Accreditation and Quality Assurance in Nigerian Universities

This paper examines the impact of accreditation on quality assurance in Nigerian universities. Descriptive survey design was used for the study. A sample of 74 universities out of 124 universities in Nigeria presently (22 federal owned and 22 state owned, and 30 private universities} was drawn using proportionate stratified random sampling technique. Also, simple random sampling was used to select 20 respondents (including teaching and non-teaching staff, who are in charge of the data needed for the study) were selected from each university, which amounted to 1480 staff. The study developed and used two sets of questionnaire tagged “Accreditation Procedures and Minimum Academic Standard Questionnaire (APMASQ), and Quality Assurance Questionnaire (QAQ)” with correlation coefficient (r) of 0.73 and 0.69 respectively and complimented with secondary data from NUC records. Pearson product moment correlation coefficient (r) was employed to analyse the data. While the null hypotheses developed for the study were tested at .05 level of significance. The findings revealed that there is significant relationship between accreditation and resource input into Nigerian universities, quality of output, quality of process, and no significant relationship between accreditation and quality of academic content. It is therefore recommended among others that human capacities should be built in the area of quality assurance so as to ensuring quality in Nigerian universities. Also, accreditation exercises and conducts should be properly manipulated and supervised without playing politics so as to achieve education standards, quality and effectiveness for purpose of accomplishing goals of university education in Nigeria. Key words: Quality assurance, Quality, University education,    Accreditation, Standards, Quality improvement

Facial Image Verification and Quality Assessment System -FaceIVQA

Although several techniques have been proposed for predicting biometric system performance using quality values, many of the research works were based on no-reference assessment technique using a single quality attribute measured directly from the data. These techniques have proved to be inappropriate for facial verification scenarios and inefficient because no single quality attribute can sufficient measure the quality of a facial image. In this research work, a facial image verification and quality assessment framework (FaceIVQA) was developed. Different algorithms and methods were implemented in FaceIVQA to extract the faceness, pose, illumination, contrast and similarity quality attributes using an objective full-reference image quality assessment approach. Structured image verification experiments were conducted on the surveillance camera (SCface) database to collect individual quality scores and algorithm matching scores from FaceIVQA using three recognition algorithms namely principal component analysis (PCA), linear discriminant analysis (LDA) and a commercial recognition SDK. FaceIVQA produced accurate and consistent facial image assessment data. The Result shows that it accurately assigns quality scores to probe image samples. The resulting quality score can be assigned to images captured for enrolment or recognition and can be used as an input to quality-driven biometric fusion systems.DOI:http://dx.doi.org/10.11591/ijece.v3i6.503

PREDICTING SOCIAL NETWORK ADDICTION USING VARIANT SIGMOID TRANSFER FEED-FORWARD NEURAL NETWORKS (FNN-SNA)

Researchers have reflected on personal traits that may predict Social Networking Sites (SNS) addiction. However, most of the researchers involved in the findings of personality traits predictor for social networking addiction either postulate or based their conclusions on analytical tools. Moreso, a review of the literature reveals that the prediction of social networking addiction using classifiers have not been well researched. We examined the prediction of SNS addiction from a well-structured questionnaire consisting of sixteen (16) personality traits. The questionnaire was administered on the google form with a response rate of 95% out of the 102-sample size. Additionally, a three (3) variant sigmoid transfer feed- forward neural networks was developed for the prediction of SNS addiction. Result indicated that pertinence (β = 0.251, p  0.01) was the most powerful predictor of social networking addiction in general and less obscurity addiction (β = 0.244, p  0.01). Experimental results also showed that the developed classifier correctly predict SNS addiction with 98% accuracy compared to similar classifiers.     &nbsp

Effects of mothers' socio-economic status on the management of febrile conditions in their under five children in a resource limited setting

BACKGROUND: Public health research is shifting focus to the role of socioeconomic indicators in the promotion of health. As such an understanding of the roles that socio-economic factors play in improving health and health-seeking behaviour is important for public health policy. This is because the share of resources devoted to different policy options should depend on their relative effectiveness. OBJECTIVE: To measure the effect of socio-economic status (age, education, occupation, income, religion and family structure) of mothers on the management of febrile conditions in under-fives children METHOD: Two hundred mothers who brought their febrile under-five children to a health facility were interviewed on the treatment they gave to their children before reporting at health facility. Data collected were entered and analyzed using the SPSS software. Binary logistic regression was adopted for the quantitative analysis of the effect of socio-economic variables on the mothers' actions prior to utilizing the health facility. RESULTS: Results showed that while mothers' age was negatively correlated (-0.13), occupation was positively correlated (0.17) with under-fives mothers' action. Education, religion, income and family structure were however insignificant at 5% level CONCLUSION: This poses a lot of challenges to policy makers in the developing nations where women's education and earning capacity is low. There is therefore a need to increase the number of women benefiting from micro credit. This will ensure that more women are engaged in a form of occupation that is profitable and can sustain the economic and health needs of the family

Exploiting ICT For Accelerated Development In Tanzania

ICT plays a great role in nation building today. Its relevance in rural development cannot be overemphasized, owing to it's immerse contributions through the use of services made possible by new technological developments. A comprehensive analysis on the status of ICT in Tanzania was carried out; this analysis was done to ascertain the depth to which ICT has gone in the country. Factors hindering a progressive ICT in Tanzania were also studied, analyzed so as to proffer concrete solutions to them. Also highlighted in this research paper are the potential uses of ICT coupled with the role of ICT in human development and reduction of poverty which is the focal point of the research work. It was discovered at the end of the research, that though Tanzania has embraced ICT in most areas, some other areas still needs attention. The paper highlights among others the need for proper awareness on the relevant of ICT to human and economic development and also . ~he need for manpower development to ensure maximum exploitation of ICT in Tanzania

Performance analysis of grid-tied photovoltaic system under varying weather condition and load

Model and simulation of the impact of the distribution grid-tied photovoltaic (PV) system feeding a variable load with its control system have been investigated in this study. Incremental Conductance (IncCond) algorithm based on maximum power point tracking (MPPT) was implemented for the PV system to extract maximum power under different weather conditions when solar irradiation varies between 250W/m2 and 1000W/m2. The proposed system is modelled and simulated with MATLAB/Simulink tools. Under different weather conditions, the dynamic performance of the PV system is evaluated. The results obtained show the efficacy of the proposed MPPT method in response to rapid daytime weather variations. The results also show that the surplus power generated is injected into the grid when the injected power from the PV system is higher than the load demand; otherwise, the grid supplies the load

BLACKFACE SURVEILLANCE CAMERA DATABASE FOR EVALUATING FACE RECOGNITION IN LOW QUALITY SCENARIOS

Many face recognition algorithms perform poorly in real life surveillance scenarios because they were tested with datasets that are already biased with high quality images and certain ethnic or racial types. In this paper a black face surveillance camera (BFSC) database was described, which was collected from four low quality cameras and a professional camera. There were fifty (50) random volunteers and 2,850 images were collected for the frontal mugshot, surveillance (visible light), surveillance (IR night vision), and pose variations datasets, respectively. Images were taken at distance 3.4, 2.4, and 1.4 metres from the camera, while the pose variation images were taken at nine distinct pose angles with an increment of 22.5 degrees to the left and right of the subject. Three Face Recognition Algorithms (FRA), a commercially available Luxand SDK, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were evaluated for performance comparison in low quality scenarios. Results obtained show that camera quality (resolution), face-to-camera distance, average recognition time, lighting conditions and pose variations all affect the performance of FRAs. Luxand SDK, PCA and LDA returned an overall accuracy of 97.5%, 93.8% and 92.9% after categorizing the BFSC images into excellent, good and acceptable quality scales.

Indoor environmental conditions of selected shopping malls in Nigeria: A comparative study of microclimatic conditions, noise levels, and microbial burdens

oai:repository.uel.ac.uk:8x3x4The activities of people and equipment used within shopping malls are major factors that contribute to air pollution and increased sound levels, thereby affecting indoor environmental quality and the well-being of mall operators. This study assessed indoor environmental quality through microbial characterization and measurement of environmental conditions present in selected shopping malls. Investigations were conducted at three shopping malls in Ibadan selected through convenience sampling technique. Environmental parameters such as noise level, relative humidity, temperature, PM₂.₅ levels, total volatile organic compound (TVOC) levels, microbial characterization, and quantity were determined. Microclimatic parameters (temperature and relative humidity) were measured using a 4-in-1 Precision Gold N09AQ multi-tester. Culturable airborne microbes were collected using the settle plate technique. PM₂.₅ and TVOC levels were measured using a Thermo Scientific MIE pDR-1500 PM monitor and sf200-TVOC meter respectively. Two bacteria species and five fungi species were isolated across the malls. The noise levels ranged from 61.27 to 81.20 dB. The mean temperatures (highest mean of 33.44 ± 1.42 °C), PM₂.₅ (highest mean of 114.06 ± 25.64 μg/m³), and TVOC (highest mean of 55.21 ± 8.28 ppm) concentrations were higher than the permissible limits stipulated by the WHO guidelines and NESREA standard limits across all the selected malls. A positive correlation was found to exist between particulate matter and TVOC (r = 0.174, p = 0.004). The total bacteria count was generally high with the highest mean of 1965.33 ± 368.56 CFU/m³, while the total fungi count was generally low with the highest mean of 579.82 ± 51.55 CFU/m³. Bacillus spp. and Candida spp. were found to the consistent from all sample points across the three malls. The bacteria isolated are Gram-positive bacteria associated with human skin which suggests a high rate of indoor pollution from humans. In conclusion, this research has demonstrated the necessity to monitor noise levels and indoor air quality in malls. Also, there is need for government policies to improve indoor air quality which must be enforced and regulated, especially within shopping malls

Workability and rheological properties of EVA-modified bitumen compared with PG 76 binder

The failures of the flexible pavements are not only caused by harsh climatic conditions prevailing in most of the tropical countries but also due to increase in traffic. The ethylene vinyl acetate (EVA) modification of the bitumen can strengthen the properties of binders and also improve the quality of bitumen used for pavements construction. This paper reports the changes in physical and rheological properties of unaged 80-100 grade bitumen modified with different percentages of EVA and compared with the properties of PG 76 binder. The penetration, softening point and viscosity properties were studied. The rheological properties were measured using dynamic shear rheometer and the test was performed at temperatures ranging from 46 to 76 ⁰C at intervals of 6 ⁰C. It was noted that, after modification, the properties of binders had improved. The results show that 5% EVA content by weight in modified binder is adequate in terms of physical and rheological properties studied. In addition, the properties of 5% EVA modified 80-100 grade bitumen are similar to PG 76 binder

DeepCOVID-19: A model for identification of COVID-19 virus sequences with genomic signal processing and deep learning

The spread of Coronavirus Disease-2019 worldwide necessitates the development of accurate identification methods and the determination of genetic relatedness. The result of genomic methods involving nucleotide alignment informed the considerations of several alignment-free techniques for virus detection. This paper presents a genomic sequence identification model, developed based on Genomic Signal Processing (GSP), deep learning, and genomic datasets of Coronavirus 2 (SARS-CoV-2), Severe Acute Respiratory Syndrome CoV (SARS-CoV), and Middle East Respiratory Syndrome CoV (MERS-CoV). Our results showed that the Z-Curve images for the three viral strains depicted high visual similarities in texture and color, thus making it difficult to differentiate the strains by visual inspection. However, the homogeneity distance showed that SARS-CoV-2 is closer to SAR-CoV than MERS-CoV. Following a validation accuracy of 98.33%, it became clear that Z-Curve images for MERS-CoV, SARS-CoV and SARS-CoV-2 have distinct features after transformation by the Convolutional Neural Network (CNN) classifier. The divergence in texture and color reflects genetic variation among the strains, which is too insignificant for differentiation via visual inspection. Our results showed that higher layers of CNN amplify aspects of input images that are critical for discrimination, thereby confirming the importance of deep learning and GSP in accurate viral detectio