Reviewer at Journal of Intelligent & Robotic Systems

I was selected as a reviewer at Journal of Intelligent & Robotic Systems that is published by Springer. This journal aims at publishing peer-reviewed and original contributed papers, invited and survey papers that promote and disseminate scientific knowledge and information to the readers in the fields of system theory, control systems, bioengineering, robotics and automation, human-robot interaction, robot ethics, mechatronics, unmanned systems, multi-robot teams and networked swarms, machine intelligence, learning, system autonomy, human-machine interfaces, cyber physical systems, and other related areas in which cutting edge technologies have been developed and applied to model, design, build and test complex engineering and autonomous systems.

You can find more information about this journal via the link.

The paper entitled "FSB-System: A Detection System for Fire, Suffocation, and Burn Based on Fuzzy Decision Making, MCDM, and RGB Model in Wireless Sensor Networks"

The paper entitled "FSB-System: A Detection System for Fire, Suffocation, and Burn Based on Fuzzy Decision Making, MCDM, and RGB Model in Wireless Sensor Networks" was published by Wireless Personal Communications (Springer) on 25 March 2019. This paper proposes a detection system, called FSB-System, to predict the fire, suffocation, and burn probabilities over areas using fuzzy theory, MCDM, and an RGB model. The system uses sensing data of the temperature, smoke, and light sensors to determine appropriate, assorted decisions under different conditions. Three fuzzy controllers are suggested in FSB-System: fire fuzzy controller (namely FFC), suffocation fuzzy controller (namely SFC), and burn fuzzy controller (namely BFC). FFC determines the fire probability, SFC measures the suffocation probability, and BFC calculates the burn probability. Sensor nodes are randomly scattered over areas in a way that they form multiple clusters. Non-cluster heads (NCHs) transmit their sensing data to cluster heads (CHs). Furthermore, CHs transmit the gathered data to the native sink to report environmental conditions toward a base station (e.g., a fire department). The number of sinks is determined by a suggested MCDM controller based on network size and the number of clusters. Simulation results demonstrate that the proposed system surpasses the threshold methods in terms of remaining energy, the number of alive nodes, network lifetime, the number of wrong alerts, and financial losses. This system can be applied in various environments including forests, buildings, etc.

You can find more information about this paper via the link.

The paper "Behavior-Based Decision Making: A Tutorial"

The paper entitled "Behavior-Based Decision Making: A Tutorial" was published by International Journal of Dynamics and Control in December 2018. This paper proposes a novel, knowledge and learning based method called behavior-based decision making, BBDM, in control and system engineering. It is an expert decision support system containing the learning ability to work based on humanistic behavioral reasoning. The knowledge base is built by the system based on various behavioral styles (e.g., safe) associated to other systems and humans. BBDM uses the knowledge-based information to make appropriate decisions when any desired behavioral style is requested from the system. It specifies a success rate for any desired style based on the obtained knowledge base with the aid of a behavioral inference system. This procedure can be used to select a proper system or human to accomplish a requested job. All operations of the BBDM method are performed by a proposed behavioral decision system, called BDS, which consists of three main units: decomposition, behavioral inference, and composition. The decomposition unit splits any behavioral style into several optional features (e.g., safety). The behavioral aggregation sub-unit aggregates all behavioral styles obtained by the system to define the total behavior. The behavioral inference unit produces a success set for any desired behavioral style. Finally, the composition unit converts success set to success rate to specify the success probability of the desired style.

You can find more information about this paper via the link.