Aida Mustapha

Data quality: A survey of data quality dimensions

Nowadays, activities and decisions making in an organization is based on data and information obtained from data analysis, which provides various services for constructing reliable and accurate process. As data are significant resources in all organizations the quality of data is critical for managers and operating processes to identify related performance issues. Moreover, high quality data can increase opportunity for achieving top services in an organization. However, identifying various aspects of data quality from definition, dimensions, types, strategies, techniques are essential to equip methods and processes for improving data. This paper focuses on systematic review of data quality dimensions in order to use at proposed framework which combining data mining and statistical techniques to measure dependencies among dimensions and illustrate how extracting knowledge can increase process quality.

Naive Bayes-Guided Bat Algorithm for Feature Selection

When the amount of data and information is said to double in every 20 months or so, feature selection has become highly important and beneficial. Further improvements in feature selection will positively affect a wide array of applications in fields such as pattern recognition, machine learning, or signal processing. Bio-inspired method called Bat Algorithm hybridized with a Naive Bayes classifier has been presented in this work. The performance of the proposed feature selection algorithm was investigated using twelve benchmark datasets from different domains and was compared to three other well-known feature selection algorithms. Discussion focused on four perspectives: number of features, classification accuracy, stability, and feature generalization. The results showed that BANB significantly outperformed other algorithms in selecting lower number of features, hence removing irrelevant, redundant, or noisy features while maintaining the classification accuracy. BANB is also proven to be more stable than other methods and is capable of producing more general feature subsets.

A Review of Norms and Normative Multiagent Systems

Norms and normative multiagent systems have become the subjects of interest for many researchers. Such interest is caused by the need for agents to exploit the norms in enhancing their performance in a community. The term norm is used to characterize the behaviours of community members. The concept of normative multiagent systems is used to facilitate collaboration and coordination among social groups of agents. Many researches have been conducted on norms that investigate the fundamental concepts, definitions, classification, and types of norms and normative multiagent systems including normative architectures and normative processes. However, very few researches have been found to comprehensively study and analyze the literature in advancing the current state of norms and normative multiagent systems. Consequently, this paper attempts to present the current state of research on norms and normative multiagent systems and propose a norms life cycle model based on the review of the literature. Subsequently, this paper highlights the significant areas for future work.

A Dynamic Measurement of Agent Autonomy in the Layered Adjustable Autonomy Model

In a dynamically interactive systems that contain a mix of humans’ and software agents’ intelligence, managing autonomy is a challenging task. Giving an agent a complete control over its autonomy is a risky practice while manually setting the agent’s autonomy level is an inefficient approach. In this paper, we propose an autonomy measurement mechanism and its related formulae for the Layered Adjustable Autonomy (LAA) model. Our model provides a mechanism that optimizes autonomy distribution, consequently, enabling global control of the autonomous agents that guides or even withholds them whenever necessary. This is achieved by formulating intervention rules on the agents’ decision-making capabilities through autonomy measurement criteria. Our aim is to create an autonomy model that is flexible and reliable.

Effect of Temporal Relationships in Associative Rule Mining for Web Log Data

The advent of web-based applications and services has created such diverse and voluminous web log data stored in web servers, proxy servers, client machines, or organizational databases. This paper attempts to investigate the effect of temporal attribute in relational rule mining for web log data. We incorporated the characteristics of time in the rule mining process and analysed the effect of various temporal parameters. The rules generated from temporal relational rule mining are then compared against the rules generated from the classical rule mining approach such as the Apriori and FP-Growth algorithms. The results showed that by incorporating the temporal attribute via time, the number of rules generated is subsequently smaller but is comparable in terms of quality.

Norms Detection and Assimilation in Multi-agent Systems: A Conceptual Approach

In this paper, we propose a technique for a software agent to detect the norms of a community of agents and assimilate its behavior to comply with the local normative protocol, failing which, the agent is refused services and resources. In this technique, the software agent is equipped with an algorithm, which detects and analyzes the normative interactions between local agents. When the detection is successful, it launches another algorithm to request for its assimilation to the local normative protocol, indicating its acceptance by the group of local agents.

Potential norms detection in social agent societies

In this paper, we propose a norms mining algorithm that detects a domain’s potential norms, which we called the Potential Norms Mining Algorithm (PNMA). According to the literature, an agent changes or revises its norms based on variables of local environment and amount of thinking about its behaviour. Based on these variables, the PNMA is used to revise the norms and identify the new normative protocol to comply with the domain’s norms. The objective of this research is to enable an agent to revise its norms without a third party enforcement unlike most of the work on norms detection and identification, which entail sanctions by an authority. We demonstrate the execution of the algorithm by testing it on a typical scenario and analyse the results on several issues.

A Framework for Extracting Information from Semi-Structured Web Data Sources

Nowadays, many users use web search engines to find and gather information. User faces an increasing amount of various semi-structured information sources. The issue of correlating, integrating and presenting related information to users becomes important. When a user uses a search engine such as Yahoo and Google to seek a specific information, the results are not only information about the availability of the desired information, but also information about other pages on which the desired information is mentioned. The number of selected pages is enormous. Therefore, the performance capabilities, the overlap among results for the same queries and limitations of web search engines are an important and large area of research. Extracting information from the web data sources also becomes very important because the massive and increasing amount of diverse semi-structured information sources in the Internet that are available to users, and the variety of web pages making the process of information extraction from web a challenging problem. This paper proposes a framework for extracting, classifying and browsing semi-structured web data sources. The framework is able to extract relevant information from different web data sources, and classify the extracted information based on the standard classification of Nokia products.

A review of cyberbullying detection: An overview

With the growth of Web 2.0, online communication and social networks are emerging. This alternation helps users to share their information and collaborate with each other easily. In addition, these internet services help establish new connections between persons or reinforce existing ones. However, they can also lead to misbehaviors or cyber criminal acts for example, cyberbullying. At the same time, it can make children and adolescents to use the technologies for the intention of harming another person. Due to the negative effect of cyberbullying, some techniques and methods are proposed to overcome this problem. This paper illustrates a survey covering some methods and challenges in cyberbullying. Next, we offer suggestions for continued research in this area.

A fuzzy logic control in adjustable autonomy of a multi-agent system for an automated elderly movement monitoring application

Autonomous agents are being widely used in many systems, such as ambient assisted-living systems, to perform tasks on behalf of humans. However, these systems usually operate in complex environments that entail uncertain, highly dynamic, or irregular workload. In such environments, autonomous agents tend to make decisions that lead to undesirable outcomes. In this paper, we propose a fuzzy-logic-based adjustable autonomy (FLAA) model to manage the autonomy of multi-agent systems that are operating in complex environments. This model aims to facilitate the autonomy management of agents and help them make competent autonomous decisions. The FLAA model employs fuzzy logic to quantitatively measure and distribute autonomy among several agents based on their performance. We implement and test this model in the Automated Elderly Movements Monitoring (AEMM-Care) system, which uses agents to monitor the daily movement activities of elderly users and perform fall detection and prevention tasks in a complex environment. The test results show that the FLAA model improves the accuracy and performance of these agents in detecting and preventing falls.