Search

The target of this work is to assess the presentation involving arbitrary woodland and contrast and KNN for the recognition of mind growth with Novel division. In this work, an AI approach is acquain ted with identifying growths in an MRI picture of the cerebrum. A sum of 31619 examples was gathered from three liver illness datasets accessible in Kaggle. These examples were partitioned into preparing dataset bunch 1 (n = 22133 [70%]) and test dataset b unch 2 (n = 9486 [30%]). The necessary examples for this examination are finished by G power computation is 80%. Irregular backwoods accomplished exactness, accuracy, and review upsides of 91.66 %, 92.43 % and 92.53 % are huge qualities and better contrasted with KNN calculation which showed exactness, accuracy, and review upsides of 81.48 %, 95.65 % and 95.65 % with P < 0.02. In this study, it is seen that the KNN calculation displayed essentially preferable execution over Random Forest calculation for mind growth recognition of the datasets. [ABSTRACT FROM AUTHOR]

An improved analysis with considerable linear and nonlinear banking service and user data classification utilising random forests and k nearest neighbours is the goal of this research. Objectives: The following are the materials and procedures used in this project: N=10 sample iterations are used to test the model's accuracy in classifying clients for the banking service allocation system. These two groups include random forest algorithm and k closest neighbour method. Classifier accuracy can go as high as (98.26 percent) with the Random Forest classifier, while the K Nearest Neighbor model can go as accurate as (88.98 percent). When comparing the Random forest to K Nearest Neighbor, a statistically significant difference was found (p=0.005) Conclusion: The Random Forest algorithm outperforms the K Nearest Neighbor algorithm in banking application systems for service allocation in terms of performance and significance. [ABSTRACT FROM AUTHOR]

To estimate precision and recall for movie star rating using sentiment content. K-Nearest Neighbour with sample size of 10 and novel Naive Bayes with sample size of 10 was iterated at different times for predicting accuracy percentage of movie review. The F1 measure used in prediction to probabilities which helps to improve the prediction of accuracy percentage. The sigmoid function used in KNN prediction to probability which helps to improve the prediction of accuracy. Result: Results proved that Naive Bayes got significant results with 82% accuracy compared to KNN with 74% accuracy. There was a statistical significance between Naive Bayes and KNN (p=0.00) Naive Bayes is a simple and most effective algorithm to build fast machine learning models. Naive bayes with f1 measure helps in predicting with more accuracy percentage of movie review. [ABSTRACT FROM AUTHOR]

Generating a stereophonic presentation from a monophonic audio signal is a challenging open task, especially if the goal is to obtain a realistic spatial imaging with a specific panning of sound elements. In this work, we propose to convert mono to stereo by means of predicting parametric stereo (PS) parameters using both nearest neighbor and deep network approaches. In combination with PS, we also propose to model the task with generative approaches, allowing to synthesize multiple and equallyplausible stereo renditions from the same mono signal. To achieve this, we consider both autoregressive and masked token modelling approaches. We provide evidence that the proposed PS-based models outperform a competitive classical decorrelation baseline and that, within a PS prediction framework, modern generative models outshine equivalent non-generative counterparts. Overall, our work positions both PS and generative modelling as strong and appealing methodologies for mono-to-stereo upmixing. A discussion of the limitations of these approaches is also provided. [ABSTRACT FROM AUTHOR]

PT. Rariza Putra is a company engaged in the distribution of Liquefied Petroleum Gas (LPG) 3 kg. The distribution route starts from the Oil Fuel Filling Station (SPBE) to each customer venue. In the distribution method, the company determines the route only by decisions made by the driver, because the company does not have a fixed distribution route. It means that the resulting route is not an optimal route. With the existing problems, the company needs a problem-solving method that would provide a solution to calculate the shorter route to reduce transportation and fuel costs. This research was conducted on the Vehicle Routing Problem (VRP) with the Capacitated Vehicle Routing Problem (CVRP) approach, which determines the route by taking into account the capacity of the vehicle, and also Vehicle Routing Problem with Multiple Trips (VRPMT), which determines the route for each vehicle to make more than one delivery, and Vehicle Routing Problem with Split Delivery (VRPSD), where each base can be visited more than once. The method used in solving this problem is the Saving Matrix and also the Nearest Neighbor methods. These two methods will be compared to the initial route that exists in the company, and the method that gives the optimal results will be chosen. Based on the processing that has been carried out with the both methods, the Nearest Neighbor method is chosen which gives more optimal results in determining the route and also affects the fuel costs incurred. The route generated by the nearest neighbor method in one week is 283,37 km with a comparison of the initial route length of 391,17 km. While the total fuel cost savings that occur every week on the nearest neighbor method is Rp. 208.475 with a cost comparison on the initial route of Rp. 289.773 with a difference of Rp. 81.298. The new distribution route provides 28% savings compared to the initial route in the company. [ABSTRACT FROM AUTHOR]

DKI Jakarta often experiences flood disasters when the rainy season hits. The floods caused a number of residents to be affected so that residents needed to evacuate to safer places, such as refugee posts. Citizens who secure themselves at the post certainly need some necessary needs such as humanitarian logistics goods. Humanitarian logistics goods need to be distributed quickly and precisely so that optimal distribution lines are needed. This research aims to find out the route of distribution of humanitarian logistics goods that occur in Jakarta, especially East Jakarta. This problem is solved by using the Saving Matrix Method with the Nearest Neighbour and Nearest Insertion Method as a route sorting method. The results of the route determination are known that the optimal distribution path is obtained from the first alternative using the Nearest Neighbour Method with a total distance of 130.25 m. [ABSTRACT FROM AUTHOR]

The Vehicle Routing Problem (VRP) is a problem related to the distribution of products using the optimal and involving more than one vehicle by taking into account several obstacles in serving a number of agents according to the request of each related agent. The Capacitated Vehicle Routing Problem (CVRP) is one of the variations of VRP, namely by adding constraint in the form of vehicle capacity used in product distribution. This paper applies the formation of a CVRP model to the problem of the distribution route of the Solopos daily mail and its solution using the Sweep Algorithm method which aims to optimize the distribution route. The Sweep Algorithm is an algorithm consisting of two stages, the first stage is clustering agents, then the second stage is the formation of routes for each cluster with the Nearest Neighbor method. Based on the calculations carried out in solving CVRP problems using the Sweep algorithm, the results obtained that the total vehicle mileage is 216,5 km with a time windows of 6 hours 5 minutes. [ABSTRACT FROM AUTHOR]

The rapidly growing communication technology brings not only an opportunity but also a challenge to the printing industry. Consumers are significantly moving from conventional media to digital. Determining the optimal distribution route to ensure a smooth distribution flow is crucial to retaining customers by providing an optimal service. One of the limitations in the distribution process is related to vehicle capacity (Capacitated Vehicle Routing Problem). Previous research has succeeded in determining a combined route between three newspaper industry companies in Surakarta, but each company still needs optimal independent routes. Using the saving matrix method with the milk-run system, this study aims to determine the optimal route for distributing newspapers independently, focusing on one of the newspaper companies by considering three conditions: when demand increases, decreases, and is static. Nearest Insert and Nearest Neighbor methods are used to optimize the distribution route. The results show that each of the three conditions has two optimal routes. When the demand is static, the total monthly cost is IDR 618,750. When the demand increases, the total cost per month will be IDR 562,650, and when the demand decreases, the total cost per month will be IDR 592,350/month. [ABSTRACT FROM AUTHOR]

XYZ Ltd. is a daily newspaper company, one of which is located in Kartasura, Sukoharjo. Newspapers were distributed to 11 locations around Kartasura and Klaten. In the delivery process, there are limitations such as the number of vehicles and vehicle capacity. The company wants to find a better or shorter route in distributing its products to agents to minimize transportation costs. This issue can be considered a Capacity Vehicle Routing Problem. In this study, the sweep algorithm was first used to find the initial solution. The local search procedure is used for the solution obtained by the sweep and nearest neighbor algorithm. The new first route is Depot-A2-A4-A5-A6-A10-A1-Depot, while the second route is Depot-A7-A3-A8-A9-A11-Depot. Those routes are found using local search combination of 1-insertion intra route, and swap intra route. After that, the minimum cost is calculated. The results are a decrease in the distance, time, and fuel costs from the previous route. The improvements are saving IDR 1,119,323 per year, 8 minutes faster, and 4.3 km shorter than the previous route for route 1 and IDR 728,862 per year, 6 minutes faster and 2.8 km shorter than the previous route for route 2. [ABSTRACT FROM AUTHOR]

In this paper, we report numerical calculations of the localization length in a non-interacting one-dimensional tight-binding model at zero tem¬perature, holding a correlated disorder model with an algebraic power-spectrum (de Moura-Lyra model). Our calculations were based on a Kernel Polynomial implementation of the Thouless formula for the inverse localization length of a general nearest-neighbor 1D tight-binding model with open boundaries. Our results confirm the delocalization of all eigenstates in de Moura-Lyra model for α > 1 and a localization length which diverges as ξ ∝ (1 – α)–1 for α → 1–, at all energies in the weak disorder limit (as previously seen in [12]). [ABSTRACT FROM AUTHOR]

Modeling and estimation of ore grade are very essential in geostatistical ore resource estimation. Resource modeling is generally carried out on gold, copper, nickel and bauxite ores. This study applies the geostatistical method for modeling and estimation of iron ore grade. The objective of the study is to apply estimation techniques (OK, ordinary kriging; IDW, inverse distance weighting, and NNP, nearest neighbor polygon) and evaluate the accuracy of these techniques in iron ore resources. This study uses detailed exploration, which are 68 drill holes with 170 iron ore grade composite data. In the iron ore resource estimation, the block modeling method is applied. The results showed RMSE (root mean square error) values of various estimation techniques. Based on statistical analysis, visualization of comparisons between borehole data and models, and probability plots, the accuracy of each iron ore resource estimation technique in the study area can be determined. All estimation techniques have the same accuracy on low CV (coefficient of variance) values. The relative kriging standard deviation values determine the classification of measured iron ore resources. [ABSTRACT FROM AUTHOR]

The increasing of global energy demand by 2.1% in 2017 which is more than twice the previous year’s rate resulting in increasing of carbon dioxide emissions by 1.4% in the previous year after three years of remaining flat. Energy demand can be supplied by renewable energy which is more clean and help reducing carbon emissions. Solar energy has become the dominant renewable energy in Malaysia since it is situated at the equatorial region with an average solar radiation of 400-600 MJ/m2 per month. In this paper, factors that affected solar power generation are studied. All data from these factors are collected and the correlation analysis is done to determine which factor has strong correlation with solar power generation. The factors that have strong correlation with power generation will be used to predict solar power generation for next month. The results from this study showed that k-nearest neighbor method provides a better prediction result than artificial neural network since its root mean square error is the lowest value. [ABSTRACT FROM AUTHOR]

Graph applications develop quite rapidly in the transportation system. One of the applications is used to search for travel routes. The route from a terminal to an intersection that must be passed exactly once and must return to the original terminal is a very important issue. Determination of the TMB travel route (Trans Metro Bandung) using the Nearest Neighbor method. The results show that the TMB bus journey on corridor 2 (Cibeureum - Cicaheum) can be formulated into graph form. The bus transportation system is modeled in graph using the point symbol (Vertex) as a shelter and edge symbol as a path that connects between shelters. The TMB bus route in the graph is a closed track and is called the Hamilton Cycle. The results of route calculation and search from TMB corridor 2 produce different routes and distances from the initial, including: St. Ah. Nasution - St. Ahmad Yani - St. Ibrahim Adjie - St. Jakarta - St. Ahmad Yani - St. Asia Afrika - St. Sudirman - St. Rajawali Barat - St. Elang - St. Rajawali Timur - St. Kebon Jati - St. Perintis Kemerdekaan - St. Lembong - St. Ahmad Yani - St. Ah. Nasution. From a distance of 28.4 km to 27.65 km. [ABSTRACT FROM AUTHOR]

Using exact numerical diagonalizations as well as the semiclassical spin-wave approach, we study the quantum phase diagram of the spin-1/2 Heisenberg kagome strip, which is a particular cut-out from the kagome lattice. The unit cell contains five spins placed on the central cite and on the four end sites building two spin-1/2 along the strip. The model can be characterized by three different constants for the nearest-neighbor exchange bonds between (i) the central and chain spins (J1), (ii) the end spins in a cluster (J2 > 0), and (ii) the spins of neighboring clusters (J′2>0) In the present work we extend the previous studies of the model, which were restricted to antiferromagnetic J1 bonds (J1 > 0), by including the region with ferromagnetic J1 couplings (J1 <0). It is demonstrated that for dominating ferromagnetic J1 couplings there is another gapless phase, which can be related to the critical ground state of the spin-5/2 Heisenberg chain. The phase boundaries separating the discussed critical phases, as well as the behavior of their low-lying excitations close to these boundaries, are examined as well. [ABSTRACT FROM AUTHOR]

We have studied the conditions for the existence and stability of solitons in an anisotropic ferromagnetic chain with first- and second-neighbor interactions. The effects of the second-neighbor interactions and the anisotropy for the homogeneous case and arbitrary wave number in the Brillouin zone are analyzed. Analytical solutions are obtained for static solitons bound to a linear point defect. The type of the soliton solutions and their form depend on both, the anisotropy parameters and the exchange interactions. [ABSTRACT FROM AUTHOR]

This poster aims to find out a quick and accurate way for feature selection in hard landing prediction. To make the performance evaluation effective, a general evaluation framework is designed. Then seven classic feature selection methods are carried out by applying four classification algorithms, nine comparison metrics on a real QAR dataset with a total of 3,040 instances. At last, TOPSIS is employed for overall performance evaluation. Results indicate that the data‐driven category is preferable in feature selection of hard landing prediction. Moreover, Gradient Boosting Decision Tree combining with K‐Nearest Neighbor classifier on a balanced training dataset outperforms among 56 possible combination models. [ABSTRACT FROM AUTHOR]

Stocks are known as high-risk and high-return investments. Forecasting stock prices movement is the challenging problem for researchers and financial analysts. Support Vector Machines (SVM) with K Nearest Neighbor (KNN) approach will be applied to forecast stock prices of a listed company in Indonesia Stock Exchange (IDX). The stock data are collected from January 2013 to December 2016. First, this paper used feature selection method to select important indicators from thirteen technical indicators using Support Vector Regression (SVR). Second, the stock data are classified using SVM to represent profit or loss and the output helps to find the best nearest neighbor from the training set. Next, stock prices are forecasted using KNN. The performance of this model is computed using Root Mean Square Error (RMSE) and relative error. In this case, the experiment result shows that three indicators selected from feature selection present good prediction capability and the accuracy for close prices prediction is 93.33 % accurately. [ABSTRACT FROM AUTHOR]

In this current and recent decade, various data and information are actively collected. The problem with increasing data from year to year has made it difficult for people to make the right decision. This is because when data increased, the number of options to choose from will also increase. Therefore, recommendation systems are needed to address this problem and help recommend to users some options that meet their desirable requirements only. In this study, recommendation systems in the field of filming were conducted to provide movie recommendations services for users by using the content-based recommendation system and collaborative filtering recommendation system. For content-based recommendation system, movie recommendations are done by looking for similarities between active user profiles and movie genres. The similarities between active user profiles and movie genres are calculated by using the cosine similarity measure. For collaborative filtering recommendation system, movie recommendations are made by calculating the predicted rating for active users based on the rating values from their nearest neighbours. The nearest neighbours are identified by calculating the cosine similarity measure between the active users and existing users in the data set. Comparisons for these two recommendation systems are performed to identify which system works best in recommending movies to active users. The results show that the collaborative filtering recommendation system is more suitable in recommending movies to active users because this system is more successful in producing desirable recommendations compared to content-based recommendation system. [ABSTRACT FROM AUTHOR]

In this paper, we consider the λ-model on Cayley tree for order two with Potts competing nearest-neighbor and prolonged next-nearest neighbor interactions. We described the construction of the Gibbs measure for the considered model. We proved the existence of the translation-invariant limiting Gibbs measures for the model. [ABSTRACT FROM AUTHOR]

Collaborative filtering is one of the most widely used recommendation technologies, but the data sparsity and cold start problem of collaborative filtering algorithms are difficult to solve effectively. In order to alleviate the problem of data sparsity in collaborative filtering algorithm, firstly, a weighted improved SimRank algorithm is proposed to compute the rating similarity between users in rating data set. The improved SimRank can find more nearest neighbors for target users according to the transmissibility of rating similarity. Then, we build trust network and introduce the calculation of trust degree in the trust relationship data set. Finally, we combine rating similarity and trust to build a comprehensive similarity in order to find more appropriate nearest neighbors for target user. Experimental results show that the algorithm proposed in this paper improves the recommendation precision of the Collaborative algorithm effectively. [ABSTRACT FROM AUTHOR]

The Collaborative filtering recommendation algorithm is one of the most widely used recommendation algorithm in personalized recommender systems. The key is to find the nearest neighbor set of the active user by using similarity measure. However, the methods of traditional similarity measure mainly focus on the similarity of user common rating items, but ignore the relationship between the user common rating items and all items the user rates. And because rating matrix is very sparse, traditional collaborative filtering recommendation algorithm is not high efficiency. In order to obtain better accuracy, based on the consideration of common preference between users, the difference of rating scale and score of common items, this paper presents an improved similarity measure method, and based on this method, a collaborative filtering recommendation algorithm based on similarity improvement is proposed. Experimental results show that the algorithm can effectively improve the quality of recommendation, thus alleviate the impact of data sparseness [ABSTRACT FROM AUTHOR]

The relationship between the burden surface and the gas flow in blast furnace was studied in this paper using the improving k-means algorithm and graded case based matching method. An improved k-means classification algorithm was proposed based on the evaluation of effectiveness index to study the relationship between the burden surface and the gas flow in blast furnace from historical data, which proved the proposed algorithm has high accuracy according to the experimental data and different standard data sets. The paper also proposed a matching algorithm on the basis of the above clustering algorithm to obtain the most matched historical burden surface with the current burden surface. At last, compared with both of the improved grey similarity matching algorithm and Euclidean nearest neighbor matching algorithm, the results showed that the proposed method has higher efficiency and matching accuracy, and it is more suitable for the research of the relationship between burden surface and gas flow to assist the monitoring of the blast furnace to control burden surface. [ABSTRACT FROM AUTHOR]

Data mining classification methods are affected when the data is imbalanced, that is, when one class is larger than the other class in size for the case of a two-class dependent variable. Many new methods have been developed to handle imbalanced datasets. In handling a binary classification task, Support Vector Machine (SVM) is one of the methods reported to give a high accuracy in predictive modeling compared to the other techniques such as Logistic Regression and Discriminant Analysis. The strength of SVM is the robustness of its algorithm and the capability to integrate with kernel-based learning that results in a more flexible analysis and optimized solution. Another popular method to handle imbalanced data is the random sampling method, such as random undersampling, random oversampling and synthetic sampling. The application of the Nearest Neighbours techniques in sampling approach has been seen as having a bigger advantage compared to other methods, as it can handle both structured and non-structured data. There are some studies that implement an ensemble method of both SVM and Nearest Neighbours with good results. This paper discusses the various methods in handling imbalanced data and an illustration of using SVM and k-Nearest Neighbours (k-NN) on a real-data set. [ABSTRACT FROM AUTHOR]

We investigate an Ising model with two restricted competing interactions (nearest neighbors, and one-level neighbors) on the Cayley tree of order 5. The translation Gibbs measures is considered for this model. Our result of the critical curve shows that the phase transition occurs in this model, further it confirms a particular case of a conjecture. [ABSTRACT FROM AUTHOR]

The Nearest Neighbour Method (NNM) is a data-driven and non-parametric scheme established on the similarity characteristics of hydrological phenomena. One of the important parts of NNM is to choose a proper distance measure. The Euclidean distance (EUD) is a commonly used distance measure, which represents the absolute distance of a spatial point and is directly related to the coordinate of the point, but is not sensitive to the direction of the feature vector. This paper used the cosine angle distance (CAD) for the similarity measure, which reflects more differences in the direction, and compared it to EUD. This technique is applied to annual runoff at YiChang station on the Yangtze River. The results show the NNM with CAD has a better performance than that of EUD. [ABSTRACT FROM AUTHOR]

The current paper suggests an alternative to the Nearest-neighbor (NN) algorithm, which requires comparable or less computational time and memory in many applications of the Direct Simulation Monte Carlo (DSMC) method. The new approach uses the Simplified Bernoulli Trials (SBT) collision algorithm in combination with the transient adaptive subcell (TAS) technique. The Direct Simulation Monte Carlo (DSMC) is a particle-based method used to solve the Boltzmann equation through statistical schemes. The major role of any DSMC method is played by its collision algorithm, which tries to solve the most sophisticated term of the Boltzmann equation, and at the same time preserving its statistical restrictions by using specified number of particles per cell. The Simplified Bernoulli-trials (SBT) collision algorithm has already been introduced as a scheme that provides accurate results with a smaller number of particles and its combination with transient adaptive subcell (TAS) technique will enable SBT to have smaller grid sizes. In this paper, in order to have a closer look up on SBT, the Nearest neighbor (NN) algorithm in Bird DS2V code is replaced by SBT-TAS and comparisons between it and NN are made over an appropriate test case that is designed to have a wide spectrum of collision frequency. Hypersonic gas flow passing a cylinder, suggested by G. Bird, is a well-known benchmark problem that provides a wide collision frequency range from the downstream back-cylinder till the upstream stagnation point. Unlike NN, SBT does not need to calculate the required selection number of collision pairs and instead of that it lets its probability function to do this job. Since the probability function and subcell volumes are dependent, the necessity of having a logical volume approximation is very important. This volume calculation scheme, on the one hand needs to preserve the SBT logic well enough that it doesn't change the collision frequency, and on the other hand it must be easy and simple without adding any further burden on calculation costs. It'll be shown that SBT-TAS combination will reduce the desired number of cells and particle per cells while it still preserves the accuracy of NN. [ABSTRACT FROM AUTHOR]

The New Colombo Plan (NCP) program was launched by the Department of Foreign Affairs and Trade (DFAT) in 2014. It aims to enhance Australia's public diplomacy in the Indo-Pacific region, whilst sending Australian students to learn in various countries, including Indonesia More than 2,000 Australian students have been studying in Indonesia under the NCP scheme, however, little is known about how this opportunity is improving Australia's public diplomacy in its nearest neighbour. Using survey and interview approaches, this paper examines the experiences of Australian students in conducting the NCP program in Indonesia between 2018 and 2020. The findings suggest that students have excellent experience during their time in Indonesia, and the program has been instrumental in improving Australia's public diplomacy in Indonesia. [ABSTRACT FROM AUTHOR]

We report here a tight binding model study of the interplay of antiferromagnetic and orbital orderings in CMR manganites. The model consists of double exchange interaction, Heisenberg type antiferromagnetic spin interaction and band Jahn-Teller (JT) interaction along with kinetic energies of conduction band and core band electrons. Further, first and second nearest neighbor interactions are considered in the band energy dispersion and Heisenberg spin interaction. The model Hamiltonian is solved using Zubarev's Green's function technique and the interplay between transverse spin fluctuation and JT distortion is studied. The electron specific heat exhibits two peak structure near the transition temperatures as observed in experiments. [ABSTRACT FROM AUTHOR]

Random deposition model with surface diffusion over several next nearest neighbours is studied. Several extensions of diffusion models to include multistep diffusion gives Family's surface diffusion model in the nearest neighbour diffusion limit. The results for the various extensions agree with the results obtained by Family for the case of nearest neighbour diffusion. However, for larger allowed diffusion length, the growth exponent and roughness exponent show interesting dependence on diffusion length. The variation of values of exponents are fitted to empirical equations. The probable mechanism for dependence of exponents on the diffusion length is discussed. [ABSTRACT FROM AUTHOR]

This article presents new heuristics concept for solving Vehicle Routing Problem with Time Window. New concept, which is a skip concept, derives from a solution of the traditional sweep and nearest neighbor technique, which gives poor vehicle's utilization. Hence, skip - sweep algorithm and skip - nearest neighbor search focus on increasing the vehicle's utilization. The case study of cash distribution has been applied to proof efficiency of the skip concept. New concepts have been resulting in using the same amount of vehicle as the traditional one. The favorable result is the increasing of the vehicle's utilization. The decreasing of total travel time, which is an unexpected result, has found from the case study. It turns to be a favorable result since it associates to the variable cost of fuel. [ABSTRACT FROM AUTHOR]

Optimization of the interaction distance between qubits to map a quantum circuit into one-dimensional quantum architectures is addressed. The problem is formulated as the Minimum Linear Arrangement (MinLA) problem. To achieve this, an interaction graph is constructed for a given circuit, and multiple instances of the MinLA problem for selected subcircuits of the initial circuit are formulated and solved. In addition, a lookahead technique is applied to improve the cost of the proposed solution which examines different subcircuit candidates. Experiments on quantum circuits for quantum Fourier transform and reversible benchmarks show the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

This study was conducted to provide evidence of the chaotic behavior of the daily river flow data at Lubuk Paku station on the Pahang River in the Pahang River Basin in Malaysia. Four nonlinear dynamic methods are employed: (1) phase space reconstruction; (2) average mutual information algorithm; (3) false nearest neighbors algorithm; and (4) correlation dimension method. First, average mutual information method is used to determine a first minimum of delay time. Second, the sufficient embedding dimension is estimated using the false nearest neighbour algorithm. The time delay and sufficient dimension are used in phase space reconstruction. The presence of chaos in river flow has been analyzed through the correlation dimension method. The correlation dimension is less than 3. Hence, we can conclude that low correlation dimension presence by examined river flow time series data Lubuk Paku station on the Pahang River, Malaysia. [ABSTRACT FROM AUTHOR]

Skewing of the scanned image is an inevitable process and its detection is an important issue for document recognition systems. The skew of the scanned document image specifies the deviation of the text lines from the horizontal or vertical axis. This paper surveys methods to detect this skew in two steps, dimension reduction and skew estimation. These methods include projection profile analysis, Hough Transform, nearest neighbor clustering, cross-correlation, piece-wise painting algorithm, piece-wise covering by parallelogram, transition counts, morphology. [ABSTRACT FROM AUTHOR]

We propose a new hybrid recommendation algorithm to optimization the cold-start problem with Collaborative Filtering (CF). And we use neighborhood-based collaborative filtering algorithm has obtained great favor due to simplicity, justifiability, and stability. However, when faced with large-scale, sparse, or noise affected data, nearest-neighbor collaborative filtering performs not so well, as the calculation of similarity between user or item pairs is costly and the accuracy of similarity can be easily affected by noise and sparsity. We introduce a new model comprising both In the training stage, user-item and film-item relationships in recommender systems, and describe how to use algorithm generates recommendations for cold-start items based on the preference model. Our experiments model provides a relatively efficient and accurate recommendation technique. [ABSTRACT FROM AUTHOR]

Breast cancer is the leading cause of deaths among women. To reduce the number of deaths, early diagnosis and treatment have been pointed at as the most reliable approach. This paper introduces the application of cluster-knearest neighbor for breast cancer diagnosis. First, we apply wavelet transform to extract features. Feature selection is applied to select the most relevant features out of the huge number of coefficients that are extracted. After that, we apply the cluster-k-nearest neighbor classifier for classification. [ABSTRACT FROM AUTHOR]

Symmetry and electrostatic equilibrium in lanthanide compounds are discussed with the aim of improving the role of obtaining the charge factors, gj, of the nearest neighbours (NN) of the lanthanide site, j running over the NN, now using the gj vs Rj (the NN position) linear behaviour. The simple overlap model (SOM) and the method of equivalent nearest neighbours (MENN), are used in order to calculate the crystal field parameters and the 7F1 level splitting in complexes, crystals and glass. An inferior limit of the gj position is predicted for crystals and complexes, which is around the limit of 5s and 5p filled shells and may indicate different contraction/extension of the 4f wave functions. Further, it is discussed the possibility that, perhaps, the B4q and B6q parameters are not dependent on Rj in the same way as the B2q parameter is. [ABSTRACT FROM AUTHOR]

Co-occurrence matrix has been applied successfully for echographic images characterization because it contains information about spatial distribution of grey-scale levels in an image. The paper deals with the analysis of pixels in selected regions of interest of an US image of the liver. The useful information obtained refers to texture features such as entropy, contrast, dissimilarity and correlation extract with co-occurrence matrix. The analyzed US images were grouped in two distinct sets: healthy liver and steatosis (or fatty) liver. These two sets of echographic images of the liver build a database that includes only histological confirmed cases: 10 images of healthy liver and 10 images of steatosis liver. The healthy subjects help to compute four textural indices and as well as control dataset. We chose to study these diseases because the steatosis is the abnormal retention of lipids in cells. The texture features are statistical measures and they can be used to characterize irregularity of tissues. The goal is to extract the information using the Nearest Neighbor classification algorithm. The K-NN algorithm is a powerful tool to classify features textures by means of grouping in a training set using healthy liver, on the one hand, and in a holdout set using the features textures of steatosis liver, on the other hand. The results could be used to quantify the texture information and will allow a clear detection between health and steatosis liver. [ABSTRACT FROM AUTHOR]

In this paper we tackle the problem of creating a reference corpus for the classification of news items in finegrained multi-label scenarios. These scenarios are particularly challenging for text classification techniques, and the availability of reference corpora is one important bottleneck for developing and testing new classification strategies. We propose a semiautomatic approach for creating a reference corpus that uses three auxiliary classification methods - one based on Support Vector Machines, one based on Nearest Neighbor Classifiers and another based on a dictionary-based classification heuristic - for suggesting to human annotators topic-related labels that can be used to describe different facets of a given news item being annotated. Using such approach, we semi-automatically produce a corpus of 1,600 news items with 865 different labels, having in average 3.63 labels per news item. We evaluate the contribution of each of the auxiliary classification methods to the annotation process and we conclude that: (i) none of the methods alone is capable of suggesting all relevant labels, (ii) a dictionary-based classification heuristic contributes significantly and (iii) the Nearest Neighbor classifier performs very efficiently in the most extreme multi-label part of the problem and is robust to the very unbalanced item-to-class distribution. [ABSTRACT FROM AUTHOR]

The orderly propagation of electrical excitation through the sinoatrial node (SAN) in the heart is fundamental to its pacemaking properties. This is achieved via the synchronization of the electrical cell cycle of the nearest neighbour cells within the node. This is mediated via gap junction protein channels physically connecting the intracellular space of the cells. In this study, the synchronization characteristics of the cells in the central region were investigated. The characteristics and density of the gap junctions determine the overall electrical conductance coupling the cells. The simulations show that a small number of gap junction is sufficient for synchronisation to occur where the coupled cells share a common inter beat interval, determined by phase resetting of the pairs. This common value is not constant, however, but drifts with further increases in coupling conductance. [ABSTRACT FROM AUTHOR]

Content based retrieval and recognition of objects represented in images is a challenging problem making it an active research topic. Shape analysis is one of the main approaches to the problem. In this paper we propose the use of a reduced set of features to describe 2D shapes in images. The design of the proposed technique aims to result in a short and simple to extract shape description. We conducted several experiments for both retrieval and recognition tasks and the results obtained demonstrate usefulness and competiveness against existing descriptors. For the retrieval experiment the achieved bull's eye performance is about 60%. Recognition was tested with three different classifiers: decision trees (DT), k-nearest neighbor (kNN) and support vector machines (SVM). Estimated mean accuracies range from 69% to 86% (using 10- fold cross validation). The SVM classifier presents the best performance, followed by the simple kNN classifier. [ABSTRACT FROM AUTHOR]

Aiming at the feature of ad hoc networks, the paper proposed a novel ad hoc network routing algorithm based on previous people's basis, which used dynamic neighborhood decomposition at the same time to parallel optimization of the calculation of partition. And simulation was carried out, we adopt neighborhood of each sub-regions to be satisfied with the global connectivity solution in the end. Simulation results show that performance is superior to a number of related algorithms, it can reduce end-to-end transmission delay and effectively improved network transmission performance and communication efficiency, at the same time the power and protocol of data packet transmission has been improved. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to develop and implement new algorithms of classification and show that the method of the nearest neighbors rule can be also applied successfully to deal with the medical classification problems. In this context, we developed two original algorithms of classification by the method of the nearest neighbors rule and we validated them by a real application in the field of classification for the assistance to the treatment of the breast cancer to detect possible benign or malignant tumors. [ABSTRACT FROM AUTHOR]