the goal of this paper is to investigate which requirements engineering techniques have been applied in the development of Multi-Agent Systems (MAS) and how they were applied. We performed a systematic review of 58 of...
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ISBN:
(纸本)9783642043932
the goal of this paper is to investigate which requirements engineering techniques have been applied in the development of Multi-Agent Systems (MAS) and how they were applied. We performed a systematic review of 58 of a total of 835 papers found in scientific digital libraries. the results show that most of the proposals for dealing with requirements (79%) use already defined methods or techniques from other software development paradigms and that 69% of these techniques are based on the goal-oriented paradigm. A total of 95% of the reviewed papers focus on techniques for analyzing requirements, and only 45% of them explicitly consider some type of elicitation technique. Finally, only 5% of the papers give some empirical evidence about the effectiveness of their approaches by conducting empirical studies. the results of our study are particularly important in the determination of current research activities in Requirements engineering for MAS and in the identification of research gaps for further investigation.
A novel hybrid genetic algorithm(GA)/Support Vector Machine (SVM) system, which selects features from the protein sequences and trains the SVM classifier simultaneously using a multi-objective genetic algorithm, is pr...
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An approach is being explored that involves embedding a fuzzy logic based resource manager in an electronic game environment. Game agents can function under their own autonomous logic or human control. this approach a...
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High-dimensional data indexing and query is a challenging problem due to the inherent sparsity of the data. Fast algorithms are in an urgent need in this field. In this paper, an automatic subspace dimension selection...
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Changes in data distribution for in-sample training and out-sample validation can be unavoidable due to presence of random dynamic noises created by external uncontrollable environmental factors. To compensate for the...
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Effective diagnostic techniques are still needed for the early diagnosis and treatment of skin cancer, which is still a serious health concern. this paper presents a Generative AI-powered approach for the diagnosis of...
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As e-business software prevails worldwide, large amount of data are accumulated automatically in databases of most sizable companies. Managers in organizations now face the problems of making sense out of the data. In...
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Software that needs to fulfill many tasks requires a large number of components. Users of these software need a lot of time to find the desired functionality or follow a particular workflow. Recommendation systems can...
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ISBN:
(纸本)9783030336073;9783030336066
Software that needs to fulfill many tasks requires a large number of components. Users of these software need a lot of time to find the desired functionality or follow a particular workflow. Recommendation systems can optimize a user's working time by recommending the next features he/she needs. Given that, we evaluate the use of three algorithms (Markov Chain, IndRNN, and LSTM) commonly applied in sequence recommendation/classification in a dataset that reflects the use of the accounting software from Fortes Tecnologia. We analyze the results under two aspects: accuracy for top-5 recommendations and training time. the results show that the IndRNN achieved the highest accuracy, while the Markov Chain reached the lowest training time.
A novel approach is presented to the categorisation of non-rigid biological objects from unsegmented scenes in an unsupervised manner. the biological objects investigated are five phytoplankton species from the coasta...
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In many applications, data is non-vector in nature. For example, one might have transaction data from a dialup access system, where each customer has an observed time-series of dialups which are different on start tim...
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ISBN:
(纸本)354040550X
In many applications, data is non-vector in nature. For example, one might have transaction data from a dialup access system, where each customer has an observed time-series of dialups which are different on start time and dialup duration from customer to customer. It's difficult to convert this type of data to a vector form, so that the existing algorithms oriented on vector data [5] are hard to cluster the customers withtheir dialup, events. this paper presents an efficient model-based algorithm to cluster individuals whose data is non-vector in nature. then we evaluate on a large data set of dialup transaction, in order to show that this algorithm is fast and scalable for clustering, and accurate for prediction. At the same time, we compare this algorithm with vector clustering algorithm by predicting accuracy, to show that the former is fitter for non-vector datathan the latter.
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