This study is designed to consider the two important yet often neglected factors, which are factory recommendation and Bit features, in Optimum Bit Selection. Image Processing Techniques have been used to consider the Bit features. A mathematical equation, which is derived from a Neural Network model, is used for drill Bit Selection to obtain the Bit’ s maximum penetration rate that corresponds to the Optimum parameters for Drilling. At the end, the Bit with the maximum penetration rate is chosen. The results of this study showed that Bit pattern can be inserted in the calculation through a proper Bit Image Processing technique. This is to ensure that each unique Bit can be discriminated from other Bits. The values of mean square error and coefficient of determination (R2) were respectively found as 0. 0037 and 0. 9473, for the rate of penetration model. The Image Processing Techniques were used to extract the Bit features. The Artificial Neural Network black box was converted to white box in order to extract a mathematical equation and visibility of the model.

Coagulation process is of vital importance for achieving good performance in water pre-treatment units. It usually implements Optimum operating conditions that results in highest turbidity removal. The choice, dosage, pH and rate of mixing of coagulant and coagulant-aid are the variables that define Optimum operating conditions. In this research, coagulation process of Fajr petrochemical company has been studied. Several jar tests are conducted to determine performance of Alum, Ferric chloride and Poly aluminum chloride as coagulants and anionic poly electrolyte and wheat starch as coagulant-aids at different pH and mixing rates.Optimum operating conditions data obtained in jar tests are used in developing a Neural Network model. This model allows operators to have an estimation of the operating conditions. The success of water pre-treatment depends on fixed feed water quality without frequent need to run jar tests. The predicted result of this Network for operating parameters, presented the high consistency with operating data of industrial unit. The maximum relative error for prediction of turbidity is 0.6 % and for total hardness is 2.3 %.The number of neurons of Neural Network hidden layer is optimized and the developed model is validated and tested using a fraction of data that was not utilized in Network training. The estimated operating condition for a given feed water quality is implemented in practice and the result of pre-treated water quality matched the expected quality.

In naturally fractured reservoirs, fractures play a main role in production, and fracture identification is very important in reservoir development and management. Borehole Image log, which is a high resolution “pseudo picture” of the borehole wall, is a powerful tool for fracture study. These logs provide critical information about the orientation, depth, and type of natural fractures. Currently, there is no comprehensive Algorithm for the automatic identification of fracture parameters in Image logs, and the interpretation of these logs is often done manually. This process might become erroneous when the interpreter is less experienced. The present study uses Image analysis and Processing Techniques, as well as Genetic Algorithms to detect fractures in Image logs automatically. In this method, the points related to fractures are first extracted from the Image by a classification method. Then, the number, depth, dip, and dip direction of fractures are determined on the extracted points by using Genetic Algorithm. This method is performed on a part of two Image logs (4 and 8 pads) of two wells located in two oilfields in the south of Iran. Despite the sensitivity of the proposed method to the noises of the Image, it successfully estimated the number, dip, and dip direction of fractures for both studied wells with an accuracy of 70%.

Appropriate drill Bit Selection can lead to enhance remarkable Drilling Operation efficiency and cost-saving. There are several factors affecting the Bit Selection process. Among these parameters, some factors such as specific energy (SE), cost per foot (CPF), formation drillability (FD), and rate of penetration (ROP) are considered the most important ones. One of the best approaches to select the Optimum drill Bit is to apply Multiple Criteria Decision-Making (MCDM) Techniques. In this research work, Fuzzy Technique for Order-Preference by similarity to Ideal Solution (FTOPSIS) is used to choose the Optimum Bit for Drilling Operations in Sarvak and Asmari formations due to higher accuracy and validity of findings achieved from the FTOPSIS. With this respect, three types of Bits (i. e. 517, 527, and 537) were considered as available candidates and were then ranked through the FTOPSIS, resulting in the best option (Bit). In Asmari formation, similarity factors for Bit types of 517, 527, and 537 Bits were obtained as 0. 479, 0. 438, and 0. 382, respectively indicating Bit type 517 can be chosen as a proper option compared to other ones. Similarly, in Sarvak formation, results showed 0. 5405, 0. 5019, and 0. 5622 values for 517, 527, and 537 Bit types respectively, demonstrating the Bit-type 537 is the most appropriate alternative in such formation.

Due to the high speed and accuracy of intelligent pest detection in warehouse products, in this study, the detection of chickpea fourpoint beetle pest was simulated by Image Processing technique using Artificial Neural Networks. To prepare the Images, a glass box was prepared and the chickpea seeds were placed in the center of the box. The light was then illuminated from all six sides and photographed with a digital camera from all sides. The Image properties were then extracted by Wavelet Gabor using MATLAB software and applied to the ANN as training data. To train the Network, 69 Images of chickpeas damaged and 59 healthy chickpeas were used. Then, to evaluate the Network, a set of data that did not play a role in Network training as test data was applied to the Network and its results were evaluated. In this study, Perceptron and Elman Neural Networks were used which had better results than Elman Network. The proposed method was able to detect the high rate of damaged with 6. 17% non-detection error and 4. 86% error-detection error. After Image Processing by the Neural Network and detection of damage points, the amount of crop damage was also calculated. For this purpose, the level of detected damage was calculated and divided by the area of total area of chickpea seed and percentage of damage. After identifying the injury sites, the damage was estimated 2. 3% in the studied Images.

Cry as the only way of communication of babies with the surrounding environment can be happened for many reasons such as diseases, suffocation, hunger, cold and heat feeling, pain and etc. So, the analysis and detection of its source are very important for parents and health care providers. So the present study designed with the aim to test the performance of Neural Networks in the identification of the source of babies crying. The present study combines the Genetic Algorithm and Artificial Neural Network with (Linear Predictive Coding) LPC and MFCC (Mel-Frequency Cepstral Coefficients) to classify the babies crying. The results of this study indicate the superiority of the proposed method compared to the other previous methods. This method could achieve the highest accuracy in the classification of newborns crying among the previous studies. Developing methods for classification audio signal analysis are promising and can be effectively applied in different areas such as babies crying.

Weeds normally grow as patches and spatially distributed in field. Patch spraying to control weeds has advantages such as cost reduction, herbicide saving and reduction of environmental pollution. Machine vision system should obtain and process digital Images to make control decisions. Proper identification and classification of weeds are the key steps to make control decisions and use of any spraying Operation performed. In this study, a robust method based on Image Processing and computational intelligence was developed for segmentation from other parts of Image and classification of weeds. Large crabgrass, common lamb’ s quarter, velvetleaf, common barnyard grass, European black nightshade, red-rooted pigweed and European heliotrope were the weeds in the experiment. Results showed that this Algorithm was precisely separated weeds from the soil. In the next step, the feature vector, which includes shape features and color features, was composed. Finally, classification of seven classes of weeds was carried out by Artificial Neural Network (ANN). Among different ANN structures, the most Optimum classifier was the 43-15-15-7 topology with accuracy 88/71 %. The results of this research indicate that the proposed system has the ability to accurately detection of weeds.

The optimized portfolio formation is one of the important decisions making for corporations, accordingly a portfolio Selection with top efficiency rate and controlled risk is one of problems that scholars attend them. In this research, we submit a method with multi objectives Genetic Algorithm based to portfolio formation and we lionize the value at risk as a paragon for risk measuring. Also we use 50 top companies’ data of stock exchange in time period from 1385 to 1389.The results show that multi objectives Genetic Algorithm can used to optimized portfolio formation and designed portfolio Operation via Genetic Algorithm is different from with 50 top companies Operation with equal weights.

Accurately and promptly monitoring the nutritional conditions of fruit orchards is crucial for providing optimal fertilizer recommendations, which in turn improves yield and enhances the quality of agricultural products. The current laboratory methods used to evaluate nutritional condition in fruit trees are expensive, challenging, time-consuming, and require an expert. In this study, Image Processing methods and Neural Network models was utilized to determine the stages of iron deficiency in peach trees. Therefore, a database containing 800 Images of peach leaf samples was acquired. These Images were then classified into four categories using the KNN clustering method: no deficiency, low deficiency, moderate deficiency, and severe deficiency. The preProcessing, feature extraction, and modeling Operations were performed in the MATLAB software, version 2017. Features such as mean and standard deviation were extracted from the RGB, HSV, and Lab color space components of each Image. Subsequently, the principal component analysis (PCA) Algorithm was applied to the feature vector. To determine the optimal structure of the Network, criteria including precision, accuracy, recall, and the F1-score were evaluated. These criteria helped ascertain the number of optimal inputs and the corresponding number of neurons for each combination of input features (PCs). Results indicated that the Neural Network model, structured as 6-36-4, achieved an accuracy of 89.73 ± 0.54%, precision of 89.59 ± 0.57%, recall of 89.52 ± 0.51%, and an F1-score of 89.55 ± 0.54% in detecting levels of iron deficiency in peach tree leaves. The findings from the confusion matrix and the developed model reveal that this method can effectively and efficiently detect the severity of iron deficiency in peach tree leaves.