Development of a Feature Extraction Technique for Online Character Recognition System

Character recognition has been a popular research area for many years because of its various application potentials. Some of its application areas are postal automation, bank cheque processing, automatic data entry, signature verification and so on. Nevertheless, recognition of handwritten characters is a problem that is currently gathering a lot of attention. It has become a difficult problem because of the high variability and ambiguity in the character shapes written by individuals. A lot of researchers have proposed many approaches to solve this complex problem but none has been able to solve the problem completely in all settings. Some of the problems encountered by researchers include selection of efficient feature extraction method, long network training time, long recognition time and low recognition accuracy. This paper developed a feature extraction technique for online character recognition system using hybrid of geometrical and statistical features. Thus, through the integration of geometrical and statistical features, insights were gained into new character properties, since these types of features were considered to be complementary. Keywords: Character recognition, Feature extraction, Geometrical Feature, Statistical Feature, Character

Comparison of Machine Learning Classifiers for Recognition of Online and Offline Handwritten Digits*

Automated recognition of handwritten digits has applications in several industries such as Postal and Banking for reading of addressed packages and cheques respectively. This paper compares four machine learning classifiers namely Naive Bayes, Instance Based Learner, Decision Tree and Neural Network for single digit recognition. Our experiments were conducted using the WEKA machine learning tool on two datasets; the MNIST offline handwritten digits and a collection of online ISGL handwritten digits acquired with a pen digitiser. Experiments were designed to allow for comparison within the datasets in a cross validation and across them where the online dataset is used for training and the offline dataset for testing and vice versa. We also compared classification accuracy at different levels of down sampling. Results indicate that the lazy learning instance based classifier performed slightly better than the neural network with a maximal accuracy of 97.86% and they both outperformed the other two classifiers: Naive Bayes and Decision Tree. The decision tree gave the worst performance of the four classifiers. We also discovered that better results were obtained with using the online digits when tested in a cross validation experiment. However, the pre-processed MNIST offline digits gave higher accuracies when used for training and tested with the online ISGL digits not vice versa. Also, we discovered down sampled size of 14x14 gave the best results for most of the four classifiers although these were not significantly different from the other down sampled sizes of 7x7, 21x21 and 28x28. We intend to investigate the performance of these classifiers in recognition of other characters (alphabets, punctuation and other symbols) as well as extend the recognition task to other levels of text granularity such as words, sentences and paragraphs. Keywords: Digits recognition, machine learning, classifiers, handwritten character recognition, Wek

Comparative analysis of features extraction techniques for black face age estimation

A computer-based age estimation is a technique that predicts an individual's age based on visual traits derived by analyzing a 2D picture of the individual's face. Age estimation is critical for access control, e-government, and effective human–computer interaction. The other-race effect has the potential to cause techniques designed for white faces to underperform when used in a region with black faces. The outcome is the consequence of intermittent training with faces of the same race and the encoding structure of the trained face images, which is based on the feature extraction technique used. This study contributes to a constructive comparison of three feature-extraction techniques, namely, local binary pattern (LBP), Gabor Wavelet (GW), and wavelet transformation, used in the development of a genetic algorithm (GA)- artificial neural network (ANN)-based age estimation system. The feature extraction techniques used are proven to produce a wealth of shape and textural information. The GA-ANN constitutes the age classifier module. The correct classification rate was chosen as the performance metrics in this study. The results demonstrated that the LBP is a more robust representation of the black face than the GW and Wavelet transformations, as evidenced by its accuracy rate of 91.76 compared to 89.41 and 84.71 achieved with the GW and Wavelet transformation age estimation systems, respectively

Performance Evaluation of a Developed Fuzzy-Based Model for

Soil degradation is a phenomenon that has always had an adverse effect on productivity of soil. It occurs when soil loses its quality as a result of human activities resulting from improper use usually for agricultural, industrial or urban purposes. Right from the beginning of human existence, soil has played a major part in human survival by being the backbone of Agriculture. But over the years, man’s activities on the soil such as farming, use of fertilizers, deforestation, bush burning, etc. have all had adverse effect on the soil. Erosion has invariably led to degradation of the soil nutrients hence a necessity to monitor the rate and state of soil’s degradation in order to take adequate measures it. In order to achieve this, fuzzy model was used to predict the degradation after some factors have been quantified. Fuzzy model as an artificial intelligence technique has proven to be useful approach for addressing problems associated with simulating complex processes and environment in variety of Earth science disciplines. The model used was Fuzzy Based Dynamic Soil Erosion Model (FuDSEM). The model was used with different parameters and data to help its predictive ability. The results obtained from the output using the FuDSEM model shows that the area has low runoff potential. The results show that Fuzzy Logic model is reasonably accurate in predicting reliability of farm tractors. The fuel system was observed to be the most reliable of the tractor systems

A Review of the Underlying Concepts of Electronic Voting

Elections and voting are fundamental to any consensus-based society. They are one of the most critical functions of democracy. There are a number of voting systems adopted all over the world with each of them having its peculiar problems. The manual voting system still appears prominent among the developed and developing nations, but with considerations being given to an electronic alternative with a view to showing most of the short comings. Furthermore, with the increased interest and attention on e-government, e-democracy and e-governance, e-voting initiatives have gained more significance. Thus, many countries are piloting with various e-voting models and systems in order to enable voting from anywhere; also, international organisations are developing standards and recommendations in this area. This paper details a review of the underlying concepts of e-voting and discusses some of the salient issues on the subject. Also, a review of common e-voting models, existing elections schemes and explanation of the usual terminologies associated with e-voting were presented. KEYWORDS: voting, election, democracy, e-voting, cryptograph

Development of a Timed Coloured Petri Net Model for Time-of-Day Signal Timing Plan Transitions

In many countries, traffic signal control is one of the most cost effective means of improving urban mobility. Nevertheless, the signal control can be grouped into two principal classes, namely traffic-response and fixed-time. Precisely, a traffic response signal controller changes timing plan in real time according to traffic conditions while a fixed-time signal controller deploys multiple signal timing plans to cater for traffic demand changes during a day. To handle different traffic scenarios via fixed-time signal controls, traffic engineers determine such time-of-day intervals manually using one or two days worth of traffic data. That is, owing to significant variation in traffic volumes, the efficient use of fixed-time signal controllers depends primarily on selecting a number of signal timing plans within a day. In this paper, a Timed Coloured Petri Net (TCPN) formalism was explored to model transition between four signal timing plans of a traffic light control system such that a morning peak signal timing plan handles traffic demand between the hours of 6:00 am and 8:30 am, followed by afternoon I and afternoon II signal timing plans which handle traffic demands from 8:30 am to 3:00 pm and from 3:00 pm to 7:00 pm respectively, while the off peak plan handles traffic demands from 7:00 pm to 9:00 pm. Other hours of the day are ignored since they are characterized by low traffic demands. Keywords: Signal timing plan, Petri nets, Time-of-day, Model, Traffic, Fixed-time

Modelling of a Sequential Low-level Language Program Using Petri Nets

Petri nets were devised for use in the modelling of a specific class of problems. Typical situations that can be modelled by Petri nets are synchronization, sequentiality, concurrency and conflict. This paper focuses on a low-level language program representation by means of Petri nets. In particular, Petri net formalisms were explored with emphasis on the application of the methodology in the modelling of a sequential low-level language program using a Motorola MC68000 assembly language program as an example. In the Petri net representation of the sequential low-level language program under consideration, tokens denote the values of immediate data as well as availability of the data. Thus, the developed petri net model shows that Petri net formalism can be conveniently used to represent flows of control and not flows of data. Keywords: Petri nets, model, low-level language, microprocessor, instructions

Review of Livestock Feed Formulation Techniques

This paper reviews animal feed formulation methods, the conventional methods and intelligent system method. Highlighting their cons and pros. The intelligent system method (neuro-fuzzy) incorporated fuzzy conjunctive into levenberge training of artificial neural network. The neuro-fuzzy system was trained with dataset and validated using Amino acid elements of chicks feed. With 0.05 level of significance on NCCS 2000 platforms, output of the neuro-fuzzy system produced a correlation coefficient of 0.888608 and p-value of 0.97. Intelligent system can be employed to increase productivity in the field of animal feed formulation. Keywords: animal feed formulation, linear programming, neuro-fuzzy, ration

A Survey of Cryptographic and Stegano-Cryptographic Models for Secure Electronic Voting System

The success rate of an electronic voting system in electronic decision making is dependent on security, authenticity and integrity of pre-electoral, electoral and post electoral phases of the electioneering process. Various Information Security and Privacy Technologies including steganography, cryptography, and combination of both as well as watermarking have been formulated in literatures to make e-democratic decision through e-voting systems to be fair and credible. In this paper, we present a survey of existing cryptographic and stegano-cryptographic schemes in securing e-voting systems. We established critical security requirements for secure e-voting systems for all phases of the electioneering processes, formulated an adaptable conceptual framework for secure e-voting systems for developing countries and proposed a multilayer, multi-domain and multimedia model for electronic voting system for the delivery of fair, transparent, better participatory and credible elections in future e-democratic dispensation in developing countries with significant digital divides.Keywords: Authentication, E-Voting, Confidentiality, Cryptography, Security, Steganography, Image