Meysam Ghaffari

Meysam Ghaffari, Ashok Srinivasan, Xiuwen Liu

Timely and high-resolution estimates of the home locations of a sufficiently large subset of the population are critical for effective disaster response and public health intervention, but this is still an open problem. Conventional data sources, such as census and surveys, have a substantial time lag and cannot capture seasonal trends. Recently, social media data has been exploited to address this problem by leveraging its large user-base and real-time nature. However, inherent sparsity and noise, along with large estimation uncertainty in home locations, have limited their effectiveness. Consequently, much of previous research has aimed only at a coarse spatial resolution, with accuracy being limited for high-resolution methods. In this paper, we develop a deep-learning solution that uses a two-phase dynamic structure to deal with sparse and noisy social media data. In the first phase, high recall is achieved using a random forest, producing more balanced home location candidates. Then two deep neural networks are used to detect home locations with high accuracy. We obtained over 90% accuracy for large subsets on a commonly used dataset. Compared to other high-resolution methods, our approach yields up to 60% error reduction by reducing high-resolution home prediction error from over 21% to less than 8%. Systematic comparisons show that our method gives the highest accuracy both for the entire sample and for subsets. Evaluation on a real-world public health problem further validates the effectiveness of our approach.

Ghassem Alikhajeh, Abdolreza Mirzaei, Mehran Safayani et al.

Copy-move forgery is the most popular and simplest image manipulation method. In this type of forgery, an area from the image copied, then after post processing such as rotation and scaling, placed on the destination. The goal of Copy-move forgery is to hide or duplicate one or more objects in the image. Key-point based Copy-move forgery detection methods have five main steps: preprocessing, feature extraction, matching, transform estimation and post processing that matching and transform estimation have important effect on the detection. More over the error could happens in some steps due to the noise. The existing methods process these steps separately and in case of having an error in a step, this error could be propagated to the following steps and affects the detection. To solve the above mentioned problem, in this paper the steps of the detection system interact with each other and if an error happens in a step, following steps are trying to detect and solve it. We formulate this interaction by defining and optimizing a cost function. This function includes matching and transform estimation steps. Then in an iterative procedure the steps are executed and in case of detecting error, the error will be corrected. The efficiency of the proposed method analyzed in diverse cases such as pixel image precision level on the simple forgery images, robustness to the rotation and scaling, detecting professional forgery images and the precision of the transformation matrix. The results indicate the better efficiency of the proposed method.

Meysam Ghaffari, Nasser Ghadiri, Mohammad Hossein Manshaei et al.

The location-based services provide an interesting combination of cyber and physical worlds. However, they can also threaten the users' privacy. Existing privacy preserving protocols require trusted nodes, with serious security and computational bottlenecks. In this paper, we propose a novel distributed anonymizing protocol based on peer-to-peer architecture. Each mobile node is responsible for anonymizing a specific zone. The mobile nodes collaborate in anonymizing their queries, without the need not get access to any information about each other. In the proposed protocol, each request will be sent with a randomly chosen ticket. The encrypted response produced by the server is sent to a particular mobile node (called broker node) over the network, based on the hash value of this ticket. The user will query the broker to get the response. All parts of the messages are encrypted except the fields required for the anonymizer and the broker. This will secure the packet exchange over the P2P network. The proposed protocol was implemented and tested successfully, and the experimental results showed that it could be deployed efficiently to achieve user privacy in location-based services.

Mohammad Tafaghodi, Meysam Ghaffari, Alimohammad Latif et al.

With the fast development of communication and multimedia technology, the rights of the owners of multimedia products is vulnerable to the unauthorized copies and watermarking is one of the best known methods for proving the ownership of a product. In this paper we prosper the previous watermarking method which was based on Tabu search by Chaos. The modification applied in the permutation step of watermarking and the initial population generation of the Tabu search. We analyze our method on some well known images and experimental results shows the improvement in the quality and speed of the proposed watermarking method.

Mostafa Sepahvand, Ghasem Alikhajeh, Meysam Ghaffari et al.

With the technology development, the need of analyze and extraction of useful information is increasing. Bayesian networks contain knowledge from data and experts that could be used for decision making processes But they are not easily understandable thus the rule extraction methods have been used but they have high computation costs. To overcome this problem we extract rules from Bayesian network using genetic algorithm. Then we generate the graphical chain by mutually matching the extracted rules and Bayesian network. This graphical chain could shows the sequence of events that lead to the target which could help the decision making process. The experimental results on small networks show that the proposed method has comparable results with brute force method which has a significantly higher computation cost.

Meysam Ghaffari, Nasser Ghadiri

As a well-known clustering algorithm, Fuzzy C-Means (FCM) allows each input sample to belong to more than one cluster, providing more flexibility than non-fuzzy clustering methods. However, the accuracy of FCM is subject to false detections caused by noisy records, weak feature selection and low certainty of the algorithm in some cases. The false detections are very important in some decision-making application domains like network security and medical diagnosis, where weak decisions based on such false detections may lead to catastrophic outcomes. They are mainly emerged from making decisions about a subset of records that do not provide enough evidence to make a good decision. In this paper, we propose a method for detecting such ambiguous records in FCM by introducing a certainty factor to decrease invalid detections. This approach enables us to send the detected ambiguous records to another discrimination method for a deeper investigation, thus increasing the accuracy by lowering the error rate. Most of the records are still processed quickly and with low error rate which prevents performance loss compared to similar hybrid methods. Experimental results of applying the proposed method on several datasets from different domains show a significant decrease in error rate as well as improved sensitivity of the algorithm.