Using Image Processing Techniques to Increase Safety in Shooting Ranges

Accidents are a leading cause of deaths in armed forces. The Aim of this paper is to minimize the accidents caused using weapons in the armed forces. Developing artificial intelligence technologies aim to increase efficiency more and more wherever people exist. Giving guns to inexperienced, untrained, or unpredictable mentally unhealthy people in shooting ranges used for gun training can be risky and fatal. With the use of image processing technologies in these shooting ranges, it is aimed to minimize the risk of life-threatening accidents that may be caused by this people. Artificial intelligence is trained for the targets to be used in shooting ranges. When the camera of weapon sees these targets, it switches from safe mode to firing mode. When a risky situation occurs in shooting range, the gun turns itself into safe mode with various additional security measures.Comment: 10 pages, 7 figures, 2 table

A Fuzzy Rule Based Approach to Geographic Classification of Virgin Olive Oil Using T-Operators

Olive oil is an important agricultural food product. Especially, protected designation of origin (PDO) and protected geographic indications (PGI) are useful to protect the intellectual property rights of the consumers and producers. For this reason, the importance of the geographic classification increases to trace geographical indications. This chapter suggests a geographical classification system for the virgin olive oils. This system is formed on chemical parameters. These parameters include fuzziness. Novel proposed system constructs the rules by using fuzzy decision tree algorithm. It produces rules over fuzzy ID3 algorithm. It uses fuzzy entropy on the fuzzified data. The reasoning procedure depends on weighted rule-based system and is adapted into the fuzzy reasoning handled with different T-operators. Fuzzification is performed with fuzzy c-means algorithm for the olive oil data set. The cluster numbers of each variable are selected based on partition coefficient validity criteria. The model is examined by using different decision tree approaches (C4.5 and standard version fuzzy ID3 algorithm) and FID3 reasoning method with eight different T-operators. Also, the conclusions are supported by statistical analysis. Experimental results support that the weights have important manner on fuzzy reasoning method for the geographic classification system

Analysis of time series for Malaysian currency exchange rate to the United States currency

Currency exchange rate is one of the external factors that will affect the financial status of Malaysia. Therefore, forecasting the foreign currency exchange rate is important for the financial decision makers, bankers, academic researchers and business practitioners. Time series method is an important area of predicting future data based on the past data. In this study, Auto-Regressive Integrated Moving Average (ARIMA), Double Exponential Smoothing method and Holt-Winter additive method will be used to forecast the data of currency exchange rate of Malaysia Ringgit (RM) to United States of America Dollar (USD). The Mean Absolute Percentage Error (MAPE) for ARIMA, Double Exponential Smoothing method and Holt-Winter additive method are 0.9400, 0.9035 and 2.2686 respectively. In conclusion, the model generated by using Double exponential Smoothing method is the best model to forecast the currency data with the lowest value of MAPE, Mean Absolute Error (MAE) and Mean Square Error (MSE) compared to ARIMA method and Holt-Winter Additive method

3D FcRM modelling in miles per gallon of cars

The new fuzzy c-regression modeling (FcRM) are widely used in order to fit switching regression models. Minimization of objective function yields immediate estimates for different c regression models. The functions of model, estimation technique and results are discussed in this paper. A case study in miles per gallon (MPG) of different cars using the FcRM modeling was carried out. The 3D graph for significant independent variables for FcRM clustering is shown in this study. The comparison between multiple linear regression and FcRM modeling were done. The mean square error (MSE) was used to find the better model. It was found that the FcRM modeling with lower MSE to be the better model and has great capability in predicting the dependent variable effectively

A ROBUST ALGORITHM FOR SOLUTION OF THE FUZZY CLUSTERING PROBLEM ON THE BASIS OF THE FUZZY JOINT POINTS METHOD

The approach called the method of Fuzzy Joint Points (FJP) is considered in which the fuzziness of clusterization lies in the detailedness of taking into account properties of elements in forming sets of similar elements. Based on this approach, a new robust variant of the FJP algorithm is proposed. The properties of this FJP algorithm are analyzed and a sufficient condition for the correct recognition of the hidden structure of clusters is proved

A problem of identification of states of a system from fuzzy values of informational features

The present study considers the problem of identification of the states of complex systems from a group of fuzzy values of several informational features. Using methods from the theory of fuzzy sets, the degree of admissible measurement error is established for arriving at a unique identification of the states of the system

An algorithm for constructing an admissible solution to the bin packing problem with fuzzy constraints

A new statement of the bin packing problem with the evaluation of the quality of packing under fuzzy source constraints is considered. An interactive algorithm for solving the problem is developed, its accuracy is evaluated, and its finiteness is proved. Estimates for the a priori determination of the maximum degree of quality of packing that accelerate the process of the solution of the problem are presented

A Problem of Task Allocation with Fuzzy Information and Two-Stage Solution Algorithm

The problem of high-performance allocation of tasks between executives, where the competence of each for implementation of tasks is specified in the form of fuzzy relations, is considered. Maximization of the aggregated degree of competence of the entire allocation and maximization of the degree of the overall level of employment of standard executives are the optimization criteria. Aggregation is performed by means of the Hurwicz operator and the Ordered Weighted Average (OWA) operator. A two-stage heuristic algorithm is proposed for the solution of the problem. An analysis of different algorithms and assessment of the results of computational experiments is conducted

Certain integral characteristics of fuzzy numbers and a visual interactive method for choosing the strategy of their calculation

Certain integral characteristics of fuzzy numbers such as the averaged representative and the width of a fuzzy number are considered. Their properties are studied and a visual interactive method for finding the distribution density function for the significance of degrees and coefficients of significance of sides of a fuzzy number is proposed