by Terungwa Simon Yange, Oluoha Onyekwere and Oluchi Blessing Okeke
Special Issue
This research deals with the design, developing and testing of web-based duty scheduling system to schedule staff on a monthly basis so that a staff can have access to their duty roster irrespective of his/her location. The Federal University of Agriculture Veterinary Teaching Hospital was used the case study for this work. A computer web based system called “Veterinary Duties Scheduler” was developed to this effect using PHP as the programming language, MySQL as the database and Unified Modelling Language (UML) as system design tool. After the new system was implemented, the results gotten showed clearly that staffs were now able to view their duty roster from any location by simply logging into the system using their staff-id and password. Therefore, the new system was able to solve most of the problems associated with the existing manual system of duty scheduling as witnessed in the case study.
Journal of Computer Sciences and Applications. 2020, 8(1), 30-39. DOI: 10.12691/jcsa-8-1-5
Pub. Date: June 04, 2020
5161 Views603 Downloads
by Terungwa Simon Yange, Oluoha Onyekwere and Yakubu Musa Abdulmuminu
Special Issue
Network intrusion detection systems are becoming an important tool for information security and technology world. Given the rise of attacks across the network, there is a pressing need to develop an improved security system to combat these growing threats on the computer network. The quality of an intrusion detection system is determined by the number of attacks its able to classify correctly. This research developed a data analytics system for network intrusion detection to combat the ever growing threats as well as classify them so as to ease the task of data scientists and network administrators. Decision tree algorithm and python programming language were used. KDD’99 was used as the data source. Decision tree assists the network administrator to decide about the incoming traffic, i.e., whether the coming data is malicious or not by providing a model that separates malicious and non-malicious traffic. It allows taking less number of attributes and provides acceptable accuracy in reasonable account of time. From the results of the experiments, it is concluded that the system is more efficient with respect to finding attacks in the network with less number of features and it takes less time to construct the model. Also, the efficiency of the system has little or no regards for the size of the dataset and the number of features used to construct the decision tree.
Journal of Computer Sciences and Applications. 2020, 8(1), 21-29. DOI: 10.12691/jcsa-8-1-4
Pub. Date: June 04, 2020
4648 Views590 Downloads
by Daniel Makpeme Olojakpoke and Adebola Kehinde Ojo
Original Research
Monitoring of students who are undergoing the Students’ Industrial Work Experience Scheme (SIWES) program by school-based supervisors is a difficult task because the current paper based logbook system currently employed is not adequate enough to determine how well students are undergoing the program. It is difficult for school-based supervisors to know whether students actually filled their logbooks daily, showing what they have done or whether they filled it all at the end of a long period of time which means that such entries are very likely to be fraudulent. Which is why school-supervisors try to visit students on the program to physically monitor such students, however due to distance and other logistical issues school-based supervisors are only able to visit such students once or at most twice or sometimes never. The application was developed following the incremental model. Node.Js was used for the backend, MongoDB was used as the database while React Native was used to create the front-end. This application helps school-based supervisors monitor students on the SIWES program more effectively and also makes grading and commenting on logbook entries a lot easier. It can therefore be deployed to tertiary institutions in Nigeria to assist them in the running of their respective SIWES programmes.
Journal of Computer Sciences and Applications. 2020, 8(1), 15-20. DOI: 10.12691/jcsa-8-1-3
Pub. Date: May 20, 2020
6098 Views503 Downloads
by Guanghui Wang
Original Research
The sequence prediction theory of wavelet neural network is applied to short-term temperature prediction. By using wavelet function as the activation function of the hidden layer of BP neural network, a "compact" wavelet neural network prediction model is established. The structural characteristics of the model are analyzed and the specific steps of building the model are described. Based on two sets of temperature observation data, internal characteristics and constraints of different data series are revealed using statistical analysis. Then, short-term temperature changes are predicted using wavelet neural network and predicted results are compared with the actual temperature. Finally, the prediction accuracy of wavelet neural network based on different data sequences is compared and analyzed. The results show that the wavelet neural network has a good accuracy for the prediction of short-term temperature change.
Journal of Computer Sciences and Applications. 2020, 8(1), 5-14. DOI: 10.12691/jcsa-8-1-2
Pub. Date: March 27, 2020
5969 Views1041 Downloads
by Tukhtasinov I. M, Lutfillaev M. H, Abdullaeva S. A, Mamadalieva N. S, Lutfillaeva F. M and Fayziev N. A.
Original Research
This project explores the design and implementation of virtual resources in the learning process on the basis of computational simulation models for higher educational establishments.
Journal of Computer Sciences and Applications. 2020, 8(1), 1-4. DOI: 10.12691/jcsa-8-1-1
Pub. Date: January 25, 2020
5572 Views964 Downloads