Browsing by Author "Maskeliunas, Rytis"

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Citation Count: 0
Attitude of Mobile Telecommunication Subscribers Towards Sim Card Registration in Lagos State, Southwestern Nigeria
(Springer india, 2019) Oyediran, O.; Mısra, Sanjay; Omoshule, A.; Misra, Sanjay; Maskeliunas, Rytis; Damasevicius, Robertas; Computer Engineering
Despite the concerted effort of the Nigeria Communication Commission (NCC) to ensure that Nigeria mobile phone subscribers register their SIM cards, there has been some level of apathy on the part of the mobile phone subscribers. This study investigated the attitude of mobile telecommunication subscribers towards SIM card registration in Lagos Metropolis, Nigeria. The theories of planned behaviour and reasoned action were adapted for the study because they provide the necessary constructs that help to investigate the attitudes of telecommunication subscribers. The purposive sampling technique was adopted in selecting five local government areas within Lagos. Random sampling method was used to select 300 mobile phone subscribers. In total, 290 responses were collected and were found usable. Data analysis was performed using statistical methods, and Spearman's correlation analysis was used to test relationship between the variables of interest. The results of the study revealed that SIM card users have positive attitude towards SIM card registration. Perceived usefulness and perceived ease of use significantly influenced subscribers attitude towards SIM card registration with both of them having negative significant relationship with attitude towards registration (r = -.116, r = -.132, p < 0.05) respectively.
Citation Count: 78
Cassava Disease Recognition From Low-Quality Images Using Enhanced Data Augmentation Model and Deep Learning
(Wiley, 2021) Abayomi-Alli, Olusola Oluwakemi; Mısra, Sanjay; Damasevicius, Robertas; Misra, Sanjay; Maskeliunas, Rytis; Computer Engineering
Improvement of deep learning algorithms in smart agriculture is important to support the early detection of plant diseases, thereby improving crop yields. Data acquisition for machine learning applications is an expensive task due to the requirements of expert knowledge and professional equipment. The usability of any application in a real-world setting is often limited by unskilled users and the limitations of devices used for acquiring images for classification. We aim to improve the accuracy of deep learning models on low-quality test images using data augmentation techniques for neural network training. We generate synthetic images with a modified colour value distribution to expand the trainable image colour space and to train the neural network to recognize important colour-based features, which are less sensitive to the deficiencies of low-quality images such as those affected by blurring or motion. This paper introduces a novel image colour histogram transformation technique for generating synthetic images for data augmentation in image classification tasks. The approach is based on the convolution of the Chebyshev orthogonal functions with the probability distribution functions of image colour histograms. To validate our proposed model, we used four methods (resolution down-sampling, Gaussian blurring, motion blur, and overexposure) for reducing image quality from the Cassava leaf disease dataset. The results based on the modified MobileNetV2 neural network showed a statistically significant improvement of cassava leaf disease recognition accuracy on lower-quality testing images when compared with the baseline network. The model can be easily deployed for recognizing and detecting cassava leaf diseases in lower quality images, which is a major factor in practical data acquisition.
Citation Count: 18
Distributed Centrality Analysis of Social Network Data Using Mapreduce
(Mdpi, 2019) Behera, Ranjan Kumar; Mısra, Sanjay; Rath, Santanu Kumar; Misra, Sanjay; Damasevicius, Robertas; Maskeliunas, Rytis; Computer Engineering
Analyzing the structure of a social network helps in gaining insights into interactions and relationships among users while revealing the patterns of their online behavior. Network centrality is a metric of importance of a network node in a network, which allows revealing the structural patterns and morphology of networks. We propose a distributed computing approach for the calculation of network centrality value for each user using the MapReduce approach in the Hadoop platform, which allows faster and more efficient computation as compared to the conventional implementation. A distributed approach is scalable and helps in efficient computations of large-scale datasets, such as social network data. The proposed approach improves the calculation performance of degree centrality by 39.8%, closeness centrality by 40.7% and eigenvalue centrality by 41.1% using a Twitter dataset.
Citation Count: 12
An E-Environment System for Socio-Economic Sustainability and National Security
(Politechnika Lubelska, 2018) Okewu, Emmanuel; Mısra, Sanjay; Misra, Sanjay; Fernandez Sanz, Luis; Maskeliunas, Rytis; Damasevicius, Robertas; Computer Engineering
Though there are adequate institutional frameworks and legal instruments for the protection of the Sub-Saharan African environment, their impact on the development and conservation (protection) of the environment leaves much to be desired. This assertion is substantiated by the reality that inspite of these regulatory frameworks, the environment is largely degraded with negative ramifications for the twin goals of attaining sustainable socio-economic advancement and realization of environmental rights. Both national and regional state of environment (SoE) reports show that degradation is apparent. It is worthy of mention that almost all African countries have ratified and domesticated the various regional and subregional environmental agreement. Efforts to solve the puzzle have revealed that corruption and environmental degradation in Sub-Saharan Africa are closely linked. Financial impropriety in ecological funds management, poorly equipped environmental protection institutions, and inadequate citizens' environmental management awareness campaigns are outcomes of corruption in the public sector. Since corruption thrives in the absence of transparency and accountability, this study proposes a cutting-edge technology-based solution that promotes participatory environmental accountability using an e-Environment system. The web-based multi-tier e-Environment system will empower both citizens and government officials to deliberate online real-time on environmental policies, programmes and projects to be embarked upon. Both parties will equally put forward proposals on the use of tax payers money in the environment sector while monitoring discrepancies between amount budgeted, amount released and actual amount spent. We applied design and software engineering skills to actualize the proposed solution. Using Nigeria as case study, our research methodology comprised literature review, requirements gathering, design of proposed solution using universal modelling language (UML) and development/implementation on the Microsoft SharePoint platform. In view of our determination to evolve a zero-defect software, we applied Cleanroom Software Engineering techniques. The outcome obtained so far has proved that the model supports our expectations. The system is not only practical, but ecologically sound. It is anticipated that the full-scale implementation of such an enterprise e-Environment system will decrease the current tide of corruption in the environment sector, mitigate environmental degradation and by extension, reduce social-economic tensions and guarantee national security.
Citation Count: 0
Embryo Spatial Model Reconstruction
(Springer international Publishing Ag, 2020) Mısra, Sanjay; Maskeliunas, Rytis; Raudonis, Vidas; Misra, Sanjay; Computer Engineering
Time lapse microscopy offered new solutions to study embryo development process. It allows embryologist to monitor embryo growth in real time and evaluate them without interfering into their growth environment. Embryo evaluation during growth process is one of the key criteria in embryo selection for fertilization. Live embryo monitoring is time consuming and new tools are offered to automate part of process. Our proposed algorithm gives new possibilities for embryo monitoring. It uses embryo images which are taken from different embryo layers, extracts embryo cell features and returns metrical evaluation to compare different embryos. High number of extracted features shows embryo fragmentation. Other tool whichwe present is spatial embryo model. Features extracted from embryo layers are combined together to spatial model. It allows embryologist to examine embryo model and compare different layers in one space. The obtained spatial embryo model will be later used to develop new algorithms for embryo analysis tasks.
Citation Count: 17
Few-Shot Learning With a Novel Voronoi Tessellation-Based Image Augmentation Method for Facial Palsy Detection
(Mdpi, 2021) Abayomi-Alli, Olusola Oluwakemi; Mısra, Sanjay; Damasevicius, Robertas; Maskeliunas, Rytis; Misra, Sanjay; Computer Engineering
Face palsy has adverse effects on the appearance of a person and has negative social and functional consequences on the patient. Deep learning methods can improve face palsy detection rate, but their efficiency is limited by insufficient data, class imbalance, and high misclassification rate. To alleviate the lack of data and improve the performance of deep learning models for palsy face detection, data augmentation methods can be used. In this paper, we propose a novel Voronoi decomposition-based random region erasing (VDRRE) image augmentation method consisting of partitioning images into randomly defined Voronoi cells as an alternative to rectangular based random erasing method. The proposed method augments the image dataset with new images, which are used to train the deep neural network. We achieved an accuracy of 99.34% using two-shot learning with VDRRE augmentation on palsy faces from Youtube Face Palsy (YFP) dataset, while normal faces are taken from Caltech Face Database. Our model shows an improvement over state-of-the-art methods in the detection of facial palsy from a small dataset of face images.
Citation Count: 15
Fusion of Smartphone Sensor Data for Classification of Daily User Activities
(Springer, 2021) Sengul, Gokhan; Şengül, Gökhan; Ozcelik, Erol; Misra, Sanjay; Özçelik, Erol; Damasevicius, Robertas; Mısra, Sanjay; Maskeliunas, Rytis; Computer Engineering
New mobile applications need to estimate user activities by using sensor data provided by smart wearable devices and deliver context-aware solutions to users living in smart environments. We propose a novel hybrid data fusion method to estimate three types of daily user activities (being in a meeting, walking, and driving with a motorized vehicle) using the accelerometer and gyroscope data acquired from a smart watch using a mobile phone. The approach is based on the matrix time series method for feature fusion, and the modified Better-than-the-Best Fusion (BB-Fus) method with a stochastic gradient descent algorithm for construction of optimal decision trees for classification. For the estimation of user activities, we adopted a statistical pattern recognition approach and used the k-Nearest Neighbor (kNN) and Support Vector Machine (SVM) classifiers. We acquired and used our own dataset of 354 min of data from 20 subjects for this study. We report a classification performance of 98.32 % for SVM and 97.42 % for kNN.
Citation Count: 15
Gender Detection Using 3d Anthropometric Measurements by Kinect
(Polska Akad Nauk, Polish Acad Sciences, 2018) Camalan, Seda; Şengül, Gökhan; Sengul, Gokhan; Misra, Sanjay; Çamalan, Seda; Maskeliunas, Rytis; Damasevicius, Robertas; Mısra, Sanjay; Computer Engineering; Information Systems Engineering
Automatic gender detection is a process of determining the gender of a human according to the characteristic properties that represent the masculine and feminine attributes of a subject. Automatic gender detection is used in many areas such as customer behaviour analysis, robust security system construction, resource management, human-computer interaction, video games, mobile applications, neuro-marketing etc., in which manual gender detection may be not feasible. In this study, we have developed a fully automatic system that uses the 3D anthropometric measurements of human subjects for gender detection. A Kinect 3D camera was used to recognize the human posture, and body metrics are used as features for classification. To classify the gender, KNN, SVM classifiers and Neural Network were used with the parameters. A unique dataset gathered from 29 female and 31 male (a total of 60 people) participants was used in the experiment and the Leave One Out method was used as the cross-validation approach. The maximum accuracy achieved is 96.77% for SVM with an MLP kernel function.
Citation Count: 51
Hybrid Microgrid for Microfinance Institutions in Rural Areas - a Field Demonstration in West Africa
(Elsevier, 2019) Ayodele, Esan; Mısra, Sanjay; Misra, Sanjay; Damasevicius, Robertas; Maskeliunas, Rytis; Computer Engineering
We present a hybrid energy microgrid optimization model for a microbank in a remote rural residential area. The model is based on the use of renewable (wind turbines & solar photovoltaic (PV)) and conventional (gasoline generators) energy sources and battery storage systems. We conducted a detailed assessment of a typical microbank's load, residential loads and energy resources in a village called Ajasse-Ipo in Kwara State, Nigeria. We performed the modeling of a hybrid microgrid system, followed by an economic analysis and sensitivity analysis to optimize the hybrid system design. We performed simulations based on the energy resources available (solar PV, wind, gasoline generator & battery energy storage system) to satisfy the energy demands of the microbank, while the excess energy was supplied to meet the demand of the community loads, i.e. water pumping machine and rural home lighting. The results obtained showed that the hybrid system comprising the solar PV/battery/diesel was most techno-economically viable with a Net Present Cost (NPC) and Cost of Energy (COE) of $468,914 and 0.667$/kWh, respectively. Comparing these results with those obtained using analytical methods, the solar PV, battery and converter sizes obtained were slightly higher than the optimal system configurations as produced by HOMER. The proposed hybrid energy system also allowed to achieve almost 50% reductions in CO2, CO, unburned hydrocarbons, particulate matter, SO2 & NO2. The system can be applicable for other rural regions in the developing countries with similar environmental conditions.
Citation Count: 21
Identifying Phishing Attacks in Communication Networks Using Url Consistency Features
(inderscience Enterprises Ltd, 2020) Azeez, Nureni Ayofe; Mısra, Sanjay; Salaudeen, Balikis Bolanle; Misra, Sanjay; Damasevicius, Robertas; Maskeliunas, Rytis; Computer Engineering
Phishing is a fraudulent attempt by cybercriminals, where the target audience is addressed by a text message, phone call or e-mail, requesting classified and sensitive information after presenting himself/herself as a legitimate agent. Successful phishing attack may result into financial loss and identity theft. Identifying forensic characteristics of phishing attack can help to detect the attack and its perpetuators and as well as to enable defence against it. To shield internet users from phishing assaults, numerous anti-phishing models have been proposed. Currently employed techniques to handle these challenges are not sufficient and capable enough. We aim at identifying phishing sites in order to guard internet users from being vulnerable to any form of phishing attacks by verifying the conceptual and literal consistency between the uniform resource locator (URL) and the web content. The implementation of the proposed PhishDetect method achieves an accuracy of 99.1%; indicating that it is effective in detecting various forms of phishing attacks.
Citation Count: 3
An Intelligent Advisory System To Support Managerial Decisions for a Social Safety Net
(Mdpi, 2019) Okewu, Emmanuel; Mısra, Sanjay; Misra, Sanjay; Okewu, Jonathan; Damasevicius, Robertas; Maskeliunas, Rytis; Computer Engineering
Social investment programs are designed to provide opportunities to the less privileged so that they can contribute to the socioeconomic development of society. Stakeholders in social safety net programs (SSNPs) target vulnerable groups, such as the urban poor, women, the unemployed, and the elderly, with initiatives that have a transformative impact. Inadequate policy awareness remains a challenge, resulting in low participation rates in SSNPs. To achieve all-inclusive development, deliberate policies and programs that target this population have to be initiated by government, corporate bodies, and public-minded individuals. Artificial intelligence (AI) techniques could play an important role in improving the managerial decision support and policy-making process of SSNPs and increasing the social resilience of urban populations. To enhance managerial decision-making in social investment programs, we used a Bayesian network to develop an intelligent decision support system called the Social Safety Net Expert System (SSNES). Using the SSNES, we provide an advisory system to stakeholders who make management decisions, which clearly demonstrates the efficacy of SSNPs and inclusive development.
Citation Count: 32
Large Scale Community Detection Using a Small World Model
(Mdpi, 2017) Mısra, Sanjay; Rath, Santanu Kumar; Misra, Sanjay; Damasevicius, Robertas; Maskeliunas, Rytis; Computer Engineering
In a social network, small or large communities within the network play a major role in deciding the functionalities of the network. Despite of diverse definitions, communities in the network may be defined as the group of nodes that are more densely connected as compared to nodes outside the group. Revealing such hidden communities is one of the challenging research problems. A real world social network follows small world phenomena, which indicates that any two social entities can be reachable in a small number of steps. In this paper, nodes are mapped into communities based on the random walk in the network. However, uncovering communities in large-scale networks is a challenging task due to its unprecedented growth in the size of social networks. A good number of community detection algorithms based on random walk exist in literature. In addition, when large-scale social networks are being considered, these algorithms are observed to take considerably longer time. In this work, with an objective to improve the efficiency of algorithms, parallel programming framework like Map-Reduce has been considered for uncovering the hidden communities in social network. The proposed approach has been compared with some standard existing community detection algorithms for both synthetic and real-world datasets in order to examine its performance, and it is observed that the proposed algorithm is more efficient than the existing ones.
Citation Count: 22
Network Intrusion Detection With a Hashing Based Apriori Algorithm Using Hadoop Mapreduce
(Mdpi, 2019) Azeez, Nureni Ayofe; Mısra, Sanjay; Ayemobola, Tolulope Jide; Misra, Sanjay; Maskeliunas, Rytis; Damasevicius, Robertas; Computer Engineering
Ubiquitous nature of Internet services across the globe has undoubtedly expanded the strategies and operational mode being used by cybercriminals to perpetrate their unlawful activities through intrusion on various networks. Network intrusion has led to many global financial loses and privacy problems for Internet users across the globe. In order to safeguard the network and to prevent Internet users from being the regular victims of cyber-criminal activities, new solutions are needed. This research proposes solution for intrusion detection by using the improved hashing-based Apriori algorithm implemented on Hadoop MapReduce framework; capable of using association rules in mining algorithm for identifying and detecting network intrusions. We used the KDD dataset to evaluate the effectiveness and reliability of the solution. Our results obtained show that this approach provides a reliable and effective means of detecting network intrusion.
Citation Count: 28
Optimizing Green Computing Awareness for Environmental Sustainability and Economic Security as a Stochastic Optimization Problem
(Mdpi, 2017) Mısra, Sanjay; Misra, Sanjay; Maskeliunas, Rytis; Damasevicius, Robertas; Fernandez-Sanz, Luis; Computer Engineering
The role of automation in sustainable development is not in doubt. Computerization in particular has permeated every facet of human endeavour, enhancing the provision of information for decision-making that reduces cost of operation, promotes productivity and socioeconomic prosperity and cohesion. Hence, a new field called information and communication technology for development (ICT4D) has emerged. Nonetheless, the need to ensure environmentally friendly computing has led to this research study with particular focus on green computing in Africa. This is against the backdrop that the continent is feared to suffer most from the vulnerability of climate change and the impact of environmental risk. Using Nigeria as a test case, this paper gauges the green computing awareness level of Africans via sample survey. It also attempts to institutionalize green computing maturity model with a view to optimizing the level of citizens awareness amid inherent uncertainties like low bandwidth, poor network and erratic power in an emerging African market. Consequently, we classified the problem as a stochastic optimization problem and applied metaheuristic search algorithm to determine the best sensitization strategy. Although there are alternative ways of promoting green computing education, the metaheuristic search we conducted indicated that an online real-time solution that not only drives but preserves timely conversations on electronic waste (e-waste) management and energy saving techniques among the citizenry is cutting edge. The authors therefore reviewed literature, gathered requirements, modelled the proposed solution using Universal Modelling Language (UML) and developed a prototype. The proposed solution is a web-based multi-tier e-Green computing system that educates computer users on innovative techniques of managing computers and accessories in an environmentally friendly way. We found out that such a real-time web-based interactive forum does not only stimulate the interest of the common man in environment-related issues, but also raises awareness about the impact his computer-related activities have on mother earth. This way, he willingly becomes part of the solution to environment degradation in his circle of influence.
Citation Count: 9
Prospects of Ocean-Based Renewable Energy for West Africa's Sustainable Energy Future
(Emerald Group Publishing Ltd, 2021) Adesanya, Ayokunle; Mısra, Sanjay; Misra, Sanjay; Maskeliunas, Rytis; Damasevicius, Robertas; Computer Engineering
Purpose The limited supply of fossil fuels, constant rise in the demand of energy and the importance of reducing greenhouse emissions have brought the adoption of renewable energy sources for generation of electrical power. One of these sources that has the potential to supply the world's energy needs is the ocean. Currently, ocean in West African region is mostly utilized for the extraction of oil and gas from the continental shelf. However, this resource is depleting, and the adaptation of ocean energy could be of major importance. The purpose of this paper is to discuss the possibilities of ocean-based renewable energy (OBRE) and analyze the economic impact of adapting an ocean energy using a thermal gradient (OTEC) approach for energy generation. Design/methodology/approach The analysis is conducted from the perspective of cost, energy security and environmental protection. Findings This study shows that adapting ocean energy in the West Africa region can significantly produce the energy needed to match the rising energy demands for sustainable development of Nigeria. Although the transition toward using OBRE will incur high capital cost at the initial stage, eventually, it will lead to a cost-effective generation, transmission, environmental improvement and stable energy supply to match demand when compared with the conventional mode of generation in West Africa. Originality/value The study will contribute toward analysis of the opportunities for adopting renewable energy sources and increasing energy sustainability for the West Africa coast regions.
Citation Count: 21
Reconstruction of 3d Object Shape Using Hybrid Modular Neural Network Architecture Trained on 3d Models From Shapenetcore Dataset
(Mdpi, 2019) Kulikajevas, Audrius; Mısra, Sanjay; Maskeliunas, Rytis; Damasevicius, Robertas; Misra, Sanjay; Computer Engineering
Depth-based reconstruction of three-dimensional (3D) shape of objects is one of core problems in computer vision with a lot of commercial applications. However, the 3D scanning for point cloud-based video streaming is expensive and is generally unattainable to an average user due to required setup of multiple depth sensors. We propose a novel hybrid modular artificial neural network (ANN) architecture, which can reconstruct smooth polygonal meshes from a single depth frame, using a priori knowledge. The architecture of neural network consists of separate nodes for recognition of object type and reconstruction thus allowing for easy retraining and extension for new object types. We performed recognition of nine real-world objects using the neural network trained on the ShapeNetCore model dataset. The results evaluated quantitatively using the Intersection-over-Union (IoU), Completeness, Correctness and Quality metrics, and qualitative evaluation by visual inspection demonstrate the robustness of the proposed architecture with respect to different viewing angles and illumination conditions.
Citation Count: 92
Relationship Between Convenience, Perceived Value, and Repurchase Intention in Online Shopping in Vietnam
(Mdpi, 2018) Quoc Trung Pham; Mısra, Sanjay; Xuan Phuc Tran; Misra, Sanjay; Maskeliunas, Rytis; Damasevicius, Robertas; Computer Engineering
Electronic commerce (e-commerce) is an increasingly popular trend in modern economy concomitant with the development of the Internet. E-commerce has developed considerably, making Vietnam one of the fastest growing markets in the world. However, its growth rate has not matched its potential, leading to the question how online retailers could improve their practices and thus contribute to the sustainable development of emerging markets such as Vietnam. Therefore, with the goal of providing online retailers with many methods to improve their online shopping service, this study examined the direct and indirect influence of the dimensions of online shopping convenience on repurchase intention through customer-perceived value. A survey of 230 Vietnamese customers was conducted to test the theoretical model. A structural equation model was used for data analysis. The results determined that the five dimensions of online shopping convenience are: access, search, evaluation, transaction, and possession/post-purchase convenience. All dimensions have a direct impact on perceived value and repurchase intention. The results also show the important role of perceived value when a factor both directly influences repurchase intention and mediates the relationship between convenience and repurchase intention.
Citation Count: 22
Secure Ear Biometrics Using Circular Kernel Principal Component Analysis, Chebyshev Transform Hashing and Bose-Chaudhuri Error-Correcting Codes
(Springer London Ltd, 2020) Olanrewaju, L.; Mısra, Sanjay; Oyebiyi, Oyediran; Misra, Sanjay; Maskeliunas, Rytis; Damasevicius, Robertas; Computer Engineering
Ear biometrics has generated an increased interest in the domain of biometric identification systems due to its robustness and covert acquisition potential. The external structure of the human ear has a bilateral symmetry structure. Here, we analyse ear biometrics based on ear symmetry features. We apply iterative closest point and kernel principal component analysis with circular kernel for feature extraction while using a circular kernel function, combined with empirical mode decomposition into intrinsic mode functions perceptual hashing using and fast Chebyshev transform, and a secure authentication approach that exploits the discrete logarithm problem and Bose-Chaudhuri-Hocquenghem error-correcting codes to generate 128-bit crypto keys. We evaluate the proposed ear biometric cryptosecurity system using our data set of ear images acquired from 103 persons. Our results show that the ear biometric-based authentication achieved an equal error rate of 0.13 and true positive rate TPR of 0.85.
Citation Count: 31
Smart irrigation system for environmental sustainability in Africa: An Internet of Everything (IoE) approach
(Amer inst Mathematical Sciences-aims, 2019) Mısra, Sanjay; Misra, Sanjay; Maskeliunas, Rytis; Damasevicius, Robertas; Kazanavicius, Egidijus; Computer Engineering
Water and food are two of the most important commodities in the world, which makes agriculture crucial to mankind as it utilizes water (irrigation) to provide us with food. Climate change and a rapid increase in population have put a lot of pressure on agriculture which has a snowball effect on the earth's water resource, which has been proven to be crucial for sustainable development. The need to do away with fossil fuel in powering irrigation systems cannot be over emphasized due to climate change. Smart Irrigation systems powered by renewable energy sources (RES) have been proven to substantially improve crop yield and the profitability of agriculture. Here we show how the control and monitoring of a solar powered smart irrigation system can be achieved using sensors and environmental data from an Internet of Everything (IoE). The collected data is used to predict environment conditions using the Radial Basis Function Network (RBFN). The predicted values of water level, weather forecast, humidity, temperature and irrigation data are used to control the irrigation system. A web platform was developed for monitoring and controlling the system remotely.
Citation Count: 9
A Systematic Literature Review on Compliance Requirements Management of Business Processes
(Springer india, 2020) Mustapha, A. M.; Mısra, Sanjay; Arogundade, O. T.; Misra, Sanjay; Damasevicius, Robertas; Maskeliunas, Rytis; Computer Engineering
One crucial aspect that had cost business organizations so much is management of compliance requirements from various regulatory sources. In a bid to avoid being penalized, some organizations have adopted various techniques to accomplish this task. However, literature revealed that few thorough reviews have been centered on this subject in a systematic way. This implies that a review that systematically captured the entire crucial elements such as implementation environment, constraints types addressed, main contributions and strengths of the existing techniques is missing. This has led to the lack of sufficiently good context of operation. A systematic review on existing literatures is presented in this paper, which focuses on the management of business process compliance requirements in order to present summarized evidences and provide a lead-up for appropriately positioning new research activities. The guideline for conducting systematic literature review in software engineering by Kitchenham was employed in carrying out the systematic review as well as a review planning template to execute the review. Results showed that control flow and data flow requirements have been addressed most in recent time. The temporal and resource allocation requirements have been under researched. The approaches that have been employed in business process compliance requirements management are model checking, patterns, semantic, formal, ontology, goal-based requirements analysis and network analysis. The traditional business environment has been put into consideration more than the cloud environment. The summary of research contributions revealed that the approaches have been more of formal techniques compared to model checking and semantics. This shows that there is a need for more research on business process compliance that will be centered on the cloud environment. Researchers will be able to suggest the technique to be adopted based on the combined importance of each criterion that was defined in this work.