Effect of globalisation on quantity surveying practice in an emerging economy

Abstract: In the era where agencies, parastatals and stakeholders in various sector of the economy around the world are clamoring for sustainable developments that are financially viable, socially harmless and friendly to the environment, it is of note that globalization has played a great role and will continue to do now and in the future. A major stakeholders to the actualization of sustainable infrastructural development are quantity surveyors. This study investigates the impacts of globalization on quantity surveying (QS) practice in Nigeria in the quest of equipping quantity surveyor to contribute positively to sustainability of infrastructure projects. Data were obtained from quantity surveyors (QSs) practicing in quantity surveying firms (QSFs) that are registered and licensed to operate by the Nigerian Institute of Quantity Surveyors (NIQS) in the study area. Compare to the benchmark of importance level, it was observed that level of awareness of quantity surveyors of the six basic elements of globalization is low. It was also observed that international trade, foreign direct investment and capital market flow are basic measures of globalization affecting the practice. These have resulted in increase in clients' demand, more opportunities for QSFs including participation in international projects, rise in usage of ICT and general development of the firms. It is therefore imperative for government, NIQS and Quantity Surveyors Registration Board of Nigeria (QSRBN) to formulate appropriate industrial and trade policies that will foster the competitiveness of QSFs, to enjoy emerging global opportunities and compete adequately without resorting to protective measures

Utilizing RoBERTa model for Churn prediction through clustered contextual conversation opinion mining

In computational study and automatic recognition of opinions in free texts, certain words in sentences are used to decide its sentiments. While analysing each customer’s opinion per time in churn management will be effective for personalised recommendations. Oftentimes, the opinion is not sufficient for contextualised content mining. While personalised recommendations are time consuming, it also does not provide complete picture of an overall sentiment in the business community of customers. To help businesses identify widespread issues affecting a large segment of their customers towards engendering patterns and trends of different customer churn behaviour, here, we developed a clustered contextualised conversation as opinions set for integration with Roberta Model. The developed churn behavioural opinion clusters disambiguated short messages while charactering contents collectively based on context beyond keyword-based sentiment matching for effective mining. Based on the predicted opinion threshold, customer churn category for groupbased personalised decision support was generated, with matching concepts. The baseline RoBERTa model on the contextually clustered opinions, trained with a batch size of 16, a learning rate of 2e-5, over 8 epochs, using a maximum sequence length of 128 and standard hyperparameters, achieved an accuracy of 92%, Precision of 88%, Recall of 86% and F1 score of 84% over a test set of 30%

An Enhanced Multi-Level Authentication Electronic Voting System

Originally, manual voting systems are surrounded with issues like results manipulation, errors and long result computation time, ineligible voters, void votes among others. Electronic voting system helped in overcoming the challenges with manual voting system, to engendered other problems of phishing, men in the middle attack alongside voter’s impersonation. By these challenges, the integrity of an election results in a distributed system has become another top concern for e-voting system based on reliability. To achieve an improved voters’ authentication and result validation with excellent user experience, here, a Facial Recognition Electronic Voting System that is power-driven by Blockchain Technology was developed. The entire election engineering activities are decentralised with improved security features to enhance transparency, verifiability, and accountability for each vote count. The self-service voting system was built by smart contract and implemented on the Ethereum network. The obtained reports and evaluations reflected a non-editable and self-sufficiently certifiable system for voting. It also has a competitive edge over fingerprint enabled e-voting system. Aside it’s excellent usability and general acceptance, the developed method discarded to a larger extend, intended fraudulent actions from election activities by eliminating the involvement of a middleman while facilitating privacy, convenience, eligibility and satisfactory voters’ righ

Predictive System for Heart Disease Using a Machine Learning Trained Model

Heart as one of the essential organ of the human body and with its related disease such as cardiovascular diseases accounts for the death of many in our society over the last decades, and also regarded as one of the most life-threatening diseases in the world.  Hence we seek to predict a system for Heart disease using a supervised Machine Learning (ML) trained model in MATLAB2018 workflow in a real-time environment. To develop the system, 299 heart sounds from patients were obtained and labeled as normal and abnormal heart sound. Features were extracted and labeled as dataset; K Nearest Neighbour (KNN), Support Vector Machine (SVM) and Decision Tree (DT) algorithm were used as the training platform. From the classification analysis developed using the supervised ML trained model in MATLAB2018 in conjunction with system software features for the prediction of the heartbeat for both current and predefined of a heart condition algorithms used in training the dataset for the prediction when principle component analysis was enabled, the result shows that KNN algorithm has the highest and best accuracy of 94.4%, followed by the SVM with 84.4% and DT had 81.1%.  while from the evaluation analysis, KNN on Receive Operation Characteristic Curve (ROC) with 90% variance and training time of 12.88 seconds on positive class of abnormal over false classes of normal heart sound has AUC as 0.94 and on ROC curve with PCA 90% variance and training time of 1.7119 seconds on positive class of normal over negative classes of abnormal heart sound has AUC as 0.89 efficiency. Hence the analysis from the result shows that out of the three classified algorithms used, KNN predicts and have the highest accuracy and is more efficient with respect to real-time environment

A MACHINE LEARNING MODEL FOR SOBRIETY AND RELAPSE ANALYSIS IN DRUG REHABILITATION

Drug abuse has become so paramount among members of society. Although, the initial decision to take drugs is typically voluntary among victims. As drugs are constantly been used, the ability to exert self-control on them is relatively impaired. Thus, abuse is witnessed in different age groups, gender, and celebrities from all walks of life. While addicts become such owing to several factors of curiosity and peer pressure, recreational purpose, source of inspiration, and more, the effect of these drugs can lead to depression, brain stimulation, and hallucination. Managing drug abuse through behavioral or pharmacological means is intended to help addicts stop habitual drug use. Oftentimes, rehab is not effective because the desired change is absent while a proper technological-driven approach to track sobriety and relapse in compulsive drug seeking and usage is also missing. Consequently, in this research, user-friendly and interactive sobriety and relapse predictive management application is developed. Here, addicts' behavioral and demographics with major relapse monitoring factors were clustered to predict the likelihood of relapse. The relapse predictive system using cognitive behavioral patterns, adopts the logistic regression algorithm of the Bayesian network for both training and testing. The essence is to ascertain users’ addiction level, analyze and track sobriety and relapse in order to uncover drug addiction patterns, discover the probability of relapse occurrence towards recommending sustainable rehabilitation decision suppor

Knowledge Based Performance Evaluation and Predictive Model for Undergraduate Students

In educational data mining, the process of analysing and predicting from a pool of acquired data is a big challenge due to the influence of behavioural, environmental, parental, personal and social traits of students. While existing education predictive systems have used patterns generated from mined common factors to predict student performance based on subject, faculty, and grade amongst others, explicit traits, which defines a student are often neglected. Thus, such existing models are too general for specific and targeted analysis in more recent times when predictive features are although common but in real essence unique to individual students to a certain degree. Here, a Self-Academic Appraisal and Performance Predictive (SAAPP) system was developed to analyse and predict the overall performance of students before the expiration of their course duration. The inherent knowledge driven model analyses common available predictive internal and external factors, with probabilistic analysis of student academic history and pending courses. The system then builds a personal data centric system for individual student through a decision support expert system and a probabilistic optimal grade point analysis for more effective recommendation. The developed system is more accurate, reliable and precise in student performance classification with targeted recommendations

Hydrokinetic Energy Opportunity for Rural Electrification in Nigeria

This paper is part of the ongoing research by the Power, Energy, Machine and Drive (PEMD) research group of the Electrical Engineering Department of the University of Ibadan. The paper presents various sites with possible hydrokinetic energy potential in Nigeria with the aim of quantifying their energy potential for rural electrification application. Overview of hydrokinetic technology is also presented with the view of highlighting the opportunities and the challenges of the technology for rural electrification. A case study of using hydrokinetic turbine technology in meeting the energy demand of a proposed civic center in a remote community is demonstrated.  Some of the key findings revealed that Nigeria has many untapped hydrokinetic potential site and if adequately harnessed can improve the energy poverty and boost economic activities especially in the isolated and remote rural communities, where adequate river water resource is available. The total estimated untapped hydrokinetic energy potential in Nigeria is 111.15MW with the Northern part of the country having 68.18MW while the Southern part has 42.97MW. The case study shows that harnessing hydrokinetic energy of potential site is promising for rural electrification. This paper is important as it will serve as an initial requirement for optimal investment in hydrokinetic power development in Nigeria. Article History: Received November 16th 2017; Received in revised form April 7th 2018; Accepted April 15th 2018; Available online How to Cite This Article: Olatunji, O.A.S., Raphael, A.T. and Yomi, I.T. (2018) Hydrokinetic Energy Opportunity for Rural Electrification in Nigeria. Int. Journal of Renewable Energy Development, 7(2), 183-190. http://dx.doi.org/10.14710/ijred.7.2.183-19

Socio-Transactional Impact of Recency, Frequency, and Monetary Features oN Customers’ Behaviour in Telecoms’ Churn Prediction

Due to the increasing competitiveness in telecom’s market, it has now become more necessary for operators to start building personal relationship with customers for targeted retention strategies. Achieving this goal requires the development of an effective churn prediction model that will solve the problem of churn misclassification, which is persistent in current churn prediction models. With several existing segment-oriented churn prediction models failing to harness the power of associative networking provided by telecoms users, churn prediction accuracy remains unguaranteed while targeted decision support is not enhanced. Here, the research introduced the Customer’s Influence Degree (I) to the existing Recency, Frequency, and Monetary (RFM) values as an additional predictive factor, towards determining the churn class of a customer. The essence is to utilise the socio-transactional affinities of customers’ direct dependent to targeted communication nodes through customers RFM analysis to determine the dominance of a customer in the community. The newly introduced predictive factor helped to minimise churn misclassification rate through appropriate reclassification of customers who were wrongly classified as churner or non-churner when using the existing RFM churn scores only

Numerical investigation and sensitivity analysis of turbulent heat transfer and pressure drop of Al2O3/H2O nanofluid in straight pipe using response surface methodology

In this paper, investigation of the effect of Reynolds number, nanoparticle volume ratio, nanoparticle diameter and entrance temperature on the convective heat transfer and pressure drop of Al2O3/H2O nanofluid in turbulent flow through a straight pipe was carried out. The study employed a computational fluid dynamic approach using single-phase model and response surface methodology for the design of experiment. The Reynolds average Navier-Stokes equations and energy equation were solved using k-ε turbulent model. The central composite design method was used for the response-surface-methodology. Based on the number of variables and levels, the condition of 30 runs was defined and 30 simulations were performed. New models to evaluate the mean Nusselt number and pressure drop were obtained. Also, the result showed that all the four input variables are statistically significant to the pressure drop while three out of them are significant to the Nusslet number. Furthermore, sensitivity analysis carried out showed that the Reynolds number and volume fraction have a positive sensitivity to both the mean Nusselt number, and pressure drop, while the entrance temperature has negative sensitivities to both