, particularly for the automated recognition of baby broccoli heads through computer vision, involves the creation of a rich dataset that sufficiently captures the characteristics of baby broccoli heads, as such a dataset has not yet been collected or published. This paper marks the first step towards automating baby broccoli harvesting by creating a novel dataset and testing its accuracy for precise identification. We have gathered data using custom software and a RealSense D435 Depth camera, known for its depth and stereo vision capabilities. The initial results of the model that was trained from our dataset had mAP values ranging from 0.869 to 0.942. This initial training shows that the dataset fits the purpose of detecting baby broccoli heads. Further results shows that when the model is 97.7% confident or more, its predictions are 100% precise. © 2024 IEEE.

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A novel dynamic software-defined networking approach to neutralize traffic burst

- Sharma, Aakanksha, Balasubramanian, Venki, Kamruzzaman, Joarder

Authors: Sharma, Aakanksha , Balasubramanian, Venki , Kamruzzaman, Joarder
Date: 2023
Type: Text , Journal article
Relation: Computers Vol. 12, no. 7 (2023), p.
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Description: Software-defined networks (SDN) has a holistic view of the network. It is highly suitable for handling dynamic loads in the traditional network with a minimal update in the network infrastructure. However, the standard SDN architecture control plane has been designed for single or multiple distributed SDN controllers facing severe bottleneck issues. Our initial research created a reference model for the traditional network, using the standard SDN (referred to as SDN hereafter) in a network simulator called NetSim. Based on the network traffic, the reference models consisted of light, modest and heavy networks depending on the number of connected IoT devices. Furthermore, a priority scheduling and congestion control algorithm is proposed in the standard SDN, named extended SDN (eSDN), which minimises congestion and performs better than the standard SDN. However, the enhancement was suitable only for the small-scale network because, in a large-scale network, the eSDN does not support dynamic SDN controller mapping. Often, the same SDN controller gets overloaded, leading to a single point of failure. Our literature review shows that most proposed solutions are based on static SDN controller deployment without considering flow fluctuations and traffic bursts that lead to a lack of load balancing among the SDN controllers in real-time, eventually increasing the network latency. Therefore, to maintain the Quality of Service (QoS) in the network, it becomes imperative for the static SDN controller to neutralise the on-the-fly traffic burst. Thus, our novel dynamic controller mapping algorithm with multiple-controller placement in the SDN is critical to solving the identified issues. In dSDN, the SDN controllers are mapped dynamically with the load fluctuation. If any SDN controller reaches its maximum threshold, the rest of the traffic will be diverted to another controller, significantly reducing delay and enhancing the overall performance. Our technique considers the latency and load fluctuation in the network and manages the situations where static mapping is ineffective in dealing with the dynamic flow variation. © 2023 by the authors.

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A novel ensemble of hybrid intrusion detection system for detecting internet of things attacks

- Khraisat, Ansam, Gondal, Iqbal, Vamplew, Peter, Kamruzzaman, Joarder, Alazab, Ammar

Authors: Khraisat, Ansam , Gondal, Iqbal , Vamplew, Peter , Kamruzzaman, Joarder , Alazab, Ammar
Date: 2019
Type: Text , Journal article
Relation: Electronics (Switzerland) Vol. 8, no. 11 (2019), p.
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Description: The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack to the end nodes. Due to the large number and diverse types of IoT devices, it is a challenging task to protect the IoT infrastructure using a traditional intrusion detection system. To protect IoT devices, a novel ensemble Hybrid Intrusion Detection System (HIDS) is proposed by combining a C5 classifier and One Class Support Vector Machine classifier. HIDS combines the advantages of Signature Intrusion Detection System (SIDS) and Anomaly-based Intrusion Detection System (AIDS). The aim of this framework is to detect both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the Bot-IoT dataset, which includes legitimate IoT network traffic and several types of attacks. Experiments show that the proposed hybrid IDS provide higher detection rate and lower false positive rate compared to the SIDS and AIDS techniques. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

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A novel OFDM format and a machine learning based dimming control for lifi

- Nowrin, Itisha, Mondal, M., Islam, Rashed, Kamruzzaman, Joarder

Authors: Nowrin, Itisha , Mondal, M. , Islam, Rashed , Kamruzzaman, Joarder
Date: 2021
Type: Text , Journal article
Relation: Electronics (Switzerland) Vol. 10, no. 17 (2021), p.
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Description: This paper proposes a new hybrid orthogonal frequency division multiplexing (OFDM) form termed as DC‐biased pulse amplitude modulated optical OFDM (DPO‐OFDM) by combining the ideas of the existing DC‐biased optical OFDM (DCO‐OFDM) and pulse amplitude modulated discrete multitone (PAM‐DMT). The analysis indicates that the required DC‐bias for DPO‐OFDM-based light fidelity (LiFi) depends on the dimming level and the components of the DPO‐OFDM. The bit error rate (BER) performance and dimming flexibility of the DPO‐OFDM and existing OFDM schemes are evaluated using MATLAB tools. The results show that the proposed DPO‐OFDM is power efficient and has a wide dimming range. Furthermore, a switching algorithm is introduced for LiFi, where the individual components of the hybrid OFDM are switched according to a target dimming level. Next, machine learning algorithms are used for the first time to find the appropriate proportions of the hybrid OFDM components. It is shown that polynomial regression of degree 4 can reliably predict the constellation size of the DCO‐OFDM component of DPO‐OFDM for a given constellation size of PAM‐DMT. With the component switching and the machine learning algorithms, DPO‐OFDM‐based LiFi is power efficient at a wide dimming range. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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A robust forgery detection method for copy-move and splicing attacks in images

- Islam, Mohammad, Karmakar, Gour, Kamruzzaman, Joarder, Murshed, Manzur

Authors: Islam, Mohammad , Karmakar, Gour , Kamruzzaman, Joarder , Murshed, Manzur
Date: 2020
Type: Text , Journal article
Relation: Electronics Vol. 9, no. 9 (2020), p. 1-22
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Description: Internet of Things (IoT) image sensors, social media, and smartphones generate huge volumes of digital images every day. Easy availability and usability of photo editing tools have made forgery attacks, primarily splicing and copy-move attacks, effortless, causing cybercrimes to be on the rise. While several models have been proposed in the literature for detecting these attacks, the robustness of those models has not been investigated when (i) a low number of tampered images are available for model building or (ii) images from IoT sensors are distorted due to image rotation or scaling caused by unwanted or unexpected changes in sensors' physical set-up. Moreover, further improvement in detection accuracy is needed for real-word security management systems. To address these limitations, in this paper, an innovative image forgery detection method has been proposed based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) and a new feature extraction method using the mean operator. First, images are divided into non-overlapping fixed size blocks and 2D block DCT is applied to capture changes due to image forgery. Then LBP is applied to the magnitude of the DCT array to enhance forgery artifacts. Finally, the mean value of a particular cell across all LBP blocks is computed, which yields a fixed number of features and presents a more computationally efficient method. Using Support Vector Machine (SVM), the proposed method has been extensively tested on four well known publicly available gray scale and color image forgery datasets, and additionally on an IoT based image forgery dataset that we built. Experimental results reveal the superiority of our proposed method over recent state-of-the-art methods in terms of widely used performance metrics and computational time and demonstrate robustness against low availability of forged training samples.
Description: This research was funded by Research Priority Area (RPA) scholarship of Federation University Australia.

A rule based inference model to establish strategy-process relationship

- Dinh, Loan, Karmakar, Gour, Kamruzzaman, Joarder, Stranieri, Andrew

Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder , Stranieri, Andrew
Date: 2017
Type: Text , Conference proceedings
Relation: 30th International Business Information Management Association Conference - Vision 2020: Sustainable Economic development, Innovation Management, and Global Growth, IBIMA 2017; Madrid, Spain; 8th-9th November 2017 Vol. 2017-January, p. 4544-4556
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Description: An effective relationship between business processes and their relevant strategies helps enterprises achieve their goals. As a business organisation changes quickly, business processes implement their relevant business operations for efficiency. It is important to know which business process achieves which business strategies dynamically. To the best of our knowledge, there exists a framework which aims to automatically determine the strategy-process relationship (Morrison et al. 2011). However, this framework can only work when the effect of the business process is known, but it is difficult to determine such effect accurately. Moreover, by optimising business processes to satisfy business strategies, higher efficiency may be achieved but there is a high chance of losing discriminative information. It therefore creates certain level of uncertainty in achieving accurate strategy-process relationship. To reduce this uncertainty and determine the relationship accurately between business processes and their relevant strategies as defined by business domain experts, in this paper, we introduce a rule-based inference model. This model not only helps business organisations realize which business processes need to be involved for the organisation to achieve their goals when strategies are made, but also reduces the possibility of losing important details from business process optimisation. We have developed a business case to validate our proposed model and the results show that our model can infer the relation accurately for each rule defined for the related business case.

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