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Agent-Based Faults Monitoring in Automatic Teller Machines
(African Institute of Development Informatics and Policy - Computing, Information Systems and Development Informatics Journal, 3(4), 1 – 6, 2012) Bashir, S.A.; Mohammed, I.K.; Abdulsalam, S.O.
Automated Teller Machine (ATM) has gained widespread acceptance as a convenient medium to facilitate financial transaction without need for human agent. However, ATM deployers are facing challenges in maximizing the uptime of their ATMs as a result of wide gap in fault detection, notification and correction of the ATMs. One way to ameliorate this situation is through intelligent monitoring of ATM by resident software agents that monitor the device real time and report faulty components real time to facilitate quick response. We proposed an architecture for rule-based, intelligent agent based monitoring and management of ATMs. Agents are used to perform remote monitoring on the ATMs and control function such software maintenance. Such agents can detect basic events or correlate existing events that are stored in a database to detect faults. A system administrator can securely modify the monitoring policies and control functions of agents. The framework presented here includes software fault monitor, hardware fault monitor and transaction monitor. A set of utility support agents: caller agent and log agent are used to alert network operator and log error and transaction information in a database respectively. at-1, stuck-at-0 faults in digital circuits validate the point that faulty circuits dissipates more and hence draw more power. Key words: Automated Teller Machine (ATM), Intelligent Agents, Mobile Agents, Event Monitoring
Data Mining for Knowledge Discovery from Financial Institution Database
(Society for Science and Nature - International Journal of Engineering and Management Sciences. 3(4), 409 – 415, 2012) Abdulsalam, S.O.; Adewole, K.S.; Bashir, S.A.; Jimoh, R.G.; Olagunju, M.
Due to its importance for the investment decision-making and risk management, describing and predicting stock represents a key topic in stock analysis. Stock or shares are valued and analyzed by stock investors using fundamental analysis and technical analysis. Stock investors describe and predict stock manually based on individual experiences. Employing manual procedure in analyzing stock, most especially technical analysis is always very cumbersome and inefficient; because of much time that is consumed in examining the past records of a respective firm, an investor is willing to invest in. This paper presents a better way of describing and predicting stock, especially technical analysis, by employing data mining techniques. A database was developed employing 360 records of daily activity summary (equities) and 78 records of weekly activity summary (equities) spanning through 18 months that is, from January 2007 to June 2008. These data were obtained from the daily official list of the prices of all shares traded on the stock exchange published by the Nigerian Stock Exchange being the financial institution that runs Nigerian stock market; using banking sector of Nigerian economy with three banks namely:- First Bank of Nigeria Plc, Zenith Bank Plc, and Skye Bank Plc. A data mining software tool was developed and employed in identifying patterns and relationships from the database to generate new knowledge about the data set in the database through the use of data mining techniques that employ regression analysis. KEYWORDS: Data Mining, Stock Exchange, Financial Institution, Decision-Making, Risk Management, Regression Analysis
Predicting Nigeria Budget Allocation Using Regression Analysis: A Data Mining Approach
(Nigeria Computer Society (NCS), Lagos, Nigeria - The Journal of Computer Science and Its Application, An International Journal of the Nigeria Computer Society. 21(1), 73 – 82, 2014) Adewole, K.; Mabayoje, M.; Abdulsalam, S.; Ajao, J.
Budget is used by the Government as a guiding tool for planning and management of its resources to aid in effective decision-making. Data mining is one of the most vital areas of research with the objective of finding meaningful information from large datasets. The delay in the preparation of budget of the Federation by the Government has become incessant issue in the running of affairs of the country. This is evident in the delay in implementation of the previous budgets in the country; hence, the need for automated system to tackle the setback. In this paper, regression analysis which is one of the data mining techniques is employed to predict budget allocation from Nigeria budget dataset. 200 records consisting of the budget allocation summary for the year 2008, 2009, 2010, 2011, and 2012 across 40 data points containing Ministries, Departments, Commissions and Agencies (MDCAs) were used. A web-based data mining tool that employed linear regression to predict both Nigeria budget allocation across the 40 data points and the overall budget summary allocation of the Federation is proposed. The proposed data mining software predicted N1,803,196,024,657.40, N1,871,754,338,112.68 and N2,007,780,403,902.98 for the year 2013, 2014 and 2015 respectively. The tool is found capable of discovering interesting patterns in the data and for predicting budget allocation. Keywords: Budget, Data Mining, Dataset, Linear Regression, Prediction
Development of an Intrusion Detection System in a Computer Network
(Council for Innovative Research - International Journal of Computers and Technology, 12(5), 3479 – 3485, 2014) Babatunde, R.S.; Adewole, K.S.; Abdulsalam, S.O.; Isiaka, R.M.
The development of network technologies and application has promoted network attack both in number and severity. The last few years have seen a dramatic increase in the number of attacks, hence, intrusion detection has become the mainstream of information assurance. A computer network system should provide confidentiality, integrity and assurance against denial of service. While firewalls do provide some protection, they do not provide full protection. This is because not all access to the network occurs through the firewall. This is why firewalls need to be complemented by an intrusion detection system (IDS).An IDS does not usually take preventive measures when an attack is detected; it is a reactive rather than proactive agent. It plays the role of an informant rather than a police officer. In this research, an intrusion detection system that can be used to deny illegitimate access to some operations was developed. The IDS also controls the kind of operations performed by users (i.e. clients) on the network. However, unlike other methods, this requires no encryption or cryptographic processing on a per-packet basis. Instead, it scans the various messages sent on a network by the user. The system was developed using Microsoft Visual Basic. Indexing terms: Intrusion detection system, illegitimate, misuse, network.
Frequent Pattern and Association Rule Mining from Inventory Database Using Apriori Algorithm
(Institute of Electrical & Electronics Engineers (IEEE) - African Journal of Computing and ICT, A Journal of the Institute of Electrical & Electronics Engineers (IEEE) Computer Chapter Nigeria Section. 7(3), 35 – 42, 2014) Adewole, K.S.; Akintola, A.G.; Ajiboye, A.R.; Abdulsalam, S.O.
Recently, data mining has attracted a great deal of attention in the information industry and in a Society where data continue to grow on a daily basis. The availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge is the major focus of data mining. The information and knowledge obtained from large data can be used for applications ranging from market analysis, fraud detection, production control, customer retention, and science exploration. A record in such data typically consists of the transaction date and the items bought in the transaction. Successful organizations view such databases as important pieces of the marketing infrastructure. This paper considers the problem of mining association rules between items in a large database of sales transactions in order to understand customer-buying habits for the purpose of improving sales. Apriori algorithm was used for generating strong rules from inventory database. It was found that for a transactional database where many transaction items are repeated many times as a superset in that type of database, Apriori is suited for mining frequent itemsets. The algorithm was implemented using PHP, and MySQL database management system was used for storing the inventory data. The algorithm produces frequent itemsets completely and generates the accurate strong rules. Keywords: Apriori Algorithm, data mining, database, strong rules & inventory
Development of an Intrusion Detection System in a Computer Network.
(International Journal of Computers & Technology., 2014-03-14) Babatunde, R. S., Adewole K. S., Abdulsalam S. O. and Isiaka R. M. (2014)
The development of network technologies and application has promoted network attack both in number and severity. The last few years have seen a dramatic increase in the number of attacks, hence, intrusion detection has become the mainstream of information assurance. A computer network system should provide confidentiality, integrity and assurance against denial of service. While firewalls do provide some protection, they do not provide full protection. This is because not all access to the network occurs through the firewall. This is why firewalls need to be complemented by an intrusion detection system (IDS).An IDS does not usually take preventive measures when an attack is detected; it is a reactive rather than proactive agent. It plays the role of an informant rather than a police officer. In this research, an intrusion detection system that can be used to deny illegitimate access to some operations was developed. The IDS also controls the kind of operations performed by users (i.e. clients) on the network. However, unlike other methods, this requires no encryption or cryptographic processing on a per-packet basis. Instead, it scans the various messages sent on a network by the user. The system was developed using MicrosoftVisual Basic.
Development of Fingerprint Biometric Attendance System for Non-Academic Staff in a Tertiary Institution.
(Computer Engineering and Intelligent Systems., 2014-04-13) Adewole, K. S., Abdulsalam S. O., Babatunde R. S., Shittu T. M. and Oloyede M. O. (2014)
Institutions, companies and organisations where security and net productivity is vital, access to certain areas must be controlled and monitored through an automated system of attendance. Managing people is a difficult task for most of the organizations and maintaining the attendance record is an important factor in people management. When considering the academic institute, taking the attendance of non-academic staff on daily basis and maintaining the records is a major task. Manually taking attendance and maintaining it for a long time adds to the difficulty of this task as well as wastes a lot of time. For this reason, an efficient system is proposed in this paper to solve the problem of manual attendance. This system takes attendance electronically with the help of a fingerprint recognition system, and all the records are saved for subsequent operations. Staff biometric attendance system employs an automated system to calculate attendance of staff in an organization and do further calculations of monthly attendance summary in order to reduce human errors during calculations. In essence, the proposed system can be employed in curbing the problems of lateness, buddy punching and truancy in any institution, organization or establishment. The proposed system will also improve the productivity of any organization if properly implemented.
Enhanced Automated Teller Machine Using Short Service Authentication Service
(African Journal of Computing and ICTs, 2014-05-16) Jimoh, R. G. & Babatunde, A.N.
The use of Automated Teller Machine (ATM) has become an important tool among commercial banks, customers of banks have come to depend on and trust the ATM conveniently meet their banking needs. Although the overwhelming advantages of ATM cannot be over-emphasized, its alarming fraud rate has become a bottleneck in it’s full adoption in Nigeria. This study examined the menace of ATM in the society imposing additional security cost of running ATM services by banks in the country. The researchers developed a prototype of an enhanced Automated Teller Machine Authentication using Short Message Service (SMS) Verification. The usability of the developed prototype was examined using heuristic evaluation with the aid of a questionnaire designed based on validated software usability metrics. Responses from ten (10) respondents who are users of ATM cards in the country and the data collected were analyzed using Statistical Package for Social Science (SPSS). Based on the results of the analysis, it is revealed that the developed prototype will go a long way in reducing the alarming rate of ATM fraud in Nigeria. Keywords—ATM, ATM Fraud, E-banking, Prototyping.
An Appropriate Search Algorithm for Finding Grid Resources
(International Journal of Emerging Trends and Technology in Computer Science, 2014-06-16) Olusegun, O. A., Babatunde, A. N., Omotehinwa, T. O., Aremu, D. R. & Balogun, B. F.
Abstract: A grid is a collection of computing resources that perform tasks. Searching within a grid resource is very challenging considering the potential size of the grid and wide range of resources that are represented. The aim of this study is to find the search algorithm that is appropriate for a search problem in a grid environment. Breadth first search, Depth first search and Depth first iterative deepening search algorithms were studied and analyzed to find the one with minimal complexity. The algorithms were implemented using graph with a collection of nodes. They were analyzed based on their completeness and optimality in terms of running time. Octave was used as the analysis tool to measure the time complexity of these search algorithms. From the analysis of these algorithms, Depth First Iterative deepening search algorithm was adopted as a search strategy for grid resources because of its completeness and optimality in terms of running time. The research shows that Depth First Iterative Deepening search algorithm is asymptotically optimal in terms of cost of solution, running time, and space required for uninformed searches. Therefore, DFID search is the best search strategy suitable for grid resources among the three search strategy studied in this work. Keywords: Grid, Breadth first search, Depth first search, Depth first iterative deepening search.
A Survey of Cloud Computing Awareness, Security Implication and Adoption in Nigeria IT Based Enterprises
(Faculty of Communication and Information Sciences, University of Ilorin, Ilorin, Nigeria - International Journal of Information Processing and Communication, 3(1&2), 210 – 220, 2015) Bashir, S.A.; Adebayo, O.S.; Abdulsalam, S.O.; Sadiku, J.S.; Mabayoje, M.A.
The advancement in information technology (IT) infrastructures and the overwhelming pervasive Internet accessibility has tremendously change the way computing is done in IT based enterprises. The recent hype of Cloud Computing that utilizes the existing Internet infrastructure to provide pay-as-you-go services to diverse community of businesses has emerged to alleviate and reduce the cost of computing tremendously. This paper examined the awareness of the Nigerian business enterprisesand their readiness in adopting cloud computing. It was found that the trend of awareness and adoption were very minimal with many being sceptical although few businesses were aware of the cloud technology. The survey also revealed that the stakeholders were precarious of the security-level of the cloud-based computing. Keywords: Cloud-Based Computing, Security Implications, Cloud Awareness, Cloud Adoption, Enterprises, Technology
Comparative Analysis of Decision Tree Algorithms for Predicting Undergraduate Students’ Performance in Computer Programming
(Faculty of Science, Adeleke University, Ede, Nigeria - Journal of Advances in Scientific Research & Applications, A Multidisciplinary Journal Publication of the Faculty of Science, Adeleke University, Ede, Nigeria, 2015) Abdulsalam, S.O.; Babatunde, A.N.; Hambali, M.A.; Babatunde, R.S.
Educational data are increasing tremendously with little exploration by the education managers. Hidden knowledge can be discovered from the huge data sets available in educational databases through the use of data mining techniques. Educational data mining (EDM) is an emerging discipline, concerned with developing methods for exploring the unique types of data that reside in educational databases. One of the significant areas of the application of EDM is the development of student models for predicting students’ performances in their educational institutions. The focus of this work is to identify the optimal decision tree algorithms for predicting students’ performance in a computer programming course taken in 200 level based on their ordinary level results in Mathematics and Physics and their 100 level results in Mathematics and Physics courses. One hundred and thirty one (131) students’ records from computer science programme at Kwara State University (KWASU) between 2009 and 2013 were used. The attributes used are students’ ordinary level scores in Mathematics and Physics, 100 level results in Mathematics and Physics courses and the score in a 200 level computer programming course (CSC 203). C4.5 (known as J48 in WEKA) Classification and Regression Tree (CART), and Best-First Tree (BF Tree) decision tree algorithms were used in Waikato Environment for Knowledge Analysis data mining software to generate three classification models employed in predicting students’ performance in CSC 203. The results of these algorithms were compared using 10-fold cross validation method in terms of prediction accuracy and computational time. Our results showed that J48 tree has the highest prediction accuracy of 70.37% and least execution time of 0.02 seconds while CART and BFTree has prediction accuracy of 60.44% and 60.30% respectively and both having execution time of 0.22 seconds based on the data set used in this study. This study also revealed that previous knowledge of Mathematics and Physics both at Ordinary level and 100 level are essential determinants of students’ performance in a computer programming course. Keywords: Data Mining, Educational Data Mining, Decision Tree algorithm, Students’ Performance
Local Binary Pattern and Ant Colony Optimization Based Feature Dimensionality Reduction Technique for Face Recognition Systems.
(Journal of Advances in Mathematics and Computer Science, 2015-04-13) Babatunde, R.S, Olabiyisi, S. O., Omidiora, E. O. and Ganiyu, R. A. (2015)
Feature dimensionality reduction is the process of minimizing the number of features in high dimensional feature space. It encompasses two vital approaches: feature extraction and feature selection. In face recognition domain, widely adopted face dimensionality reduction techniques include Principal component analysis, Discrete wavelet transform, Linear discriminant analysis and Gabor filters. However, the performances of these techniques are limited by strict requirement of frontal face view, sensitivity to signal shift and sample size, computational intensiveness amongst others. In this paper, a feature dimensionality reduction technique that employed Local binary pattern for feature extraction and Ant colony optimization algorithms for the selection of optimal feature subsets was developed. The developed technique identified and selected the salient feature subsets capable of generating accurate recognition. The average training time, recognition time and recognition rate obtained from the experiment on locally acquired face data using cross-validation evaluation approach indicate an efficient performance of the potential combination of both methods in a two-level technique for dimensionality reduction
Design and Implementation of Ayo Olopon Game
(Computing, Information Systems, Development Informatics and Allied Research Journal, 2015-05-13) Babatunde, A. N., Abikoye, O. C., Mabayoje, M. A., Akintola, G. A. & Oderemi, C.
The difficulty encountered with the present manual mode of the “Ayo olopon” game is majorly on the tools used in playing it. The game utilizes the earth as the board of the game and uses stones, leaves as other parameters in playing it. Though some still use boards carved from wood and seeds but there is still a problem with portability. This research which is centred on automating the “Ayo olopon” game is inevitable because of the increasing need for system automation. There are many difficulties associated with the existing manual approach of playing the “Ayo olopon” game which ranges from loss or misplacing of seeds to inaccuracy in scores calculation due to human errors. This system is designed to efficiently handle the entire process of the game play. Two algorithms were implemented for the game manipulation. The first for handling the incrementing of the next cell while the other an entire play turn. Out of the diverse rules for the game play, one was selected and implemented. With the proposed system, “Ayo olopon” game playing is more efficient when compared to its present mode of play. The study outlined the concepts of the analysis and design methodology of the proposed system, compares it with the existing system and explains the design and implementation of the system using Microsoft C# as its programming language on the Visual Studio.NET platform serving as the Integrated Development Environment (IDE). The research was tested using the Windows operating system and worked successful. Keywords :- Ayo Olopon, Mancala Games, Cell, Seeds.
: Assessing the performance of Random Partitioning and K-Fold Cross Validation methods of evaluation of a Face Recognition System.
(Journal of Advances in Image and Video Processing, 2015-06-08) Babatunde, R. S, Olabiyisi S. O, Omidiora E. O, Ganiyu, R. A. and Isiaka, R. M. (2015)
Face recognition has been an active research area in the pattern recognition and computer vision domains due to its many potential applications in surveillance, credit cards, passport and security. However, the problem of correct method of partitioning the face data into train and test set has always been a challenge to the development of a robust face recognition system. The performance of the System was tested on locally acquired face database when the face database was randomly partitioned and when k-fold Cross Validation partition was used. The face database was captured under the condition of significant variations of rotation, illumination and facial expression. Quantitative evaluation experimental results showed that Random Sampling technique has a higher average recognition rate (96.7%) than Cross Validation partition method (95.3%). However, recognition time in Cross Validation is faster (0.36 secs) than that of Random Sampling (0.38 secs).
Comparative Analysis of Decision Tree Algorithms for Predicting Undergraduate Students’ Performance in Computer Programming
(Journal of Advances in Scientific Research and Applications, 2015-06-15) Abdulsalam, S. O., Babatunde, A. N., Hambali, M. A. & Babatunde, R. S.
Educational data are increasing tremendously with little exploration by the education managers. Hidden knowledge can be discovered from the huge data sets available in educational databases through the use of data mining techniques. Educational data mining (EDM) is an emerging discipline, concerned with developing methods for exploring the unique types of data that reside in educational databases. One of the significant areas of the application of EDM is the development of student models for predicting students’ performances in their educational institutions. The focus of this work is to identify the optimal decision tree algorithms for predicting students’ performance in a computer programming course taken in 200 level based on their ordinary level results in Mathematics and Physics and their 100 level results in Mathematics and Physics courses. One hundred and thirty one (131) students’ records from computer science programme at Kwara State University (KWASU) between 2009 and 2013 were used. The attributes used are students’ ordinary level scores in Mathematics and Physics, 100 level results in Mathematics and Physics courses and the score in a 200 level computer programming course (CSC 203). C4.5 (known as J48 in WEKA) Classification and Regression Tree (CART), and Best-First Tree (BF Tree) decision tree algorithms were used in Waikato Environment for Knowledge Analysis data mining software to generate three classification models employed in predicting students’ performance in CSC 203. The results of these algorithms were compared using 10-fold cross validation method in terms of prediction accuracy and computational time. Our results showed that J48 tree has the highest prediction accuracy of 70.37% and least execution time of 0.02 seconds while CART and BFTree has prediction accuracy of 60.44% and 60.30% respectively and both having execution time of 0.22 seconds based on the data set used in this study. This study also revealed that previous knowledge of Mathematics and Physics both at Ordinary level and 100 level are essential determinants of students’ performance in a computer programming course. Keywords: Data Mining, Educational Data Mining, Decision Tree algorithm, Students’ Performance
Redundant Residue Number System Based Fault Tolerant Architecture for Communication System
(Proceedings of the 1St International Conference of IEEE Nigeria Computer Chapter In collaboration with Department of Computer Science, University of Ilorin, Ilorin, Nigeria - 2016, 2016) Kadri Akeem Femi; Saheed Yakub Kayode; Gbolagade Kazeem Alagbe
This paper proposed the Redundant Residue Number System based Code Division Multiple Access over a communication channel. The Coded transmission technique applied in a multipath environment has a Bit Error Rate comparable to that of a narrow band radio channel due to the fact that the fading of each subcarrier is frequency non-selective. The existing scheme requires large block of data and higher overhead. Moreover, simple error detection such as parity check bit is too weak for communication in which quality of radio channel is often poor and burst error often occurs. These aforementioned shortcomings reduce the performance of the CDMA. In this paper, an alternative scheme to detect and correct error, in order to increase performance and also to eliminate the higher overhead in the present CDMA scheme is presented. We use length five (5) moduli set [2n, 2n – 1, 2n + 1, 2n - 2(n+1)/2 + 1, 2n +2(n+1)/2 + 1], where (2n - 2(n+1)/2 + 1, 2n +2(n+1)/2 + 1) is the redundant moduli set which is used for the correction of error. The proposed scheme increases the performance of CDMA and provides more capability for fault-tolerance than those similar of the state-of the-art.
A Feature Selection Based on One-Way ANOVA for Microarray Data Classification
(College of Natural Sciences, Al-Hikmah University, Ilorin, Nigeria - Al-Hikmah Journal of Pure and Applied Sciences, 2016) Arowolo, M.O.; Abdulsalam, S.O.; Saheed, Y.K.; Salawu, M.D.
High dimensionality of microarray data and expressions of thousands of features in a much smaller number of samples is a challenge affecting the applicability of the analytical results. However Support Vector Machine (SVM) has been commonly used in the classification of microarray datasets, yet the problem of high dimensionality of the feature space of data still exist. This study deals with the reduction of gene expression data into a minimal subset of genes, by introducing feature selection, to greatly reduce computational burden and noise arising from irrelevant genes that can perform a classification of cancer from microarray data using machine learning. Various statistical theory and Machine Learning (ML) algorithms to select important features, remove redundant and irrelevant features have been proposed, but it is unclear how these algorithms respond to conditions like small sample-sizes. This paper presents combination of Analysis of Variance (ANOVA) for feature selection; to reduce high data dimensionality of feature space and SVM algorithms technique for classification; to reduce computational complexity and effectiveness. Computational burden and noise arising from redundant and irrelevant features are eliminated. It reduces gene expression data to a lesser number of genes rather than thousands of genes, which can drop the cost for cancer testing significantly. The proposed approach selects most informative subset of features for classification to obtain a high performance accuracy, sensitivity, specificity and precision. Key words: Gene expressions, Microarray, One-Way-ANOVA, Support Vector Machines
Gender Recognition Using Local Binary Pattern and Naive Bayes Classifier
(Nigeria Computer Society (NCS), Lagos, Nigeria - The Journal of Computer Science and Its Application, An International Journal of the Nigeria Computer Society. 23(1), 65 – 74, 2016) Babatunde, R.S.; Abdulsalam, S.O.; Yusuff, S.R.; Babatunde, A.N.
Human face provides important visual information for gender perception. Ability to recognize a particular gender is very important for the purpose of differentiation. Automatic gender classification has many important applications, for example, intelligent user interface, surveillance, identity authentication, access control and human-computer interaction amongst others. Gender recognition is a fundamental task for human beings, as many social functions critically depend on the correct gender perception. Consequently, real-world applications require gender classification on real-life faces, which is much more challenging due to significant appearance variations in unconstrained scenarios. In this study, Local Binary Pattern is used to detect the occurrence of a face in a given image by reading the texture change within the regions of the image, while Naive Bayes Classifier was used for the gender classification. From the results obtained, the gender correlation was 100% and the highest accuracy of the result obtained was 99%.The system can be employed for use in scenarios where real time gender recognition is required. Keywords: Gender, Local Binary Pattern, Naïve Bayes, Recognition
Design and Implementation of Electronic Election Campaign System
(College of Natural Sciences, Al-Hikmah University, Ilorin - Al-Hikmah Journal of Pure and Applied Sciences. 2(2), 122 – 129, 2016) Salau-Ibrahim, T.T.; Abdulsalam, S.O.
Researches have been conducted over the years till date on the importance of election campaign in the fields of electoral studies, political studies and the use of information systems to assist the process. The use of computer technology in developing information systems to support several aspects of election campaign has also continued to evolve. System analysis best practices on the use of qualitative research methodology, Unified Modelling Language (UML) to enhance communication as well as rapid prototyping tools were explored and those well suited were used effectively. The emphasis of this work is to deduce how to provide computer support to improve gathering, analysis of data and information from face-to-face canvassing as well as conducting effective get-out-the-vote (GOTV) activities before and on election day. The system will be web based in order to ease communication and user interaction with the system. As one might expect for web based systems, Hyper Text Markup Language (HTML) will be used to display results in the web browser that will be visible to the user. Therefore, in this work, PHP Hypertext Preprocessor (PHP) was used for coding the core logic of the system and this runs on Apache web server, JavaScript for validation and printing facility, cascading style sheet (CSS) and lastly MySQL for database all residing on Apache web server. The present application will assist campaign managers, staff, as well as volunteers to carry out their duties more accurately and effectively. Keywords: Information Systems, System Analysis, Software Engineering, Election Campaign, Campaign Violence, face-to- face canvassing
Handwritten Character Recognition using Brainnet Library,
(Annals. Computer Science Series Journal, Published by Faculty of Computers and Applied Computer Science, Tibiscus University of Trinisoara, Romania., 2016-02-15) Babatunde, A. N., Abikoye, O.C., Babatunde, R.S. and Kawu, R.O. (2016)
Handwriting has continued to persist as a means of communication and recording information in dayto- day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. An off-line handwritten alphabetical character recognition system using multilayer feed forward neural network is described, and a method, called, diagonal based feature extraction is used for extracting the features of the handwritten alphabets. This project implements this methodology using BrainNet Library. Ten data sets, each containing 26 alphabets written by various people, are used for training the neural network and 130 different handwritten alphabetical characters are used for testing. The proposed recognition system performs quite well yielding higher levels of recognition accuracy compared to the systems employing the conventional horizontal and vertical methods of feature extraction. This system, if modified will be suitable for converting handwritten documents into structural text form and recognizing handwritten names.

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