• International Journal of Computer Science & Network Security
• /
• v.22 no.10
• /
• pp.374-388
• /
• 2022

Cloud computing has been one of the most critical technology in the last few decades. It has been invented for several purposes as an example meeting the user requirements and is to satisfy the needs of the user in simple ways. Since cloud computing has been invented, it had followed the traditional approaches in elasticity, which is the key characteristic of cloud computing. Elasticity is that feature in cloud computing which is seeking to meet the needs of the user's with no interruption at run time. There are traditional approaches to do elasticity which have been conducted for several years and have been done with different modelling of mathematical. Even though mathematical modellings have done a forward step in meeting the user's needs, there is still a lack in the optimisation of elasticity. To optimise the elasticity in the cloud, it could be better to benefit of Machine Learning algorithms to predict upcoming workloads and assign them to the scheduling algorithm which would achieve an excellent provision of the cloud services and would improve the Quality of Service (QoS) and save power consumption. Therefore, this paper aims to investigate the use of machine learning techniques in order to predict the workload of Physical Hosts (PH) on the cloud and their energy consumption. The environment of the cloud will be the school of computing cloud testbed (SoC) which will host the experiments. The experiments will take on real applications with different behaviours, by changing workloads over time. The results of the experiments demonstrate that our machine learning techniques used in scheduling algorithm is able to predict the workload of physical hosts (CPU utilisation) and that would contribute to reducing power consumption by scheduling the upcoming virtual machines to the lowest CPU utilisation in the environment of physical hosts. Additionally, there are a number of tools, which are used and explored in this paper, such as the WEKA tool to train the real data to explore Machine learning algorithms and the Zabbix tool to monitor the power consumption before and after scheduling the virtual machines to physical hosts. Moreover, the methodology of the paper is the agile approach that helps us in achieving our solution and managing our paper effectively.

• Natural Product Sciences
• /
• v.28 no.2
• /
• pp.45-52
• /
• 2022

The identification of Plasmodium falciparum enoyl acyl-carrier protein reductase (pfENR) is considered as a potential biological target against malaria. Trema orientalis is considered a rich source of phytochemicals useful in malaria treatment. This study evaluated the in-vitro inhibitory activity of the extract and isolated compounds of T. orientalis leaf; the isolated compounds and the analogues of the most active compound were subjected to in-silico molecular docking studies on pfENR. The methanolic extract of T. orientalis was subjected to repeated chromatographic separation which led to the isolation of some compounds. The isolated compounds from the plant were examined for their antimalarial activity using β-hematin inhibition assay. Virtual screening via molecular docking and ADMET studies were conducted to gain insight into the mechanism of binding of ligand and to identify effective pfENR inhibitors. The isolated compounds and the analogues of the most active isolates were gotten from PubChem library for use in docking study. Hexacosanol and β-sitosterol showed inhibition of the β-hematin formation. The docking results showed that hexacosanol, β-sitosterol and the analogues of β-sitosterol displayed binding energy ranging between -6.1 kcal/mol and -11.6 kcal/mol. Sitosterol glucoside has the highest docking score. Some of the ligands showed more binding affinity than known bioactive compounds used as reference. Analogues of β-sitosterol has been shown to be potential inhibitors of pfENR, therefore, the findings from this study suggest that sitosterol glucoside and ergosterol peroxide could act as antimalarial agents after further lead optimisation investigations.

• Proceedings of the Korean Society of Grassland Science Conference
• /
• 2002.09b
• /
• pp.103-127
• /
• 2002

The main problem of the forage-based livestock farming in Germany at the moment is the high yielding cow requiring high energy concentration in feed which can be obtained lot easier with corn and grain than with grass. Therefore milk production tends out of the grassland region and into the forage crop region. Nutrient surplus due to concentrates in milk production in future probably will be limited by the government. The problem can only be solved by using best swards and optimal silage techniques as well as optimisation of manure utilization. Most important steps of sustainable forage production are care of grassland as well as regular resowing, especially of silage meadows. About 40% of Hessian grassland is managed in agri-environmental problems to keep it in use and to protect the natural resources. Selective measures are realized, to solve special problmes of nature and landscape conservation.

• International Journal of Computer Science & Network Security
• /
• v.23 no.11
• /
• pp.99-109
• /
• 2023

The darknet is frequently referred to as the hub of illicit online activity. In order to keep track of real-time applications and activities taking place on Darknet, traffic on that network must be analysed. It is without a doubt important to recognise network traffic tied to an unused Internet address in order to spot and investigate malicious online activity. Any observed network traffic is the result of mis-configuration from faked source addresses and another methods that monitor the unused space address because there are no genuine devices or hosts in an unused address block. Digital systems can now detect and identify darknet activity on their own thanks to recent advances in artificial intelligence. In this paper, offer a generalised method for deep learning-based detection and classification of darknet traffic. Furthermore, analyse a cutting-edge complicated dataset that contains a lot of information about darknet traffic. Next, examine various feature selection strategies to choose a best attribute for detecting and classifying darknet traffic. For the purpose of identifying threats using network properties acquired from darknet traffic, devised a hybrid deep learning (DL) approach that combines Recurrent Neural Network (RNN) and Bidirectional LSTM (BiLSTM). This probing technique can tell malicious traffic from legitimate traffic. The results show that the suggested strategy works better than the existing ways by producing the highest level of accuracy for categorising darknet traffic using the Black widow optimization algorithm as a feature selection approach and RNN-BiLSTM as a recognition model.

• International conference on construction engineering and project management
• /
• 2011.02a
• /
• pp.299-303
• /
• 2011

Congestion is a growing concern of many global cities and the demands on Infrastructure services within a locale coupled by the rising expectations from the growing population places stress on these cities. This entails the ability to build a sustainable community that requires an understanding and recognition of Population growth, changing demographics and the ever changing urban development on both a macro and micro level. Infrastructure is an integral part of Australian economy, particularly the 'Infrastructure Assets Management' which highlights the importance towards the development of sustainable communities for Melbourne's future. Melbourne 2030 is a comprehensive representation of government's response to a wide-ranging population growth within Melbourne metropolitan and surrounding areas. Urban plan and specific Infrastructure Assets Planning needs not only to provide sufficient Infrastructure to a community, but it must also be efficient and innovative so that it produces an optimised management system. A system that incorporates engineering techniques that will be sustainable for decades to come by maintaining an acceptable level of services to its intended community in an effective manner, which also strengthens service delivery. The fundamental challenges for optimization of Infrastructure with the Melbourne urban plan is, the ability to manage and sustain maintenance of Infrastructure to provide the acceptable level of service required by the community in a most effective manner which also strengthens service delivery to contribute towards Melbourne 2030. This paper particularly investigates some of the fundamental issues within the Melbourne urban plan such as Infrastructure Asset Management, AusLink and the Australian Road Management Act 2004, which the Governments at all levels must deal with to provide an economically viable solution to the changing Infrastructure so it may suits the needs and services the strategies of a metropolis.