37 results
Search Results
Now showing 1 - 10 of 37
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: 5Particle Swarm Optimization of the Spectral and Energy Efficiency of an Scma-Based Heterogeneous Cellular Network(Wiley, 2022) Noma-Osaghae, Etinosa; Misra, Sanjay; Ahuja, Ravin; Koyuncu, MuratBackground The effect of stochastic small base station (SBS) deployment on the energy efficiency (EE) and spectral efficiency (SE) of sparse code multiple access (SCMA)-based heterogeneous cellular networks (HCNs) is still mostly unknown. Aim This research study seeks to provide insight into the interaction between SE and EE in SBS sleep-mode enabled SCMA-based HCNs. Methodology A model that characterizes the energy-spectral-efficiency (ESE) of a two-tier SBS sleep-mode enabled SCMA-based HCN was derived. A multiobjective optimization problem was formulated to maximize the SE and EE of the SCMA-based HCN simultaneously. The multiobjective optimization problem was solved using a proposed weighted sum modified particle swarm optimization algorithm (PSO). A comparison was made between the performance of the proposed weighted sum modified PSO algorithm and the genetic algorithm (GA) and the case where the SCMA-based HCN is unoptimized. Results The Pareto-optimal front generated showed a simultaneous maximization of the SE and EE of the SCMA-based HCN at high traffic levels and a convex front that allows network operators to select the SE-EE tradeoff at low traffic levels flexibly. The proposed PSO algorithm offers a higher SBS density, and a higher SBS transmit power at high traffic levels than at low traffic levels. The unoptimized SCMA-based HCN achieves an 80% lower SE and a 51% lower EE than the proposed PSO optimized SCMA-based HCN. The optimum SE and EE achieved by the SCMA-based HCN using the proposed PSO algorithm or the GA are comparable, but the proposed PSO uses a 51.85% lower SBS density and a 35.96% lower SBS transmit power to achieve the optimal SE and EE at moderate traffic levels. Conclusion In sleep-mode enabled SCMA-based HCNs, network engineers have to decide the balance of SBS density and SBS transmit power that helps achieve the desired SE and EE.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: 37Citation - Scopus: 59Career Abandonment Intentions among Software Workers(Wiley, 2014) Colomo-Palacios, Ricardo; Casado-Lumbreras, Cristina; Misra, Sanjay; Soto-Acosta, PedroWithin the software development industry, human resources have been recognized as one of the most decisive and scarce resources. Today, the retention of skilled IT (information technology) personnel is a major issue for employers and recruiters as well, since IT career abandonment is a common practice and means not only the loss of personnel, knowledge, and skills, but also the loss of business opportunities. This article seeks to discover the main motivations young practitioners abandon the software career. To achieve this objective, two studies were conducted. The first study was qualitative (performed through semistructured interviews) and intended to discover the main variables affecting software career abandonment. The second study was quantitative, consisting of a Web-based survey developed from the output of the first study and administered to a sample of 148 IT practitioners. Results show that work-related, psychological, and emotional variable are the most relevant group of variables explaining IT career abandonment. More specifically, the three most important variables that motivate employees to abandon the career are effort-reward imbalance, perceived workload, and emotional exhaustion. In contrast, variables such as politics and infighting, uncool work, and insufficient resources influence to a lesser extent the decision to leave the career. (c) 2012 Wiley Periodicals, Inc.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: 31Citation - Scopus: 50Neural Network and Classification Approach in Identifying Customer Behavior in the Banking Sector: a Case Study of an International Bank(Wiley, 2015) Ogwueleka, Francisca Nonyelum; Misra, Sanjay; Colomo-Palacios, Ricardo; Fernandez, LuisThe customer relationship focus for banks is in development of main competencies and strategies of building strong profitable customer relationships through considering and managing the customer impression, influence on the culture of the bank, satisfactory treatment, and assessment of valued relationship building. Artificial neural networks (ANNs) are used after data segmentation and classification, where the designed model register records into two class sets, that is, the training and testing sets. ANN predicts new customer behavior from previously observed customer behavior after executing the process of learning from existing data. This article proposes an ANN model, which is developed using a six-step procedure. The back-propagation algorithm is used to train the ANN by adjusting its weights to minimize the difference between the current ANN output and the desired output. An evaluation process is conducted to determine whether the ANN has learned how to perform. The training process is halted periodically, and its performance is tested until an acceptable result is obtained. The principles underlying detection software are grounded in classical statistical decision theory.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: 10Citation - Scopus: 18An Ontology-Based Information Extraction System for Organic Farming(Igi Global, 2021) Abayomi-Alli, Adebayo Adewumi; Arogundade, Oluwasefunmi 'Tale; Misra, Sanjay; Akala, Mulkah Opeyemi; Ikotun, Abiodun Motunrayo; Ojokoh, Bolanle AdefowokeIn the existing farming system, information is obtained manually, and most times, farmers act based on their discretion. Sometimes, farmers rely on information from experts and extension officers for decision making. In recent times, a lot of information systems are available with relevant information on organic farming practices; however, such information is scattered in different context, form, and media all over the internet, making their retrieval difficult. The use of ontology with the aid of a conceptual scheme makes the comprehensive and detailed formalization of any subject domain possible. This study is aimed at acquiring, storing, and providing organic farming-based information available to current and intending software developer who may wish to develop applications for farmers. It employs information extraction (IE) and ontology development techniques to develop an ontology-based information extraction (OBIE) system called ontology-based information extraction system for organic farming (OBIESOF). The knowledge base was built using protege editor; Java was used for the implementation of the ontology knowledge base with the aid of the high-level application programming language for working web ontology language application program interface (OWLAPI). In contrast, HermiT was used to checking the consistencies of the ontology and for submitting queries in order to verify their validity. The queries were expressed in description logic (DL) query language. The authors tested the capability of the ontology to respond to user queries by posing instances of the competency questions from DL query interface. The answers generated by the ontology were promising and serve as positive pointers to its usefulness as a knowledge repository.
