The use of soft computing to classification of some weeds based on video processing

ВПЛИВ БУР’ЯНІВ НА РІВЕНЬ ВОДНОГО ЖИВЛЕННЯ ПОСІВІВ БУРЯКІВ ЦУКРОВИХ

Aim: Prediction of coronary illness utilizing novel Novel k nearest neighbor (KNN) and contrasting its accuracy with decision tree algorithm. Materials and Methods: Two gatherings are proposed for foreseeing the accuracy (%) of coronary illness. To be specific, novel Novel k nearest neighbor and decision tree algorithm. Here we take 20 examples each for assessment and look at. The sample size was calculated using G power with pretest power at 80% and the alpha of 0.05 value. Result: The decision tree gives better accuracy (84.95%) contrasted with the Novel k nearest neighbor accuracy (76.29 %). Along these lines the factual meaning of the decision tree is superior to the novel k nearest neighbor algorithm with significance value of 0.115. Conclusion: From the outcome, it may very well be inferred that the decision tree helps in anticipating the coronary illness with more precision contrasted with the novel k nearest neighbor algorithm.

For the pattern classification problems the neuro-pattern recognition which is the pattern recognition based on the neural network approach has been paid an attention since it can classify various patterns like human beings. In this paper, we adopt the learning vector quantization (LVQ) method to classify the various kinds of money. The reasons to use the LVQ are that it can process the unsupervised classification and treat many input data with small computational burdens. We will construct the LVQ network to classify the Italian liras. Compared with a conventional pattern matching technique, which has been adopted as a classification method, the proposed method has shown excellent classification results.

A bill money classification has become automated and it is important that the classifier has higher accuracy. Generally, the accuracy of classification is represented as the recognition rate of sample data. However, when classifying bill money, we must evaluate the accuracy more strictly. For pattern recognition a neural network (NN) is studied and its ability is highly estimated. Among NNs a competitive NN has a simple structure and can be analyzed by the relation between the inputs and the outputs more easily than a layered NN based on the backpropagation method. Because of this, we use a competitive NN for bill money classification and use the learning vector quantization (LVQ) method for training the NN. We propose a reliability criterion based on a probability distribution for the classification by the LVQ method. Then we classify US dollars by the LVQ and apply the reliability criterion to the classification. We show that the proposed method of bill money classification has higher accuracy.

The Research aimst to compare backpropagation and Learning Vector Quantization (LVQ) methods in exploring the potential of new students at STMIK PalComTech. Comparisons in this study involve four input variables used which consist of four basic subjects of informatics engineering and information systems (math, basic programming, computer networks and management bases) which then make informatics techniques and information systems as outputs, to get the accuracy level high in this study, the researchers used several variations of parameters which eventually produced the best accuracy of the two methods. From 120 data tested using variations in test data and training data which are then processed using variations in the learning rate parameters and epochs. From the test results obtained the level of accuracy of pattern recognition in the backpropagation method is 99.17% with a learning rate variation of 0.1 and epoch 100, the learning vector quantization method has an accuracy rate of 96.67% with a variation of learning rate 1 and epoch 20 From the results of the comparison the Backpropagation method is superior in terms of accuracy so that it becomes the right method to use in exploring the potential of new students at STMIK PalComTech.

Aim: Coronary illness is one of the main sources of mortality in this current world. A basic test in the field of clinical information assessment is the expectation of cardiovascular infection. The fundamental target of this paper is to work on the exactness of the Data mining algorithms being used to improve heart disease prediction. We will be comparing the novel Random forest with K Nearest Neighbor to find out which of these can give us the best accuracy. Material and Methods: The study used 98 samples with Novel Random Forest and K Nearest Neighbor is executed with varying training and testing splits for predicting the accuracy for heart disease prediction with the 80% of G-power value and data about cardiovascular disease was gathered from numerous web sources, including latest research findings and threshold 0.05%, confidence interval 95% mean and standard deviation. The performance of the accuracy rate of the classifiers is examined using the heart disease dataset.. There was a statistical insignificant difference among Innovative Random Forest and K Nearest Neighbor p = 0.484 (p>0.05). Results and Discussion: The accuracy of predicting heart disease in Novel Random Forest 90.16% and K Nearest Neighbor 67.21% is obtained. Conclusion: Prediction of Heart disease using the innovative Random Forest (RF) technique appears to be a significant improvement over the K Nearest Neighbor (KNN) algorithm in terms of accuracy.

The nearest neighbor (NN) classifiers, especially the k-NN algorithm, are among the simplest and yet most efficient classification rules and are widely used in practice. We introduce three adaptation rules that can be used in iterative training of a k-NN classifier. This is a novel approach both from the statistical pattern recognition and the supervised neural network learning points of view. The suggested learning rules resemble those of the well-known learning vector quantization (LVQ) method, but at the same time the classifier utilizes the fact that increasing the number of samples that the classification is based on leads to improved classification accuracy. The performances of the suggested learning rules are compared with the usual K-NN rules and the LVQ1 algorithm.

This paper presents the yield and evolution of weeding level in wheat crop, as part of a three-year soybean-winter wheat-corn rotation, with the implementation of minimum soil tillage variants with: rotary harrow, paraplow and chisel plough. The experiment has been conducted on a Phaeozem argic soil type in Transylvania Plain, with conventional and four minimum tillage systems of soil cultivation. The level of weeding was influenced by the soil tillage system in the phase of springing up to the harvest of the wheat crop. At crop spring­ing, the total number of weeds was 31.8 weeds/m2 in the variant with plough + rotary har­row and between 31.6-34.7 weeds/m2 in minimum tillage variants. A greater number of perennial dicotyledonous weeds can be recorded in the minimum tillage variant (between 1.2-1.6 weeds/m2), when compared with plough + rotary harrow variant (0.9 weeds/m2). At the crop harvest stage, the number of weeds was 8.2-50.6% higher in the variants with minimum tillage system, when compared to the conventional tillage system. A decrease in the percent of annual dicotyledonous weeds is evident, from 85.9% in the variant with plough, to 71.5-78.9% in the variants with minimum tillage systems, with an increase in perennial dicotyledonous (from 7.9% to 8.4-13.8%) and monocotyledonous weeds (from 6.2 to 11.6-20%). The changes in the floristic content of weeding level indicates the need for changing the strategies of weed control in winter wheat crop in the variants with mini­mum soil tillage systems. Increasing the percent of perennial dicotyledonous weeds by 59% (from 7.9% to 13.8%) makes the wheat cultivation in crop rotation with leguminous nec­essary, completed with the use of specific herbicide methods, in previous crop and wheat crop. The presence of monocotyledonous weeds of about 11.6% to 20.0% completes the necessity for the application of different strategies of control and correlation, for the weed­ing level as well as the type of weed, including their annual or perennial character. Yields obtained by applying minimum tillage were 97.8-98.2%, when paraplow and chisel are compared to plough and were significantly lower in the rotary harrow variant, while weed­ing level was the highest.

The face is one of the media to identify someone, a human face has a very high level of variability. Many methods have been introduced by researchers and scientists in recognizing one's face, one of the methods introduced is the Feature Extraction of Gray Level Co-Occurrence Matrix (GLCM) and Learning Vector Quantization (LVQ). GLCM feature extraction is used for data extraction/learning process whereas a data analysis process (face recognition, cropping and storing data) the LVQ method is used for the data training process where the data that has been processed in GLCM feature extraction which still has large dimensions are processed to be smaller dimensions. So this test uses data of 190 photos and gets a match of 90%, the authors conclude that the GLCM feature extraction and LVQ method can very well recognize faces contained in the database.

FSKNN: Multi-label text categorization based on fuzzy similarity and k nearest neighbors

<p>Satellite based rainfall estimates (SBRE) are used as an alternative to gauge rainfall in hydrological studies particularly for basins with data issues. However, these data products exhibit errors which cannot be always corrected by bias correction methods such as Ratio Bias Correction (RBC). Data fusion or data merging can be a potentially good approach in merging various SBREs to obtain a fused dataset, which can benefit from all the data sources and may minimise the error in rainfall estimates. Data merging methods which are commonly applied in meteorology and hydrology are: Arithmetic merging method (AMM), Inverse error squared weighting (IESW) and Error variance (EV). Among these methods EV is popular, which merges bias corrected SBREs using the minimisation of variance principle.</p><p>In this research we propose using K Nearest Neighbour (KNN) as a data merging method. KNN has a particular advantage as it does not depend upon any specific statistical model to merge data and presents a great flexibility as the value of K (the number of neighbours to be chosen) can be varied to suit the purpose (for example, choosing different K values for different seasons). In this research it is proposed to compute the distances of bias corrected SBREs of the training data from the gauge data and to assign the SBRE with the minimum distance as the class C where C = 1, 2, 3,…, number of SBREs. In validation each data point consisting of a value of each SBRE may be compared with the data points from the training set and the class of the data point(s) closest to this data point is assigned as the class of the validation data point.</p><p>The KNN approach as a data merging method was applied to the Indus basin in Pakistan. Three satellite rainfall products CMORPH, PERSIANN CDR and TRMM 3B42 with 0.25° x 0.25° spatial and daily temporal resolution were used. Based on the climatic and physiographic features the Indus basin was divided into four zones. Rainfall products were compared at daily, weekly, fortnightly, monthly and seasonally whereas spatial scales were gauge location, zonal scales and basin scale. The RBC method was used to correct the bias. The KNN method with K=1, 3 and 5 was used and compared with other merging methods namely AMM, IESW and EV. The results were compared in two seasons i.e. non-wet and wet season. AMM and EV methods performed similarly whereas IESW performed poorly at zonal scales. KNN merging method outperformed all other merging methods and gave lowest error across the basin. The daily normalised root mean square error at the Indus basin scale was reduced to 0.3, 0.45 and 0.45 respectively with KNN, AMM and EV whereas this error was 0.8, 0.65 and 0.53 respectively in CMORPH, PERSIANN CDR and TRMM datasets. The KNN merged product gave lowest error at daily scale in calibration and validation period which justifies that merging with KNN improves rainfall estimates in sparsely gauged basins.</p><p> </p><p><strong>Key words:</strong> Merging, data fusion, K nearest neighbour, KNN, error variance, Indus.</p>

Traditional k-nearest neighbor methods couldn’t be able to correctly classify objects when their k nearest neighbors are dominated by other classes. This paper formulates a two-class classification problem, and applies a modified k-nearest neighbors (KNN) classifier algorithm based on maximal coherence, validity ratings, and k-fold cross validation to classify the test samples. We build a validity score for the pairs of sample and their surroundings according to their labels. The k nearest neighbors (including the unknown test object) of each sample in the training set as well as the unknown test object itself will be determined. The unknown test object will be tentatively assigned to a class membership. Then we use the validity scores to quantify the degree to which a pre-determined group of samples resemble their k nearest neighbors. A classifier is designed which take into account the coherence and validity ratings. A numerical example demonstrates the effectiveness of the algorithm in detail. The enhanced KNN method is compared with the conventional KNN and the modified KNN method on both real world wine data and photo-thermal infrared imaging spectroscopy (PT-IRIS) data for up to 20 different k values. Classification accuracy of KNN method and our method in terms of various combinations of k-value and k-fold cross validation are compared. The experimental results show that the proposed enhanced KNN method outperforms the conventional KNN and the modified KNN method on both real world wine data and PT-IRIS data. In addition, the classification accuracy of both the conventional KNN and our method increase drastically when k = 5. The average classification accuracy of our method on the PT-IRIS data featuring small sample size and high overlap is 97.87%.

Research on the Identification of Stroke with MRI Imagery Using the Learning Vector Quantization (LVQ) Method Based on Texture Features has been carried out. This study aims to determine the program's best parameters and the highest accuracy level of the stroke identification program. This research was conducted at Haji Sukolilo Hospital - Surabaya by obtaining 57 images of stroke patients and 15 images of regular patients. The study used the intelligence of stroke, tumor, and standard images to determine each category's image characteristics. After knowing the differences in each class, the next process is digital image processing, followed by feature extraction used is the Gray-Level Co-occurrence matrix (GLCM) with four parameters: contrast, correlation, diversity, and homogeneity. These four parameters are the best input parameters with an intelligence rate of 0.100 with a decrease in intelligence rate of 0.100, so the best accuracy value for training is 74.97%, and test data is 78.60%. Regarding the program's ability to correctly identify 11 data from 14 data tested, the program is feasible to be used as a second opinion.

Deaths of people with breast diseases have reached 40% and are increasing every year. Breast Disease arises from wounds in breast tissue is breast cancer. Wounds of breast can be seen and discriminated the type of wound using Electrical Impedance Spectroscopy(EIS). The results of this EIS data can be used as an initial diagnosis before the doctor carries out treatment. This research tried to classify the types of wounds from breast tissue by using the Learning Vector Quantization (LVQ) method with the Genetic Algorithm (GA) to find out whether the solution provides the expected optimum results. The dataset we used for this study was very small which did not represent the existing class, with 106 data for 6 classes. The results of the comparison between LVQ and GA-LVQ obtained LVQ added by GA has higher accuracy values than LVQ with 73% accuracy and compared to LVQ, GA increased accuracy by ± 50%.

Learning Vector Quantization (LVQ) is a popular method for multiclass classification. Several variants of LVQ have been developed recently, of which Robust Soft Learning Vector Quantization (RSLVQ) is a promising one. Although LVQ methods have an intuitive design with clear updating rules, their dynamics are not yet well understood. In simulations within a controlled environment RSLVQ performed very close to optimal. This controlled environment enabled us to perform a mathematical analysis as a first step in obtaining a better theoretical understanding of the learning dynamics. In this talk I will discuss the theoretical analysis and its results. Moreover, I will focus on the practical application of RSLVQ to a real world dataset containing extracted features from facial expression data.