Mohsen Afsharchi

• Doctor of Philosophy
• Professor (Associate) at University of Zanjan

Wildfire in the forest leads to a huge amount of financial and human losses. That is it causes damage to the forest and the life of firefighters. To reduce the amount of such damage, unmanned aerial vehicles are among the best options to do the job of firefighting. Their orchestration of effective fire extinguishing is a must and essential. This wo...

The analysis of brain tumors plays a significant role in medical applications and provides a huge amount of anatomic and functional information. Automatic tumor segmentation is one of the most challenging issues among radiologists and other specialists intent on lowering and eliminating manual detection errors and speeding up the detection of tissu...

This paper presents a novel method, called Modular Grammatical Evolution (MGE), towards validating the hypothesis that restricting the solution space of NeuroEvolution to modular and simple neural networks enables the efficient generation of smaller and more structured neural networks while providing acceptable (and in some cases superior) accuracy...

This paper proposes a NeuroEvolution algorithm, Modular Grammatical Evolution (MGE), that enables the evolution of both topology and weights of neural networks for more challenging classification benchmarks like MNIST and Letter with 10 and 26 class counts. The success of MGE is mainly due to (1) restricting the solution space to regular network to...

Some events which happen in the future could be important for companies, governments, and even our personal life. Prediction of these events before their establishment is helpful for efficient decision-making. We call such events emerging entities. They have not taken place yet, and there is no information about them in KB. However, some clues exis...

Learning-based techniques could be an alternative approach to solve Dynamic Distributed Constraint Optimization Problems (DDCOPs) and are computationally cheaper than sequential DCOP solvers. This paper, proposes a learning-based solution to solve DDCOPs in which the environment is stochastic due to the presence of multiple agents. In our approach...

An inherent difficulty in dynamic distributed constraint optimization problems (dynamic DCOP) is the uncertainty of future events when making an assignment at the current time. This dependency is not well addressed in the research community. This paper proposes a reinforcement-learning-based solver for dynamic distributed constraint optimization. W...

Recommender Systems (RSs) aim to model and predict the user preference while interacting with items, such as Points of Interest (POIs). These systems face several challenges, such as data sparsity, limiting their effectiveness. In this paper, we address this problem by incorporating social, geographical, and temporal information into the Matrix Fac...

Recommender Systems (RSs) aim to model and predict the user preference while interacting with items, such as Points of Interest (POIs). These systems face several challenges, such as data sparsity, limiting their effectiveness. In this paper, we address this problem by incorporating social, geographical, and temporal information into the Matrix Fac...

This article presents a novel method, called Modular Grammatical Evolution (MGE), toward validating the hypothesis that restricting the solution space of NeuroEvolution to modular and simple neural networks enables the efficient generation of smaller and more structured neural networks while providing acceptable (and in some cases superior) accurac...

Demand‐side management (DSM) enables customers to decide consciously on how to seek and obtain power from the grid. The prevailing method available in DSM is load shifting. The grid is assisted through reducing load demands during the peak hours and altering the demand time into the off‐peak hours in a manner that the consumption sources could be m...

With the rapid growth of Location-Based Social Networks, personalized Points of Interest (POIs) recommendation has become a critical task to help users explore their surroundings. Due to the scarcity of check-in data, the availability of geographical information offers an opportunity to improve the accuracy of POI recommendation. Moreover, matrix f...

Automatic test case generation has been received great attention by researchers. Evolutionary algorithms have increasingly gained special places as means of automating the test data generation for software testing. Genetic algorithm (GA) is the most commonplace algorithm in search-based software testing. One of the key issues of search-based testin...

Inspired by recent attention to multi-agent reinforcement learning (MARL), the effort to provide efficient methods in this field is increasing. But, there are many issues which make this field challenging. Decision making of an agent depends on the other agents’ behavior while sharing information is not always possible. On the other hand, predictin...

Recommender systems attempt to suggest information that is of potential interest to users helping them to quickly find information relevant to them. In addition to historical user–item interaction data, such as users’ ratings on items, social recommendation methods use social relationships between users to improve the accuracy of recommendations. H...

With the rapid growth of Location-Based Social Networks, personalized Points of Interest (POIs) recommendation has become a critical task to help users explore their surroundings. Due to the scarcity of check-in data, the availability of geographical information offers an opportunity to improve the accuracy of POI recommendation. Moreover, matrix f...

Recently, Point of interest (POI) recommendation has gained ever-increasing importance in various Location-Based Social Networks (LBSNs). With the recent advances of neural models, much work has sought to leverage neural networks to learn neural embeddings in a pre-training phase that achieve an improved representation of POIs and consequently a be...

With the rapid growth of Location-Based Social Networks, personalized Points of Interest (POIs) recommendation has become a critical task to help users explore their surroundings. Due to the scarcity of check-in data, the availability of geographical information offers an opportunity to improve the accuracy of POI recommendation. Moreover, matrix f...

This paper investigates the problem of prime and test paths generation, which is an important problem in ensuring path coverage in software testing. Most existing methods for prime/test paths generation have little success in generating the set of all prime/test paths of structurally complex programs with high Npath complexity. This paper puts forw...

One of the most important and costly tests in software engineering is regression testing, which is performed after any change made to the software. One way to reduce the cost of this test is using test case prioritization, in which the two strategies of Total and Additional have been of most interest. Each of these two strategies have their own lim...

Supervised Word Sense Disambiguation (WSD) systems use features of the target word and its context to learn about all possible samples in an annotated dataset. Recently, word embeddings have emerged as a powerful feature in many NLP tasks. In supervised WSD, word embeddings can be used as a high-quality feature representing the context of an ambigu...

Recently, Point of interest (POI) recommendation has gained ever-increasing importance in various Location-Based Social Networks (LBSNs). With the recent advances of neural models, much work has sought to leverage neural networks to learn neural embeddings in a pre-training phase that achieve an improved representation of POIs and consequently a be...

Recommender systems are intelligent programs to suggest relevant contents to users according to their interests which are widely expressed as numerical ratings. Collaborative filtering is an important type of recommender systems which has established itself as the principal means of recommending items. However, collaborative filtering suffers from...

Social recommendation systems use social relations (such as trust, friendship, etc.) among users to find preferences and provide relevant suggestions to users. Historical ratings of items provided by the users are also used to predict unseen items in the systems. Therefore, it is an important issue to calculate the sufficient number of the historic...

Trust-aware recommender systems are advanced approaches which have been developed based on social information to provide relevant suggestions to users. These systems can alleviate cold start and data sparsity problems in recommendation methods through trust relations. However, the lack of sufficient trust information can reduce the efficiency of th...

Smart grids, to facilitate the electricity production, distribution, and consumption, employ information and communication technologies simultaneously. Electricity markets, through stabilizing the electricity prices, attempt to alleviate the challenges of power exchange. On one hand, buyers, by considering their full demand satisfaction, endeavor t...

Recommender systems are techniques to make personalized recommendations of items to users. In e-commerce sites and online sharing communities, providing high quality recommendations is an important issue which can help the users to make effective decisions to select a set of items. Collaborative filtering is an important type of the recommender sys...

In this paper we present a novel approach to reinforcement learning for continuous state–action control problems. This approach is obtained by combining least square policy iteration (LSPI) with zero-order Takagi–Sugeno fuzzy system, which we call it, “fuzzy least square policy iteration (FLSPI).” FLSPI is a critic-only method and has advantages of...

Energy efficiency is one of the most challenging issues in wireless sensor networks, particularly in the target tracking. The main purpose of these networks is to preserve the distributed important sites from the targets who intend to destroy the sites in an environment. This paper proposes a distributed energy-efficient mechanism for sleep schedul...

Currently the Dempster-Shafer based algorithm and Uniform Random Probability based algorithm are the preferred method of resolving security games, in which defenders are able to identify attackers and only strategy remained ambiguous. However this model is inefficient in situations where resources are limited and both the identity of the attackers...

Background/Objectives: Database systems are one of the largest communicative interfaces between users and service providing organizations. Generally, all the organizations and companies which deal with sensitive financial data and online customer services, such as banks, the telephone company and their payments need to provide services with high ac...

In this paper we study multi robot cooperative task allocation issue in a situation where a swarm of robots is deployed in a confi?ned unknown environment where the number of colored spots which represent tasks and the ratios of them are unknown. The robots should cover this spots as far as possible to do cleaning and sampling actions desirably. It...

The smart grid makes use of two-way streams of electricity and information to constitute an automated and distributed energy delivery network. Coming up with multi-agent systems for resource allocation, chiefly comprises the design of local capabilities of single agents, and therefore, the interaction and decision-making mechanisms that make them c...

Network vertices are often divided into groups or communities with dense connections within communities and sparse connections between communities. Community detection has recently attracted considerable attention in the field of data mining and social network analysis. Existing community detection methods require too much space and are very time c...

The smart grid makes use of two-way streams of electricity and information to constitute an automated and distributed energy delivery network. Coming up with multi-agent systems for resource allocation, chiefly comprises the design of local capabilities of single agents, and therefore, the interaction and decision-making mechanisms that make them c...

In this paper, we intend to have a game theoretic study on the concept learning problem in a multi-agent system. Concept learning is a very essential and well-studied domain of machine learning when it is studied under the characteristics of a multi-agent system. The most important reasons are the partiality of the environment perception for any ag...

This research work presents a framework to build a hybrid expert recommendation system that integrates the characteristics of content-based recommendation algorithms into a social network-based collaborative filtering system. The proposed method aims at improving the accuracy of recommendation prediction by considering the social aspect of experts’...

Graph vertices are often divided into groups or communities with dense connections within communities and sparse connections between communities. Community detection has recently attracted considerable attention in the field of data mining and social network analysis. Existing community detection methods require too much space and are very time con...

Traditionally, communication among agents has been established based on the group commitment to a common ontology which is unfortunately often too strong or unrealistic. In the real world of communicating agents, it is preferred to enable agents to exchange information while they keep their own individual ontology. While this assumption makes agent...

We present a hybrid method for an expert recommendation system that integrates the characteristics of content-based recommendation algorithms into a social network-based collaborative filtering system. Our method aims at improving the accuracy of the recommendation prediction by considering the social aspect of experts' behaviors. For this purpose,...

Software estimations are regarding based on prediction properties of system, with attention to development methodology. In object-oriented analysis, Use Case models describe the functional requirements of a software system, so they can be basis for software measurement and sizing. Use Case points method that suggested by karner, is based on size an...

In this research we focus on understanding the nature of the knowledge used during the various phases of the software development process. We have found that there are two types of knowledge involved in software development: (1) descriptive knowledge represented by conversion and coding rules, e.g., a rule for splitting a class into two; and (2) pr...

A significant body of work in multi-agent systems over more than two decades has focused on multi-agent coordination (Levesque et al., 1990). Many challenges in multi-agent coordination can be modeled as Distributed Constraint Optimizations (DCOPs). Many complete and incomplete algorithms have been introduced for DCOPs, but complete algorithms are...

A significant body of work in multiagent systems over more than two decades has focused on multi-agent coordination. Many challenges in multi-agent coordination can be modeled as Distributed Constraint Optimizations (DCOPs). Many complete and incomplete algorithms have been introduced for DCOPs but complete algorithms are often impractical for larg...

This article deals with the issue of concept learning and tries to have a game theoretic view over the process of cooperative concept learning among agents in a multi-agent system, in which an extreme sense of competition has arisen. This gives birth to a new realm labeled as ”Learning Games”. We study the cooperative view and give a novel idea to...

Mobile ad-hoc networks (MANETs) are random, self-configurable and rapidly-deployable networks. The main goal of developing the MANETs is not only obtaining better service, but also having networks that can serve in situations in which no other means of communications can operate. Examples include networks that are used in battlefields, in search-an...

We present an extension to the definition of a concept in an ontology that allows an agent to simultaneously communicate with a group of agents that might have different understandings of some concepts. We also provide a way to learn such non-unanimous concepts by using a method for learning concepts from a group of teachers. The general idea of no...

Given a set of observations or new information, agents should be able to update their understandings of the world. As a part of any agents' world ontology, concepts need to evolve in time. In this paper we present a new representation for non-unanimous concepts based on the combination of feature-values and their probabilities. This representation...

Currently, search systems are based on commitment to a common ontology. In the real world, it is preferred to enable Web repositories to exchange information freely while keeping their own ontology. This helps contents providers to represent information independently in the repositories at the expense of bringing complexity to the communication and...

The ability to share knowledge is a necessity for agents in order to achieve both group and individual goals. To grant this ability many researchers have assumed to not only establish a common language among agents but a complete common understanding of all the concepts the agents communicate about. But these assumptions are often too strong or unr...

ABSTRACT Inthis paper we present ,(1) a method ,for semantic search supported,by ontological ,concept ,learning; and ,(2) a prototype multiagent system that can handle semantic search and encapsulate the complexity of ,such process from the users. Agents which conduct semantic search on behalf of a user, deploy ontologies to organize structured and...

We present a statistical approach for software agents to learn ontology concepts from peer agents by asking them whether they can reach consensus on significant differences between similar concepts. This method allows agents that are not sharing common ontologies to establish common grounds on concepts known only to some of them, when these common...

We present a method to improve the positive examples selec- tion by teaching agents in a multi-agent system in which a team of agent peers teach concepts to a learning agent. The basic idea in this method is to let a teacher agent expand the features it uses to describe a concept in its ontology by additional features. This resembles the typical be...

We present a general method for agents using ontologies as part of their knowledge representation to teach each other concepts to improve their communication and thus cooperation abilities. Our method aims at getting positive and negative examples for a concept only very vaguely understood by a particular agent from the other agents. This agent the...

We present an extension to the definition of a concept in an ontology that allows an agent to simultaneously com- municate with a group of agents that might have different understandings of some concepts. We also provide a way to learn such non-unanimous concepts by using a method for learning concepts from a group of teachers. The gen- eral idea o...

This research addresses the formation of new concepts and their corresponding ontology in a multi-agent system where individual autonomous agents try to learn new concepts by consulting several other agents. In this research individual agents create and learn their distinct conceptualization and rather than a commitment to a common ontology they us...

Software agents are knowledgeable, autonomous, situated and interactive software entities. Agents' interactions are of special importance when a group of agents interact with each other to solve a problem that is beyond the capability and knowledge of each individual. Efficiency, performance and overall quality of the multi-agent applications depen...

A substantial amount of study in multi-agent systems has fo-cused on multi-agent coordination for over twenty years. Many challenges in multi-agent coordination can be modeled as Distributed Constraint Optimization (DCOP). Finding the optimal solution for a DCOP is NP-hard, so using incomplete algorithms that are faster are more desirable. Many inc...

Computer networks are nowadays subject to an increasing number of attacks. Intru-sion Detection Systems (IDS) are designed to protect them by identifying malicious behaviors or improper uses. Since the scope is different in each case (register already-known menaces to later recognize them or model legitimate uses to trigger when a variation is dete...