Ehab Ezzat

Towards a Conceptual Framework for Early Warning Information Systems (EWIS) for Crisis Preparedness

This paper highlights the need of many organizations nowadays for early warning information systems (EWIS) that can predict the future and help prevent crises or reduce their negative effects. These EWIS should be based on a reliable and consistent framework. The frameworks currently available are mostly deterministic, simplified or inconsistent in application and assumption; thus making them unreliable and impractical. The goal of this paper is twofold. Firstly, it provides guidelines for system analysts, designers, engineers and experts seeking to deal with crisis or disaster information systems. Secondly, it aims to present a novel framework for EWIS that can be adapted to the dynamic needs of the field of crisis management, and that can also be used efficiently in crisis preparedness. Finally, the paper will describe a case study in the law enforcement sector as a proof-of-concept for the conceptual framework; to demonstrate both the theoretical and practical approaches.

An efficient hybridized index technique for moving object database

Indexes are needed in order to index a number of moving object’s positions to provide answers to different types of queries as fast as possible. The most popular types of querying techniques in moving object databases are K-Nearest Neighbor and Rang queries. In KNN, a set of k points of interest that can be reached in a minimum response time are retrieved. For Range Query, all objects whose positions fall within a predefined rectangular or circular range are retrieved. Creating an efficient index for objects’ locations is looked upon as the most critical problem in connection with spatial–temporal data management. Indexes are different based on their structures, query processing, and update performance. In this context, this paper aims to hybridize both tree and grid based structures to enhance update, search, and insert in the index. To achieve this goal, the current paper will discuss the design of the proposed index.

Datacentre Total Cost of Ownership (TCO) Models : A Survey

Datacenter total cost of ownerships (TCO) tools and spreadsheets can be used to estimate the capital and operational costs required for running datacenters. These tools are helpful for business owners to improve and evaluate the costs and the underlying efficiency of such facilities or evaluate the costs of alternatives, such as off-site computing. Well understanding of the cost drivers of TCO models can provide more opportunities to business owners to control costs .In addition, they also introduce an analytical structure in which anecdotal information can be cross-checked for consistency with other well-known parameters driving data center costs. This work focuses on comparing between number of proposed tools and spreadsheets which are publicly available to calculate datacenter total cost of ownership (TCO) ,The comparison is based on many aspects such as what are the parameters included and not included in such tools and whether the tools are documented or not. Such an approach presents a solid ground for designing more and better tools and spreadsheets in the future.

Swarming Behaviors of Chicken for Predicting Posts on Facebook Branding Pages

The rapid increase in social networks data and users present an urgent need for predicting the performance of posted data over these networks. It helps in many industrial aspects such as election, public opinion detection and advertising or branding over social networks. This paper presents a new posts’ prediction system for Facebook’s branding pages concerning the user’s attention and interaction. CSO is utilized to optimize the ANFIS parameters for accurate prediction. CSO-ANFIS is compared with several methods including ANFIS, particle swarm optimization, genetic algorithm and krill herd optimization.

Building Relation Extraction Templates via Unsupervised Learning

The vast amount of text published daily over the internet pose an opportunity to build unsupervised text mining models with a better or a comparable performance than existing models. In this paper, we investigate the problem of relation extraction and generation from text using an unsupervised model learned from news published online. We propose a clustering-based method to build a dataset of relations examples. News articles are clustered and once a cluster of sentences for each event in each piece of news is formed, relations between important entities in each event cluster are extracted and considered as examples of relations. Relations examples are used to build extraction templates in order to extract and generate readable relations summaries from new instances of news. The proposed unsupervised relation extraction and generation method is evaluated against multiple methods for relation extraction over different datasets where the proposed method has shown a comparable performance.

An Adaptive Approach for Community Detection Based on Chicken Swarm Optimization Algorithm

This paper presents an adaptive approach based on chicken swarm optimization algorithm (ACSO) for community detection problem in complex social networks. The proposed approach is able to define dynamically the number of communities for complex social network. The basic chicken swarm algorithm by its nature is continuous which can’t fit for community detection domain so it needs to be redesigned as a discrete chicken swarm for a better exploration of the search space. Locus-based adjacency scheme is used for encoding and decoding tasks while NMI and Modularity are used as an objective function.

Semi-supervised outlier detection via bipartite graph clustering

A considerable amount of attributes in real datasets are not numerical, but rather textual and categorical. We investigate the problem of identifying outliers in categorical and textual datasets. We propose a clustering-based semi-supervised outlier detection method which basically represents normal and unlabeled data points as a bipartite graph. We leverage the existing free of parameters clustering techniques to cluster the resulting graph. The bipartite graph is clustered with a specific end goal to distinguish unlabeled data points as either outliers or normal data points. The proposed method is evaluated using multiple categorical and textual datasets against one-class support vector machines classifier and FRaC approach for semi-supervised outlier detection where the proposed method has shown a comparable performance.

An Automated System for Measuring Similarity between Software Requirements

Recently, usage of text similarity has increased rapidly to be involved in different areas such as document clustering, information retrieval, short answer grading, text summarization, machine learning and natural language processing. Lexical-based similarity and semantic-based similarity are the two main categories of text similarity. Reusability of software components increases productivity and quality. In this paper, we propose that there is some linkage between text similarity and software reusability. In an organization, whenever a new incoming project is received, similarity test can be done to identify some similar projects and therefore some components to be reused such as design, code and test cases instead of starting building software from scratch. In this paper, we present an interactive system to measure the lexical similarity between a new incoming project and a set of completed projects exist in the repository and therefore identify some components to be reused.

Using a Multi-Criteria Approach in Early Warning Information Systems (MCEWIS)

A great deal of research has been conducted and numerous software programs have been created around the world in order to monitor events that cause threats. Such efforts had sought to predict possible disastrous events and help minimize their adverse effects on human life and property. The authors have developed a multi-criteria early warning information system (MCEWIS) to detect risks or expected hazards in the law enforcement sector. The distinguishing feature is using the EWS with a multi-factor/ multi-criteria approach to select the most important factors affecting the risk or even stimulating it. The EWS will automatically calculate the weight of each criterion/indicator and compare all criteria according to this weight. If one criterion has more weight than the others; this reflects the importance of this criterion in stimulating or increasing the risk. The paper describes the new approach and demonstrates the proof-of-concept.