A Genetic-Fuzzy System Algorithm Method for the Breast Cancer Diagnosis Problem

Breast cancer is an important medical problem, especially for women, computer-aided medical diagnosis is very important in terms of prevention and early detection. This paper presents early detection of breast cancer using two methods, namely genetic algorithm and fuzzy inference system which will be used for early detection of breast cancer which will be used by doctors with computer assistance to obtain medical diagnosis of breast cancer in Indonesia. Our research shows that the diagnosis of breast cancer using these two methods has a high level of accuracy.

Re-examination of the internet financial reporting determinants

PurposeThe purpose of this paper is to shed light on the relationship between the Internet Financial Reporting (IFR) levels and corporate characteristics. It is assumed that the relationship between the disclosure level and its determinants is known. Nevertheless, the results of the empirical studies confirm that it is a naive assumption. As a result, the author suggests refusing the conventional methods of econometric analysis.Design/methodology/approachThe research methodology consisted of four stages: First, the author tried to select the “best” model using the Akaike Information Criterion (AIC). Second, the author checked out the stability of the relationship between corporate disclosure level and its determinants. Third, the regression analysis was used. Finally, the author proposed a “genetic-fuzzy system” for studying the determinants of corporate disclosure. The firms' yearly data collected consisted of a random sample of 152 Tunisian companies' websites.FindingsThe results show that the variables that should be used to explain the level of IFR are firm size, ownership concentration, firm performance and liquidity. The Chow forecast test shows that there is a significant and large difference between the actual and the predicted values. Consequently, the author suggests using non-parametric methods, particularly a methodology based on fuzzy logic concepts and genetic algorithms. This technique would allow the author to discover the true form of the relationship between the disclosure level and its determinants. Regarding the hypotheses of this study, the findings of the “genetic-fuzzy system” validate all the hypotheses. Indeed, the arguments of the agency theory, the signaling theory, and the political cost hypothesis were supported using the “genetic-fuzzy system.”Originality/valueThe originality of the paper lies in providing a new research methodology based on several statistical tools for dealing with an important research topic in accounting and finance, i.e. the determinants of IFR. The results of this study can be considered as a starting point to develop a unified methodology.

Intelligent Multi-Robot System for Collaborative Object Transportation Tasks in Rough Terrains

Human missions on other planets require constructing outposts and infrastructures, and one may need to consider relocating such large objects according to changes in mission spots. A multi-robot system would be a good option for such a transportation task because it can carry massive objects and provide better system reliability and redundancy when compared to a single robot system. This paper proposes an intelligent and decentralized approach for the multi-robot system using a genetic fuzzy system to perform an object transportation mission that not only minimizes the total travel distance of the multi-robot system but also guarantees the stability of the whole system in a rough terrain environment. The proposed fuzzy inference system determines the multi-robot system’s input for transporting an object to a target position and is tuned in the training process by a genetic algorithm with an artificially generated structured environment employing multiple scenarios. It validates the optimality of the proposed approach by comparing the training results with the results obtained by solving the formulated optimal control problem subject to path inequality constraints. It highlights the performance of the proposed approach by applying the trained fuzzy inference systems to operate the multi-robot system in unstructured environments.

Genetic Fuzzy Based Scalable System of Distributed Robots for a Collaborative Task

This paper introduces a new genetic fuzzy based paradigm for developing scalable set of decentralized homogenous robots for a collaborative task. In this work, the number of robots in the team can be changed without any additional training. The dynamic problem considered in this work involves multiple stationary robots that are assigned with the goal of bringing a common effector, which is physically connected to each of these robots through cables, to any arbitrary target position within the workspace of the robots. The robots do not communicate with each other. This means that each robot has no explicit knowledge of the actions of the other robots in the team. At any instant, the robots only have information related to the common effector and the target. Genetic Fuzzy System (GFS) framework is used to train controllers for the robots to achieve the common goal. The same GFS model is shared among all robots. This way, we take advantage of the homogeneity of the robots to reduce the training parameters. This also provides the capability to scale to any team size without any additional training. This paper shows the effectiveness of this methodology by testing the system on an extensive set of cases involving teams with different number of robots. Although the robots are stationary, the GFS framework presented in this paper does not put any restriction on the placement of the robots. This paper describes the scalable GFS framework and its applicability across a wide set of cases involving a variety of team sizes and robot locations. We also show results in the case of moving targets.

A Particle Fuzzy Decisive Framework for Moving Target Detection in the Multichannel SAR Framework

Target detection is one of the important subfields in the research of Synthetic Aperture Radar (SAR). It faces several challenges, due to the stationary objects, leading to the presence of scatter signal. Many researchers have succeeded on target detection, and this work introduces an approach for moving target detection in SAR. The newly developed scheme named Adaptive Particle Fuzzy System for Moving Target Detection (APFS-MTD) as the scheme utilizes the particle swarm optimization (PSO), adaptive, and fuzzy linguistic rules in APFS for identifying the target location. Initially, the received signals from the SAR are fed through the Generalized Radon-Fourier Transform (GRFT), Fractional Fourier Transform (FrFT), and matched filter to calculate the correlation using Ambiguity Function (AF). Then, the location of target is identified in the search space and is forwarded to the proposed APFS. The proposed APFS is the modification of standard Adaptive genetic fuzzy system using PSO. The performance of the MTD based on APFS is evaluated based on detection time, missed target rate, and Mean Square Error (MSE). The developed method achieves the minimal detection time of 4.13[Formula: see text]s, minimal MSE of 677.19, and the minimal moving target rate of 0.145, respectively.