Interlocking Institutional Worlds (IWs) is a concept explaining the need to interoperate between institutions (or players), to solve problems of common interest in a given domain. Managing knowledge in the IWs domain is complex; however, promoting knowledge sharing based on standards and common terms agreeable to all players is essential and is something that must be established. In this context, ontologies, as a conceptual tool and a key component of knowledge-based systems, have been used by organizations for effective knowledge management, better decision-making, and interoperability among diverse institutions of an IWs domain. The development of ontology involves structural and logical complexity, and requires a well-designed, mature, and widely accepted methodology, to ensure its reliability. Many methodologies for ontology development have been proposed by several researchers; however, most of the developed methodologies have not included several important phases. Furthermore, several methodologies have not provided the complete details of the techniques and activities involved in the ontology construction process. Fewer details make it difficult to follow a methodology for designing ontologies. This study aims to compare existing methodologies based on sixteen important criteria and proposes an improved methodology for ontology development for IWs domains. The proposed methodology has included several important phases such as the

SQL injection happened in electronic records in database and it is still exist even after two decades since it first happened. Most of the web-based applications are still vulnerable to the SQL injection attacks. Although technology had improved a lot during these past years, but, hackers still can find holes to perform the SQL injection. There are many methods for this SQL injection to be performed by the hackers and there is also plenty of prevention for the SQL injection to be happened. The vulnerability to SQL injection is very big and this is definitely a huge threat to the web based application as the hackers can easily hacked their system and obtains any data and information that they wanted anytime and anywhere. This paper can conclude that several proposed techniques from existing journal papers used for preventing SQL injection. Then, it comes out with Blockchain concept to prevent SQL injection attacks on database management system (DBMS) via IP.

Deforestation is the long-term or permanent conversion of forest land to other uses, such as agriculture, mining, and urban development. As a result, deforestation has catastrophic consequences for the environment, including the loss of biodiversity, disruption of clean water supplies, and the acceleration of climate change. According to statistics, the deforestation trend in developing countries is at an alarming rate including Malaysia where plantation activities are the primary cause of forest loss. Recent anecdotal studies have demonstrated the effectiveness of the deep learning-based (DL) approach in producing deforestation maps. However, there are limited studies concentrating on DL approach for synthetic aperture radar (SAR) imaging due to complexity of the computational concepts of the method. The SAR imagery can be challenging to interpret but its all-weather and all-day capability can be critical in forest monitoring compared to optical imagery. Thus, in this study, we propose to map deforestation areas in Permanent Forest Reserve (HSK) using multi-temporal Sentinel-1 SAR data. Deep learning-based U-Net was employed to classify the SAR imagery as forest and non-forest due to its semantic segmentation capabilities. The experiment results showed that the proposed deep learning-based technique successfully achieved 0.993 of intersection over union (IoU) and 0.980 of overall accuracy (OA). Also, we explained the entire procedure from beginning to end as simple as possible for beginners to comprehend. In brief, the findings of this study have the potential to improve monitoring of damaged HSK areas, prioritize the restoration of the affected forest areas and protecting the forest lands from illegal deforestation activities.

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