Seyed Abdollah Amin Mousavi

Bio: Seyed Abdollah Amin Mousavi is an academic researcher from Islamic Azad University. The author has contributed to research in topics: Graph (abstract data type) & Behavioral activation. The author has an hindex of 2, co-authored 2 publications receiving 26 citations.

Combined fuzzy clustering and firefly algorithm for privacy preserving in social networks

Abstract: In recent years, an explosive growth of social networks has been made publicly available for understanding the behavior of users and data mining purposes. The main challenge in sharing the social network databases is protecting public released data from individual identification. The most common privacy preserving technique is anonymizing data by removing or changing some information, while the anonymized data should retain as much information as possible of the original data. K-anonymity and its extensions (e.g., L-diversity and T-closeness) have widely been used for data anonymization. The main drawback of the existing anonymity techniques is the lack of protection against attribute/link disclosure and similarity attacks. Moreover, they suffer from high amount of information loss in the released database. In order to overcome these drawbacks, this paper proposes a combined anonymizing algorithm based on K-member Fuzzy Clustering and Firefly Algorithm (KFCFA) to protect the anonymized database against identity disclosure, attribute disclosure, link disclosure, and similarity attacks, and significantly minimize the information loss. In KFCFA, at first, a modified K-member version of fuzzy c-means is utilized to create balanced clusters with at least K members in each cluster. Then, firefly algorithm is performed for further optimizing the primary clusters and anonymizing the network graph and data. To achieve this purpose, a constrained multi-objective function is introduced to simultaneously minimize the clustering error rate and the generated information loss, while satisfying the defined anonymity constraints. The proposed methodology can be utilized for both network graph structures and micro data. Simulation results over four social network databases from Facebook, Google+, Twitter and YouTube demonstrate the efficiency of the proposed KFCFA algorithm to minimize the information loss of the published data and graph, while satisfying K-anonymity, L-diversity and T-closeness conditions.

The Relationship between Behavioral Activation/Inhibition Systems (BAS/BIS) and Bullying/ Victimization Behaviors among Male Adolescents

Abstract: Objective: This research was conducted to investigate the relationship between behavioral activation-inhibition systems and bullyingvictimization behaviors among adolescents. Method: This was a correlational and cross-sectional study. Two hundred and thirty school boys were selected randomly by multistage cluster sampling method, and participated in this research. This sample responded to a demographic questionnaire, the Revised Olweus Bully/ Victim questionnaire and the child version of behavioral inhibition/activation systems Scale in their classrooms and in the presence of the researcher. The collected data were analyzed by Pearson’s correlation and multiple regressions. Result: The results showed that bullying and victimization were correlated with both behavioral activation and behavioral inhibition systems (p<0.01). The results also showed that 18% of the variance in victimization and 31 % of the variance in bullying were explained by behavioral inhibition and behavioral activation systems respectively . Conclusion: The results of this study implied that BAS and BIS may play a role in the manifestation of bullying in adolescents.

An improved firefly algorithm for global continuous optimization problems

Abstract: Global continuous optimization is populated by its implementation in many real-world applications. Such optimization problems are often solved by nature-inspired and meta-heuristic algorithms, including the firefly algorithm (FA), which offers fast exploration and exploitation. To further strengthen FA's search for global optimum, a Levy-flight FA (LF-FA) has been developed through sampling from a Levy distribution instead of the traditional uniform one. However, due to its poor exploitation in local areas, the LF-FA does not guarantee fast convergence. To address this problem, this paper provides an adaptive logarithmic spiral-Levy FA (AD-IFA) that strengthens the LF-FA's local exploitation and accelerates its convergence. Our AD-IFA is integrated with logarithmic-spiral guidance to its fireflies’ paths, and adaptive switching between exploration and exploitation modes during the search process. Experimental results show that the AD-IFA presented in this paper consistently outperforms the standard FA and LF-FA for 29 test functions and 6 real cases of global optimization problems in terms of both computation speed and derived optimum.

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

Abstract: Anonymization is a practical solution for preserving user’s privacy in data publishing. Data owners such as hospitals, banks, social network (SN) service providers, and insurance companies anonymize their user’s data before publishing it to protect the privacy of users whereas anonymous data remains useful for legitimate information consumers. Many anonymization models, algorithms, frameworks, and prototypes have been proposed/developed for privacy preserving data publishing (PPDP). These models/algorithms anonymize users’ data which is mainly in the form of tables or graphs depending upon the data owners. It is of paramount importance to provide good perspectives of the whole information privacy area involving both tabular and SN data, and recent anonymization researches. In this paper, we presents a comprehensive survey about SN (i.e., graphs) and relational (i.e., tabular) data anonymization techniques used in the PPDP. We systematically categorize the existing anonymization techniques into relational and structural anonymization, and present an up to date thorough review on existing anonymization techniques and metrics used for their evaluation. Our aim is to provide deeper insights about the PPDP problem involving both graphs and tabular data, possible attacks that can be launched on the sanitized published data, different actors involved in the anonymization scenario, and major differences in amount of private information contained in graphs and relational data, respectively. We present various representative anonymization methods that have been proposed to solve privacy problems in application-specific scenarios of the SNs. Furthermore, we highlight the user’s re-identification methods used by malevolent adversaries to re-identify people uniquely from the privacy preserved published data. Additionally, we discuss the challenges of anonymizing both graphs and tabular data, and elaborate promising research directions. To the best of our knowledge, this is the first work to systematically cover recent PPDP techniques involving both SN and relational data, and it provides a solid foundation for future studies in the PPDP field.

Inclusive Multiple Model Using Hybrid Artificial Neural Networks for Predicting Evaporation

Abstract: Predicting evaporation is essential for managing water resources in basins. Improvement of the prediction accuracy is essential to identify adequate inputs on evaporation. In this study, artificial neural network (ANN) is coupled with several evolutionary algorithms, i.e., capuchin search algorithm (CSA), firefly algorithm (FFA), sine cosine algorithm (SCA), and genetic algorithm (GA) for robust training to predict daily evaporation of seven synoptic stations with different climates. The inclusive multiple model (IMM) is then used to predict evaporation based on established hybrid ANN models. The adjusting model parameters of the current study is a major challenge. Also, another challenge is the selection of the best inputs to the models. The IMM model had significantly improved the root mean square error (RMSE) and Nash Sutcliffe efficiency (NSE) values of all the proposed models. The results for all stations indicated that the IMM model and ANN-CSA could outperform other models. The RMSE of the IMM was 18, 21, 22, 30, and 43% lower than those of the ANN-CSA, ANN-SCA, ANN-FFA, ANN-GA, and ANN models in the Sharekord station. The MAE of the IMM was 0.112 mm/day, while it was 0.189 mm/day, 0.267 mm/day, 0.267 mm/day, 0.389 mm/day, 0.456 mm/day, and 0.512 mm/day for the ANN-CSA, ANN-SCA, and ANN-FFA, ANN-GA, and ANN models, respectively, in the Tehran station. The current study proved that the inclusive multiple models based on improved ANN models considering the fuzzy reasoning had the high ability to predict evaporation.