The increase of developments of electronic and computer has been resulted with the commonly use of these technologies in everyday life in many applications. One of these applications is emerged in agricultural applications as precision agriculture studies. The use of technology in agriculture has lots of benefits such as energy saving, yield increase, time saving and etc. In this study, a novel learning based, pixel-wise segmentation method which uses Common Vector Approach (CVA) for the first time in the literature for segmentation is proposed. In the proposed method, first of all color regions belong to both vegetation and soil are manually cropped and then three different color space representations such as HSV, Lab and Luv of RGB images for each color region are encoded as 3rd color tensors. Then by unfolding the color tensor in the mode-3 direction, 2-D color matrix is obtained. The columns of this 2-D color matrix are the dimensional vectors that include (H, S a, b, u, v) components of HSV, Lab and Luv color spaces of an image pixel, and each column vector is accepted as an object. By applying CVA in object space consisting of column vectors of 2-D color matrix, a common color vector which represents common properties of that color region is obtained and used for segmentation purposes. In the experimental studies two different datasets proposed for open computer vision tasks in precision agriculture before in the literature are used. Three different experimental studies are performed for different dataset combinations in terms of training set and the test set. The performance of the proposed method has been compared with the performance of a deep learning method, Convolutional Neural Networks (CNN) based semantic segmentation method. In all of the three experimental studies proposed method achieves extremely high performance according to CNN, especially in the second and in the third experimental studies where dataset combinations include the two of the datasets.

This study proposes the use of geopolymer lime mortar, activated with NaOH and Na₂SiO₃ alkalis, and made from lime, fly ash, brick aggregate, and blast furnace slag (BFS), as an alternative to Portland cement-based concrete. The geopolymer lime mortar samples used in the experimental analysis were produced under controlled laboratory conditions. Compressive strength tests were conducted on the produced samples. The sample with the highest BFS content yielded the best compressive strength results. However, experimental studies are time-intensive. To shorten the experimental time and minimize the material and equipment costs associated with the experiments, a hybrid regression algorithm was proposed for the prediction of compressive strength. Instead of labratory tests the compressive strength of the produced samples was determined using a hybrid regression algorithm has never been used before for this purpose in the literature. This hybrid algorithm is the principal components analysis extreme learning machine algorithm obtained by integrating the PCA method, an effective feature selection method in machine learning, and the ELM method, a regression method that has increased its popularity in recent years. The performance of the proposed algorithm has been compared with other neural network models such as Artificial Neural Network and ELM algorithms and also compared with frequently used algorithms such as random forest regressor, ada boosting, gradient boosting, and extreme gradient boosting algorithms. The results obtained demonstrated the ability of the proposed PCA-ELM algorithm to capture complex relationships within the data by exhibiting superior performance compared to commonly used methods in compressive strength estimation of geopolymer lime mortar.

In this paper, a new algorithm using 2DPCA and Gram-Schmidt Orthogonalization Procedure for recognition of face images is proposed. The algorithm consists of two parts. In the first part, a common feature matrix is obtained; and in the second part, the dimension of the common feature matrix is reduced. Resulting common feature matrix with reduced dimension is used for face recognition. Column and row covariance matrices are obtained by applying 2DPCA on the column and row vectors of images, respectively. The algorithm then applies eigenvalue-eigenvector decomposition to each of these two covariance matrices. Total scatter maximization is achieved taking the projection of images onto d eigenvectors corresponding to the largest d eigenvalues of column covariance matrix, yielding the feature matrix. The each column of the feature matrix represents a feature vector. Minimization of within class scatter is achieved by reducing the redundancy of the corresponding feature vectors of the different images in the same class. A common feature vector for each d^{th} eigenvector direction is obtained by applying Gram-Schmidt Orthogonalization Procedure. A common feature matrix is established by gathering d common feature vectors in a matrix form. Then, the dimension of common feature matrix is reduced to d \times d taking the projection of common feature matrix onto d eigenvectors which corresponds to the largest d eigenvalues of row covariance matrix. The performance of the proposed algorithm is evaluated experimentally by measuring the recognition rates. The developed algorithm produced better recognition rates compared to Eigenface, Fisherface and 2DPCA methods. Ar-Face and ORL face databases are used in the experimental evaluations.