Matin Pirouz

California State University, Fresno | Fresno State · Department of Computer Science

The development of the Internet of Things has facilitated the rapid development of various industries. With the improvement in people’s living standards, people’s health requirements are steadily improving. However, owing to the scarcity of medical and health care resources in some areas, the demand for remote surgery has gradually increased. In th...

Estimating similarity using multiple similarity measures or machine learning prediction models is a popular solution to the link prediction problem. The Relation Pattern Deep Learning Classification (RPDLC) technique is proposed in this study, and it is based on multiple neighbor-based similarity metrics and convolution neural networks. The RPDLC f...

Detecting communities in complex networks gives rise to a new data mining challenge known as community ranking. Community ranking can be computed based on the influence of each community on the neighboring communities as well as on the entire network. Our main research question is to verify if there is a significant correlation between community ra...

In Cyber-Physical Systems (CPS), especially in human-in-the-loop situations (also known as HitLCPS), the security and privacy for keeping sensitive information private is considered an emerging topic in recent decades. Many techniques in privacy-preserving data mining (PPDM) can be applied directly to HitLCPS. However, most of them to date have foc...

In the ever-growing world, the concepts of High-utility Itemset Mining (HUIM) as well as Frequent Itemset Mining (FIM) are fundamental works in knowledge discovery. Several algorithms have been designed successfully. However, these algorithms only used one factor to estimate an itemset. In the past, skyline pattern mining by considering both aspect...

One key methodology to understand the structure of complex networks is through community detection and analysis. Such information is used to find relationships and hidden structures in social communities. Existing community detection algorithms are either computationally expensive in large-scale real-world networks or require specific information s...

Autism Spectrum Disorder (ASD) is a neurodevelopment disorder associated with impairments in socio-communication, relationships, restrictions in thoughts, imagination, etc. Autism being identified as genetic, depending upon person to person and their socio-communication, it is important for computer science researchers to analyze the big data visua...

The Internet of Vehicles (IoV) aims to establish a network of autonomous vehicles that exchange messages with one another and the infrastructure such as road-side units and a central trust authority. These messages must be secure in transmission to ensure network security and utilize as little resources as possible. Currently, there is much researc...

High-utility itemset mining (HUIM) is considered as an emerging approach to detect the high-utility patterns from databases. Most existing algorithms of HUIM only consider the itemset utility regardless of the length. This limitation raises the utility as a result of a growing itemset size. High average-utility itemset mining (HAUIM) considers the...

Within the current transportation infrastructure, we have seen a steady increase in the use of sensor technologies. These sensors, individually produce large amounts of data that then need to be fused and understood. Data commingling and data integration are difficult tasks when it comes to processing such data centrally, which can require costly h...

The Internet of Vehicles (IoV) is an emerging research framework, with network and graph theories as two of the major fields. Researchers in these topics use a variety of tools and approaches to simulate and perform experimentation on their proposed methodologies. A comprehensive study to facilitate the selection of such simulation tools is lacking...

The Internet of Vehicles (IoV) aims to establish a network of autonomous and connected vehicles that communicate with one another through facilitation led by road-side units (RSUs) and a central trust authority (TA). Messages must be efficiently and securely disseminated to conserve resources and preserve network security. Currently, research in th...

The stock market is a highly nonlinear dynamic system, not only stock prices have a certain tendency, but also it is influenced by many factors such as political, economic and psychological factors. With the flourishing development of deep learning technique, a well-designed neural network can accomplish feature learning tasks more effectively. For...

Abstract Emotion recognition using brain signals has the potential to change the way we identify and treat some health conditions. Difficulties and limitations may arise in general emotion recognition software due to the restricted number of facial expression triggers, dissembling of emotions, or among people with alexithymia. Such triggers are ide...

The emergence and proliferation of the internet of things (IoT) devices have resulted in the generation of big and uncertain data due to the varied accuracy and decay of sensors and their different sensitivity ranges. Since data uncertainty plays an important role in IoT data, mining the useful information from uncertain dataset has become an impor...

Given here is an anonymized dataset of online grocery purchases from users; we present a recommender system framework to predict future purchases. We describe the method of constructing a utility matrix to run a collaborative filtering algorithm to pair similar and dissimilar users and ultimately provide recommendations. Given those recommendations...

The growing healthcare industry generates a large amount of data on patient health conditions, demographic plans, and drugs required for such conditions. These attract the attention of the medical professionals and the data scientists alike. In this paper, we propose a drug recommendation assistant built using machine learning techniques and natura...

Natural language processing is a technique to process data such as text and speech. Some fundamental research includes named-entity recognition, which recognizes name entities (i.e., persons, companies) from texts; semantic parsing, which is used to convert a natural language utterance to the representation of logical form; and co-reference resolut...

A latent variable conditional random fields (CRF) model is proposed to improve sequence labeling, which utilizes the BIO encoding schema as latent variable to capture the latent structure of hidden variables and observation data. The proposed model automatically selects the best encoding schema for each given input sequence. Through experimentation...

We present a neural-encoded mention-hypergraph (named as NEMH in this paper) model for mention-extraction and classification in this paper. Through extraction of textual mention entities, a model is proposed that applies a hypergraph-encoding schema to neural networks. Comparing the results of the proposed model with the previous approaches, the pr...

High-utility itemset mining (HUIM) has been gaining popularity in the field of data mining. Frequent itemset mining (FIM) used to be the main tool to reveal high-frequency patterns but failed to consider the concept of profit. HUIM, on the other hand, obtains the itemsets and is practical in commercial applications. A main challenge in HUIM is that...

In this study, FAST Personalized PageRank is utilized to find the target node set. Using the mentioned target set, the algorithm gives an estimation of the closeness of any pair of nodes in the graph. Personalized Page Vector is used to find the most popular nodes, also known as hubs, in the network. The time taken by the estimation of Personalized...

Identifying community structures and subnetwork patterns for complex networks provide us with great knowledge about network. Community detection has been getting lots of attention and interest in recent years. The application for such knowledge goes from target marketing to biology, social studies, and physics. The existing algorithms either lack a...

In the past, frequent itemset mining (FIM) revealed the high-frequent patterns but ignored the more important concepts such as unit of profit and quality of the items. Recently, high-utility mining (HUIM) has caused wide public concern in the data mining field. A principal problem in HUIM is that the HUIM needs to handle the exponential search spac...

The advancement in technology has transformed Cyber Physical Systems and their interface with IoT into a more sophisticated and challenging paradigm. As a result, vulnerabilities and potential attacks manifest themselves considerably more than before, forcing researchers to rethink the conventional strategies that are currently in place to secure s...

In the era of Big Data , reduced models capable of reducing big data graph to estimate Personalized PageRank are limited. Personalized PageRank is a page rank calculation where random jumps are only allowed to a subset of start nodes. The resources of current process of calculation of Personalized PageRank is highly prohibitive, in this paper, we p...

Community structures and relation patterns, and ranking them for social networks provide us with great knowledge about network. Such knowledge can be utilized for target marketing or grouping similar, yet distinct, nodes. The ever-growing variety of social networks necessitates detection of minute and scattered communities, which are important prob...

Clickjacking attacks are emerging threats to websites of different sizes and shapes. They are particularly used by threat agents to get more likes and/or followers in Online Social Networks (OSNs). This paper reviews the clickjacking attacks and the classic solutions to tackle various forms of those attacks. Different approaches of Cross-Site Scrip...

This paper proposes an algorithm called optimized relativity search to reduce the number of nodes in a graph when attempting to decrease the running time for personalized page rank (PPR) estimation. Even though similar estimations have been done, this method significantly increases the speed of computation, making it a feasible candidate for large...