Characteristic Feature Vectors Research Articles

Fuzzy deep learning-based crop yield prediction model for sustainable agronomical frameworks

The evolution in science and innovation has to lead to an immense volume of information from various agricultural fields to be accumulated in the public domain. As a result, an objective arises from the investigation of the accessible information and incorporating them with processes like foreseeing crop yield, plant diseases examination, crops enhancement, etc. Machine learning has grown with tremendous processing methods to conceive new innovations in the multi-disciplinary agricultural sector. In experimenting with machine learning models, there exist certain limitations like improvident nonlinear mapping between the raw data and crop yield values. Hence, deep learning models are comprehensively used to extricate critical crop parameters for prediction. Foreseeing the crop yield depending on climate, soil and water parameters has been a potential research subject. This paper proposes a hybrid deep learning-based crop yield prediction system using deep belief network (DBN) and fuzzy neural networks system (FNN). DBN is a combination of statistics and probability with neural networks. Though DBN performs better for nonlinear systems, the algorithm alone cannot provide satisfactory results in terms of robustness, model accuracy and learning speed, which is predominantly due to gradient diffusion. Hence, a DBN along with FNN has been proposed to overcome the nonlinearity and gradient diffusion problems. The proposed model initially performs an efficient pre-training technique by DBN for enhanced model development and feature vector generation. This characteristic feature vector is fed as an input to the FNN for further processing. The superiority of the proposed fuzzy neural network-based deep belief network is analyzed by comparing it with other deep learning algorithms. The proposed model efficiently predicts the results outperforming the other models by preserving the original data distribution with an accuracy of 92%.

Transformation of FASTA files into feature vectors for unsupervised compression of short reads databases.

FASTA data sets of short reads are usually generated in tens or hundreds for a biomedical study. However, current compression of these data sets is carried out one-by-one without consideration of the inter-similarity between the data sets which can be otherwise exploited to enhance compression performance of de novo compression. We show that clustering these data sets into similar sub-groups for a group-by-group compression can greatly improve the compression performance. Our novel idea is to detect the lexicographically smallest k-mer (k-minimizer) for every read in each data set, and uses these k-mers as features and their frequencies in every data set as feature values to transform these huge data sets each into a characteristic feature vector. Unsupervised clustering algorithms are then applied to these vectors to find similar data sets and merge them. As the amount of common k-mers of similar feature values between two data sets implies an excessive proportion of overlapping reads shared between the two data sets, merging similar data sets creates immense sequence redundancy to boost the compression performance. Experiments confirm that our clustering approach can gain up to 12% improvement over several state-of-the-art algorithms in compressing reads databases consisting of 17-100 data sets (48.57-197.97[Formula: see text]GB).

Development of a surrogate model by extracting top characteristic feature vectors for building energy prediction

In early stage of building design, design team has to consider and simulate energy consumption for several combinations of various input parameters to analyze the building energy consumption. In a scenario considering five parameters, each with ten variations, one has to simulate hundred thousand combinations. It requires a lot of computation to simulate energy consumption for all the input combinations. This paper aims at reducing the computation required to compute the energy consumption of all the combinations. This is done by identifying appropriate training samples, computing their energy consumption using EnergyPlus and estimating energy consumption of the rest of the data using machine learning techniques. This paper presents two sampling methods along with various regression techniques to predict energy consumption of a building in the early phase. It involves usage of efficient sampling methods for identifying the training data. The key contribution of this method of surrogate modeling is saving a lot of computation by reducing the computation by ~100-fold. This method is tested for Jaipur and Hyderabad cities of India. Approximately hundred thousand simulations are performed for each location using parallel computation. By simulating approximately one percent of the input combinations, annual energy consumption for the large set of combinations are predicted using SVR and k-means clustering for Jaipur with accuracy greater than 93% for 99.8% of the input combinations. When the same model is trained for Hyderabad, it produced accuracy greater than 93% for 98% of the input combinations.

A Review On Content Based Image Retrieval

In current years, very huge collections of images and videos have grown swiftly. In parallel with this boom, content-based image retrieval and querying the indexed collections of images from the large database are required to access visible facts and visual information. Three of the principle additives of the visual images are texture, shape and color. Content based image retrieval from big sources has a wide scope in many application areas and software’s. To accelerate retrieval and similarity computation, the database images are analyzed and the extracted regions are clustered or grouped together with their characteristic feature vectors. As a result of latest improvements in digital storage technology, it's easy and possible to create and store the large quantity of images inside the image database. These collections may additionally comprise thousands and thousands of images and terabytes of visual information like their shape, texture and color. For users to make the most from those image databases, efficient techniques and mechanisms of searching should be devised. Having a computer to do the indexing primarily based on a CBIR scheme attempts to deal with the shortcomings of human-based indexing. Since anautomated process on a computer can analyze and process the images at a very quick and efficient rate that human can never do alone. In this paper, we will discuss the structure of CBIR with their feature vectors.

COMPREHENSIVE STUDY ON CONTENT BASED IMAGE RETRIEVAL WITH THEIR FEATURES

In current years, very huge collections of images and videos have grown swiftly. In parallel with this boom, content-based image retrieval and querying the indexed collections of images from the large database are required to access visible facts and visual information. Three of the principle additives of the visual images are texture, shape and color. Content based image retrieval from big sources has a wide scope in many application areas and software’s. To accelerate retrieval and similarity computation, the database images are analyzed and the extracted regions are clustered or grouped together with their characteristic feature vectors. As a result of latest improvements in digital storage technology, it's easy and possible to create and store the large quantity of images inside the image database. These collections may additionally comprise thousands and thousands of images and terabytes of visual information like their shape, texture and color. For users to make the most from those image databases, efficient techniques and mechanisms of searching should be devised. Having a computer to do the indexing primarily based on a CBIR scheme attempts to deal with the shortcomings of human-based indexing. Since an automated process on a computer can analyze and process the images at a very quick and efficient rate that human can never do alone. In this paper, we will discuss the structure of CBIR with their feature vectors.

Cardiac Arrhythmia Classification Using Boosted Decision Trees

An intelligent system for the classification of Electrocardiograph (ECG) beat signal would play an important role in the diagnosis of cardiac arrhythmias. This paper employed a recently invented C5.0 decision trees (DTs) algorithm to develop a supervised ECG beat classifier. In general, decision tree algorithms have proved remarkable ability to derive meaning from complicated or imprecise data. Accordingly, they can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computational techniques. They are nonparametric methods with no assumptions about the space distribution and the classifier structure. This study investigated the performance of the C5.0 decision tree model with boosting on the diagnosis of ECG features' dataset. Boosting process significantly improves the accuracy of a C5.0 model. The algorithm builds up multiple decision-tree models in a sequential manner; the first model is built in the standard way. Then, each of the subsequent models focuses on the misclassified samples by the preceding model. Finally, new samples are classified by ensemble these models using a weighted voting procedure to combine the separate decisions into one overall choice. The objective of this work is to classify an ECG characteristic feature vector as either normal or arrhythmia. The classification performance of boosted C5.0 DTs is evaluated and compared to the one that achieved by multilayer feed-forward neural network. Experimental results showed that the boosted C5.0 DTs model has achieved a remarkable performance that reached 99% classification accuracy on both training and testing subsets.

Real time electromagnetic target classification using a novel feature extraction technique with PCA-based fusion

This work introduces an efficient technique to design an electromagnetic target classifier whose reference database is constructed using scattered data at only a few aspects. The suggested technique combines a natural-resonance related feature extraction process with a novel, multiaspect feature fusion scheme. First, moderately aspect-variant late-time features are extracted from scattered field of a given candidate target at several different reference aspects using the Wigner transformation to characterize the target's scattered energy distribution over a selected late-time segment of the joint time-frequency plane. Then, these features are fused using the principal component analysis to obtain a single characteristic feature vector that can effectively represent the target of concern over a broad range of aspect angles. The suggested technique is demonstrated to design a classifier that is verified to be highly accurate and robust even in the presence of excessive noise. Due to the computational efficiency of the technique, the classifier needs very small memory space to store the reference information and quite fast lending itself suitable for real-time target classification.

Automated geostatistical seafloor classification—Principles, parameters, feature vectors, and discrimination criteria

A geostatistical method for automated seafloor classification is developed and applied to bathymetrie data for a 150 × 100 km area at 26 °N on the western flank of the Mid-Atlantic Ridge. The objective of seafloor classification is to characterize seafloor properties quantitatively, and to use such spatial characteristics to distinguish roughness provinces, and geologic and morphologic units automatically. The method presented here is based on the calculation of directional variograms as spatial structure functions. Parameters determined from filtered variogram functions are used to compose feature vectors, which are shown to be characteristic of morphologic prototypes and surface roughness types, and therefore facilitate a classification. Discrimination criteria include spacing and strike of abyssal hill terrain, smoothness resulting from sediment cover, and parameters related to complexity and morphological significance of abyssal hills and their slopes. Complications of automating the process concern robustness of parameter estimation, optimal window size, and subselection of data. By moving the classification operation through the study area and color-coding property classes, seafloor classification maps are obtained. The concepts of characteristic parameters, feature vectors and discrimination criteria are illustrated in applications to bathymetric data from the western flank of the Mid-Atlantic Ridge. Resultant classification maps are presented, with classes including roughness provinces and morphologic units.

Minimum Cross-Entropy Pattern Classification and Cluster Analysis

This paper considers the problem of classifying an input vector of measurements by a nearest neighbor rule applied to a fixed set of vectors. The fixed vectors are sometimes called characteristic feature vectors, codewords, cluster centers, models, reproductions, etc. The nearest neighbor rule considered uses a non-Euclidean information-theoretic distortion measure that is not a metric, but that nevertheless leads to a classification method that is optimal in a well-defined sense and is also computationally attractive. Furthermore, the distortion measure results in a simple method of computing cluster centroids. Our approach is based on the minimization of cross-entropy (also called discrimination information, directed divergence, K-L number), and can be viewed as a refinement of a general classification method due to Kullback. The refinement exploits special properties of cross-entropy that hold when the probability densities involved happen to be minimum cross-entropy densities. The approach is a generalization of a recently developed speech coding technique called speech coding by vector quantization.