http://laujci.lautech.edu.ng/index.php/laujci/issue/feed LAUTECH JOURNAL OF COMPUTING AND INFORMATICS 2020-10-14T11:43:42+00:00 Professor Justice O. Emuoyibofarhe eojustice@gmail.com Open Journal Systems <p><strong>Aim and Scope</strong></p> <p>The Lautech Journal of Computing and Informatics (LAUJCI) ISSN: 2714-4194 is an institutional based peer-reviewed research publishing journal with the aim of promoting and publishing original high quality research outputs in all areas of Computing and Informatics. All technical or research papers and research results submitted to LAUJCI should be original in nature, never previously published in any journal or presented in a conference or undergoing such process across the globe. All the submission will be peer-reviewed by the panel of experts associated with particular fields. Submitted papers should meet the internationally accepted criteria and manuscripts should follow the journal format which is available in the journal website for the purpose of both reviewing and editing.</p> <p>Some of the topics and subject areas of LAUJCI include but not limited to:</p> <ul> <li>Computer Science &amp; Computer Engineering</li> <li>Information Security, Cyber Security &amp; Computer Forensics</li> <li>Cloud, Cluster &amp; Green Computing</li> <li>Signal &amp; Image Processing</li> <li>Artificial Intelligence (includes pattern recognition, evolutionary computation, logic, etc.)</li> <li>Human-Computer Interaction</li> <li>Mobile Technologies &amp; Mobile Web Services</li> <li>Remote Sensing</li> <li>IP mobility protocols</li> <li>Communications software and services</li> </ul> <ul> <li>Applied ICT, including but not limited to: <ul> <li>ICTs in Industrialization &amp; Manufacturing</li> <li>ICT applications in Energy (Renewable, Oil, Hydro, Clean Coal &amp; Nuclear)</li> <li>Internet applications</li> <li>Virtual environments and social networks</li> <li>ICT applications in education &amp; training, including eLearning, Distance Education &amp;</li> </ul> </li> </ul> <p>Innovative Educational Platforms</p> <ul> <li>E-government, e-governance &amp; e-skills for development</li> <li>eHealth &amp; mHealth</li> <li>Remote tracking, logistics &amp; monitoring technologies</li> <li>Wireless Sensor Networks</li> <li>ICT for creative industries &amp; technology innovations</li> <li>Electrical &amp; Electronic Engineering, including but not limited to:</li> <li>Electronics Design</li> <li>Smart Utility Systems</li> <li>Power line communications and their applications</li> <li>Biomedical engineering</li> <li>Consumer electronics and components</li> <li>Measurements and modelling of signal propagation</li> <li>Communications, including but not limited to:</li> </ul> <ul> <li> <ul> <li>Green communications</li> <li>Telecommunications</li> <li>Wireless and fibre networks</li> <li>Satellite communications</li> <li>Software-Defined and Cognitive Radios</li> <li>Long term evolution networks</li> <li>Computer Networking</li> <li>Other Related Areas</li> </ul> </li> </ul> <p> </p> http://laujci.lautech.edu.ng/index.php/laujci/article/view/7 Predicting Groundwater Potentials Using Case Based Reasoning 2020-10-14T04:27:21+00:00 A. Adeyanju Ibrahim ibrahim.adeyanju@fuoye.edu.ng O. Oduntan Esther esteemviac@gmail.com Israel O. Ojeyinka bisioje75@gmail.com <p><span class="fontstyle0">Water is one of the basic necessities of life but the available surface water is inadequate to meet up with human’s demand for water. Hence, groundwater which is the subsurface water beneath the earth’s surface is exploited to complement surface water based on different parameters. These parameters include its potentiality, amount of water stored/ found in a particular media at a time and the ability to recharge. The potential to recharge is the major indicator in groundwater search; this is done by carrying out extensive geological and geophysical survey of the area. This paper proposes the prediction of groundwater potentials using Case Based Reasoning (CBR). Geological and geophysical survey data including electrical resistivity survey data also known as Vertical Electricity Survey (VES) was acquired from three local government areas in the southern part of Oyo State, Nigeria. The problem attributes of each case include the coefficients of resistivity, VES data, geo-electric section data while the solution attribute is the groundwater potential with values poor, moderate or high. Given a new set of problem attributes, our CBR technique retrieves the most similar case from our casebase as a potential solution. The retrieved solution is then adapted as solution to the current problem is the similarity meets a particular threshold. An accuracy of 80% was obtained from our experiments where moderate and high groundwater potential were assumed to be identical. We intend to experiment with data from other parts of the country to further validate the CBR technique and investigate the effects of a three-way classification, instead of the two solution classes used in this study.</span> &nbsp;</p> 2020-10-14T00:00:00+00:00 Copyright (c) 2020 LAUJCI http://laujci.lautech.edu.ng/index.php/laujci/article/view/8 Development of an Android Based Medication Reminder and Adherence System 2020-10-14T04:43:15+00:00 O. J Emuoyibofarhe eojustice@gmail.com T.O. Adeyemi adeyemitaofeek84@gmail.com I. A. Jenyo jenyoifeoluwani@gmail.com E. A. Amusan lizzydoyin@yahoo.com <p><span class="fontstyle0">Android based medication reminder and adherence system is a system in which an alarm and notification system is implemented<br>and also provides a platform for doctors, healthcare givers and patients’ interaction. Patients can set a customized alarm tone in<br>their local language or select from a list of default tones. The application allows specialists to automatically see the list of patients<br>connected to them and their chat messages. Specialist can send health tips and other broadcast messages to his entire patients or fix an appointment date with any of their patients at will.<br>Agile software engineering process was used for the development of the system. The front end was implemented using android<br>studio and the back end was designed using firebase frame work. The android based application runs on mobile devices, such as<br>smart phones, tablet computers and PDAs. The application was implemented and tested on the mobile phones of several patients, healthcare givers and Computer programmers, it was found to be very helpful in care management and easing travelling stress and fatigue and the reminder system assisted in medication adherence. Survey results shows 100% likes for the system’s reminder and notification module, 80% likes for the chart module, another 80% likes for the search for specialist module while 70% likes were recorded for broadcast messages.<br>This paper presents the development of the reminder and adherence system. The application is light weight, very easy to use and<br>support medication adherence. The application will assist patients with chronic illness like Cancer, Diabetes, Asthma and<br>HIV/AIDS, to get notifications from medical personnel about the availability of drugs and also served as a reminder system,<br>thereby promoting adherence</span> </p> 2020-09-30T00:00:00+00:00 Copyright (c) 2020 LAUJCI http://laujci.lautech.edu.ng/index.php/laujci/article/view/17 A Modified Genetic Algorithm Used for Dimensionality Reduction in Record Classification 2020-10-14T10:40:35+00:00 Kamal Bakari Jilahi kamalbakari@gmail.com Ahmet Ünveren ahmet.unveren@emu.edu.tr <p>This work proposes a modified Genetic Algorithm and compares its performance with the conventional Genetic Algorithms (GA) used for Dimensionality Reduction in record classification. A specialized elite voting crossover and mutation was introduced to the conventional GA and the population composition of every generation was compartmented into elite and non-elite individuals, and a proportion of offspring generated in each generation are derived from the elite individuals using the introduced voting crossover and mutation. The performance of the two algorithms was tested with 3 datasets from the UCI ML repository using different levels of elitism, crossover and mutation with the Extreme Learning Machine classifier. At higher rate of elitism, the results were highly in favor of the modified GA in both convergence time and classifier accuracy. While, at lower levels of elitism the two algorithms seen to be comparable in convergence time but the modified algorithm had better classifier accuracy. Furthermore, at higher rate of crossover, the modified algorithm tends to be slower in convergence than the conventional algorithm but better classifier accuracy. On the other hand, at higher mutation rate the modified algorithm tends to be faster in convergence than the conventional algorithm. In conclusion, except for the added computational cost due to the specialized voting crossover and mutation in the modified algorithm the results are in favor of the modified algorithm.</p> 2020-09-30T00:00:00+00:00 Copyright (c) 2020 http://laujci.lautech.edu.ng/index.php/laujci/article/view/20 Development of a Forecasting Model for Farm Produce using Fuzzy Cognitive Map 2020-10-14T11:05:43+00:00 O. O Alo ooalo@lautech.edu.ng <p>A Fuzzy Cognitive Maps (FCMs) is a modeling methodology based on exploiting knowledge and experience. It comprises the main advantages of fuzzy logic and neural networks, representing a graphical model that consists of nodes-concepts which are connected with weighted edges (representing the cause and effect relationships among the concepts). FCMs have proved to be a promising modeling methodology with many successful applications in different areas especially for simulating system design, modeling and control. Improving the crop yield has always been a major challenge for farming community as well for agricultural scientists. Though various computational approaches (qualitative and quantitative analysis) have been followed traditionally in practice, still a persistent decision making method to improve crop yield is not yet predicted.<br>In this work, FCMs are introduced to model a decision support system for precision agriculture (PA). The FCM model developed<br>consists of nodes which describe soil properties and agricultural crop yield and of the weighted relationships between these nodes. The nodes of the FCM model represent the main factors influencing crop production i.e. essential soil properties such as soil texture, temperature, soil fertility, bulk density, pH, annual rainfall, pest infestation among others.<br>This work provides a clear understanding to agricultural products yield forecasting. The information obtained at the end of this work will be useful to agricultural scientists, farmers and other stakeholders</p> 2020-09-30T00:00:00+00:00 Copyright (c) 2020 http://laujci.lautech.edu.ng/index.php/laujci/article/view/21 Fault Tolerance and Real Time Monitoring Infant Incubator Model 2020-10-14T11:11:42+00:00 I. S. Marafa marafasalman@fcaishiagu.edu.ng <p>This paper present the development of fault redundant infant incubator model, the framework used here is both hardware fault monitoring and software fault monitoring. The purpose of a Fault tolerant system is to ensure that faults do not result in malfunctioning and system failure and to achieve the best performance even with minimum number of sensors working. It is developed in such a way that the hardware fault monitoring module connects the two basic functional units with separate components to a central controlling unit. This control unit performs the function of a back up to support the whole system, that is redundant Sensing for the critical parameter such as temperature and the humidity has been incorporated using triple modular redundancy. Software fault monitoring involves transmitting of information and notifying of personnel about faulty parts through the graphical user interface (GUI) during operation for better performance of the incubator. The proposed model was developed using a PIC16F688 micro controller, Temperature sensors (DS18S20), Humidity sensor (DHT11).</p> 2020-09-30T00:00:00+00:00 Copyright (c) 2020 http://laujci.lautech.edu.ng/index.php/laujci/article/view/22 Automated Lectures-Based Timetabling Generation Using Evolutionary Algorithm 2020-10-14T11:16:56+00:00 Ismaila W. Oladimeji woismaila@lautech.edu.ng <p>A timetable management system is designed and created to handle as much course data as fed while ensuring the avoidance of redundancy. Every school year, institutions of education face the rigorous task of drawing up timetables that satisfies the various courses offered by the different department. The difficulty is due to the great complexity of the construction of timetables for lectures, due to the scheduling size of the lectures, the high number of constraints and criteria of allocation, usually circumvented with the use of little strict heuristics, based on solutions from previous years. Also, the former timetabling systems did not consider the requests of lecturers as par the time convenient to fix their classes. This work employed Genetic Algorithm to generate timetable for faculty of agriculture courses. The hard, soft and float constraints for the system were formulated. The float constraint was included in hard constraints (system A) and then in soft constraints (system B). The repair strategy is also used for initializing a random population. The system was run with different parameters settings to obtain optimum results.<br>The results of the systems are: SA produced total of 4 soft constraints; SB produced a total of 9 soft constraints while SC produced 15 soft constraints. Thus, the accuracies of SA, SB and SC timetabling systems are 95.8%, 89.45 and 87.2%. This established that the lecturers request should be part of strong constraints for a conflict free timetabling.</p> 2020-09-30T00:00:00+00:00 Copyright (c) 2020 http://laujci.lautech.edu.ng/index.php/laujci/article/view/23 Improved Genetically Optimized Neural Network Algorithm for Classification of Distributed Denial of Service Attack 2020-10-14T11:22:57+00:00 Emmanuel Hamman Gadzama ehgadzama@gmail.com Olawale Surajudeen Adebayo waleadebayo@futminna.edu.ng <p>This paper proposes a classification of distributed denial of service (DDOS) attack using neural network-based genetic algorithm (NNGA). The genetic algorithm was used to optimize neural network for the detection of DDoS attacks in order to improve the effectiveness and efficiency of classification accuracy and performance. In order to improve the NNGA, a fitness function was introduced in genetic algorithm that improved the performance of NNGA. The features of DDOS attacks from KDD 99 intrusion detection datasets were obtained to train the NNGA. The results show the improved genetically optimized neural network algorithm has better accuracy and lower false positive rate in comparison with the conventional neural network.</p> 2020-09-30T00:00:00+00:00 Copyright (c) 2020 http://laujci.lautech.edu.ng/index.php/laujci/article/view/24 A Fingerprint Based Attendance Monitoring System with SMS Alert 2020-10-14T11:34:19+00:00 Temilola M. Adepoju atemilola@gmail.com Matthias O. Oladele oladelematthias@gmail.com Michael A. Oni mickystunt@yahoo.com <p>Management of the attendance of students in an institution can be rigorous using the conventional method of paper sheets and the old file system method. The approach and accuracy of this attendance record are marred with various issues, such as the manipulation of the attendance record by fraudulent students, updating of the current attendance record to the already collated previous attendance records, and so on. Previous attendance management systems monitor attendance of the students without giving feedback such as sending Short Message Service (SMS). A fingerprint-based attendance management system with an SMS alert was developed to monitor the attendance of the student and inform them of their attendance status before the examination. The system utilizes a portable fingerprint scanner as the input to acquire fingerprint images and notebook personal computers as the mobile terminal for the processing of the images and to record attendance. The system consists of two modules, the enrolment and the authentication modules. The enrolment module takes the fingerprint of an individual, extract its unique features and stores it in a database while the authentication module takes a fingerprint, extract its features and compare it with the fingerprints stored in the database. A database was developed to store student’s information, lecturer’s information, and attendance records of the students. Java was used as the programming language, SQLite as the database, and eBulkSMS API as the means of SMS. The system was tested with ten courses and an accuracy of 90% was achieved.</p> 2020-09-30T00:00:00+00:00 Copyright (c) 2020 http://laujci.lautech.edu.ng/index.php/laujci/article/view/25 Implementation of an Improved Interface Complexity Metric on Regular Language for Next Generation 2020-10-14T11:43:42+00:00 Chistopher A. Oyeleye caoyeley@lautech.edu.ng Kehinde A. Sotonwa adebola.sotonwa@calebuniversity.edu.ng Elijah O. Omidiora eoomidiora@lautech.edu.ng Stephen O. Olabiyisi soolabiyisi@lautech.edu.ng Emmanuel Abiodun etabiodun1492@gmail.com Bello O. Alabi bellooa@abuad.edu.ng <p>User Interface design metric assist developers to evaluate interface designs in early phase before delivering the software to end users. Controlling and minimizing software complexity is one of the most important objectives of each software development paradigm because it affects all other software quality attributes like reusability, reliability, testability, maintainability etc. This paper presents Improved Interface Complexity (IIC) Metric using Number of Equivalence Class (NEC), Frequency Occurrence of Class (FOCi), Number of Elements (NE) of the schema documents, the Number of Attributes (NA) and Element Fanning (EF) of an RNG. The proposed metric was applied on real schemas documents data acquired from Web Service Description Language (WSDL) and implemented in Regular Language for Next Generation (RNG). The result showed that RNG reduce complexity of class elements, showed more reusability and flexibility traits and overall understanding of the schema documents becomes much easier which reduces maintenance effort.</p> 2020-09-30T00:00:00+00:00 Copyright (c) 2020