scholarly journals Fuzzy based binary feature profiling for modus operandi analysis

2016 ◽  
Vol 2 ◽  
pp. e65 ◽  
Author(s):  
Mahawaga Arachchige Pathum Chamikara ◽  
Akalanka Galappaththi ◽  
Roshan Dharshana Yapa ◽  
Ruwan Dharshana Nawarathna ◽  
Saluka Ranasinghe Kodituwakku ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Open Data ◽  
New Method ◽  
Classification Algorithms ◽  
Data Sets ◽  
Deviation Probability ◽  
Modus Operandi ◽  
Inference System ◽  
Binary Feature

It is a well-known fact that some criminals follow perpetual methods of operations known as modi operandi. Modus operandi is a commonly used term to describe the habits in committing crimes. These modi operandi are used in relating criminals to crimes for which the suspects have not yet been recognized. This paper presents the design, implementation and evaluation of a new method to find connections between crimes and criminals using modi operandi. The method involves generating a feature matrix for a particular criminal based on the flow of events of his/her previous convictions. Then, based on the feature matrix, two representative modi operandi are generated: complete modus operandi and dynamic modus operandi. These two representative modi operandi are compared with the flow of events of the crime at hand, in order to generate two other outputs: completeness probability (CP) and deviation probability (DP). CP and DP are used as inputs to a fuzzy inference system to generate a score which is used in providing a measurement for the similarity between the suspect and the crime at hand. The method was evaluated using actual crime data and ten other open data sets. In addition, comparison with nine other classification algorithms showed that the proposed method performs competitively with other related methods proving that the performance of the new method is at an acceptable level.

scholarly journals Fuzzy based binary feature profiling for modus operandi analysis

2015 ◽  
Author(s):  
Mahawaga Arachchige Pathum Chamikara ◽  
Akalanka Galappaththi ◽  
Roshan D Yapa ◽  
Ruwan D Nawarathna ◽  
Saluka Ranasinghe Kodituwakku ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Roc Analysis ◽  
Fuzzy Inference ◽  
Open Data ◽  
Classification Algorithms ◽  
Data Sets ◽  
Deviation Probability ◽  
Modus Operandi ◽  
Inference System ◽  
Binary Feature

It is a well-known fact that some criminals follow perpetual methods of operations, known as modus operandi (MO) which is commonly used to describe the habits in committing something especially in the context of criminal investigations. These modus operandi are then used in relating criminals to other crimes where the suspect has not yet been recognized. This paper presents a method which is focused on identifying the perpetual modus operandi of criminals by analyzing their previous convictions. The method involves in generating a feature matrix for a particular suspect based on the flow of events. Then, based on the feature matrix, two representative modus operandi are generated: complete modus operandi and dynamic modus operandi. These two representative modus operandi will be compared with the flow of events of the crime in order to investigate and relate a particular criminal. This comparison uses several operations to generate two other outputs: completeness probability and deviation probability. These two outcomes are used as inputs to a fuzzy inference system to generate a score value which is used in providing a measurement for the similarity between the suspect and the crime at hand. The method was evaluated using actual crime data and four other open data sets. Then ROC analysis was performed to justify the validity and the generalizability of the proposed method. In addition, comparison with five other classification algorithms showed that the proposed method performs competitively with other related methods.

scholarly journals Fuzzy based binary feature profiling for modus operandi analysis

2015 ◽  
Author(s):  
Mahawaga Arachchige Pathum Chamikara ◽  
Akalanka Galappaththi ◽  
Roshan D Yapa ◽  
Ruwan D Nawarathna ◽  
Saluka Ranasinghe Kodituwakku ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Roc Analysis ◽  
Fuzzy Inference ◽  
Open Data ◽  
Classification Algorithms ◽  
Data Sets ◽  
Deviation Probability ◽  
Modus Operandi ◽  
Inference System ◽  
Binary Feature

It is a well-known fact that some criminals follow perpetual methods of operations, known as modus operandi (MO) which is commonly used to describe the habits in committing something especially in the context of criminal investigations. These modus operandi are then used in relating criminals to other crimes where the suspect has not yet been recognized. This paper presents a method which is focused on identifying the perpetual modus operandi of criminals by analyzing their previous convictions. The method involves in generating a feature matrix for a particular suspect based on the flow of events. Then, based on the feature matrix, two representative modus operandi are generated: complete modus operandi and dynamic modus operandi. These two representative modus operandi will be compared with the flow of events of the crime in order to investigate and relate a particular criminal. This comparison uses several operations to generate two other outputs: completeness probability and deviation probability. These two outcomes are used as inputs to a fuzzy inference system to generate a score value which is used in providing a measurement for the similarity between the suspect and the crime at hand. The method was evaluated using actual crime data and four other open data sets. Then ROC analysis was performed to justify the validity and the generalizability of the proposed method. In addition, comparison with five other classification algorithms showed that the proposed method performs competitively with other related methods.

scholarly journals Estimate reliability of component-based software sys-tem using modified neuro fuzzy model

2017 ◽  
Vol 6 (2) ◽  
pp. 45 ◽  
Author(s):  
Ravi Kumar Sharma ◽  
Dr. Parul Gandhi
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Software Systems ◽  
Data Sets ◽  
Component Reliability ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Estimate Reliability

There are many algorithms and techniques for estimating the reliability of Component Based Software Systems (CBSSs). Accurate esti-mation depends on two factors: component reliability and glue code reliability. Still much more research is expected to estimate reliability in a better way. A number of soft computing approaches for estimating CBSS reliability has been proposed. These techniques learnt from the past and capture existing patterns in data. In this paper, we proposed new model for estimating CBSS reliability known as Modified Neuro Fuzzy Inference System (MNFIS). This model is based on four factors Reusability, Operational, Component dependency, Fault Density. We analyze the proposed model for diffent data sets and also compare its performance with that of plain Fuzzy Inference System. Our experimental results show that, the proposed model gives better reliability as compare to FIS.

A New Method to Identify the Preisach Distribution Function of Hysteresis

2005 ◽  
Vol 475-479 ◽  
pp. 2107-2110 ◽  
Author(s):  
Fan Li ◽  
Jian Qin Mao ◽  
Hai Shan Ding ◽  
Wen Bo Zhang ◽  
Hui Bin Xu ◽  
...  
Keyword(s):  
Distribution Function ◽  
Least Squares ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Least Squares Method ◽  
Input Sequence ◽  
Least Square Method ◽  
Least Square ◽  
New Method ◽  
Inference System

In this paper, a new method which combines the least square method with Tree-Structured fuzzy inference system is presented to approximate the Preisach distribution function. Firstly, by devising the input sequence and measure the output, discrete Preisach measure can be identified by the use of the least squares method. Then, the Preisach function can be obtained with Tree-Structured fuzzy inference system without any special smoothing means. So, this new method is not sensitive to noise, and is a universal approximator of the Preisach function. It collect the merit and overcome the deficiency of the existing methods.

Application of Adaptive Neuro-Fuzzy Inference System in Field Balancing

2005 ◽  
Author(s):  
Shi Liu ◽  
Liangsheng Qu
Keyword(s):  
Computer Simulation ◽  
Fuzzy Set ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
New Method ◽  
Inference System ◽  
Flexible Rotors ◽  
Neuro Fuzzy ◽  
Vibration Responses ◽  
Single Sensor

The field balancing of flexible rotors is one of the key techniques to reduce vibration of large rotating machinery. Although in recent decades the balancing theory has been thoroughly studied and various balancing techniques have been well developed, the present balancing methods are still remain for further improvements in accuracy and efficiency. Firstly, most balancing methods need large numbers of trial runs to obtain the vibration responses of trial weights in different correcting planes. Secondly, the vibration response in each measured section is always taken from a single sensor, and thus are lack of comprehensive vibration information of rotor. In fact, the movement of rotor is a complex spatial motion, which can’t be objectively and reliably described just with a single sensor in each bearing section. In order to overcome above shortcomings of traditional balancing methods, this paper presents a new field balancing method for flexible rotors, which is based on adaptive neuro-fuzzy inference system (ANFIS). The new method successfully applies the information fusion, ANFIS and computer simulation together. It integrates and fully utilizes the information supplied from all proximity sensors by holospectrum for enhancing the balancing efficiency and accuracy. A fuzzy model is established to simulate the mapping relationship between vibration responses and balancing weights by using the ANFIS. The inputs into ANFIS are the amplitudes and phases of integrated vibration responses, while the outputs are the mass and azimuth of balancing weights. A fuzzy set with three membership functions (MFs) is used to describe the magnitude of vibration amplitudes or of balancing weights. Another fuzzy set with five MFs is used to describe the quadrant of vibration phases or of balancing weights. Based on the historical balancing data, a combination of least-square and back-propagation gradient descent methods is then used for training ANFIS membership function and node-parameters to model input (vibration response)/output (balancing weight) data. The simulation study shows that the ANFIS can obtain satisfactory balancing result after a single trial run. At the same time, with the help of computer simulation, different correction schemes can be compared and rapidly simulated to direct balancing operation. Finally, the effectiveness of the new method was validated by the experiments on balancing rig and in the field balancing practice of several 300MW turbo-generator units.

Adaptive Neuro-Fuzzy Inference System for Predicting Compressive Strength of Fibres Self Compacting Concrete

2019 ◽  
Vol 892 ◽  
pp. 46-54
Author(s):  
L.V. Prasad Meesaraganda ◽  
Prasenjit Saha
Keyword(s):  
Compressive Strength ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Data Sets ◽  
Fine Aggregate ◽  
Self Compacting Concrete ◽  
Anfis Model ◽  
Inference System

This research focused on the applicability of Adaptive Network-Based Fuzzy Inference System (ANFIS) for predict the compressive strength of fibers self-compacting concrete. An ANFIS model combines the benefit of ANN and fuzzy logic. The data developed experimentally for fibers self-compacting concrete and the data sets of a total 99 concrete samples were used in this work. In this paper research is computational based for prediction of concrete compressive strength. A model was developed using ANFIS with five input nodes as w/p ratio, course aggregate, fine aggregate, fiber and superplastizers. In this model Feed-forward three-layer back-propagation neural networks with 10 hidden nodes were examined using learning algorithm. ANFIS model proposed analytically that gives more compatible results. Hence, the model is adopted to predict the strength of fibrous self-compacting concrete.

scholarly journals Non-Weighted Evaluation Function in Multi-Objective Problems

2018 ◽  
Vol 6 (5) ◽  
Author(s):  
Bayadir Abbas Himyari ◽  
Azman Yasin ◽  
Horizon Gitano
Keyword(s):  
Genetic Algorithm ◽  
Computational Complexity ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Data Sets ◽  
Evaluation Function ◽  
Inference System ◽  
Multi Objective ◽  
Composition Method ◽  
Rules Extraction

An evaluation function is proposed to deal with multi-objective problems without weight using a new composition method. Improving the evaluation function by reducing its complexity through discarding the weights. The evaluation function is utilized for the optimization of fuzzy rules.A genetic algorithm is applied as a multi-objective algorithm for fuzzy rules extraction. Simplicity during building fuzzy inference system and reducing the computational complexity is required. The algorithm is applied on AFR data sets.

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