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Article Citation - WoS: 19Software Development Outsourcing: Challenges and Opportunities in Nigeria(Taylor & Francis inc, 2014) Casado-Lumbreras, Cristina; Colomo-Palacios, Ricardo; Ogwueleka, Francisca N.; Misra, SanjayIn recent years, several emergent regions have become software development sourcing countries. This article investigates the possibilities of sub-Saharan Africa as a sourcing destination in the software field. To find out the reasons why sub-Saharan Africa countries, in general, and Nigeria, in particular, are not considered a destination for global software development projects, the authors interviewed a set of professionals from Europe and Africa. Results indicate that there are many disadvantages and difficulties impeding Nigeria from becoming a preferred sourcing destination, mainly the absence of a strong software industry and the concerns about legislative, fiscal, and commercial premises. On the other hand, it is observed that there are also relevant added values and competitive advantages in Nigeria (English-speaking country, same time zone, and cost); therefore, it can become a potential target for software development outsourcing in the medium and long terms.Article Citation - WoS: 21Citation - Scopus: 34An Artificial Neural Network Model for Road Accident Prediction: a Case Study of a Developing Country(Budapest Tech, 2014) Ogwueleka, Francisca Nonyelum; Misra, Sanjay; Ogwueleka, Toochukwu Chibueze; Fernandez-Sanz, L.; Computer EngineeringRoad traffic accidents (RTA) are one of the major root causes of the unnatural loses of human beings all over the world. Although the rates of RTAs are decreasing in most developed countries, this is not the case in developing countries. The increase in the number of vehicles and inefficient drivers on the road, as well as to the poor conditions and maintenance of the roads, are responsible for this crisis in developing countries. In this paper, we produce a design of an Artificial Neural Network (ANN) model for the analysis and prediction of accident rates in a developing country. We apply the most recent (1998 to 2010) data to our model. In the design, the number of vehicles, accidents, and population were selected and used as model parameters. The sigmoid and linear functions were used as activation functions with the feed forward-back propagation algorithm. The performance evaluation of the model signified that the ANN model is better than other statistical methods in use.Article Citation - WoS: 10Citation - Scopus: 18Evaluation Criteria for Object-Oriented Metrics(Budapest Tech, 2011) Misra, Sanjay; Computer EngineeringIn this paper an evaluation model for object-oriented (OO) metrics is proposed. We have evaluated the existing evaluation criteria for OO metrics, and based on the observations, a model is proposed which tries to cover most of the features for the evaluation of OO metrics. The model is validated by applying it to existing OO metrics. In contrast to the other existing criteria, the proposed model is simple in implementation and includes the practical and important aspects of evaluation; hence it suitable to evaluate and validate any OO complexity metric.Article Citation - WoS: 3Citation - Scopus: 4Predictive Rental Values Model for Low-Income Earners in Slums: the Case of Ijora, Nigeria(Taylor & Francis Ltd, 2023) Iroham, Chukwuemeka O.; Misra, Sanjay; Emebo, Onyeka C.; Okagbue, Hilary, IIt is well known most often that values of properties tend to hike at the effluxion of time. This has necessitated the adoption of predictive models in interpreting outcomes in the property market in the future. Earlier studies have been oblivious of such models' outcomes as it affects any focal group, particularly the vulnerable. This present study focuses on the low-income earners found in the slum. The Ijora community in Lagos was the highlight of this study, particularly Ijora Badia and Ijora Oloye, regarded as slums according to the UNDP report. The entire fifty-two (52) local agents in the Ijora community were surveyed in cross-sectional survey research that entailed the questionnaire's issuance. The nexus of data collection, pre-processing, data analysis, algorithm application, and model evaluation resulted in retrieving rental values within the years 2010 and 2019 on two predominant residential property types of self-contain and one-bedroom flats found within the community. Three selected algorithms, Artificial Neural Network (ANN), Support Vector Machine, and Logistic Regression, were essentially used as classifiers but trained to predict the continuous values. These algorithms were implemented through the use of Python's SciKit-learn Library and RapidMiner. The findings revealed that though all three models gave accurate predictions, Logistic Regression was the highest with low error values. It was recommended that Logistic Regression be applied but with much data set of property values of low-income earners over much more period. This study will contribute to the Sustainable development goals(SDG) 11(Sustainable cities and communities) of the United Nations to benefit developing countries, especially in sub-Saharan Africa.Article Citation - WoS: 11Citation - Scopus: 17Software Measurement Activities in Small and Medium Enterprises: an Empirical Assessment(Budapest Tech, 2011) Pusatli, O. Tolga; Misra, Sanjay; Computer EngineeringAn empirical study for evaluating the proper implementation of measurement/metric programs in software companies in one area of Turkey is presented. The research questions are discussed and validated with the help of senior software managers (more than 15 years' experience) and then used for interviewing a variety of medium and small scale software companies in Ankara. Observations show that there is a common reluctance/lack of interest in utilizing measurements/metrics despite the fact that they are well known in the industry. A side product of this research is that internationally recognized standards such as ISO and CMMI are pursued if they are a part of project/job requirements; without these requirements, introducing those standards to the companies remains as a long-term target to increase quality.Article Citation - WoS: 103Citation - Scopus: 160Cassava Disease Recognition From Low-Quality Images Using Enhanced Data Augmentation Model and Deep Learning(Wiley, 2021) Abayomi-Alli, Olusola Oluwakemi; Damasevicius, Robertas; Misra, Sanjay; Maskeliunas, RytisImprovement 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.Article Citation - WoS: 2Multi-Paradigm Metric and Its Applicability on Java Projects(Budapest Tech, 2013) Misra, Sanjay; Cafer, Ferid; Akman, Ibahim; Fernandez-Sanz, LuisJAVA is one of the favorite languages amongst software developers. However, the numbers of specific software metrics to evaluate the JAVA code are limited In this paper, we evaluate the applicability of a recently developed multi paradigm metric to JAVA projects. The experimentations show that the Multi paradigm metric is an effective measure for estimating the complexity of the JAVA code/projects, and therefore it can be used for controlling the quality of the projects. We have also evaluated the multi-paradigm metric against the principles of measurement theory.Article Citation - WoS: 18Citation - Scopus: 35Distributed Centrality Analysis of Social Network Data Using Mapreduce(Mdpi, 2019) Behera, Ranjan Kumar; Rath, Santanu Kumar; Misra, Sanjay; Damasevicius, Robertas; Maskeliunas, RytisAnalyzing 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.Article Citation - WoS: 33Citation - Scopus: 51Optimizing Green Computing Awareness for Environmental Sustainability and Economic Security as a Stochastic Optimization Problem(Mdpi, 2017) Okewu, Emmanuel; Misra, Sanjay; Maskeliunas, Rytis; Damasevicius, Robertas; Fernandez-Sanz, LuisThe 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.Review Citation - WoS: 22Citation - Scopus: 39Cybersecurity Deep: Approaches, Attacks Dataset, and Comparative Study(Taylor & Francis inc, 2022) Barik, Kousik; Misra, Sanjay; Konar, Karabi; Fernandez-Sanz, Luis; Murat, KoyuncuCyber attacks are increasing rapidly due to advanced digital technologies used by hackers. In addition, cybercriminals are conducting cyber attacks, making cyber security a rapidly growing field. Although machine learning techniques worked well in solving large-scale cybersecurity problems, an emerging concept of deep learning (DL) that caught on during this period caused information security specialists to improvise the result. The deep learning techniques analyzed in this study are convolution neural networks, recurrent neural networks, and deep neural networks in the context of cybersecurity.A framework is proposed, and a real-time laboratory setup is performed to capture network packets and examine this captured data using various DL techniques. A comparable interpretation is presented under the DL techniques with essential parameters, particularly accuracy, false alarm rate, precision, and detection rate. The DL techniques experimental output projects improvise the performance of various real-time cybersecurity applications on a real-time dataset. CNN model provides the highest accuracy of 98.64% with a precision of 98% with binary class. The RNN model offers the second-highest accuracy of 97.75%. CNN model provides the highest accuracy of 98.42 with multiclass class. The study shows that DL techniques can be effectively used in cybersecurity applications. Future research areas are being elaborated, including the potential research topics to improve several DL methodologies for cybersecurity applications.
