The usage of cybersecurity is growing steadily because it is beneficial to us. When people use cybersecurity, they can easily protect their valuable data. Today, everyone is connected through the internet. It’s much easier for a thief to connect important data through cyber-attacks. Everyone needs cybersecurity to protect their precious personal data and sustainable infrastructure development in data science. However, systems protecting our data using the existing cybersecurity systems is difficult. There are different types of cybersecurity threats. It can be phishing, malware, ransomware, and so on. To prevent these attacks, people need advanced cybersecurity systems. Many software helps to prevent cyber-attacks. However, these are not able to early detect suspicious internet threat exchanges. This research used machine learning models in cybersecurity to enhance threat detection. Reducing cyberattacks internet and enhancing data protection; this system makes it possible to browse anywhere through the internet securely. The Kaggle dataset was collected to build technology to detect untrustworthy online threat exchanges early. To obtain better results and accuracy, a few pre-processing approaches were applied. Feature engineering is applied to the dataset to improve the quality of data. Ultimately, the random forest, gradient boosting, XGBoost, and Light GBM were used to achieve our goal. Random forest obtained 96% accuracy, which is the best and helpful to get a good outcome for the social development in the cybersecurity system.

Due to the gradual growth of urbanization in cities, urban forests can play an essential role in sequestering atmospheric carbon, trapping pollution, and providing recreational spaces and ecosystem services. However, in many developing countries, the areas of urban forests have sharply been declining due to the lack of conservation incentives. While many green city spaces have been on the decline in Thailand, most university campuses are primarily covered by trees and have been serving as urban forests. In this study, the carbon sequestration of the university campuses in the Bangkok Metropolitan Region was analyzed using geoinformatics technology, Sentinal-2 satellite data, and aerial drone photos. Seventeen campuses were selected as study areas, and the dendrometric parameters in the tree databases of two areas at Chulalongkorn University and Thammasat University were used for validation. The results showed that the weight average carbon stock density of the selected university campuses is 46.77 tons per hectare and that the total carbon stock and sequestration of the study area are 22,546.97 tons and 1402.78 tons per year, respectively. Many universities in Thailand have joined the Green University Initiative (UI) and UI GreenMetric ranking and have implemented several campus improvements while focusing on environmental concerns. Overall, the used methods in this study can be useful for university leaders and policymakers to obtain empirical evidence for developing carbon storage solutions and campus development strategies to realize green universities and urban sustainability.

This study aims to explain the design of policy strengthening in forest and land fire disaster mitigation governance, through the integration of ecotourism development in Siak Regency. Based on the research topic, this study employs a qualitative approach to describe governance conditions and the design of policy strengthening in ecotourism-based disaster mitigation governance. Data analysis is performed using Nvivo 12 Plus software. The results of this study indicate that forest and land fire disaster mitigation governance based on ecotourism development still has shortcomings that need to be addressed in the principles of conservation, economy, and community involvement. Then, the design of a policy to strengthen ecotourism-based disaster mitigation governance includes three crucial policy recommendations, namely: the need for special regulations related to forest and land fire disaster mitigation prevention based on the integration of ecotourism principle development, the need for a balance of roles between actors in determining and implementing ecotourism-based disaster mitigation policies, and the need for effective and efficient implementation of ecotourism-based disaster mitigation policies through increasing the involvement of strategic actors. Substantially, the handling of forest and land fire disasters in Siak Regency can be combined with ecotourism activities, especially in tourist village areas, by developing policies to strengthen the utilization of village-owned disaster mitigation facilities such as reservoirs, lakes, or ponds that are converted into water supplies during the dry season for forest and land fire disaster prevention activities and local economy-based tourist destinations. Our findings are a strategic effort to raise awareness among actors and highlight the need for policy-strengthening design in ecotourism-based disaster mitigation. These findings can also contribute to the literature that will be useful for all stakeholders in developing future long-term disaster mitigation governance policies. This study relies heavily on information from key informants, who represent only the perspectives and expertise of the stakeholders encountered. However, it still refers to important elements based on the informants’ knowledge capabilities in the disaster and tourism sectors. Therefore, we propose to conduct future studies on a comprehensive analysis of sustainable ecotourism-based disaster mitigation governance to promote and accelerate the idea of disaster and tourism in the future.

Objective: This research aims to investigate the legal dynamics of leasing agricultural land plots integrated with protective plantings, motivated by recent legislative changes that significantly influence both agricultural productivity and environmental conservation. Methods: The authors of the article used the methods of axiological, positivist, dogmatic, historical, and comparative-legal analysis. Results: The study considers the recent legislative amendments that grant agricultural producers the right to lease land with forest belts without the need for bidding. It traces the historical development of forest plantations, highlighting their major role in intensifying agricultural production. Our results reveal that the new legislative framework allows agricultural producers to lease lands with protective forest belts without bidding, a change that highlights the complexities of balancing economic efficiency with ecological sustainability. Conclusions: The research emphasizes the unique legal challenges and opportunities presented by forest belt leasing in the agricultural context. It stipulates the need for a balanced legal framework that preserves environmental integrity, protects property rights, and supports sustainable agricultural practices. This study dwells on the evolving legal landscape of forest belt leasing and its implications for agricultural land management in Russia and similar regions. The significance of this research in its comprehensive analysis of the legal, economic, and ecological dimensions of land leasing, offering a nuanced understanding of how legislative changes shape land use strategies.

Falling is one of the most critical outcomes of loss of consciousness during triage in emergency department (ED). It is an important sign requires an immediate medical intervention. This paper presents a computer vision-based fall detection model in ED. In this study, we hypothesis that the proposed vision-based triage fall detection model provides accuracy equal to traditional triage system (TTS) conducted by the nursing team. Thus, to build the proposed model, we use MoveNet, a pose estimation model that can identify joints related to falls, consisting of 17 key points. To test the hypothesis, we conducted two experiments: In the deep learning (DL) model we used the complete feature consisting of 17 keypoints which was passed to the triage fall detection model and was built using Artificial Neural Network (ANN). In the second model we use dimensionality reduction Feature-Reduction for Fall model (FRF), Random Forest (RF) feature selection analysis to filter the key points triage fall classifier. We tested the performance of the two models using a dataset consisting of many images for real-world scenarios classified into two classes: Fall and Not fall. We split the dataset into 80% for training and 20% for validation. The models in these experiments were trained to obtain the results and compare them with the reference model. To test the effectiveness of the model, a t-test was performed to evaluate the null hypothesis for both experiments. The results show FRF outperforms DL model, and FRF has same accuracy of TTS.

This research is based on the condition of the ever-rampant events of illegal logging perpetrated by companies in various areas in Indonesia and Malaysia. The issue of corporate illegal logging happened due to a concerning level of conflict of interest between companies, the government, and local societies due to economic motives. this paper aims to analyze the law enforcement on corporate illegal logging in Indonesia and Malaysia as well as the law enforcement on corporate illegal logging that is based on sustainable forestry. this research used the normative legal approach that was supported by secondary data in the forms of documents and cases of illegal logging that happened in Indonesia and Malaysia. this paper employed the qualitative analysis. Results showed that Indonesia had greater commitment and legal action than Malaysia because Indonesia processed more illegal logging cases compared to Malaysia. But mere commitment is not enough as the illegal logging ratio in Indonesia compared to timber production is 60%. meanwhile, in Malaysia, it is 35%. This shows that the ratio of law enforcement in Malaysia is more effective when comparing the rate of illegal logging and timber production. The phenomenon of forest destruction in Indonesia happened due to a disharmonic situation or an improper social relationship between society, the regional government, the forestry sector, business owners, and the law-enforcing apparatus. The sustainable forest-based law enforcement concept against corporate illegal logging is carried out through the integration approach that involves various parties in both countries.