scholarly journals An Inference System for Classifying Oil Palm Fungal Diseases

2021 ◽  
Vol 9 (11) ◽  
pp. 611-620
Author(s):  
Olajide Blessing Olajide ◽  
Odeniyi Olufemi Ayodeji ◽  
Olabiyi Olatunji Coker ◽  
Stephen Munu ◽  
Yakubani Yakubu
Keyword(s):  
Oil Palm ◽  
Plant Disease ◽  
Fuzzy Inference ◽  
World Market ◽  
Fungal Disease ◽  
Fungal Diseases ◽  
Classification Model ◽  
Rule Base ◽  
Inference System ◽  
Oil Plant

The oil palm plant is one of the major important cash crops of the Nigerian economy and a significant contributor to the world market for vegetable oils. Unfortunately, infection with fungi has caused a decline in the productivity of oil palms and subsequently the palm oil industry. Hence the need to detect oil palm plant disease earlier before it affects it informed this research to develop a fuzzy inference model to predict the influence of fungal disease on the oil plant plant. Following extensive review of related works, the factors associated with the severity of fungal diseases in the oil palm plant were identified following validation by Botanist. Fuzzy triangular membership functions were used to formulate the input factors identified alongside the target variables for identifying the severity of fungal diseases affecting the oil palm plant. The rule base was formulated using IF-THEN statements to combine the values of the input factors with the respective values of the target severity of oil palm plant disease. The classification model for oil palm plant disease severity was simulated using the Fuzzy Logic Toolbox available in the MATLAB R2015b Software. The results showed that the developed inference system for oil palm plant was capable of classifying and predicting the degree of the fungal disease infection into four groups; no severity, low severity, moderate severity and high severity.

Intelligent cooperative collision avoidance via fuzzy potential fields

Robotica ◽  
2021 ◽  
pp. 1-20
Author(s):  
Daegyun Choi ◽  
Anirudh Chhabra ◽  
Donghoon Kim
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Collision Avoidance ◽  
Fuzzy Inference ◽  
Potential Fields ◽  
Rule Base ◽  
Local Minima ◽  
Inference System ◽  
Aerial Vehicles ◽  
Different Shapes ◽  
Simulation Results

Summary This paper proposes an intelligent cooperative collision avoidance approach combining the enhanced potential field (EPF) with a fuzzy inference system (FIS) to resolve local minima and goal non-reachable with obstacles nearby issues and provide a near-optimal collision-free trajectory. A genetic algorithm is utilized to optimize parameters of membership function and rule base of the FISs. This work uses a single scenario containing all issues and interactions among unmanned aerial vehicles (UAVs) for training. For validating the performance, two scenarios containing obstacles with different shapes and several UAVs in small airspace are considered. Multiple simulation results show that the proposed approach outperforms the conventional EPF approach statistically.

Enhancing Fuzzy Inference System-Based Criterion-Referenced Assessment with a Similarity Reasoning Technique

2012 ◽  
pp. 302-327
Author(s):  
Tze Ling Jee ◽  
Kai Meng Tay ◽  
Chee Khoon Ng
Keyword(s):  
Fuzzy Inference System ◽  
Analogical Reasoning ◽  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Large Set ◽  
Inference System ◽  
Similarity Reasoning ◽  
Criterion Referenced ◽  
Reasoning Technique

A search in the literature reveals that the use of fuzzy inference system (FIS) in criterion-referenced assessment (CRA) is not new. However, literature describing how an FIS-based CRA can be implemented in practice is scarce. Besides, for an FIS-based CRA, a large set of fuzzy rules is required and it is a rigorous work in obtaining a full set of rules. The aim of this chapter is to propose an FIS-based CRA procedure that incorporated with a rule selection and a similarity reasoning technique, i.e., analogical reasoning (AR) technique, as a solution for this problem. AR considers an antecedent with an unknown consequent as an observation, and it deduces a conclusion (as a prediction of the consequent) for the observation based on the incomplete fuzzy rule base. A case study conducted in Universiti Malaysia Sarawak is further reported.

scholarly journals M-CFIS-R: Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 707 ◽  
Author(s):  
Tran Manh Tuan ◽  
Luong Thi Hong Lan ◽  
Shuo-Yan Chou ◽  
Tran Thi Ngan ◽  
Le Hoang Son ◽  
...  
Keyword(s):  
Decision Making ◽  
Similarity Measure ◽  
Fuzzy Inference System ◽  
Granular Computing ◽  
Fuzzy Inference ◽  
Fuzzy Theory ◽  
Rule Base ◽  
Inference System ◽  
Fuzzy Measures ◽  
Fuzzy Similarity

Complex fuzzy theory has strong practical background in many important applications, especially in decision-making support systems. Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been introduced as an effective tool for handling events that are not restricted to only values of a given time point but also include all values within certain time intervals (i.e., the phase term). In such decision-making problems, the complex fuzzy theory allows us to observe both the amplitude and phase values of an event, thus resulting in better performance. However, one of the limitations of the existing M-CFIS is the rule base that may be redundant to a specific dataset. In order to handle the problem, we propose a new Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing called M-CFIS-R. Several fuzzy similarity measures such as Complex Fuzzy Cosine Similarity Measure (CFCSM), Complex Fuzzy Dice Similarity Measure (CFDSM), and Complex Fuzzy Jaccard Similarity Measure (CFJSM) together with their weighted versions are proposed. Those measures are integrated into the M-CFIS-R system by the idea of granular computing such that only important and dominant rules are being kept in the system. The difference and advantage of M-CFIS-R against M-CFIS is the usage of the training process in which the rule base is repeatedly changed toward the original base set until the performance is better. By doing so, the new rule base in M-CFIS-R would improve the performance of the whole system. Experiments on various decision-making datasets demonstrate that the proposed M-CFIS-R performs better than M-CFIS.

scholarly journals The applicability of Generic Self-Evolving Takagi-Sugeno-Kang neuro-fuzzy model in modeling rainfall–runoff and river routing

2019 ◽  
Vol 50 (4) ◽  
pp. 991-1001 ◽  
Author(s):  
Mohammad Ashrafi ◽  
Lloyd H. C. Chua ◽  
Chai Quek
Keyword(s):  
Fuzzy Inference System ◽  
Hydrological Modeling ◽  
Fuzzy Inference ◽  
Training Dataset ◽  
Rule Base ◽  
Rainfall Runoff ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Takagi Sugeno ◽  
River Routing

Abstract Recent advancements in neuro-fuzzy models (NFMs) have made possible the implementation of dynamic rule base systems. This is in comparison with static applications commonly seen in global NFMs such as the Adaptive-Network-Based Fuzzy Inference System (ANFIS) model widely used in hydrological modeling. This study underlines key differences between local and global NFMs with an emphasis on rule base dynamics, in the context of two common flow forecast applications. A global NFM, ANFIS, and two local NFMs, Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) and Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK), were tested. Results from all NFMs compared favorably when benchmarked against physically based models. Rainfall–runoff modeling is a complex process which benefits from the advanced rule generation and pruning mechanisms in GSETSK, resulting in a more compact rule base. Although ANFIS resulted in the same number of rules, this came about at the expense of having the need for a large training dataset. All NFMs generated a similar number of rules for the river routing application, although local NFMs yielded better results for forecasts at longer lead times. This is attributed to the fact that the routing procedure is less complex and can be adequately modeled by static NFMs.

Enhancing Quality of Learning Experience Through Intelligent Agent in E-Learning

2017 ◽  
Vol 25 (01) ◽  
pp. 31-52
Author(s):  
S. Bhattacharya ◽  
S. Chowdhury ◽  
S. Roy
Keyword(s):  
Fuzzy Inference ◽  
Learning Experience ◽  
Fuzzy Rule ◽  
School Level ◽  
Rule Base ◽  
Learning Tools ◽  
Inference System ◽  
Fuzzy Rule Base ◽  
Quality Of Learning

In this paper an interactive recommending agent is proposed which helps an e-learner to enhance the quality of learning experience resulting in efficient achievement of learning objectives. The agent achieves this with the help of a fuzzy rule base working on a variety of learning materials and recommending the appropriate learning path through them. In a learner-centric environment the learning behaviour of a learner may vary to a great extent due to the characteristics of the learner and his environment. Students are often misled while choosing the appropriate path of web learning tools owing to non-availability of a human teacher/guide. By the response of a learner to different positive and negative motivation factors the proposed system employs a fuzzy machine that is fed with realization parameters e.g. Satisfied, Depressed etc. The fuzzy machine working on the paradigm of fuzzy inference system processes these realization parameters with the help of a fuzzy rule base to produce the crisp measures of the learner’s cognitive states in terms of Belief, Behaviour and Attitude. On the basis of these defuzzified crisp diagnostic parameters the proposed system will enhanced the quality of learning experience of an e-learner. To ensure this the system will provide more detailed discussion on the subject matter along with some additional learning tools. Learners often get confused to select the proper tools among various. Therefore the proposed system will also suggest most popular path among those learners with the same understanding. This recommendation comes from the analysis of data mining result. The system was tested with a wide variety of school-level students. The response obtained indicates that it is able to enhance the quality of learning experience through its recommendation.

SCALABLE ARCHITECTURE FOR HIGH-SPEED MULTIDIMENSIONAL FUZZY INFERENCE SYSTEMS

2011 ◽  
Vol 20 (03) ◽  
pp. 375-400 ◽  
Author(s):  
INÉS DEL CAMPO ◽  
JAVIER ECHANOBE ◽  
KOLDO BASTERRETXEA ◽  
GUILLERMO BOSQUE
Keyword(s):  
High Speed ◽  
High Performance ◽  
Fuzzy Inference ◽  
Reconfigurable Hardware ◽  
Rule Base ◽  
Fuzzy Inference Systems ◽  
Scalable Architecture ◽  
Inference Model ◽  
Inference System ◽  
Inference Systems

This paper presents a scalable architecture suitable for the implementation of high-speed fuzzy inference systems on reconfigurable hardware. The main features of the proposed architecture, based on the Takagi–Sugeno inference model, are scalability, high performance, and flexibility. A scalable fuzzy inference system (FIS) must be efficient and practical when applied to complex situations, such as multidimensional problems with a large number of membership functions and a large rule base. Several current application areas of fuzzy computation require such enhanced capabilities to deal with real-time problems (e.g., robotics, automotive control, etc.). Scalability and high performance of the proposed solution have been achieved by exploiting the inherent parallelism of the inference model, while flexibility has been obtained by applying hardware/software codesign techniques to reconfigurable hardware. Last generation reconfigurable technologies, particularly field programmable gate arrays (FPGAs), make it possible to implement the whole embedded FIS (e.g., processor core, memory blocks, peripherals, and specific hardware for fuzzy inference) on a single chip with the consequent savings in size, cost, and power consumption. As a prototyping example, we implemented a complex fuzzy controller for a vehicle semi-active suspension system composed of four three-input FIS on a single FPGA of the Xilinx's Virtex 5 device family.

scholarly journals A Mamdani Type Fuzzy Inference System to Calculate Employee Susceptibility to Phishing Attacks

2021 ◽  
Vol 11 (19) ◽  
pp. 9083
Author(s):  
Yahya Lambat ◽  
Nick Ayres ◽  
Leandros Maglaras ◽  
Mohamed Amine Ferrag
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Human Error ◽  
Fuzzy Inference ◽  
Social Engineering ◽  
Rule Base ◽  
Inference System ◽  
Phishing Attacks ◽  
Future System ◽  
The Individual

It is a well known fact that the weakest link in a cyber secure system is the people who configure, manage or use it. Security breaches are persistently being attributed to human error. Social engineered based attacks are becoming more sophisticated to such an extent where they are becoming increasingly more difficult to detect. Companies implement strong security policies as well as provide specific training for employees to minimise phishing attacks, however these practices rely on the individual adhering to them. This paper explores fuzzy logic and in particular a Mamdani type fuzzy inference system to determine an employees susceptibility to phishing attacks. To negate and identify the susceptibility levels of employees to social engineering attacks a Fuzzy Inference System FIS was created through the use of fuzzy logic. The utilisation of fuzzy logic is a novel way in determining susceptibility due to its ability to resemble human reasoning in order to solve complex inputs, or its Interpretability and simplicity to be able to compute with words. This proposed fuzzy inference system is based on a number of criteria which focuses on attributes relating to the individual employee as well as a companies practices and procedures and through this an extensive rule base was designed. The proposed scoring mechanism is a first attempt towards a holistic solution. To accurately predict an employees susceptibility to phishing attacks will in any future system require a more robust and relatable set of human characteristics in relation to the employee and the employer.

Using Fuzzy Logic to Control Combined Cycle Gas Turbine During Ambient Computing Environment

2020 ◽  
Vol 11 (3) ◽  
pp. 106-130 ◽  
Author(s):  
Mostafa A. Elhosseini
Keyword(s):  
Fuzzy Logic ◽  
Gas Turbine ◽  
Fuzzy Inference ◽  
Combined Cycle ◽  
Operating Conditions ◽  
Superior Performance ◽  
Rule Base ◽  
Computing Environment ◽  
Inference System ◽  
Ambient Computing

The main aim of this article is to analyse and control a combined cycle gas turbine (CCGT) under normal and perturbation loading using a Fuzzy Logic Control (FLC) and an Adaptive Neuro-Fuzzy Inference System (ANFIS) through an ambient computing environment. The main characteristics of ambient computing is invisible, embedded, easy to use, and adaptive to name a few. The current article proposes the employment of FLC and to control the operation of CCGT considering the system inputs uncertainty. The target of the FLC is to maintain the system speed, exhaust temperature, and airflow within the desired interval. ANFIS helps to get the optimal control parameter and construct the proper rule base with an appropriate membership function with reasonable accuracy. The simulation results demonstrate the ANFIS controller's superior performance over FLC as well as the traditional controller for normal operating conditions and load perturbation.

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