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It is imperative to establish an automated system for the identification of neonates (1–28 days old) and infants (29 days–12 months old) through the utilisation of the readily accessible 500 ppi fingerprint reader. This measure is crucial in addressing the issue of newborn swapping, facilitating the identification of missing children, monitoring immunisation records, maintaining comprehensive medical history, and other related purposes. The objective of this study is to demonstrate the potential for future identification of infants using fingerprints obtained from a 500 ppi fingerprint reader by employing a fusion technique that combines multiple instances of fingerprints, specifically the left thumb and right index fingers. The fingerprints were acquired from babies who were between the ages of one day and six months at the enrolment session. The sum-score fusion algorithm was implemented. The approach mentioned above yielded verification accuracies of 73.8%, 69.05%, and 57.14% for time intervals of 1 month, 3 months, and 6 months, respectively, between the enrolment and query fingerprints.

Cervical dilation is the most important parameter that is assessed during childbirth to validate that a woman is truly in labour and whether labour is progressing as expected. It is the opening of a mother’s cervix from when it is closed at 0 cm to when it is fully dilated at 10 cm, for the baby to pass through and be delivered. The cervix is a cylinder-shaped tissue that connects the uterus to the vagina. Cervical dilation is majorly assessed through a highly subjective, painful, error-prone, and infection-prone Vaginal Examination method. The method involves a doctor or midwife wearing sterilized gloves and inserting his or her fingers through the vagina to manually assess cervical dilation and mentally visualize it. Hence, in this research, a prototype of a novel low-intensity light imaging probe was developed to acquire images of cervical dilation simulation models for further processing and analysis. The probe was designed with 3D computer-aided design software. Finite element analysis was carried out on the design before it was rapid prototyped. Then, a camera and a light source were inserted into the probe to capture 2,880 cervical dilation images in low-light intensities of 28 Lux and 50 Lux, due to the penetration depth of bright light intensity and heat into tissues. Image preprocessing was carried out on the images by applying a low-light image enhancement technique. This research demonstrated the use of a low-intensity light imaging probe as a possible objective alternative to the subjective insertion of fingers in vaginal examination.

5G millimeter wave (mmWave) application in mobile connectivity to realize high-speed, reliable communication is attributed with high path loss. This paper presents a detailed 3D ray-tracing technique at 28 GHz for Lagos Island to investigate five unique path loss scenarios: path loss, free space path loss with antenna pattern, free space path loss without antenna pattern, excess path loss with antenna pattern, and the excess path loss without antenna pattern for an urban environment. The Close-In (CI) model, Floating Intercept (FI) path loss model, and a root mean square error (RMSE) are used to model and evaluate the best path loss model for Lagos Island. The average achieved FI ( $\alpha,\beta, \sigma $ ) parameters were 189.92352, 0.1654, and 0.66948, While the average CI ( $\eta,X\sigma $ ) parameters were 2.309355 and 56.236425. From all the scenarios evaluated, the lowest path loss exponent achieved was 0.45, while the highest path loss exponent was 3.8. We have established that the FI path loss model accurately characterizes path loss for the Lagos Island environment with the lowest RMSE of 0.0359 dB and the highest RSME of 0.0997 dB. In contrast, the CI model over-predict the path loss at 28 GHz with the lowest RMSE of 0.0495 dB and the highest RMSE of 2.2547 dB. This work opens up a new area of research on mm-Wave at 28 GHz in Lagos Island, and the results obtained from this work can be used to benchmark future studies on mmWave in a similar environment.

This work presents an adaptive neuro-fuzzy inference system (ANFIS) artificial intelligence methodology of predicting the 2nd law efficiency and total irreversibility of a refrigeration system running on LPG/TiO2–nano-refrigerants. For this purpose, substractive clustering and grid partition approaches were utilized to train the ANFIS models required in estimating the 2nd law efficiency and total irreversibility using some experimental data. Furthermore, predictions of ANFIS models with subtractive clustering approach was found to be more accurate than ANFIS models predictions with grid partition approach. The predictions of ANFIS models with subtractive clustering approach were also compared with experimental results that were not included in the model training and predictions of already existing ANN models of authors previous publication. The comparison of variance, root mean square error (RMSE), mean absolute percentage error (MAPE) were 0.996–0.999, 0.0296–0.1726 W and 0.108–0.176 % marginal variability values. These results indicate that the ANFIS model with subtractive clustering approach having cluster radii 0.7 and 0.5 can predict the 2nd law efficiency and total irreversibility respectively, with higher accuracy than authors’ previous publication ANN models.

For decades now, a lot of radio wave path loss propagation models have been developed for predictions across different environmental terrains. Amongst these models, empirical models are practically the most popular due to their ease of application. However, their prediction accuracies are not as high as required. Therefore, extensive path loss measurement data are needed to develop novel measurement-oriented path loss models with suitable correction factors for varied frequency, capturing both local terrain and clutter information, this have been found to be relatively expensive. In this paper, a large-scale radio propagation path loss measurement campaign was conducted across the VHF and UHF frequencies. A multi-transmitter propagation set-up was employed to measure the strengths of radio signals from seven broadcasting transmitters (operating at 89.30, 103.5, 203.25, 479.25, 615.25, 559.25 and 695.25 MHz respectively) at various locations covering a distance of 145.5 km within Nigerian urban environments. The measurement procedure deployed ensured that the data obtained strictly reflect the shadowing effects on radio signal propagation by filtering out the small-scale fading components. The paper also, examines the feasibilities of applying Kriging method to predict distanced-based path losses in the VHF and UHF bands. This method was introduced to minimize the cost of measurements, analysis and predictions of path losses in built-up propagation environments.

Gender equality in access to higher education is an important factor in building a sustainable world. Although a good number of countries across the globe have achieved parity in primary education between boys and girls, the target is yet to be widely attained at tertiary level of education. In this data article, empirical data on yearly admissions into accredited tertiary institutions in Nigeria are extensively explored to reveal the existence of gender gaps in the national admission process. Details on the number of candidates admitted into all accredited universities, polytechnics, and colleges of education between 2010 and 2015 were obtained directly from the Joint Admissions and Matriculation Board (JAMB). Gender distributions of admitted candidates are analyzed across the thirty-six (36) states of the federation, the Federal Capital Territory (FCT), and the international students’ category. Gender disparity in admissions into Nigerian tertiary institutions are explored using relevant descriptive statistics, box plots, bar charts, line graphs, and pie charts. In addition, Analysis of Variance (ANOVA) is carried out on the historical data to find out if there are significant differences in the arithmetic means of females and males admitted over the six-year period. Furthermore, multiple comparison post-hoc test results are presented in tables to understand the extent of variations (if any) in gender distribution over the years. The robust data exploration reported in this data article will help national regulatory bodies and relevant stakeholders in policy formulation and decision making towards ensuring equal access to higher education in Nigeria.

Deep knowledge of how radio waves behave in a practical wireless channel is required for effective planning and deployment of radio access networks in urban environments. Empirical propagation models are popular for their simplicity, but they are prone to introduce high prediction errors. Different heuristic methods and geospatial approaches have been developed to further reduce path loss prediction error. However, the efficacy of these new techniques in built-up areas should be experimentally verified. In this paper, the efficiencies of empirical, heuristic, and geospatial methods for signal fading predictions in the very high frequency (VHF) and ultra-high frequency (UHF) bands in typical urban environments are evaluated and analyzed. Electromagnetic field strength measurements are performed at different test locations within four selected cities in Nigeria. The data collected are used to develop path loss models based on artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and Kriging techniques. The prediction results of the developed models are compared with those of selected empirical models and field measured data. Apart from Egli and ECC-33, the root mean squared error (RMSE) produced by all other models under investigation are considered acceptable. Specifically, the ANN and ANFIS models yielded the lowest prediction errors. However, the empirical models have the lowest standard deviation errors across all the bands. The findings of this study will help radio network engineers to achieve efficient radio coverage estimation; determine the optimal base station location; make a proper frequency allocation; select the most suitable antenna; and perform interference feasibility studies.

It is very important to understand the input features and the neural network parameters required for optimal path loss prediction in wireless communication channels. In this paper, an extensive investigation was conducted to determine the most appropriate neural network parameters for path loss prediction in Very High Frequency (VHF) band. Field measurements were conducted in an urban propagation environment to obtain relevant geographical and network information about the receiving mobile equipment and quantify the path losses of radio signals transmitted at 189.25 MHz and 479.25 MHz. Different neural network architectures were trained with varying kinds of input parameters, number of hidden neurons, activation functions, and learning algorithms to accurately predict corresponding path loss values. At the end of the experimentations, the performance of the developed Artificial Neural Network (ANN) models are evaluated using the following statistical metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Standard Deviation (SD) and Regression coefficient (R). Results obtained show that the ANN model that yielded the best performance employed four input variables (latitude, longitude, elevation, and distance), nine hidden neurons, hyperbolic tangent sigmoid (tansig) activation function, and the Levenberg-Marquardt (LM) learning algorithm with MAE, MSE, RMSE, SD and R values of 0.58 dB, 0.66 dB, 0.81 dB, 0.56 dB and 0.99 respectively. Finally, a comparative analysis of the developed model with Hata, COST 231, ECC-33 and Egli models showed that ANN-based path loss model has better prediction accuracy and generalization ability than the empirical models.

One of the important optimization applications (minimization and maximization) is the power grid systems. National electricity grids should be interconnected to develop larger regional grids (supergrids), and further integrated to build up a worldwide grid (global grid) for minimizing consumption of natural resources and maximizing economical useful life, recycling rate, and effective usage of natural resources. These supergrids and global grid concepts can only be developed through detailed and organized supportive research studies. This research study aims to find, define, identify, describe and select location selection factors of very large photovoltaic solar power plant investments on a global grid and supergrid concepts. Grey systems theory, fuzzy (Type-1 and 2) theories, Mamdani’s type fuzzy rule-based system, Interpretive Structural Modelling (ISM), Impact Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) tool, and Political, Economic, Social and Technological (PEST) framework and its extensions (SLEPT, PESTEL, PESTLE, STEEPLE, STEEPLED, DESTEP, STEER) are concurrently used in this study. Eleven (11) criteria are presented for preliminary screening (i.e. C1: global horizontal irradiation (GHI), C2: governments supergrid integration policy, C3: supergrid business climate and conditions, C4: High Voltage Direct Current (HVDC) and High Voltage Alternating Current (HVAC) electrification grid infrastructure, C5: land use, allocation and availability, C6: geological conditions, C7: political, war, terror & security, C8: topographical conditions, C9: climatic conditions, C10: water availability conditions, C11: natural disaster/hazard conditions), and 191 factors are presented for pre-feasibility investment stages. Findings can directly be used or taken as a basis for further analysis by researchers and practitioners. Keywords: Fuzzy logic, Global grid, Interpretive structural modelling, MICMAC, PEST, Photovoltaic, Supergrid

In this data article, an analysis on the strategies for talent retention in Covenant University and the corresponding effects on employees’ attitude to work was presented. The study population included the academic staff of Covenant University, which has a population of 530 employees, but a sample size was determined using Yamen׳s formula. The data obtained through survey questionnaires were analysed using Statistical Package for Social Sciences (SPSS). Linear regression was used to model the effect of talent retention strategy on employees’ attitude to work. This information is made publicly available to aid empirical researches on the subject of talent management in organizations. Keywords: Talent Retention, Employee, Attitude to work, Organizations

In this data article, a robust data exploration is performed on daily Internet data traffic generated in a smart university campus for a period of twelve consecutive (12) months (January–December, 2017). For each day of the one-year study period, Internet data download traffic and Internet data upload traffic at Covenant University, Nigeria were monitored and properly logged using required application software namely: FreeRADIUS; Radius Manager Web application; and Mikrotik Hotspot Manager. A comprehensive dataset with detailed information is provided as supplementary material to this data article for easy research utility and validation. For each month, descriptive statistics of daily Internet data download traffic and daily Internet data upload traffic are presented in tables. Boxplot representations and time series plots are provided to show the trends of data download and upload traffic volume within the smart campus throughout the 12-month period. Frequency distributions of the dataset are illustrated using histograms. In addition, correlation and regression analyses are performed and the results are presented using a scatter plot. Probability Density Functions (PDFs) and Cumulative Distribution Functions (CDFs) of the dataset are also computed. Furthermore, Analysis of Variance (ANOVA) and multiple post-hoc tests are conducted to understand the statistical difference(s) in the Internet traffic volume, if any, across the 12-month period. The robust data exploration provided in this data article will help Internet Service Providers (ISPs) and network administrators in smart campuses to develop empirical model for optimal Quality of Service (QoS), Internet traffic forecasting, and budgeting. Keywords: Smart campus, Internet Protocol, Internet data traffic, Nigerian university, Smart education

This article presents the frequency of distribution of editorial members of Dove Medical press, across the world based on their official stated affiliations. Uneven distributions across the six continents were observed and this was confirmed by the Chi-square test of goodness of fit. Further research can focus on data on the gender composition, distribution of the affiliations of the first or corresponding authors of the respective journals, citation and editorial board composition based on the abstraction and indexation of the journals. Keywords: Dove Medical Press, Bibliometrics, Data analysis, Random, Smart campus, Ranking analytics, Statistics

Citation is one of the important metrics that are used in measuring the relevance and the impact of research publications. The potentials of citation analytics may be exploited to understand the gains of publishing scholarly peer-reviewed research outputs in either Open Access (OA) sources or Subscription-Based (SB) sources in the bid to increase citation impact. However, relevant data required for such comparative analysis must be freely accessible for evidence-based findings and conclusions. In this data article, citation scores (CiteScores) of 2542 OA sources and 15,040 SB sources indexed in Scopus from 2014 to 2016 were presented and analyzed based on a set of five inclusion criteria. A robust dataset, which contains the CiteScores of OA and SB publication sources included, is attached as supplementary material to this data article to facilitate further reuse. Descriptive statistics and frequency distributions of OA CiteScores and SB CiteScores are presented in tables. Boxplot representations and scatter plots are provided to show the statistical distributions of OA CiteScores and SB CiteScores across the three sub-categories (Book Series, Journal, and Trade Journal). Correlation coefficient and p-value matrices are made available within the data article. In addition, Probability Density Functions (PDFs) and Cumulative Distribution Functions (CDFs) of OA CiteScores and SB CiteScores are computed and the results are presented using tables and graphs. Furthermore, Analysis of Variance (ANOVA) and multiple comparison post-hoc tests are conducted to understand the statistical difference (and its significance, if any) in the citation impact of OA publication sources and SB publication source based on CiteScore. In the long run, the data provided in this article will help policy makers and researchers in Higher Education Institutions (HEIs) to identify the appropriate publication source type and category for dissemination of scholarly research findings with maximum citation impact. Keywords: Citation impact, Open Access, CiteScore, Analytics, Citation analytics, Smart campus, Data mining

The datasets and their analyses presented in this paper revealed some frequencies of opponents׳ eliminations by entrance or order of elimination in Royal Rumble wrestling matches from 1988 to 2018. The frequency of eliminations by the order of entrant is quite different from order of eliminations. Statistical methods, algorithms and machine learning methods can be applied to the raw data to obtain more hidden trend not included in this article. Keywords: Royal Rumble, WWE, Wrestling, Smart campus, Sport analytics, Statistics

Cassava mosaic disease (CMD) is one of the most economically important viral diseases of cassava, an important staple food for over 800 million people in the tropics. Although several Cassava mosaic virus species associated with CMD have been isolated and characterized over the years, several new super virulent strains of these viruses have evolved due to genetic recombination between diverse species. In this data article, field survey data collected from 184 cassava farms in 12 South Western and North Central States of Nigeria in 2015 are presented and extensively explored. In each State, one cassava farm was randomly selected as the first farm and subsequent farms were selected at 10 km intervals, except in locations were cassava farms are sporadically located. In each selected farm, 30 cassava plants were sampled along two diagonals and all selected plant was scored for the presence or absence of CMD symptoms. Cassava mosaic disease incidence and associated whitefly vectors in South West and North Central Nigeria are explored using relevant descriptive statistics, box plots, bar charts, line graphs, and pie charts. In addition, correlation analysis, Analysis of Variance (ANOVA), and multiple comparison post-hoc tests are performed to understand the relationship between the numbers of whiteflies counted, uninfected farms, infected farms, and the mean of symptom severity in and across the States under investigation. The data exploration provided in this data article is considered adequate for objective assessment of the incidence and symptom severity of cassava mosaic disease and associated whitefly vectors in farmers’ fields in these parts of Nigeria where cassava is heavily cultivated. Keywords: Cassava mosaic disease, Whitefly vector, Zero hunger, Cassava mosaic virus

The statistical analysis of editorial board composition, Citescore and percentile of 180 Hindawi journals currently indexed in Scopus are presented in this data article. The three indicators (editorial board composition, Citescore and percentile) can be helpful for researchers to make informed decision about the impact of Hindawi journals. The last two indicators are components of Scopus Citescore metrics. Keywords: Hindawi, Bibliometrics, Data analysis, Scopus, Percentile, Smart campus, Ranking analytics, Statistics, Citescore

This article explores the editorial board composition (across the six continents) of Hindawi journals indexed in PubMed. The dataset used is the official affiliation of the board members available at the various webpages of Hindawi journal website and not the countries of origin of the editorial board members. Summary statistics were presented and the raw dataset was provided for further analysis by interested scholars. The percentage of the editorial board composition across the continents was presented, the dataset of Hindawi journals indexed in both Hindawi and Scopus were also presented and measured in terms of Citescore and percentiles. The dataset can be used in journal evaluation, auditing, bibliometric analysis, management of smart campus; ranking and the analysis can be extended to other journal indexations. Keywords: Hindawi, Bibliometrics, Data analysis, PubMed, PubMed Central, Random, Smart campus, Ranking analytics, Statistics

This data article represents academic performances of undergraduate students in a select Nigerian Private Tertiary institution from 2008 to 2013. The 2413 dataset categorizes students with respect to their origins (ethnicity), pre-university admission scores and Cumulative Grade Point Averages earned at the end of their study at the university. We present a descriptive statistics showing mean, median, mode, maximum, minimum, range, standard deviation and variance in the performances of these students and a boxplot representation of the performances of these students with respect to their origins. Keywords: Learning analytics, Cultural impact, Ethnicity, Undergraduates, Education data mining, Smart campus, Nigerian university

This data article presents data of academic performances of undergraduate students in Science, Technology, Engineering and Mathematics (STEM) disciplines in Covenant University, Nigeria. The data shows academic performances of Male and Female students who graduated from 2010 to 2014. The total population of samples in the observation is 3046 undergraduates mined from Biochemistry (BCH), Building technology (BLD), Computer Engineering (CEN), Chemical Engineering (CHE), Industrial Chemistry (CHM), Computer Science (CIS), Civil Engineering (CVE), Electrical and Electronics Engineering (EEE), Information and Communication Engineering (ICE), Mathematics (MAT), Microbiology (MCB), Mechanical Engineering (MCE), Management and Information System (MIS), Petroleum Engineering (PET), Industrial Physics-Electronics and IT Applications (PHYE), Industrial Physics-Applied Geophysics (PHYG) and Industrial Physics-Renewable Energy (PHYR). The detailed dataset is made available in form of a Microsoft Excel spreadsheet in the supplementary material of this article. Keywords: Learning analytics, STEM students, STEM, STEM education, Gender roles, Undergraduates, Education data mining, Smart campus, Nigerian university

Empirical models are most widely used for path loss predictions because they are simple, easy to use, and require less computational efficiency when compared to deterministic models. A number of empirical path loss models have been developed for efficient radio network planning and optimization in different types of propagation environments. However, data that prove the suitability of these models for path loss predictions in a typical university campus propagation environment are yet to be reported in the literature. In this data article, empirical prediction models are comparatively assessed using the path loss data measured and predicted for a university campus environment. Field measurement campaigns are conducted at 1800 MHz radio frequency to log the actual path losses along three major routes within the campus of Covenant University, Nigeria. Path loss values are computed along the three measurement routes based on four popular empirical path loss models (Okumura-Hata, COST 231, ECC-33, and Egli). Datasets containing measured and predicted path loss values are presented in a spreadsheet file, which is attached to this data article as supplementary material. Path loss prediction data of the empirical models are compared to those of the measured path loss using first-order statistics, boxplot representations, tables, and graphs. In addition, correlation analysis, Analysis of Variance (ANOVA), and multiple comparison post-hoc tests are performed. The prediction accuracies of the empirical models are evaluated based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Standard Error Deviation (SED). In conclusion, the high-resolution path loss prediction datasets and the rich data exploration provided in this data article will help radio network engineers and academic researchers to determine the empirical model that is most suitable for path loss prediction in a typical university campus environment. Keywords: Models, Forecasting, Path loss, Loss models, Radio propagation, Smart campus

In this data article, we present and analyze the demographic data of undergraduates admitted into engineering programs at Covenant University, Nigeria. The population distribution of 2649 candidates admitted into Chemical Engineering, Civil Engineering, Computer Engineering, Electrical and Electronics Engineering, Information and Communication Engineering, Mechanical Engineering, and Petroleum Engineering programs between 2002 and 2009 are analyzed by gender, age, and state of origin. The data provided in this data article were retrieved from the student bio-data submitted to the Department of Admissions and Student Records (DASR) and Center for Systems and Information Services (CSIS) by the candidates during the application process into the various engineering undergraduate programs. These vital information is made publicly available, after proper data anonymization, to facilitate empirical research in the emerging field of demographics analytics in higher education. A Microsoft Excel spreadsheet file is attached to this data article and the data is thoroughly described for easy reuse. Descriptive statistics and frequency distributions of the demographic data are presented in tables, plots, graphs, and charts. Unrestricted access to these demographic data will facilitate reliable and evidence-based research findings for sustainable education in developing countries. Keywords: Analytics, Sustainable education, Higher education, Data mining, Engineering, Smart campus

This data article contains the statistical analysis of the total, percentage and distribution of editorial board composition of 111 Hindawi journals indexed in Emerging Sources Citation Index (ESCI) across the continents. The reliability of the data was shown using correlation, goodness-of-fit test, analysis of variance and statistical variability tests. Keywords: Hindawi, Bibliometrics, Data analysis, ESCI, Random, Smart campus, Web of science, Ranking analytics, Statistics

This data article presents comparisons of energy generation costs from gas-fired turbine and diesel-powered systems of distributed generation type of electrical energy in Covenant University, Ota, Nigeria, a smart university campus driven by Information and Communication Technologies (ICT). Cumulative monthly data of the energy generation costs, for consumption in the institution, from the two modes electric power, which was produced at locations closed to the community consuming the energy, were recorded for the period spanning January to December 2017. By these, energy generation costs from the turbine system proceed from the gas-firing whereas the generation cost data from the diesel-powered generator also include data on maintenance cost for this mode of electrical power generation. These energy generation cost data that were presented in tables and graphs employ descriptive probability distribution and goodness-of-fit tests of statistical significance as the methods for the data detailing and comparisons. Information details from this data of energy generation costs are useful for furthering research developments and aiding energy stakeholders and decision-makers in the formulation of policies on energy generation modes, economic valuation in terms of costing and management for attaining energy-efficient/smart educational environment. Keywords: Smart campus, Energy consumption, Energy efficiency, Load forecasting, Energy management, Learning analytics, Nigerian university, Education data mining

Path loss models are often used by radio network engineers to predict signal coverage, optimize limited network resources, and perform interference feasibility studies. However, the propagation mechanisms of electromagnetic waves depend on the physical characteristics of the wireless channel. Therefore, efficient radio network planning and optimization requires detailed information about the specific propagation environment. In this data article, the path loss data and the corresponding information that are needed for modeling radio wave propagation in smart campus environment are presented and analyzed. Extensive drive test measurements are performed along three different routes (X, Y, and Z) within Covenant University, Ota, Ogun State, Nigeria (Latitude 6°40′30.3″N, Longitude 3°09′46.3″E) to record path loss data as the mobile receiver moves away from each of the three 1800 MHz base station transmitters involved. Also, the longitude, latitude, elevation, altitude, clutter height, and the distance information, which describes the smart campus environment, are obtained from Digital Terrain Map (DTM) in ATOLL radio network planning tool. Results of the first-order descriptive statistics and the frequency distributions of all the seven parameters are presented in tables and graphs respectively. In addition, correlation analyses are performed to understand the relationships between the network parameters and the terrain information. For ease of reuse, the comprehensive data are prepared in Microsoft Excel spreadsheet and attached to this data article. In essence, the availability of these data will facilitate the development of path loss models for efficient radio network planning and optimization in smart campus environment. Keywords: Path loss, Radio propagation, Wireless communications, GSM networks, Smart campus

In Nigerian universities, enrolment into any engineering undergraduate program requires that the minimum entry criteria established by the National Universities Commission (NUC) must be satisfied. Candidates seeking admission to study engineering discipline must have reached a predetermined entry age and met the cut-off marks set for Senior School Certificate Examination (SSCE), Unified Tertiary Matriculation Examination (UTME), and the post-UTME screening. However, limited effort has been made to show that these entry requirements eventually guarantee successful academic performance in engineering programs because the data required for such validation are not readily available. In this data article, a comprehensive dataset for empirical evaluation of entry requirements into engineering undergraduate programs in a Nigerian university is presented and carefully analyzed. A total sample of 1445 undergraduates that were admitted between 2005 and 2009 to study Chemical Engineering (CHE), Civil Engineering (CVE), Computer Engineering (CEN), Electrical and Electronics Engineering (EEE), Information and Communication Engineering (ICE), Mechanical Engineering (MEE), and Petroleum Engineering (PET) at Covenant University, Nigeria were randomly selected. Entry age, SSCE aggregate, UTME score, Covenant University Scholastic Aptitude Screening (CUSAS) score, and the Cumulative Grade Point Average (CGPA) of the undergraduates were obtained from the Student Records and Academic Affairs unit. In order to facilitate evidence-based evaluation, the robust dataset is made publicly available in a Microsoft Excel spreadsheet file. On yearly basis, first-order descriptive statistics of the dataset are presented in tables. Box plot representations, frequency distribution plots, and scatter plots of the dataset are provided to enrich its value. Furthermore, correlation and linear regression analyses are performed to understand the relationship between the entry requirements and the corresponding academic performance in engineering programs. The data provided in this article will help Nigerian universities, the NUC, engineering regulatory bodies, and relevant stakeholders to objectively evaluate and subsequently improve the quality of engineering education in the country. Keywords: Smart campus, Learning analytics, Sustainable education, Nigerian university, Education data mining, Engineering

Empirical measurement, monitoring, analysis, and reporting of learning outcomes in higher institutions of developing countries may lead to sustainable education in the region. In this data article, data about the academic performances of undergraduates that studied engineering programs at Covenant University, Nigeria are presented and analyzed. A total population sample of 1841 undergraduates that studied Chemical Engineering (CHE), Civil Engineering (CVE), Computer Engineering (CEN), Electrical and Electronics Engineering (EEE), Information and Communication Engineering (ICE), Mechanical Engineering (MEE), and Petroleum Engineering (PET) within the year range of 2002–2014 are randomly selected. For the five-year study period of engineering program, Grade Point Average (GPA) and its cumulative value of each of the sample were obtained from the Department of Student Records and Academic Affairs. In order to encourage evidence-based research in learning analytics, detailed datasets are made publicly available in a Microsoft Excel spreadsheet file attached to this article. Descriptive statistics and frequency distributions of the academic performance data are presented in tables and graphs for easy data interpretations. In addition, one-way Analysis of Variance (ANOVA) and multiple comparison post-hoc tests are performed to determine whether the variations in the academic performances are significant across the seven engineering programs. The data provided in this article will assist the global educational research community and regional policy makers to understand and optimize the learning environment towards the realization of smart campuses and sustainable education. Keywords: Smart campus, Learning analytics, Sustainable education, Nigerian university, Education data mining, Engineering

In this data article, the Key Performance Indicators (KPIs) for Quality of Service (QoS) of Global System for Mobile Communications (GSM) networks in Nigeria are provided and analyzed. The data provided in this paper contain the Call Setup Success Rate (CSSR), Drop Call Rate (DCR), Stand-alone Dedicated Channel (SDCCH) congestion, and Traffic Channel (TCH) congestion for the four GSM network operators in Nigeria (Airtel, Etisalat, Glo, and MTN). These comprehensive data were obtained from the Nigerian Communications Commission (NCC). Significant differences in each of the KPIs for the four quarters of each year were presented based on Analysis of Variance (ANOVA). The values of the KPIs were plotted against the months of the year for better visualization and understanding of data trends across the four quarters. Multiple comparisons of the mean-quarterly differences of the KPIs were also presented using Tukey's Post Hoc test. Public availability and further interpretation and discussion of these useful information will assist the network providers, Nigerian government, local and international regulatory bodies, policy makers, and other stakeholders in ensuring access of people, machines, and things to high quality telecommunications services. Keywords: Quality of service, GSM networks, Call setup success rate, Drop call rate, Stand-alone dedicated channel congestion, Traffic channel congestion

The behaviour of radio wave signals in a wireless channel depends on the local terrain profile of the propagation environments. In view of this, Received Signal Strength (RSS) of transmitted signals are measured at different points in space for radio network planning and optimization. However, these important data are often not publicly available for wireless channel characterization and propagation model development. In this data article, RSS data of a commercial base station operating at 900 and 1800 MHz were measured along three different routes of Lagos-Badagry Highway, Nigeria. In addition, local terrain profile data of the study area (terrain elevation, clutter height, altitude, and the distance of the mobile station from the base station) are extracted from Digital Terrain Map (DTM) to account for the unique environmental features. Statistical analyses and probability distributions of the RSS data are presented in tables and graphs. Furthermore, the degree of correlations (and the corresponding significance) between the RSS and the local terrain parameters were computed and analyzed for proper interpretations. The data provided in this article will help radio network engineers to: predict signal path loss; estimate radio coverage; efficiently reuse limited frequencies; avoid interferences; optimize handover; and adjust transmitted power level. Keywords: Received signal strength, GSM, Path loss, Radio network planning, Radio network optimization, Smart and connected communities

In this data article, we present a comprehensive dataset on electrical energy consumption in a university that is practically driven by Information and Communication Technologies (ICTs). The total amount of electricity consumed at Covenant University, Ota, Nigeria was measured, monitored, and recorded on daily basis for a period of 12 consecutive months (January–December, 2016). Energy readings were observed from the digital energy meter (EDMI Mk10E) located at the distribution substation that supplies electricity to the university community. The complete energy data are clearly presented in tables and graphs for relevant utility and potential reuse. Also, descriptive first-order statistical analyses of the energy data are provided in this data article. For each month, the histogram distribution and time series plot of the monthly energy consumption data are analyzed to show insightful trends of energy consumption in the university. Furthermore, data on the significant differences in the means of daily energy consumption are made available as obtained from one-way Analysis of Variance (ANOVA) and multiple comparison post-hoc tests. The information provided in this data article will foster research development in the areas of energy efficiency, planning, policy formulation, and management towards the realization of smart campuses. Keywords: Smart campus, Energy consumption, Energy efficiency, Load forecasting, Energy management

Embedded systems technology is undergoing a phase of transformation owing to the novel advancements in computer architecture and the breakthroughs in machine learning applications. The areas of applications of embedded machine learning (EML) include accurate computer vision schemes, reliable speech recognition, innovative healthcare, robotics, and more. However, there exists a critical drawback in the efficient implementation of ML algorithms targeting embedded applications. Machine learning algorithms are generally computationally and memory intensive, making them unsuitable for resource-constrained environments such as embedded and mobile devices. In order to efficiently implement these compute and memory-intensive algorithms within the embedded and mobile computing space, innovative optimization techniques are required at the algorithm and hardware levels. To this end, this survey aims at exploring current research trends within this circumference. First, we present a brief overview of compute intensive machine learning algorithms such as hidden Markov models (HMM), k-nearest neighbors (k-NNs), support vector machines (SVMs), Gaussian mixture models (GMMs), and deep neural networks (DNNs). Furthermore, we consider different optimization techniques currently adopted to squeeze these computational and memory-intensive algorithms within resource-limited embedded and mobile environments. Additionally, we discuss the implementation of these algorithms in microcontroller units, mobile devices, and hardware accelerators. Conclusively, we give a comprehensive overview of key application areas of EML technology, point out key research directions and highlight key take-away lessons for future research exploration in the embedded machine learning domain.

The article presents statistical facts on Nigeria's preparedness for Internet of everything. Copies of structured questionnaire were administered to 163 workers in Lagos State. Using descriptive statistics and charts (bar chart and histogram), the paper revealed that most of the respondents are aware of the concept of internet of everything, perceive that Nigeria is prepared for an internet enabled society and already have devices that can help them access the internet from where they are. More so, the challenges of cost, modern technology and signal coverage pose to be the greatest areas that should be addressed in the drive for an internet enabled society in Nigeria. Keywords: Internet of everything, Internet of things, Technology, Nigeria economy, Nigeria

Efficient broadband Internet access is required for optimal productivity in smart campuses. Besides access to broadband Internet, delivery of high speed and good Quality of Service (QoS) are pivotal to achieving a sustainable development in the area of education. In this data article, trends and patterns of the speed of broadband Internet provided in a Nigerian private university campus are largely explored. Data transmission speed and data reception speed were monitored and recorded on daily basis at Covenant University, Nigeria for a period of twelve months (January–December, 2017). The continuous data collection and logging were performed at the Network Operating Center (NOC) of the university using SolarWinds Orion software. Descriptive statistics, correlation and regression analyses, Probability Density Functions (PDFs), Cumulative Distribution Functions (CDFs), Analysis of Variance (ANOVA) test, and multiple comparison post-hoc test are performed using MATLAB 2016a. Extensive statistical visualizations of the results obtained are presented in tables, graphs, and plots. Availability of these data will help network administrators to determine optimal network latency towards efficient deployment of high-speed broadband communication networks in smart campuses. Keywords: Smart campus, Broadband internet access, Data bit rate, Mobile communication, Knowledge management

Nowadays, hackers take illegal advantage of distributed resources in a network of computing devices (i.e., botnet) to launch cyberattacks against the Internet of Things (IoT). Recently, diverse Machine Learning (ML) and Deep Learning (DL) methods were proposed to detect botnet attacks in IoT networks. However, highly imbalanced network traffic data in the training set often degrade the classification performance of state-of-the-art ML and DL models, especially in classes with relatively few samples. In this paper, we propose an efficient DL-based botnet attack detection algorithm that can handle highly imbalanced network traffic data. Specifically, Synthetic Minority Oversampling Technique (SMOTE) generates additional minority samples to achieve class balance, while Deep Recurrent Neural Network (DRNN) learns hierarchical feature representations from the balanced network traffic data to perform discriminative classification. We develop DRNN and SMOTE-DRNN models with the Bot-IoT dataset, and the simulation results show that high-class imbalance in the training data adversely affects the precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), geometric mean (GM) and Matthews correlation coefficient (MCC) of the DRNN model. On the other hand, the SMOTE-DRNN model achieved better classification performance with 99.50% precision, 99.75% recall, 99.62% F1 score, 99.87% AUC, 99.74% GM and 99.62% MCC. Additionally, the SMOTE-DRNN model outperformed state-of-the-art ML and DL models.

In this paper, an optimal model is developed for path loss predictions using the Feed-Forward Neural Network (FFNN) algorithm. Drive test measurements were carried out in Canaanland Ota, Nigeria and Ilorin, Nigeria to obtain path loss data at varying distances from 11 different 1,800 MHz base station transmitters. Single-layered FFNNs were trained with normalized terrain profile data (longitude, latitude, elevation, altitude, clutter height) and normalized distances to produce the corresponding path loss values based on the Levenberg–Marquardt algorithm. The number of neurons in the hidden layer was varied (1–50) to determine the Artificial Neural Network (ANN) model with the best prediction accuracy. The performance of the ANN models was evaluated based on different metrics: Mean Absolute error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), standard deviation, and regression coefficient (R). Results of the machine learning processes show that the FNN architecture adopting a tangent activation function and 48 hidden neurons produced the least prediction error, with MAE, MSE, RMSE, standard deviation, and R values of 4.21 dB, 30.99 dB, 5.56 dB, 5.56 dB, and 0.89, respectively. Regarding generalization ability, the predictions of the optimal ANN model yielded MAE, MSE, RMSE, standard deviation, and R values of 4.74 dB, 39.38 dB, 6.27 dB, 6.27 dB, and 0.86, respectively, when tested with new data not previously included in the training process. Compared to the Hata, COST 231, ECC-33, and Egli models, the developed ANN model performed better in terms of prediction accuracy and generalization ability.

Image retrieval via traditional Content-Based Image Retrieval (CBIR) often incurs the semantic gap problem—non-correlation of image retrieval results with human semantic interpretation of images. In this paper, Relevance Feedback (RF) mechanism was incorporated into a traditional Query by Visual Example CBIR (QVER) system. The inherent curse of dimensionality associated with RF mechanism was catered for by performing feature selection using Principal Component Analysis (PCA). The amount of feature dimension retained was determined based on a not more than 5% loss constrain imposed on average precision of retrieval result. While the asymmetry and small sample size nature of the resultant image dataset informed the use of a modified One-Class Support Vector Machine (OC-SVM) classifier, three image databases (DB10, DB20 and DB100) were used to test the OC-SVM RF mechanism. Across DB10, DB20 and DB100, Average Indexing Time of 0.451, 0.3017, and 0.0904s were recorded, respectively. For a critical recall value of 0.3, precision values for QVER were 0.7881, 0.7200 and 0.9112, while OC-SVM RF yielded precision of 0.8908, 0.8409, and 0.9503, respectively. Also, the use of PCA yielded tolerable degradation of 3.54, 4.39 and 7.40% in precision on DB10, DB20, and DB100, respectively, with 80% reduction in feature dimension. The OC-SVM RF increased the precision and invariably the reliability of the CBIR system by ranking most of the relevant images higher. Also, the target class was identified faster than the conventional method, thereby reducing the image retrieval time of the OC-SVM RF.