Mamdani-type Fuzzy Inference System Research Articles

A Mamdani fuzzy inference system with trapezoidal membership functions for investigating fishery production

Seas, marine ecosystems, and coastal regions are crucial components of our environment. Numerous scientific strategies have been adopted to boost fisheries and aquaculture productivity. This study proposes a fuzzy-logic-based model to produce fisheries in India, which ranks fourth worldwide for fisheries production. Five input variables, such as fish seed, export, post-harvesting, released fund, and temperature, are considered inputs, and the production of fisheries is taken as the output variable. A Mamdani-type fuzzy inference system with trapezoidal membership functions is prepared with 243 rules in the IF-THEN format. This mathematical model investigates the impacts of input parameters on the production of Indian fisheries. We fit the model with the real-world data and show that fish seed, export, released fund, and post-harvesting facilities positively impact fisheries production. However, a very high temperature is unsuitable for high production, even if all other parameters lie at their desired level.

Mamdani-type fuzzy inference system for irrigational water quality

This paper aims to evaluate the quality of groundwater resources used for irrigation purposes using a Mamdani-type fuzzy inference system (MFIS). The MFIS is used to resolve ambiguities and uncertainties in economic, social, and natural systems and also facilitates the capture of expert knowledge in ways similar to human reasoning and thought processes. In this study, 20 groundwater samples were collected from various locations within the Mayiladuthurai district, Tamil Nadu, India, between January 2016 and December 2019. These samples underwent physical and chemical analyses to assess the suitability of the collected water resources for irrigation. The analysis utilizes the Mamdani Fuzzy Inference System, which combines values of Electrical Conductivity (EC) and Sodium Adsorption Ratio (SAR). Additionally, methods from the US Salinity Laboratory Staff were also employed. Ultimately, the groundwater quality in Mayiladuthurai is graded for irrigation use by this method. The results indicate that the MFIS reduces imprecision and uncertainty in data handling through the fuzzy membership function. The comparison of irrigation suitability results clearly demonstrates that the proposed MFIS method offers an improved assessment of the irrigation water quality level of the studied groundwater resources.

Tomato quality assessment and enhancement through Fuzzy Logic: A ripe perspective on precision agriculture

This study presents a meticulous exploration of tomato quality assessment and enhancement employing Fuzzy Logic, focusing on beefsteak, Roma, and cherry varieties. The research incorporates a multivariate approach encompassing shape, size, texture, and color analysis. Utilizing a controlled imaging system, 165 tomatoes were captured at various ripening stages to ensure uniformity in lighting, camera position, and background color. Image processing and computer vision techniques, including segmentation and feature extraction, were applied to comprehensively evaluate the tomatoes. Quality assessment involved considerations of three stages of skin coloration, defect quantity, and skin texture calculated using the statistical method of entropy. The decision-making process and quality evaluation were facilitated by a Mamdani-type Fuzzy Inference System, leveraging numerical values of external characteristics such as dimension, calyx shape, ripeness stage, and defect regions. The fuzzy logic-based system employed 22 fuzzy rules and three different membership functions (trapezoidal, Gaussian, and triangular), enabling a nuanced mapping of transitions between classes within the input variables. Furthermore, the system underwent rigorous training and validation using 80% and 20% respectively of the captured images. Through the system, we were able to identify and segment areas of tomatoes exhibiting poor conditions. This approach extends beyond conventional methods of estimating fruit ripeness, which typically rely on color assessment or destructive firmness evaluations.

Modified Mamdani-fuzzy inference system for predicting the cost overrun of construction projects

Cost overruns are a common worldwide problem in the construction industry; improved proactive risk management and cost control are much needed. Several models have been proposed, but all have weaknesses, particularly in data demands and the severity of critical risks or uncertainties associated with expert judgment. In response, this study develops a new 3-part model based on the Mamdani-type fuzzy inference system (FIS) to predict the cost overrun of construction projects. The first part assesses the weight of each expert, evaluating the severity of cost overrun factors. The second part contains a list of 40 in-built cost overrun factors and their degree of severity, while the third part establishes the relationships of every factor’s occurrence probability and severity to predict the cost overrun of a specific project. The severity of each factor is assessed based on a survey of 31 randomly selected experts in the Saudi Arabian construction industry. The model is demonstrated on two completed projects in Saudi Arabia. For each project, this involves a group of project-based experts rating the probability of occurrence of each factor on that project and applying this to the factor severity list to obtain a predicted cost overrun (PCO) for the whole project. The model is validated for robustness by sensitivity analysis comparing the predicted and actual whole project cost overrun and shown to be of practical value in assessing critical risks and predicting the likely amount of cost overrun. The model is equally applicable in the early project stages.

An Experimental Private Small Hydropower Plant Investments Selection Classification System

Investment selection problems and models are crucial for humans, communities, and states. Private small hydroelectric power/ hydropower plant investments (PSHPPIs) selection problem is a unique one in those problems and models. Many available scientific methods can be implemented on this research topic. One methods group is type-2 Mamdani’s type fuzzy inference systems (FISs). This study presents an experimental one-node general type-2 4-zslices Mamdani’s type FIS model for PSHPPIs selection. There are 3 input variables (total estimated electricity generation: M1, total estimated cost: M2, change in the average runoff: M3), 1 output variable (O1: selected/unselected, selection classification of PSHPPIs), and 49 rules. M1 is designed in 5 triangular and trapezoidal general type-2 4-zslices fuzzy membership functions (MFs), M2 in 5 triangular and trapezoidal general type-2 4-zslices fuzzy MFs, M3 in 5 Gaussian type-2 4-zslices fuzzy MFs, and O1 in 2 Gaussian type-2 4-zslices fuzzy MFs. All modeling details are finalized by a human-expert decision process. JuzzyOnline V2.0, a browser-based platform of Juzzy (a Java-based library: Juzzy toolkit), is used for all FIS modeling and calculation activities. Five PSHPPI options assumed to be in their very early investment stages in Turkiye are evaluated in an experimental application. It is clear that an improved, tuned, and fine-tuned version of this model will help private investors to take more convenient/satisfying actions/decisions in a very short evaluation period in their PSHPPIs decision processes. An automatic investment selection classification system can be built in an appropriate development period after experiencing many methods and models. This study is one of the open-science humanoid robot, robot and platform development activities entitled Global Power Robots and Platforms: GPRP/GPRAP (Global Power Plants Developers: GP2D/G2PD/GPPD, Global Power Plants Engineers: GP2E/G2PE/ GPPE, Global Power Plants Owners: GP2O/G2PO/GPOO, Global Power Prediction Systems: GP2S/G2PS/GPPS), Global Profile Analyses Systems, and Global Social Network Analyses Systems.

An Ensemble Approach Based on Fuzzy Logic Using Machine Learning Classifiers for Android Malware Detection

In this study, a fuzzy logic-based dynamic ensemble (FL-BDE) model was proposed to detect malware exposed to the Android operating system. The FL-BDE model contains a structure that combines both the processing power of machine learning (ML)-based methods and the decision-making power of the Mamdani-type fuzzy inference system (FIS). In this structure, six different methods, namely, logistic regression (LR), Bayes point machine (BPM), boosted decision tree (BDT), neural network (NN), decision forest (DF) and support vector machine (SVM) were used as ML-based methods to benefit from their scores. However, through an approach involving the process of voting and routing, the scores of only three ML-based methods which were more successful in classifying either the negative instances or positive instances were sent to the FIS to be combined. During the combining process, the FIS processed the incoming inputs and determined the malicious application score. Experimental studies were performed by applying the FL-BDE model and ML-based methods to the balanced dataset obtained from the APK files downloaded in the Drebin database and Google Play Store. The obtained results showed us that the FL-BDE model had a much better performance than the ML-based models did, with an accuracy of 0.9933, a recall of 1.00, a specificity of 0.9867, a precision of 0.9868, and an F-measure of 0.9934. These results also proved that the proposed model can be used as a more competitive and powerful malware detection model compared to those of similar studies in the literature.

Predictive Model of Humidity in Greenhouses through Fuzzy Inference Systems Applying Optimization Methods

Establishing the indoor and outdoor humidity values in a greenhouse allows us to describe the crop yield during its entire developmental cycle. This study seeks to develop a predictive model of indoor relative humidity values in a greenhouse with high accuracy and interpretability through the use of optimized fuzzy inference systems, in order to offer greenhouse users a clear and simple description of their behaviour. The three-phase methodology applied made use of descriptive statistics techniques, correlation analysis, and prototyping paradigm for the iterative and incremental development of the predictive model, validated through error measurement. The research resulted in six models which define the behaviour of humidity as a result of temperature, CO2, and soil moisture, with percentages of effectiveness above 90%. The implementation of a Mamdani-type fuzzy inference system, optimized by a hybrid method combining genetic and interior point algorithms, allowed to predict the relative humidity in greenhouses with high interpretability and precision, with an effectiveness percentage of 90.97% and MSE (mean square error) of 8.2e − 3.

Mineral potential modelling of orogenic gold systems in the granites-tanami Orogen, Northern Territory, Australia: A multi-technique approach

In this study we adopted fuzzy inference system (FIS), transformed predictor map-based random forest (RF) and feed-forward deep neural network (FF-DNN) approaches to mineral potential modelling (MPM) of the Granites-Tanami Orogen (GTO), Northern Territory, Australia, to (i) predict gold prospectivity, and (ii) compare the performance of these models to those previously generated in the study area. The current study, which utilized 19 predictor maps previously developed by Roshanravan et al. (2020) in the framework of a mineral systems approach, took into consideration the learnings gained and results obtained from previous approaches to modelling the gold potential of the GTO (Roshanravan et al., 2020: continuous fuzzy gamma, geometric average, data-driven index overlay and non-transformed predictor map-based RF; Roshanravan et al., 2021: cuckoo optimization algorithm). Importantly, our study area extending over > 46,000 km2 in the GTO, represents one of the few, if not the only, areas worldwide that has been subjected to eight different modelling techniques, an approach that delivered unique insights that cannot be achieved by utilizing a single technique. For example, our multi-technique approach enabled us to study what effect predictor maps may have on model performance and resulting mineral potential maps. In the case of data-driven MPM with RF, arguably-one of the most popular machine learning techniques, the effect of non-transformed (i.e., original) and transformed (i.e., derivative) predictor maps has never been evaluated simultaneously. To achieve this, we generated a transformed predictor map-based RF potential model with the aim of comparing it to the non-transformed predictor map-based RF model of Roshanravan et al. (2020). The FF-DNN and Mamdani-type FIS approaches to MPM were carried out specifically for the purpose of comparing and further investigating the modelling results. With respect to the Mamdani-type FIS, the concentration-area (C-A) fractal technique served to more objectively determine thresholds of the underlying mathematical functions, a novel approach that aids in reducing bias resulting from human input and expert opinion. The thresholded results demonstrate that the RF potential map generated with transformed predictor maps outperformed all other data-driven and continuous models. Overall, our multi-technique approach to MPM and the comparing and contrasting of a large set of resulting gold potential models, not only offered insights that helped to develop and calibrate new tools and techniques but also delivered what we believe are more robust gold exploration targets. As a result, we strongly recommend to using a multi-technique approach to MPM, which we hope will soon replace the currently accepted and widely utilized single-technique approach.

Data preprocessing strategy in constructing convolutional neural network classifier based on constrained particle swarm optimization with fuzzy penalty function

Convolutional neural networks (CNNs) have attracted increasing attention in recent years because of their powerful abilities to extract and represent spatial/temporal information. However, for general data, its features are assumed to have weak or no correlation, and directly applying CNN to classify such data could result in poor classification performance. To address this problem, a combined technique of original data representation method of fuzzy penalty function-based constrained particle swarm optimization (FCPSO) and CNN, so-called FCPSO-CNN is designed to effectively solve the classification problems for generic dataset and applied to recognize (classify) black plastic wastes in recycling problems. In more detail, CPSO is introduced to optimize feature reordering matrix under constraints and the construction of this matrix is driven by fitness function of CNN that quantifies classification performance. The Mamdani type fuzzy inference system (FIS) is employed to realize the fuzzy penalty function (FPF) which is utilized to realize the constrained problems of CPSO as well as alleviate the issues of the original penalty function method suffering from the lack of robustness. Experimental results demonstrate that FCPSO-CNN achieves the best classification accuracy on 13 out of 17 datasets; the statistical analysis also confirms the superiority of FCPSO-CNN. An interesting point is worth to mention that some feature reordering matrices in the infeasible space come with better classification accuracy. It has been found that the proposed method results in more accurate solution than one-dimensional CNN, random reordering feature-based CNN and some well-known classifiers (e.g., Naive Bayes, Multilayer perceptron, Support vector machine).

A comparative study of multiband mamdani fuzzy classification methods for west of Iraq satellite image

In our paper, performance of four fuzzy membership function generation methods was studied. These methods were studied in the context of implementing Mamdani fuzzy classification on a set of satellite images for western Iraqi territory. The first method generate triangulate membership functions using mean, minimum (min) and maximum (max) of histogram attribute values (AV), while peak and standard deviation (STD) of these AV were used in the second. On the other hand, in the third and fourth methods, Gaussian membership functions are generated using same mentioned values in the first and second method respectively. The goal was to generate a Mamdani type fuzzy inference system the membership function (MF) of each fuzzy set and implementing the AV of western Iraqi territory training data sets. A pixel-by-pixel comparison of each method with traditional maximum likelihood method (ML) was made on data sets comprising six bands of satellite imagery of the western Iraqi region taken by the Landsat-5 satellite. Simulation results of these performance comparisons singled out that the method using Gaussian MFs together with peak and STD of the AV as the best achiever with a similarity of 83.16 percent for band (3) of the studied area.

Intrusion detection based on fuzzy logic for wireless body area networks: review and proposition

Wireless body area networks (WBANs) are very helpful for monitoring the patient’s case, due to the medical sensors. However, this technology faces several problems such as loss communication, security issues and energy consumption. Our work focused on the security and specifically the intrusion detection system (IDS), which is one of the most effective techniques used to identify the presence of intrusions in a network. To make the IDS more efficient, the fuzzy logic (FL) is one of the well-known techniques that is known for its powerful mechanism used to differentiate network traffic levels. In this paper, we start to present an overview of IDS and FL functionality. Moreover, we give a survey of recent works dealing IDS based on FL in wireless sensor and classify them on different measures. Hence, our comparative study is very helpful for the researchers, to understand the use of FL in IDS and have clear vision for developing their own security solution. In the second part, we develop a novel IDS based on Mamdani type fuzzy inference system for detecting jamming attacks in WBAN. Our IDS was built in Matlab, also we are used Castalia platform and OMNET++ simulator to simulate different scenarios of WBAN.

Fuzzy Adaptive Teaching Learning-Based Optimization for Solving Unconstrained Numerical Optimization Problems

Teaching learning-based optimization is one of the widely accepted metaheuristic algorithms inspired by teaching and learning within classrooms. It has successfully addressed several real-world optimization problems, but it may still be trapped in local optima and may suffer from the problem of premature convergence in the case of solving some challenging optimization problems. To overcome these drawbacks and to achieve an appropriate percentage of exploitation and exploration, this study presents a new modified teaching learning-based optimization algorithm called the fuzzy adaptive teaching learning-based optimization algorithm. The proposed fuzzy adaptive teaching learning-based optimization algorithm uses three measures from the search space, namely, quality measure, diversification measure, and intensification measure. As the 50-50 probabilities for exploitation and exploration in the basic teaching learning-based optimization algorithm may be counterproductive, the Mamdani-type fuzzy inference system of the new algorithm takes these measures as a crisp inputs and generates selection as crisp output to choose either exploitation or exploration based on the current search requirement. This fuzzy-based adaptive selection helps to adequately balance global search or exploration and local search or exploitation operations during the search process as these operations are intrinsically dynamic. The performance of the fuzzy adaptive teaching learning-based optimization is evaluated against other metaheuristic algorithms including basic teaching learning-based optimization on 23 unconstrained global test functions. Moreover, adaptive teaching learning-based optimization is used to search for near-optimal values for the four parameters of the COCOMO II model, which are then tested for validity on a software project of NASA. Analysis and comparison of the obtained results indicate the efficiency and competitiveness of the proposed algorithm in addressing unconstrained continuous optimization tasks.

Synthesis, characterization of (E)-3-((2‑hydroxy-5-(thiophen-2-yl)benzylidene)amino)benzonitrile and optimization of its energy by Fuzzy Logic Modelling

The title compound (E)-3-((2‑hydroxy-5-(thiophen-2-yl)benzylidene)amino)benzonitrile (3) was prepared with a good yield and experimentally characterized by the UV–Vis, FTIR, 1H and 13C NMR spectroscopy. The molecular structure of 3 was optimized with DFT/B3LYP/6–31G(d,p) and 6–311G(d,p) method by using Gaussian 09 software package. Also, using the Mamdani-type fuzzy inference system, DFT calculations of the studied molecule together and fuzzy logic approaching (FLA) have been combined. Thus, the energy structure dependant on two torsional angles SC1(C2C1C19S18) and SC2(C4C5C7N8) of the compound 3 was modelled. Thanks to the optimized energy surface of the molecule, the discrete energy data is made continuous and untested data which are time-consuming to obtain by other methods/experimentation are made computable. Then, to show the consistency of the optimization, a regression analysis was also performed between the results obtained with the DFT and FLA methods, and it was shown that the results were very close to each other at a rate of 98.16%. The theoretical characterization of 3 was carried out using the DFT/B3LYP/6–31G(d,p) and 6–311G(d,p) calculations, and the experimental and theoretical results were discussed extensively. In addition, the molecular electrostatic potential (MEP) surface, the energies of HOMO-LUMO, the atomic charges and natural bond orbital analysis (NBO) for 3 were investigated in detail by using the DFT/B3LYP/6–31G(d,p) and 6–311G(d,p) calculations.

A Takagi-Sugeno (T-S)-Type FIS-Based Controller for an Autonomous Induction Generator (AIG)

In this manuscript, a T-S-type Fuzzy Inference System (FIS)-based controller is implemented for an Autonomous Induction Generator (AIG) with a three-legged VSI. A template-based algorithm is used here for producing reference supply currents. The suggested controller controls both reactive and active power parallely of an AIG for maintaining its voltage and frequency at the defined level under varying loads. The designing aspect of T-S-type FIS and various components of three-legged VSI controller is also elaborated. In this manuscript, an initiative is taken to accomplish the performance enhancement of an AIG using T-S-type FIS-based FL controller. Finally, a comparison of various performance outcomes based on conventional PI and T-S-type FIS-based FL controller is made to verify and justify the outcomes. Then, the suggested controller is shown to be superior to the PI method and is recommended for electrical power generation in an autonomous mode. The main aspiration behind using Takagi-Sugeno FIS-based controller is its fast and computationally efficient nature, and it works efficiently with various mathematical techniques, namely linear techniques (PID control), adaptive techniques, and optimization techniques as compared to Mamdani-type FIS.

An alternative approach to determination of Covid-19 personal risk index by using fuzzy logic.

COVID-19 disease is an outbreak that seriously affected the whole world, occurred in December 2019, and thus was declared a global epidemic by WHO (World Health Organization). To reduce the impact of the epidemic on humans, it is important to detect the symptoms of the disease in a timely and accurate manner. Recently, several new variants of COVID-19 have been identified in the United Kingdom (UK), South Africa, Brazil and India, and preliminary findings have been suggested that these mutations increase the transmissibility of the virus. Therefore, the aim of this study is to construct a support system based on fuzzy logic for experts to help detect of COVID-19 infection risk in a timely and accurate manner and to get a numerical output on symptoms of the virus from every person. The decision support system consists of three different sub and one main Mamdani type fuzzy inference systems (FIS). Subsystems are Common- Serious symptoms (First), Rare Symptoms (Second) and Personal Information (Third). The first FIS has five inputs, fever-time, cough-time, fatigue-time, shortness of breath and chest pain/dysfunction; the second FIS has four inputs, Loss of Taste/Smell, Body Aches, Conjuctivitis, and Nausea/Vomiting/Diarrhea; and the third FIS has three inputs, Age, Smoke, and Comorbidities. Then, we obtain personal risk index of individual by combining the outputs of these subsystems in a final FIS. The results can be used by health professionals and epidemiologists to make inferences about public health. Numerical output can also be useful for self-control of an individual.

Fuzzy inference systems for mineral prospectivity modeling-optimized using Monte Carlo simulations

This paper uses Monte Carlo simulations to estimate the parameters of rule-based fuzzy inference systems (FISs) designed for mineral prospectivity modeling. The targeted process for the case study is gold mineralization in the Rajapalot project area in northern Finland. Mamdani-type FISs are developed and implemented for the predictive modeling of favorable structural settings and favorable chemical traps causing gold enrichment in host rocks from ore-bearing hydrothermal fluids. The parameters of the fuzzification functions control the output fuzzy membership values. Traditionally these parameters are chosen subjectively based on the expert's domain knowledge. This study uses drill core data statistics to define the distribution of the parameters. Subsequently, Monte Carlo simulations are used to simulate the corresponding fuzzy membership values and optimize the FISs.•Capturing the complexities of the multi-processes geodynamic systems and the possible interplay mineralization-related geological aspects using ‘If-Then’ rule-based fuzzy inference systems.•Implementation of Monte Carlo simulations for quantification of uncertainties related to a Mamdani-type FIS-based prospectivity modeling.•Reporting prospectivity modeling results at different confidence levels for informed decision making on selection of exploration targets.

The implementation of fuzzy logic controller for defining the ranking function on Malay retrieval system

Ranking is likely the most important process of an information retrieval (IR) system that will be used to evaluate and measure the effectiveness of an IR system. This paper aims to produce the implementation of fuzzy logic controller of Mamdani-type fuzzy inference system for defining the ranking function by using the BM25 model in the Malay IR system that also includes the Malay Stemmer. The result of the ranking function then will be compared to the result of vector space model that is also applied in Malay IR system and be evaluated using relevant document by the hadith expert. The results showed that FBMIR has slightly outperformed vector space model on three topic set of query results such as 'Iman', 'Ilmu' and 'Wuduk' on the precision at rank 10 and the percentage of no relevant document in the top ten retrieved measures.

Estimation of infection risk using symptoms of COVID-19: an approach based on fuzzy expert system

According to the published reports and studies, the symptoms of the disease caused by the COVID-19 virus have not yet been fully determined. It is a major stress on clinicians to make a correct and consistent decision about whether to apply the test or not, as many factors with extreme uncertainty need to be evaluated at once. In this study, it is aimed to provide assistance to the clinicians by processing the data using fuzzy logic based decision support system at the time of the decision-making process. In the designed fuzzy logic based decision support system, a fuzzy rule-base was created with linguistic information by interpreting the symptoms that are naturally uncertain by experts. With the help of the obtained fuzzy rule base, the input data of symptoms will be processed and the risk of a person being infected will be obtained as an output. As the results of the estimation module constructed with the existing parameters are examined, it is observed to be compatible with the data published before. In this context, a data set with 50 different patients were designed randomly to evaluate the system. For the analysis of the nonlinear mapping obtained with the Mamdani type fuzzy inference system, random test data is used and infection risk at rates varying between 12.5-83% was determined. The fuzzy logic based decision support system for COVID-19 can be accepted as applicable, flexible, and trustworthy for clinicians. It can be said that this system is not only suitable for COVID-19 but also applicable for future epidemics.

Comparative study on Mamdani-Type Fuzzy Inference Systems and Regression model for end milling process using AA 6082T6

The goal of this observes is to take a look at the effect of machining parameters on surface roughness in end milling. An incipient technique in modelling surface roughness that makes use of synthetic perspicacity implements is defined in this paper. This paper fixates on growing empirical models utilizing fuzzy logic and regression analysis. The values of surface roughness presaged with the aid of using those fashions are then in comparison. The effects confirmed that the proposed gadget can considerably boom the precision of the product profile whilst in comparison to the traditional approaches, like regression analysis. The effects designate that the regression modelling method may be effectively applied for the presage of surface roughness in dry machining.

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

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.