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Permanent URI for this communityhttp://192.248.9.226/handle/123/2105

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item: Article-Abstract
A Commonsence knowledge modeling systems for qualitaive risk assessment
Mendis, DSK; Karunananda, AS; Samaratunga, U
Knowledge is the fundamental resource that enhances to function intelligently. Knowledge can be defined into two types such as explicit and implicit. Commonsense knowledge is one type of in implicit knowledge. Explicit knowledge can be presented formally and capable of effective (fast and good quality) communication of data to the user where as implicit knowledge can be represented in informal way and further modeling needed for gaining effective communication. Constructions of risk assessment using spatial data for disaster management have a problem of effective communication because of implicit knowledge. Risk assessment is a step in a risk management process. Risk assessment is the determination of quantitative or qualitative value of risk related to a concrete situation and a recognized hazard. Quantitative risk assessment requires commonsense knowledge related with the hazard. This complicates the effective ommunication of data to the user in real-time machine processing in support of disaster management. In this paper we present an approach to modeling commonsense knowledge in Quantitative risk assessment. This gives three-phase knowledge modeling approach for modeling commonsense knowledge in, which enables holistic approach for disaster management. At the initial stage commonsense knowledge is converted into a questionnaire. Removing dependencies among the questions are modeled using principal component analysis. Classification of the knowledge is processed through fuzzy logic module, which is constructed on the basis of principal components. Further explanations for classified knowledge are derived by expert system technology. We have implemented the system using FLEX expert system shell, SPSS, XML and VB. This paper describes one such approach using classification of human constituents in Ayurvedic medicine. Evaluation of the system has shown 77% accuracy.
item:
A Multi-agent solution for managing complexity in English to Sinhala machine translation
Hettige, B; Karunananda, AS; Rzevski, G
Machine translation turns out to be an inherently complex process requiring serious attention to morphological, syntactic and semantic complexity within both the source and the target languages. Most of the existing approaches to machine translation (MT) circumvent the complexity with the assumption that morphological, syntactic and semantic analysis can be done independently and sequentially. This has resulted in depriving us of the opportunity to use the language complexity to generate high-quality translations. In view of this, research has been conducted to develop a multi-agent systems solution for MT that uses the language complexity as an opportunity for generating a more realistic translation from English to Sinhala. This multi-agent solution primarily comprises a six-agent swarm to deliberate on morphological, syntactic and semantic concerns of the source and the target languages without being constrained to operate in a sequential manner. These agents use the ontology of corpora and dictionary of two languages. This approach is inspired by the fact that people understand a sentence by incrementally reading through words while simultaneously considering the syntax and semantics. As such, when the system progresses in identification of words one by one, both syntactical and semantic concerns are entertained up to the current point of reading. As a result, initially decided words may be changed due to the present concern of morphology, syntax and semantics. A translation system has been implemented on the multi-agent system development framework named MaSMT. Experiments show that the multi-agent solution for MT gives promising results for translating sentences of an average length and further research has been carried out to accommodate translation of long sentences.
item:
Quality of service in cloud computing : a critical review
(2015-07-21) Firdhous, MFM; Hassan, S; Ghazali, O
Quality of service plays is an important factor in distributed computing. Cloud computing has been the paradigm in distributed computing. Under cloud computing, computing resources are hosted in the internet and delivered to customers as services. Prior to the commencement of services, the customers and cloud providers negotiate and enter into an agreement named service level agreement. The services level agreements clarify the roles, set charges and expectations and provide mechanisms for resolving service problems within a specified and agreed upon time period. Service level agreements also cover performance, reliability conditions in terms of quality of service guarantees. In this paper, the authors present a comprehensive survey on quality of service implementations in cloud computing with respect to their implementation details, strengths and weaknesses.
item: Conference-Abstract
State of artificial intelligence in Sri Lankan software industry
Asanka, PPGD; Fernando, MHR; Adhikari, AMTB; Pathirage, IPVV; Karunananda, AS;
This paper has examined propagations and deliberations of AI in Sri Lankan software industry. A survey has been carried out for gathering the information. Even though software industry is a rapidly growing sector in Sri Lanka, it is lagging behind in terms of using AI technologies with compared to other countries in the world. According to the survey, this is due to the lack of popularity, knowledge, experts, requirements and sponsorship for the AI related software projects. Sri Lankan software industry has the required maturity to adopt with AI technologies and they have already made their foot print on potential markets where they could find advanced AI research and development projects in different domains. However, Sri Lankan software industry has not been able to achieve the required momentum, stability and the confidence on research and development projects in AI. Large-scale companies must increase their attention on AI technologies and the required knowledge and work force should be supplied through the Sri Lankan education system. Professional organizations, research groups, academics and the industry experts got a huge role to be played jointly to overcome the barriers in order to introduc AI technologies to the Sri Lankan software industry.

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