Alaa Hamouda

Graph coloring and ACO based summarization for social networks

An unconventional GR-ACO-LS-STS for short text summarization it is proposed.The mechanism relies on graph coloring mixed with Ant colony optimization and local search.The mechanism was found more efficient than the other traditional algorithms. Due to the increasing popularity of contents of social media platforms, the number of posts and messages is steadily increasing. A huge amount of data is generated daily as an outcome of the interactions between fans of the networking platforms. It becomes extremely troublesome to find the most relevant, interactive information for the subscribers. The aim of this work is to enable the users to get a powerful brief of comments without reading the entire list. This paper opens up a new field of short text summarization (STS) predicated on a hybrid ant colony optimization coming with a mechanism of local search, called ACO-LS-STS, to produce an optimal or near-optimal summary. Initially, the graph coloring algorithm, called GC-ISTS, was employed before to shrink the solution area of ants to small sets. Evidently, the main purpose of using the GC algorithm is to make the search process more facilitated, faster and prevents the ants from falling into the local optimum. First, the dissimilar comments are assembled together into the same color, at the same time preserving the information ratio as for an original list of comment. Subsequently, activating the ACO-LS-STS algorithm, which is a novel technique concerning the extraction of the most interactive comments from each color in a parallel form. At the end, the best summary is picked from the best color. This problem is formalized as an optimization problem utilizing GC and ACO-LS to generate the optimal solution. Eventually, the proposed algorithm was evaluated and tested over a collection of Facebook messages with their associated comments. Indeed, it was found that the proposed algorithm has an ability to capture a good solution that is guaranteed to be near optimal and had realized notable performance in comparison with traditional document summarization algorithms.

Ant colony heuristic for user-contributed comments summarization

User-contributed comments (UCC) are one of the signs of the social media. Due to the high popularity of social media, it becomes already exceedingly difficult to find the most relevant, interactive information for the users. The motivation behind this work is the fact that users may interest to get an efficacious brief understanding of comments without reading the entire comments. This paper opens up an unconventional field of comments summarization predicated on Ant colony optimization mixed with JensenShannon divergence (ACO-JSD). ACO-JSD is a proposed novel technique concerning the extraction the most interactive comments from the huge amount of concise comments perspectives. This problem is unfastened utilizing ACO to generate the optimal solution. Moreover, the JSD model is employed to ensure a summary could capture the essence of the original comments. First, an acyclic semi-graph has been constructed under two constraints: (1) the longest comments will be isolated from the graph, (2) The more similarity between two comments, the greater the chance that mutual connectivity is eliminated. Next, a feasible solution is constructed to select the high-quality summarization. Finally, the proposed algorithm has been evaluated over a collection of Facebook posts with their associated comments and an excellent performance in comparison with traditional document summarization algorithms was obtained. Accordingly, the computational results show the efficiency of the proposed algorithm, as well as its ability to find a good summary that is guaranteed to be near-optimal.

A survey of multiple types of text summarization with their satellite contents based on swarm intelligence optimization algorithms

Abstract Due to the tremendous increment of data on the web, extracting the most important data as a conceptual brief would be valuable for certain users. Therefore, there is a massive enthusiasm concerning the generation of automatic text summary frameworks to constitute abstracts automatically from the text, web, and social network messages associated with their satellite content. This survey highlights, for the first time, how the swarm intelligence (SI) optimization techniques are performed to solve the text summarization task efficiently. Additionally, a convincing justification of why SI, especially Ant Colony Optimization (ACO), has been presented. Unfortunately, three types of text summarization tasks using SI indicate bit utilizing in the literature when contrasted with the other summarization techniques as machine learning and genetic algorithms, in spite of the fact that there are seriously promising outcomes of the SI methods. On the other hand, it has been noticed that the summarization task with multiple types has not been formalized as a multi-objective optimization (MOO) task before, despite that there are many objectives which can be considered. Moreover, the SI was not employed before to support the real-time summary approaches. Thus, a new model has been proposed to be adequate for achieving many objectives and to satisfy the real-time needs. Eventually, this study will enthuse researchers to further consider the various types of SI when solving the summarization tasks, particularly, in the short text summarization (STS) field.

The BSCBAS: a Balanced Scorecard-based appraisal system for improving the performance of software organizations

Performance appraisals are most commonly undertaken to let an employee know how his/her performance compares to the supervisors expectations and to identify areas that require training or development. Generally, developing an appraisal system that accurately reflects employee performance is a difficult task. In software companies, such a task is more difficult because software engineering is, by its nature, innovative work. To measure software engineers’ performance, the challenge is in having tangible key performance indicators that reflect that performance. In this paper, we present a framework for an appraisal system for software engineers using the Balanced Scorecard methodology. This framework was applied and its influence on the software engineers’ performance and the overall performance of the organization was investigated. Copyright