Ball Tree Research Articles

K-NN’S NEAREST NEIGHBORS METHOD FOR CLASSIFYING TEXT DOCUMENTS BY THEIR TOPICS

Context. Optimization of the method of nearest neighbors k-NN for the classification of text documents by their topics and experimentally solving the problem based on the method.
 Objective. The study aims to study the method of nearest neighbors k-NN for classifying text documents by their topics. The task of the study is to classify text documents by their topics based on a dataset for the optimal time and with high accuracy.
 Method. The k-nearest neighbors (k-NN) method is a metric algorithm for automatic object classification or regression. The k-NN algorithm stores all existing data and categorizes the new point based on the distance between the new point and all points in the training set. For this, a certain distance metric, such as Euclidean distance, is used. In the learning process, k-NN stores all the data from the training set, so it belongs to the “lazy” algorithms since learning takes place at the time of classification. The algorithm makes no assumptions about the distribution of data and it is nonparametric. The task of the k-NN algorithm is to assign a certain category to the test document x based on the categories k of the nearest neighbors from the training dataset. The similarity between the test document x and each of the closest neighbors is scored by the category to which the neighbor belongs. If several of k’s closest neighbors belong to the same category, then the similarity score of that category for the test document x is calculated as the sum of the category scores for each of these closest neighbors. After that, the categories are ranked by score, and the test document is assigned to the category with the highest score.
 Results. The k-NN method for classifying text documents has been successfully implemented. Experiments have been conducted with various methods that affect the efficiency of k-NN, such as the choice of algorithm and metrics. The results of the experiments showed that the use of certain methods can improve the accuracy of classification and the efficiency of the model.
 Conclusions. Displaying the results on different metrics and algorithms showed that choosing a particular algorithm and metric can have a significant impact on the accuracy of predictions. The application of the ball tree algorithm, as well as the use of different metrics, such as Manhattan or Euclidean distance, can lead to improved results. Using clustering before applying k-NN has been shown to have a positive effect on results and allows for better grouping of data and reduces the impact of noise or misclassified points, which leads to improved accuracy and class distribution.

A parallel algorithm for approximating the silhouette using a ball tree

Clustering is widely used in many scientific fields. The contribution of enumerating the value of the silhouette is twofold: firstly it can help choosing a suitable cluster count and secondly it can be used to evaluate the quality of a clustering. Enumerating the silhouette exactly is an extremely time-consuming task, especially in big data applications; it is therefore common to approximate its value. This article presents an efficient shared-memory parallel algorithm for approximating the silhouette, which uses a ball tree. The process of initialising the ball tree and enumerating the silhouette are fully parallelised using the OpenMP API. The results of our experiments show that the proposed parallel algorithm substantially increases the speed of computing the silhouette whilst retaining necessary precision for real-world applications.

A nearest neighbor multiple-point statistics method for fast geological modeling

Multiple-point statistics (MPS) is a powerful method to generate realistic geological models. Given a training image as a prior model, the program iteratively reproduces spatial patterns in the simulation grid. However, running speed becomes a limitation to practical applications. MPS has to handle complicated and high-dimensional structures at the cost of simulation time. With the objective to accelerate geostatistical modeling with categorical variables, we propose a nearest neighbor simulation (NNSIM) method. Several k-nearest neighbor (kNN) classifiers are incorporated into MPS framework. First, we identify representative patterns with a prototype selection method. Different from existing MPS programs, our method selects training patterns according to their influences on simulation quality. A pattern subset of small size has a positive effect on searching time. Second, a teacher-student architecture is suggested to improve the pattern subset. In order to address missing data, our program augments the subset with key patterns during simulation. A cosine distance metric is applied to compare the original dataset and pattern subset. Third, our program organizes patterns with a ball tree. Pattern groups with low similarity are dynamically removed to fulfill fast search. We examine the proposed NNSIM by a benchmark channel simulation, a 2D flume model, and a 3D sandstone modeling. Many quantitative approaches are employed to evaluate geometrical and physical properties. The experimental results indicate that our NNSIM significantly improves the computational efficiency while exhibits comparable simulation quality to traditional MPS programs.

PHYTOCHEMICAL ANALYSIS AND MINERAL PROFILE OF COUROUPITA GUIANENSIS AUBL. FRUIT - AN INDIGENOUS MEDICINAL TREE

Medicinal plants possess therapeutic properties and provide pharmacological effect on the human or animal body. Couroupita guianensis also called “cannon ball tree” is a medicinal tree which has numerous medicinal properties. All the parts of the plants are used for therapeutic purposes. The present review gives the information about the phytochemical and mineral profile analysis of fruit of the particular plant. The results show that fruit of the couroupita contain various phytochemicals such as alkaloid, flavanoid, Terpenoid, tannin, phenolic compounds, carbohydrate, protein and saponin whereas phytochemicals such as catchin, anthraquinone, glycosides where found to be absent. Mineral analysis indicate that iron is found to be maximum and there are no harsh minerals found in the fruit. Many recent studies show that the fruit is edible but because of unpleasant smell people refuse to consume them. Fruit extract of Couroupita guianensis showed many pharmacological activities so it is used in investigations to isolate pharmacological active compounds which are useful for the production of novel drugs for various diseases. Keywords: Couroupita guianensis, Mineral profile, phytochemicals.

Ball-Tree-Based Signal Detection for LMA MIMO Systems

In this letter, we consider the uplink multiuser load-modulated array (LMA) multiple-input multiple-output (MIMO) communication system, where multiple users transmit the phase modulation on hypersphere (PMH) signals to a base station (BS). To reduce the detection complexity, we propose a low-complexity detection scheme based on a ball-tree (BT) structure. The received codewords are stored in a space BT data structure and the detection process can be regarded as a nearest neighbor point search operation. Two partition rules are proposed in the BT codebook construction. Simulation results show that the proposed BT based signal detection achieves about the same bit error rate (BER) performance as the ML detection while the detection complexity is significantly reduced.

Optimized 2D ball trees for shape layout applications

The interactive creation of 2D layouts involving irregular shapes such as text or images is typically carried out using a pointing device to select elements and drag them to new positions on a page. We propose augmenting layout interaction with two operations: overlap avoidance and grouping by distance. Since these are computationally expensive and may hinder the fluidity of the interaction, we examine several techniques that aim at implementing these features in an efficient manner. These techniques involve the approximation of 2D shapes by collections of balls (circles) which are then organized in ball trees, a bounding volume hierarchy. In particular, we discuss a new algorithm for approximating shapes with a reduced set of balls, as well as enhanced ball tree construction algorithms. These are used to implement algorithms to group shapes hierarchically by distance and to allow dragging with collision avoidance, in the context of a proof-of-concept web application for laying out shapes extracted from segmented images.

Predicting the Price of Second-Hand Housing Based on Lambda Architecture and KD Tree

In this paper a system is designed and implemented to predict the price of second-hand housing. This system based on Lambda architecture can execute prediction in both real-time and batch modes so it can give two kinds of different price predictions that reflect current and historical conditions respectively. The kNN related algorithms are used for price prediction. By comparing the performance of brute kNN, kd tree and ball tree, kd tree is selected as the price prediction model of the system. In system implementation the kd tree model is chosen to predict prices in both real-time and batch services. The kd tree model can also recommend housings to user besides price prediction. The experiment shows the effectiveness of our system. Time and space performance of brute kNN, kd tree and ball tree are compared by experiments. And the evaluation metrics of other available maching learning models are compared. The reason of choosing the kd tree model is also explained by the experimental results.

A novel image retrieval strategy based on transfer learning and hand-crafted features for wool fabric

The application of image retrieval techniques in industrial settings aims at rapid and accurate retrieval of the same or similar products from an archive to facilitate the production process. Content-based image retrieval and its applications on fabrics focused on the appearance differences and ignored the delicate differences, thus the retrieval fineness cannot meet the demands of industrial applications. In this study, a novel image retrieval strategy was proposed to discriminate appearance differences by the classification model based on transfer learning and realize further discrimination delicate differences by hand-crafted features. The pre-trained model based on ImageNet was fine-tuned to extract features for automatic classification. The images in the wool fabric image database were classified into different categories. Meanwhile, the distribution probabilities were used to build the retrieval strategy. To form the 'feature database' in each category, oriented FAST and rotated BRIEF (ORB) was utilized for feature extractions and the vector of locally aggregated descriptors was adopted for feature aggregation. Ball tree was implemented to search the nearest neighbors for the final results. Based on the above methods, the query image was first classified automatically, and then the images were retrieved based on the retrieval strategy. A large-scale image database of wool fabrics with 82,073 images was built as the benchmark to evaluate the efficacy of the proposed method. Experiments indicated that the proposed strategy is effective for delicate image retrieval. The combination of transfer learning and hand-crafted features can discriminate the appearance and delicate differences, being superior to the existing methods on representing the delicate fabric differences. The proposed method can provide referential assistance for the production crew and reducing manual labor in the factory.

Phytochemical Analysis of Couropita guianesis Aubl of Leaves

Couroupita guianensis Aubl is also called as “Cannon ball tree”. The tree has enormous medicinal values since most of the parts like leaves; flower, fruits and stem are used as medicine to cure various diseases. During the last few decades it has been observed that there are numerous reports on anti-inflammatory activity, antiulcer activity, anticancer activity, antioxidant activity, anthelminthic activity of this plant. It is enriched with a number of compounds such as oils, ketosteroids, glycosides, courouptine, indirubin, isatin and phenolic substances. Phytochemical analysis of the extract showed the presence of major classes of phytochemicals alkaloids, phenolic compounds such as flavonoids, tannins and saponins. Studies reveal that C. guianensis. Aubl. Is a source of medicinally active compounds and have various pharmacological effects.

Anti-bacterial and biocompatibility properties of green synthesized silver nanoparticles using Parkia biglandulosa (Fabales:Fabaceae) leaf extract

The biosynthesis of silver nanoparticles is considered to be a feasible, green, and eco-friendly method. Silver nanoparticles (AgNPs) were synthesized using badminton ball tree (Parkia biglandulosa) leaf extract and their significant physiochemical properties were characterized. The aqueous extract of the leaf of P. biglandulosa act as a reducing as well as capping agent for the reduction of Ag+ ​ions. FTIR analysis was conducted to find out the compounds that were liable for the bio-reduction of silver ions and to study the functional groups present in P. bigalandulosa leaf extract. The biologically reduced silver nanoparticles showed a surface plasmon resonance at 439 ​nm as studied from the UV–visible absorption spectrum. TEM studies confirmed the shape of the nanoparticles to be spherical with an average diameter of 15 ​nm. The anti-bacterial properties of the prepared nanoparticles were tested against Bacillus cereus by well plate method. Biosynthesized silver nanoparticles (PbAgNPs) at 0.02 ​M concentration showed the most antibacterial activity with a zone of inhibition of 7 ​mm. The high biocompatibility of the PbAgNPs by using human skin fibroblast cell line was evaluated by Alamar Blue reduction assay. The PbAgNPs had shown the highest cell viability when compared to native nanoparticles, meanwhile, a significant increase in cell viability was noted for the nanoparticles. The biosynthetic approach for synthesis was found to be a facile, rapid, single-step, safe, and effective alternative to the conventional physical/chemical methods.

A framework for optimizing extended belief rule base systems with improved Ball trees

Decision support systems imposed a challenge for efficiently reasoning through utilizing stored unordered rules. The Extended Belief Rule-Base (EBRB) system, renowned for its capability of modeling data with vagueness, incompleteness, uncertainty and nonlinear features, requires to traverse over all the rules for the execution of reasoning and consequently suffers reduced inference accuracy and efficiency. To improve the performance, we propose a framework of storing and retrieving belief rules based on employing an optimized structure called improved Ball tree. The framework first constructs a Ball tree index according to the distance between different rules in the metric space, via using the k-means++ algorithm rather than traditionally employed k-centers. Then, through our proposed dynamic threshold radius adjusting method, our algorithm finds an appropriate dataset threshold and accordingly activates more related rules. Moreover, the number of retrieved and activated irrelevant rules is significantly reduced, resulting in consequently improved reasoning accuracy and efficiency. Lastly, three sets of experiments were carried out to validate our algorithm in comparison with the previous EBRB systems.

Evaluation of isoprene emission rates of tropical trees by an iterative optimization procedure for G-93 parameters

Isoprene emissions from six tropical trees were evaluated by “Ping-Pong” method that iteratively optimized the temperature (CT) and light (CL) parameters of the Guenther model, G-93. Six tropical trees; Bauhinia variegate (camel's foot tree), Calophyllum inophyllum (ball tree), Garcinia subelliptica (fukugi tree), Syzygium cumini (black plum), Syzygium samarangense (wax jambu), and Mangifera indica (mango; red and yellow fruit) were exposed to LED light in a phytotron. Saplings of Populus nigra were similarly treated under the same experimental conditions for reference. The irradiance protocol consisted of 26 steps and two cycles of increase and decrease in light intensity. Plants were exposed to a wide variation in light intensity (200–1500 μmol/m2/s), which resulted in leaf temperature variation of 29–40 °C. Predictions of isoprene emission by the optimized G-93 well captured the light and temperature dependent increase and decrease of isoprene emission rate in all tropical trees assessed. Comparatively, G-93 with default parameters showed lower efficacy in predicting emission rates during the ascending phase in the initial round, and at the peak phase in the second round of the irradiation scheme. G-93 with default parameters overestimated emissions at the initial ascending phase and underestimated at the second peak phase. Calculated Q10 values for isoprene emission from tropical trees were much higher than that of the temperate plant P. nigra under same observational conditions. These results strongly imply that isoprene emission behavior of tropical plants deviate from that of temperate plant.

First record of invasive rugose spiraling whitefly Aleurodicus rugioperculatus Martin (Hemiptera: Aleyrodidae) along with parasitoids in Karnataka

Occurrence of the invasive rugose spiraling whitefly (RSW), Aleurodicus rugioperculatus Martin was found on coconut, banana, mango, guava, sapota, Indian almond, water apple, ball tree, rubber fig, butterfly palm, and ruffled fan palm for the first time in Karnataka. Severe damage was noticed in the coastal areas of Mangalore and Udupi and the infestation ranged from 20-35% in coconut and 24-38% in banana. Morphology and molecular based taxonomic tools were used to identity the pest and its natural enemies. Survey also revealed natural parasitism by the parasitoids Encarsia guadeloupae Viggiani and E. dispersa Polaszek (Hymenoptera: Aphelinidae) with 5-15% in coconut, 7-18% in banana and 22-30% in sapota. Besides, other commonly found natural enemies viz., Stethorus sp. associated with mites and Dichochrysa astur were also recorded. Furthermore, augmentation and conservation strategies for these parasitoids for the management of this invasive pest were discussed.

Genetic Diversity Analysis of Couroupita guianensis Aubl.based on RAPD Marker

<p> <strong><em>Couroupita guianensis</em> Aubl. Commonly known as cannon ball tree, belongs to family Lecythidaceae and widely used in Chinese medicine. C. guianensis is being threatened due to habitat destruction in its native range for human settlement and other development related activities. Native populations of C. guianensis were collected from different locations of the India states and characterized through. To study the genetic diversity of C. guianensis populations we performed RAPD – PCR with several primers. The results are revealed in this study.</strong></p>

HPTLC Fingerprinting and Antifungal Efficacy of Ethyl Acetate Extract Couroupita guianensis on Selected Fungal Pathogens

The HPTLC fingerprints will help the manufacturer for quality control and standardization of herbal formulations. Such finger printing is useful in differentiating the species from the adulterant and act as a biochemical marker for this medicinally important plant in the pharmaceutical industry and plant systematic studies. Resistance to antibiotics in pathogenic fungi is a problem of special importance in the control of infections caused by these organisms.Therefore, it is of importance to investigate the HPTLC fingerprint and antifungal analysis of ethyl acetate extract of the aerial part of couroupita guianensis. Couroupita guianensis (Aubl) Family Lecythidaceae commonly known as Cannon ball tree, locally known as “Kailashpati,” is found throughout India. The leaves, flowers, fruits and bark of this medicinal plant is used to treat skin infections, antimicrobial, cancer and various traditional medicinal preparations. Our results clearly show that the antifungal efficacy of couroupita guianensis.

Extraction of Indigo dye from Couroupita guianensisand its application on cotton fabric

AbstractCouroupita guianensis (Cannon ball tree) flowers and fruits are known to contain indigotin and indirubin. In this study an attempt is made to optimize the period for effective extraction of indigo from Couroupita guianensis fruit and application of the crude extract on the cotton fabric. Fermentation of the Couroupita guianensis fruit pulp for 5 days gave intense bluer pigment and hence was selected as the optimum period for fermentation. Major colouring components from Couroupita guianensis fruit extract were separated by thin layer chromatography using single solvent system, chloroform; which gives three pigments, violet, blue and pink with the Rf values of 0.88, 0.57, and 0.25 respectively. The Rf values, the wavelength of maximum absorption from UV-Visible spectroscopy, Fourier Transform Infrared (FTIR) spectrum and 1H Nuclear magnetic resonance (1H NMR) spectrum of blue pigment matched with that of synthetic indigo standard and that of the pink pigment matched with reported values of indirubin which is an isomer of indigotin. The % purity of indigo dye was found to be 26.46%. Dyeing of cotton fabric was carried out with the crude dye powder which gave comparable fastness properties vis-a-vis synthetic indigo.

Antidiabetic Activity of Couroupita guianensis –A Review

Couroupita guianensis is also called as “Cannon ball tree”. The tree has enormous medicinal values since most of the parts like leaves; flower, fruits and stem are used as medicine to cure various diseases. During the last few decades it has been observed that there are numerous reports on anti-inflammatory activity, antiulcer activity, anticancer activity, anti oxidant activity, antihelminthic activity of this plant. It is enriched with a number of compounds such as oils, ketosteroids, glycosides, courouptine, indirubin, isatin and phenolic substances. Recent researches on this plant extract showed anti diabetic activity also. A small preliminary test was performed to confirm its anti diabetic activity.

An Extraction of the Isatin from the <i>Couroupita guianesis</i> (Cannon Ball Tree) and a Novel Synthesis of the N,N'-(2-oxo-3'H-spiro[indoline-3,2'-[1,3,4]thiadiazole]-3',5'-diyl]diacetamidefrom the Isatin

Isatins are synthetically versatile substrates, where they can be used for the synthesis of a large variety of Isatins and heterocyclic compounds. In this, the isatin were extracted from the floral material of Couroupita guianesis commonly known as an Cannon ball tree. After that, a novel heterocyclic spiro derivative having an IUPAC name N,N’-(2-oxo-3’H-spiro[indoline-3,2’-[1,3,4] thiadiazole]-3’5’-diyl)diacetamide were synthesized , which may be used as raw material for drug synthesis. A spiro compound N,N’-(2-oxo-3’H-spiro[indoline-3,2’-[1,3,4] thiadiazole]-3’5’-diyl)diacetamide were synthesise by refluxing Isatin and thiosemicarbazone in ethanol and then on heating with acetic anhydride underwent to cyclization into intermediate that on acetylation of the NH and NH2 groups afforded the spiro compound.

An Extraction of the Isatin from the <i>Couroupita guianesis</i> (Cannon Ball Tree) and a Novel Synthesis of the N,N'-(2-oxo-3'H-spiro[indoline-3,2'-[1,3,4]thiadiazole]-3',5'-diyl]diacetamidefrom the Isatin

Isatins are synthetically versatile substrates, where they can be used for the synthesis of a large variety of Isatins and heterocyclic compounds. In this, the isatin were extracted from the floral material of Couroupita guianesis commonly known as an Cannon ball tree. After that, a novel heterocyclic spiro derivative having an IUPAC name N,N’-(2-oxo-3’H-spiro[indoline-3,2’-[1,3,4] thiadiazole]-3’5’-diyl)diacetamide were synthesized , which may be used as raw material for drug synthesis. A spiro compound N,N’-(2-oxo-3’H-spiro[indoline-3,2’-[1,3,4] thiadiazole]-3’5’-diyl)diacetamide were synthesise by refluxing Isatin and thiosemicarbazone in ethanol and then on heating with acetic anhydride underwent to cyclization into intermediate that on acetylation of the NH and NH2 groups afforded the spiro compound.

Bregman Hyperplane Trees for Fast Approximate Nearest Neighbor Search

This article presents a new approximate index structure, the Bregman hyperplane tree, for indexing the Bregman divergence, aiming to decrease the number of distance computations required at query processing time, by sacrificing some accuracy in the result. The experimental results on various high-dimensional data sets demonstrate that the proposed index structure performs comparably to the state-of-the-art Bregman ball tree in terms of search performance and result quality. Moreover, this method results in a speedup of well over an order of magnitude for index construction. The authors also apply their space partitioning principle to the Bregman ball tree and obtain a new index structure for exact nearest neighbor search that is faster to build and a slightly slower at query processing than the original.