Asian Journal of Research in Computer Science

Southeast Asian countries takes rice as one of their necessary food intake to survive for a day. With a booming population of the third world countries, the need for higher production of rice was sought as one of the priorities of the countries like Thailand to identify areas which are deemed suitable for rice production. Thailand is one of the prominent exporter of rice in the whole world and it is important that Prachuap Khiri khan can go with trends of identifying areas for good rice production that would therefore contribute for the economic improvement of its very own districts up to its own country Thailand. This study aims to identify the suitable areas for good rice production in the province of Prachuap Khirikhan. Factors such as slope, aspect, elevation, land use, road proximity, stream network and rainfall were considered to identify the suitable site for good rice yields. The spatial analysis of ArcGIS software was used in the generation of the different maps. Results shows that the southern portion of the province has a very high suitability of rice production, while the Northwestern portion of the province shows the very low suitability of rice production.

The increasing complexity of information systems, as well as the rapid development of new vulnerabilities and exploits, the security of campus networks needs to be hardened to minimize or eradicate security flaws.

Aim: To discover the vulnerabilities and enhance the creation and deployment of secured campus network. 

Place and Duration of study: University of Education, Winneba – Kumasi campus.

Methodology:  The integration of De-Militarized zone and Honeypot techniques was used to beef up the security of the campus network against vulnerabilities and exploits. Penetration testing was used in the assessment of the University of Education’s network infrastructure, and to demonstrate attacks and intrusion into the network infrastructure.

Results: Two firewall DMZ architecture techniques protect sensitive resources of the campus network by sanctioning the inflow and outflow of traffic, determining which segment allow and disallow traffic,   whiles the Honeypot techniques were configured to keep the attention of attackers diverted from the main network, the full strength of an attack, until the administrators are prepared to put the effective counter in place. The fusion of DMZ and, Honeypot provide the System Administrators to effectively management the security of the campus networks.

Conclusion: Honeypots are used to detect vulnerabilities based on the attacker’s behaviour and, data collected by honeypots can be used to enhance other security technologies.  The fusion of DMZ and Honeypot into the security models of the campus network made it more robust.

Aims: In the discipline of computer vision, detecting and tracking moving objects in a succession of video frames is a critical process. Image noise, complicated object motion and forms, and video real-time processing are some of the challenges faced by existing methods. Hence, they are computationally complex and susceptible to noise. This work utilized Canny Edge and Optical Flow (CE-OF) techniques for identifying and tracking moving objects in video files.

Methodology: Video sequence datasets in Avi and Mp4 format from MathWorks and YouTube were used to evaluate the developed CE-OF technique. The video clip's frames were sampled several times and the frame rate display was calculated. The original images were converted to grayscale, preprocessed, and CE-OP was applied to identify and track the moving object. The results of the CE-OF and optical flow techniques in terms of accuracy, precision, false acceptance rate, false rejection rate, and processing time were obtained and compared. The performance of the developed technique was evaluated using accuracy, precision, false acceptance rate (FAR), false rejection rate (FRR) and processing time. The results obtained were 94.12%, 92.86%, 25.00%, 25.00%, and 19.51s for Mp4; and 93.33%, 90.91%, 20.00%, 20.00%, and 44.11s for Avi video 1 format, respectively.

Conclusion: The developed CE-OF is a better competition in terms of accuracy and time compared with well-known techniques in the literature. The CE-OF technique performed better compared with the conventional methods in detecting and tracking a moving object. Therefore, it can be adopted in the designing of intelligent surveillance systems.

The current condition is that the Village Bumdes in Sepakat does network management using the hotspot feature found on Mikrotik. This hotspot feature has Authentication, Authorization and Accounting functions (AAA) with the license used on MikroTik, which is a level 4 license which means it has a maximum active user limit of 50 users. The purpose of this research is to find out a better performance comparison between Mikhmon and Usermanager and also provide an alternative solution which is better in Mikrotik management between Mikhmon and Usermanager. The data collection methods used are observation, interviews, and literature studies and use the Quality Of Service (QoS) analysis method. The results of the research that has been carried out are in order toprovide an alternative solution which is better in managing mikrotik between Mikhmon and Usermanager.

Surveillance systems are playing a significant role in law enforcement and city safety. It is important to detect violent and suspicious behaviors automatically in video surveillance scenarios, for instance, railway stations, schools, hospitals to avoid any casualties which could cause social, economic, and ecological damage. Automatic detection of violence for quick actions is very significant and can efficiently help law enforcement departments. So, researchers are doing a lot of research on different techniques for detecting violence. This research study reviews various techniques and methods for detecting violent or anomalous activities from surveillance video that have been proposed by many researchers in recent years. The method of detection is divided into three categories. These categories are based on the classification techniques used. These categories are: traditional violence detection using machine learning, Support Vector Machine (SVM) & Deep Learning. Feature extraction & Object detection techniques are also described for each category. Moreover, dataset & video features that help in the recognition process are also discussed. The overall research finding has been discussed which will help the researcher in their future work in this field.