Template Matching Based Sign Language Recognition System for Android Devices

An android based sign language recognition system for selected English vocabularies was developed with the explicit objective to examine the specific characteristics that are responsible for gestures recognition. Also, a recognition model for the process was designed, implemented, and evaluated on 230 samples of hand gestures.  The collected samples were pre-processed and rescaled from 3024 ×4032 pixels to 245 ×350 pixels. The samples were examined for the specific characteristics using Oriented FAST and Rotated BRIEF, and the Principal Component Analysis used for feature extraction. The model was implemented in Android Studio using the template matching algorithm as its classifier. The performance of the system was evaluated using precision, recall, and accuracy as metrics. It was observed that the system obtained an average classification rate of 87%, an average precision value of 88% and 91% for the average recall rate on the test data of hand gestures.  The study, therefore, has successfully classified hand gestures for selected English vocabularies. The developed system will enhance the communication skills between hearing and hearing-impaired people, and also aid their teaching and learning processes. Future work include exploring state-of-the-art machining learning techniques such Generative Adversarial Networks (GANs) for large dataset to improve the accuracy of results. Keywords— Feature extraction; Gestures Recognition; Sign Language; Vocabulary, Android device

Parametric Modelling of Impact of Unemployment on Economic Growth in Nigeria

The aim of this study is to examine the relationship between unemployment figures in Nigeria and gross domestic product using curve fitting regression model. The following models of regression analysis were examined to determine the most suitable one to the data collected: linear, quadratic, cubic, exponential, inverse and logarithmic. Our findings revealed that the pattern of relationship between gross domestic product and unemployment is significant in all the models considered. This implies that there is significant relationship (negative or positive) between unemployment and gross domestic prodution (GDP) in the country. Also, the best model that fits the data in terms of the relationship between the two variables is the linear model because the quadratic variable is not significant in the quadratic model. The order of the next models are logarithmic, inverse and exponential models; the cubic variable and the quadratic variable are not also significant in the cubic model. Keywords: Unemployment, Domestic, linear, inverse, logarithmic, exponentia

Analysis of Independence of Available Infrastructure on Geographical Location of Public Secondary Schools in Ondo State, Nigeria

This paper has attempted to study the availability of infrastructural facilities in public secondary schools in Ondo State, Nigeria. Six local government areas (LGAs) were randomly selected with due consideration to the geo-political set-up of the State. Quantitative methods of analysis that were used include both descriptive and inferential statistics. The descriptive statistical techniques were tabular presentation and bar chart while the inferential statistical technique applied is chi-square analysis. The inferential statistics revealed that enrollment of students in secondary schools depends on gender and on Local Government Area. Also, the level of equipping of laboratories, availability of information technology facilities,  portable water supply and supply of electricity depend on Local Government Area while availability of library in secondary schools is independent of Local Government Area. Conclusively, much still need to be done in the area of infrastructural development in public primary and secondary schools in Ondo State towards the achievement of the vision 20: 2020 goal of Nigeria as one of the 20 most developed economy of the world in terms of education. Keywords:  education, infrastructure, information technology, chi-square, enrollment

DEVELOPMENT OF A CLOUD BASED STUDENT INFORMATION CHATBOT SYSTEM

The development of chatbot system is an algorithm that analyzes the student queries and reply messages. In this system, artificial intelligence is built to answer the query of the student. The specific objectives are to determine the required features for the construction of knowledge base, design and implement the model, evaluate the performance of the developed system. Samples of Frequently Asked Questions (FAQ) was collected from the department of Student Affairs, Admission Office and Information Management and Technology Center (IMTC) of the university. The collected sample was analyzed based on the category of question and the model was designed using Unified Modeling Language (UML). The model was implemented with python programming language, HTML, CSS, JavaScript for the client sever side, and also Artificial Intelligence Markup Language (AIML) () and MySQL for the back end. The developed system performance was evaluated using Alpha Beta testing. The proposed system was successfully tested to denote its effectiveness and achievability. It totally eliminates the manual process of retrieving information about a particular domain and reduces manpower, time, for any individual. The developed system will provide adequate assistance to the student on FAQ, thereby reducing the time in visiting the college to enquire about the information in respect of school activities. It will also provide an enabling environment for the students to keep them updated about the school activities

Automated classification of African embroidery patterns using cellular learning automata and support vector machines

Embroidery is the art that is majorly practised in Nigeria, which requires creativity and skills. However, differentiating between two standard embroidery patterns pose challenges to wearers of the patterns. This study developed a classification system to improve the embroiderer to user relationship. The specific characteristics are used as feature sets to classify two common African embroidery patterns (handmade and tinko) are shape, brightness, thickness and colour. The system developed and simulated in MATLAB 2016a environment employed Cellular Learning Automata (CLA) and Support Vector Machine (SVM) as its classifier. The classification performance of the proposed system was evaluated using precision, recall, and accuracy. The system obtained an average precision of 0.93, average recall of 0.81, and average accuracy of 0.97 in classifying the handmade and tinko embroidery patterns considered in this study. This study also presented an experimental result of three validation models for training and testing the dataset used in this study. The model developed an improved and refined embroiderer for eliminating stress related to the manual pattern identification process

DESIGN AND SIMULATION OF AN EFFICIENT MODEL FOR CREDIT CARDS FRAUD DETECTION

In this study a model which can improve the accuracy and reliability of credit card fraud detection was proposed. This is with a few to mitigating contentious issues regarding online transaction of credit card, such as  amount of transactions that have resulted in payment default and the number of credit card fraud cases that have been recorded, all of which have put the economy in jeopardy.   To address this challenge,sample dataset was sourced from online repository database of Kaggle. The feature extraction on the data was performed using Principal Component Analysis (PCA). The credit card fraud detection model was designed using Neuro-fuzzy logic technique, clustering was done using Hierarchical Density Based Spatial Clustering of Application with Noise (HDBSCAN) .The simulation of the proposed model was done in Python programming environment.The performance evaluation of the model was carried out by comparing the proposed model with Neuro-Fuzzy (NF) technique using performance metrics such as precision, recall, F1-score and accuracy.  The simulation result showed that the proposed model (NF + HDBSCAN) had precision of 98.75%, recall of 98.70%, F1-Score of 97.65% and accuracy 99.75% . NF had Precision of 94.60%, recall of 94.50%, F1-Score of 95.50% and accuracy 95.70% using training dataset. Likewise, when test dataset were used, the proposed (NF + HDBSCAN) had precision of 93.50%, recall of 95.50%, F1-Score of 94.50% and accuracy 95.50%. NF had Precision of 92.50%, recall of 93.00%, F1-Score of 94.00% and accuracy 93.50%.  The simulation results of the proposed model was viable, reliable and showed possibility of being designed as module which could be  integrated into the existing credit card design for lowering fraud rate and assisting fraud investigators

Response of Guinea Grass (\u3cem\u3ePanicum maximum\u3c/em\u3e Jacq) to Application of Cow Dung in South West Nigeria

The yield of forage species from the world\u27s grazing land is limited by poor soil and unproductive species (Jones & Wild, 1975; Cooke, 1982). The use of manure on pasture land not only represents a low cost disposal method but also a means of recycling nutrients for plant growth and counteracting the decreasing organic matter content in most agricultural soils. In agro-pastoral production systems, the interaction between crops and livestock is important. Manures are used mainly to complement inorganic fertiliser in the production of food crops. There is a dearth of information on the response of pasture grass to application of cow dung and so the response of two Panicum maximum ecotypes to cow dung was evaluate

OPTIMIZATION OF PROCESS PARAMETERS FOR NATURAL GAS SWEETENING USING ASPEN HYSYS AND RESPONSE SURFACE METHODOLOGY

Amines process remains the most economic and efficient technology available today for the removal of acid gases. However, the costs associated with pumping higher flow rates and cost of increased energy needs for the regeneration of solution can undermine the economic feasibility of the project portfolio. The absorber pressure, the composition and temperature at which the sour gas is available dictates sour gas and amine flow rates needed to meet the sweet gas specifications. To improve absorber capacity for a given sweet gas specification, optimization are commonly carried out using spread sheet with an adjust function to manipulate sour gas flow rate for fixed amine flow rate. This study performed optimization using response surface methodology on a simulated and calibrated amine plant. The result shows that the optimum operating conditions for 96% CO2 removal were temperature of 30 oC, mass flow rate of 868.75 kg/hr and 20 numbers of plates. In conclusion, using experimental design and response surface methodology the parameter manipulation for attainment of optimal conditions is overcome

A novel design of multi-epitope based vaccine against Escherichia coli

Background: Multi-valent based vaccines have advantage over conventional vaccines because of its multi-faceted action targeted at antigen; thereby raising hope of a more sustained actions against allergens. Escherichia coli (E. coli) is a bacterium that is commonly found in the gut of humans and warm-blooded animals. An increasing number of outbreaks are associated with the consumption of fruits and vegetables (including sprouts, spinach, lettuce, coleslaw, and salad) thereby contamination may be due to contact with faeces from domestic or wild animals at some stages during cultivation or handling. Due to the reported increase in resistance to antibiotics used for Escherichia coli control; an effective vaccine is a would-be alternative of proven interest. Hence, a need for a rational, strategic, and efficient vaccine candidate against E.coli is of paramount necessity by the use of the most current bioinformatics tools to achieve this task. Method: In this study, immunoinformatics tools mined from diverse molecular databases were used  for a novel putative epitope based oral vaccine against E.coli. The prospective vaccine proteins were carefully screened and validated to achieve a high thorough-put three-dimensional protein structure. The eventual propsective vaccine candidate proteins was evaluated for its non-allergenicity, antigenicity, solubility, appropriate molecular weight testing and isoelectric point evaluation. Conclusion: The resultant vaccine candidate could serve as a promising anti-E.coli vaccine candidate. Immunoinformatics is a new field over pharmaco-therapeutics; this newest technology should continue to be a rescue from age-long traditional approach in vaccine developments

Utilization of insecticide treated nets during pregnancy among postpartum women in Ibadan, Nigeria: a cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Pregnant women are susceptible to symptomatic malaria due to invasion of the placenta by plasmodium. Malaria increases the risk of adverse pregnancy outcomes for mothers, the foetuses and newborns. The effective use of Insecticide Treated Nets (ITNs) would be of benefit to these vulnerable women. Previous studies have focused on prenatal-women but this study sought to explore the actual trend of utilization of the proven strategy across all the pregnancy stages among postpartum women in Ibadan.</p> <p>Methods</p> <p>This cross-sectional survey utilized a validated structured questionnaire for data collection. A calculated sample of 335 postpartum women was proportionately recruited from three fee-paying facilities within Ibadan, Nigeria using a simple random sampling technique. These hospitals have high client flow for maternity cases and are known for provision of care under traditional ANC model. The data collected were analyzed using descriptive and inferential statistics by means of Statistical Package of Social Sciences (SPSS) version 15. The level of significance was set at = 0.05.</p> <p>Results</p> <p>The women's age ranged between 18 and 47 years, mean age was 29.4 ± 0.8 years. Various irregularities marked the traditional model of ANC provided at the settings and no exposure to preconception care. Also, 276 (82.4%) had heard of ITNs. Antenatal clinics formed the major source of information. Low utilization and compliance rates were observed. One hundred and twenty-seven (37.9%) of the women had high knowledge of Malaria in Pregnancy (MIP) but only 70 (20.9%) demonstrated positive attitude towards the use of ITNs. Participants' educational status, family types, employment and residential areas significantly influenced ITNs utilization.</p> <p>Conclusions</p> <p>The women knew and learned about ITNs from ANC visits. Majority of the women did not own ITNs because of lack of access to free distribution. The existing traditional model of ANC was marked by irregularities and none of the women was exposed to preconception care. In addition, negative attitude in spite of increased knowledge of MIP was observed among the women. Therefore, evaluation of free distribution of ITNs is recommended. Integration of focused ANC and preconception care are advocated to promote early access to health information.</p