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This paper examines the application of artificial neural networks (ANNs) for predicting crop yields for an agricultural region in Nepal. The neural network algorithm has become an effective data mining tool and the outcome produced by this algorithm is considered to be less error prone than other computer science techniques. The backpropagation algorithm which iteratively finds a suitable weight value is considered for computing the error derivative. Agricultural data was collected from thirteen years from paddy field cultivation in the Siraha district, an eastern region in Nepal, and used for this investigation of neural networks. Additionally, climatic parameters including rainfall, maximum temperature and minimum temperature along with the fertilizer use were also used as input values. The experiment shows that the trained neural network produced a minimum error which indicated that the test model is capable of predicting crops yield in Nepal.

This paper presents a web-based training system in eAgriculture for agricultural college in Thailand. The eAgriculture contents consist of introduction to information and communication technology (ICT) for agriculture, information technology in agriculture, agricultural management information system and precision farming. The research shows that the majority users are highly satisfied with the developed web-based training system. The users favoured four factors of the system including the content of the website; technical media production; the designing and the formatting of the website; and benefits of its uses.

Red teaming (RT) is a process that assists an organization in finding vulnerabilities in a system whereby the organization itself takes on the role of an “attacker” to test the system. It is used in various domains including military operations. Traditionally, it is a manual process with some obvious weaknesses: it is expensive, time-consuming, and limited from the perspective of humans “thinking inside the box”. Automated RT is an approach that has the potential to overcome these weaknesses. In this approach both the red team (enemy forces) and blue team (friendly forces) are modelled as intelligent agents in a multi-agent system and the idea is to run many computer simulations, pitting the plan of the red team against the plan of blue team. This research project investigated techniques that can support automated red teaming by conducting a systematic study involving a genetic algorithm (GA), a basic coevolutionary algorithm and three variants of the coevolutionary algorithm. An initial pilot study involving the GA showed some limitations, as GAs only support the optimization of a single population at a time against a fixed strategy. However, in red teaming it is not sufficient to consider just one, or even a few, opponent‟s strategies as, in reality, each team needs to adjust their strategy to account for different strategies that competing teams may utilize at different points. Coevolutionary algorithms (CEAs) were identified as suitable algorithms which were capable of optimizing two teams simultaneously for red teaming. The subsequent investigation of CEAs examined their performance in addressing the characteristics of red teaming problems, such as intransitivity relationships and multimodality, before employing them to optimize two red teaming scenarios. A number of measures were used to evaluate the performance of CEAs and in terms of multimodality, this study introduced a novel n-peak problem and a new performance measure based on the Circular Earth Movers‟

This paper describes the use of genetic algorithms (GAs) for computerized red teaming applications, to explore options for military plans in specific scenarios. A tool called Optimized Red Teaming (ORT) is developed and we illustrate how it may be utilized to assist the red teaming process in security organizations, such as military forces. The developed technique incorporates a genetic algorithm in conjunction with an agent-based simulation system (ABS) called MANA (Map Aware Non-uniform Automata). Both enemy forces (the red team) and friendly forces (the blue team) are modelled as intelligent agents in a multi-agent system and many computer simulations of a scenario are run, pitting the red team plan against the blue team plan. The paper contains two major sections. First, we present a description of the ORT tool, including its various components. Second, experimental results obtained using ORT on a specific military scenario known as Key Installation Protection, developed at DSO National Laboratories in Singapore, are presented. The aim of these experiments is to explore the red tactics to penetrate a fixed blue patrolling strategy.

Nilsook, P., Armstrong, L. , Taechatanasat, P. , & Ranjeet, T. (2014). Web-based training in e-Agriculture for Agricultural College. Proceedings of Asian Federation for Information Technology in Agriculture. (pp. 360-370). Perth, W.A. Australian Society of Information and Communication Technologies in Agriculture. Available here

Ranjeet, T. , & Armstrong, L. (2014). An Artificial Neural Network for Predicting Crops Yield in Nepal. Proceedings of Asian Federation for Information Technology in Agriculture. (pp. 371-381). Perth, W.A. Australian Society of Information and Communication Technologies in Agriculture. Available here