Iterative Neighborhood Research Articles

A novel Covid-19 and pneumonia classification method based on F-transform

Nowadays, Covid-19 is the most important disease that affects daily life globally. Therefore, many methods are offered to fight against Covid-19. In this paper, a novel fuzzy tree classification approach was introduced for Covid-19 detection. Since Covid-19 disease is similar to pneumonia, three classes of data sets such as Covid-19, pneumonia, and normal chest x-ray images were employed in this study. A novel machine learning model, which is called the exemplar model, is presented by using this dataset. Firstly, fuzzy tree transformation is applied to each used chest image, and 15 images (3-level F-tree is constructed in this work) are obtained from a chest image. Then exemplar division is applied to these images. A multi-kernel local binary pattern (MKLBP) is applied to each exemplar and image to generate features. Most valuable features are selected using the iterative neighborhood component (INCA) feature selector. INCA selects the most distinctive 616 features, and these features are forwarded to 16 conventional classifiers in five groups. These groups are decision tree (DT), linear discriminant (LD), support vector machine (SVM), ensemble, and k-nearest neighbor (k-NN). The best-resulted classifier is Cubic SVM, and it achieved 97.01% classification accuracy for this dataset.

A local knit pattern-based automated fault classification method for the cooling system of the data center

Many automated sound-based fault diagnosis or classification methods have been presented in the literature. A novel automatic fault diagnosis method is presented by using sounds for the cooling system of the data center. A novel feature generator and an iterative feature selector are used together to present an automated data center cooling system (DCCS) fault diagnosing method. A new feature generator is proposed inspired by knitting hence, it is called a local knit pattern (LKP). A multiple pooling based decomposition method is presented as a preprocessor. The LKP generates features from each signal. Iterative neighborhood component analysis (INCA) feature selector selects the most discriminative. Twelve classifiers are calculated in the classification phase. The selected classifiers were achieved greater than 90.0% classification accuracies, and the best-resulted classifier is Quadratic SVM. It reached 96.40% classification accuracy. Results show that new generation automated sound fault diagnosis applications can also be developed as novel sound-based fault detection applications.

Automated accurate speech emotion recognition system using twine shuffle pattern and iterative neighborhood component analysis techniques

Speech emotion recognition is one of the challenging research issues in the knowledge-based system and various methods have been recommended to reach high classification capability. In order to achieve high classification performance in speech emotion recognition, a nonlinear multi-level feature generation model is presented by using cryptographic structure. The novelty of this work is the use of cryptographic structure called shuffle box for feature generation and iterative neighborhood component analysis to select the features. The proposed method has three main stages: (i) multi-level feature generation using Tunable Q wavelet transform (TQWT), (ii) twine shuffle pattern (twine-shuf-pat) for feature generation, and (iii) discriminative features are selected using iterative neighborhood component analysis (INCA) and classified. The TQWT is a multi-level wavelet transformation method used to generate high-level, medium-level, and low-level wavelet coefficients. The proposed twine-shuf-pat technique is used to extract the features from the decomposed wavelet coefficients. INCA feature selector is employed to select the clinically significant features. The performance of the obtained model is validated using four speech emotion public databases (RAVDESS Speech, Emo-DB (Berlin), SAVEE, and EMOVO). Our developed twine-shuf-pat and INCA based method yielded 87.43%, 90.09%, 84.79%, and 79.08% classification accuracies using RAVDESS, Emo-DB (Berlin), SAVEE and EMOVO corpora respectively with 10-fold cross-validation strategy. A mixed database is created from four public speech emotion databases which yielded 80.05% classification accuracy. Our obtained speech emotion model is ready to be tested with huge database and can be used in healthcare applications.

An automated snoring sound classification method based on local dual octal pattern and iterative hybrid feature selector

In this research, a novel snoring sound classification (SSC) method is presented by proposing a new feature generation function to yield a high classification rate. The proposed feature extractor is named as Local Dual Octal Pattern (LDOP). A novel LDOP based SSC method is presented to solve the low success rate problems for Munich-Passau Snore Sound Corpus (MPSSC) dataset. Multilevel discrete wavelet transform (DWT) decomposition and the LDOP based feature generation, informative features selection with ReliefF and iterative neighborhood component analysis (RFINCA), and classification using k nearest neighbors (kNN) are fundamental phases of the proposed SSC method. Seven leveled DWT transform, and LDOP are used together to generate low, medium, and high levels features. This feature generation network extracts 4096 features in total. RFINCA selects 95 the most discriminative and informative ones of these 4096 features. In the classification phase, kNN with leave one out cross-validation (LOOCV) is used. 95.53% classification accuracy and 94.65% unweighted average recall (UAR) have been achieved using this method. The proposed LDOP based SSC method reaches 22% better result than the best of the other state-of-the-art machine learning and deep learning-based methods. These results clearly denote the success of the proposed SSC method.

A new stable nonlinear textural feature extraction method based EEG signal classification method using substitution Box of the Hamsi hash function: Hamsi pattern

BackgroundThe EEG signal classification is crucial for epileptic seizure prediction. Therefore, many machine learning model has been presented to classify EEG signals accurately. Material and MethodThis work presents a novel automated EEG classification method by using a novel nonlinear feature extractor, and it is called as Hamsi-Pat. It uses the substitution box (S-Box) of the Hamsi hash function. As stated in the literature, S-Boxes have generally used for diffusion in symmetric encryption (especially block ciphers) methods and cryptologic hash functions. Since it is a nonlinear structure, this work aims to illustrate the merit of an S-Box for feature generation. Therefore, a new generation feature generator, which is Hamsi-Pat, is presented by using S-Box of the Hamsi hash function, and a novel EEG classification method is proposed by using Hamsi-Pat. The presented biomedical signal classification method has three elementary phases, and these phases are Hamsi-Pat based multileveled feature generation, iterative neighborhood component analysis (INCA) selector based feature dimension reduction, and classification using k nearest neighborhood (kNN) classifier. The presented Hamsi-Pat and INCA based methods were tested on Bonn electroencephalography (EEG) datasets. ResultThis model yielded 99.20% classification accuracy on the used EEG dataset for five classes case and it yielded 100.0% accuracies for other cases. ConclusionThese results obviously denoted that the S-Boxes can be considered as a feature generator, and a novel S-Box based feature generation research area can be defined as textural feature generation and statistical feature generation.

FusedTSNet: An automated nocturnal sleep sound classification method based on a fused textural and statistical feature generation network

Nowadays, many people have suffered from sleep disorders. These diseases affect daily life and can disrupt sanity. Sleep disorders/diseases can be diagnosed by using nocturnal sleep sounds. This work presents an automated nocturnal sound classification method. The proposed nocturnal sound classification method can be used in the automated sleep disease diagnosis process. To propose a highly accurate and cognitive method, a fused feature generation network is proposed. The proposed fused feature generation network extracts both textural features and statistical features together. Therefore, this method is called as fused textural and statistical feature generation network (FusedTSNet). One-dimensional discrete wavelet transform (DWT) is employed to create levels and 7-leveled DWT is applied to nocturnal sounds. Here, DWT is utilized as a pooling/decomposition method to create a multileveled feature generation network. By using the ReliefF iterative neighborhood component analysis (RFINCA), the most valuable features are selected. To demonstrate the success of the FusedTSNet and RFINCA based nocturnal sound classification method, conventional classifiers are used. The proposed FusedTSNet and RFINCA based nocturnal sound classification method were tested on a collected nocturnal sound dataset. This dataset has 700 sounds in 7 classes. Our method achieved a 98.0% classification rate on this dataset. This work clearly indicates that the automated sleep behavior detection can be developed and the success of the proposed FusedTSNet and RFINCA based sound classification method is obviously shown.

Collaborative Optimization of Vehicle and Charging Scheduling for a Bus Fleet Mixed With Electric and Traditional Buses

To address sustainable development issues of urban traffic, electric buses will join traditional bus system, and the scheduling of bus fleet should be adjusted due to the distinct features of electric buses. To this end, this paper develops a Multi-objective Bi-level programming model to collaboratively optimize the vehicle scheduling and charging scheduling of the mixed bus fleet under the operating conditions of a single depot. The upper level determines the vehicle scheduling to minimize the operating cost and carbon emissions under the constraints of connecting time between trips and the limited driving range of electric buses. The lower level is a charging scheduling problem that considers the charging time and the limited driving distance constraint to minimize the charging cost. The proposed model is solved with an integrated heuristic algorithm. The vehicle scheduling problem is addressed with the iterative neighborhood search algorithm based on simulated annealing, while the charging scheduling problem is solved with a greedy dynamic selection strategy based on the approach of multi-stage decision. Finally, case study is carried out based on a mixed bus fleet in Beijing, and the results validate the availability of the proposed model and solution algorithm.

The vehicle rescheduling problem with retiming

When a vehicle breaks down during operation in a public transportation system, the remaining vehicles can be rescheduled to minimize the impact of the breakdown. In this paper, we discuss the vehicle rescheduling problem with retiming (VRSPRT). The idea of retiming is that scheduling flexibility is increased, such that previously inevitable cancellations can be avoided. To incorporate delays, we expand the underlying recovery network with retiming possibilities. This leads to a problem formulation that can be solved using Lagrangian relaxation. As the network gets too large, we propose an iterative neighborhood exploration heuristic to solve the VRSPRT. This heuristic allows retiming for a subset of trips, and adds promising trips to this subset as the algorithm continues. Computational results indicate that the heuristic performs well. While requiring acceptable additional computation time, we find improvements over solutions that do not allow retiming in 58% of the tested instances. By delaying only one or two trips with on average 6–8 min, the average number of cancelled trips is reduced from 1.2 to 0.5.

Individual tree segmentation from a leaf-off photogrammetric point cloud

ABSTRACTThe proliferation of small unmanned aerial vehicles and structure from motion photogrammetry provides simple, repeatable, and inexpensive techniques for creating high-resolution aerial imagery and 3D data. These methods have been quickly adopted within the forestry realm for mapping forest canopies, among other tasks. Here, we present a workflow to capture sub-canopy structure in a leaf-off deciduous forest with a hobby-level drone and geographical information system software, and a toolkit for segmenting and measuring individual tree diameter from the resultant point cloud. Our methods, which build on steps from several previously published approaches (layer stacking for identifying vertical features and iterative neighborhood searches), rely solely on structural characteristics and could therefore be applied to dense photogrammetric and laser-scanning point clouds. Across four variable study sites, the toolkit correctly identified 279 trees (recall = 0.89, precision = 0.97, F-score = 0.93) and correctly segmented 90.9% of points in the point cloud (r = 0.98, p = 0.93, F = 0.95). Point cloud measures of diameter at breast height generally correlated with field measurements (Pearson’s r = 0.82, root mean square error = 0.11 m), but were typically higher than field measurements. These results demonstrate the capacity for assessing forest structure and conducting inventories using widely accessible hardware and software.

A local search based approach for solving the Travelling Thief Problem: The pros and cons

The Travelling Thief Problem (TTP) is a novel problem that aims to provide a benchmark model of combinatorial optimization problems with multiple interdependent components. The TTP combines two other well known benchmark problems: the Travelling Salesman Problem (TSP) and the Knapsack Problem (KP). The aim of this paper is to study the interdependence between the TTP's components, and how it makes solving each sub-problem independently from the other useless for solving the overall problem. A local search approach is proposed to solve the TTP. Two simple iterative neighborhood algorithms based on our approach are presented, analyzed, and compared to other algorithms. Initialization strategies are empirically investigated. The experimental results confirm that our approach was able to find new better solutions for many TTP instances.

Face super-resolution via multilayer locality-constrained iterative neighbor embedding and intermediate dictionary learning.

Based on the assumption that low-resolution (LR) and high-resolution (HR) manifolds are locally isometric, the neighbor embedding super-resolution algorithms try to preserve the geometry (reconstruction weights) of the LR space for the reconstructed HR space, but neglect the geometry of the original HR space. Due to the degradation process of the LR image (e.g., noisy, blurred, and down-sampled), the neighborhood relationship of the LR space cannot reflect the truth. To this end, this paper proposes a coarse-to-fine face super-resolution approach via a multilayer locality-constrained iterative neighbor embedding technique, which intends to represent the input LR patch while preserving the geometry of original HR space. In particular, we iteratively update the LR patch representation and the estimated HR patch, and meanwhile an intermediate dictionary learning scheme is employed to bridge the LR manifold and original HR manifold. The proposed method can faithfully capture the intrinsic image degradation shift and enhance the consistency between the reconstructed HR manifold and the original HR manifold. Experiments with application to face super-resolution on the CAS-PEAL-R1 database and real-world images demonstrate the power of the proposed algorithm.

Sparse, mean reverting portfolio selection using simulated annealing

We study the problem of finding sparse, mean reverting portfolios based on multivariate historical time series. After mapping the optimal portfolio selection problem into a generalized eigenvalue problem, we propose a new optimization approach based on the use of simulated annealing. This new method ensures that the cardinality constraint is automatically satisfied in each step of the optimization by embedding the constraint into the iterative neighbor selection function. We empirically demonstrate that the method produces better mean reversion coefficients than other heuristic methods, but also show that this does not necessarily result in higher profits during convergence trading. This implies that more complex objective functions should be developed for the problem, which can also be optimized under cardinality constraints using the proposed approach.

AN ALGORITHM FOR OPTIMIZED SEARCHING USING NON-OVERLAPPING ITERATIVE NEIGHBOR INTERVALS

We have attempted in this paper to reduce the number of checked condition through saving frequency of the tandem replicated words, and also using non-overlapping iterative neighbor intervals on plane sweep algorithm. The essential idea of non-overlapping iterative neighbor search in a document lies in focusing the search not on the full space of solutions but on a smaller subspace considering non-overlapping intervals defined by the solutions. Subspace is defined by the range near the specified minimum keyword. We repeatedly pick a range up and flip the unsatisfied keywords, so the relevant ranges are detected. The proposed method tries to improve the plane sweep algorithm by efficiently calculating the minimal group of words and enumerating intervals in a document which contain the minimum frequency keyword. It decreases the number of comparison and creates the best state of optimized search algorithm especially in a high volume of data. Efficiency and reliability are also increased compared to the previous modes of the technical approach.

An Algorithm for Optimized Searching Using Non-Overlapping Iterative Neighbor Intervals

We have attempted in this paper to reduce the number of checked condition through saving frequency of the tandem replicated words, and also using non-overlapping iterative neighbor intervals on plane sweep algorithm. The essential idea of non -overlapping iterative neighbor search in a document lies in focusing the search not on the full space of solutions but on a smaller subspace consideringnon-overlapping intervals defined by the solutions. Subspace is defined by the range near the specified minimum keyword. We repeatedly pick a rangeup and flip the unsatisfied keywords, so the relevant ranges are detected.The proposed methodtries to improve the plane sweep algorithm by efficiently calculating the minimal group of words and enumerating intervals in a document which contain the minimum frequency keyword.It decreases the number of comparisonand creates the best state of optimized search algorithm especially in a high volume of data. Efficiency and reliability are also increased compared to the previous modes of the technical approach.

Minimum Zone Evaluation of Flatness Error Using an Adaptive Iterative Strategy for Coordinate Measuring Machines Data

In this paper, an adaptive and iterative neighborhood search strategy is proposed to precisely evaluate the flatness error. Firstly, an initial datum plane produced by least squares method (LSM) is calculated. And then, a group of candidate planes are generated by adjusting the direction angles of the LSM datum plane. The flatness errors conform to the minimum zone condition for each candidate datum plane that can be worked out. The one with minimum flatness error will be selected as the new start plane for the next search. The new search area is the zone between the new and old datum planes, with which the new search step is computed until the terminal condition is met. Numerical examples are given to validate the proposed strategy. The results show that the proposed method could get the precise value of the flatness error from the data of coordinate measuring machines and other equipment.

An iterative neighborhood search approach for minimum zone circularity evaluation from coordinate measuring machine data

An iterative neighborhood search approach (INSA) was proposed to precisely evaluate the circularity error under minimum zone conditions without directly solving nonlinear equations from coordinate measurement machine (CMM) data. The method starts with calculating the initial location and radius of an initial circular search scope. The location is the center of the circle based on an approximate least-squares method of all measurement data points uniformly sampled around the circle, and the radius is the circularity error value by using the approximate center as the datum center of two concentric circles enclosing all measurement data points. Then the circular search scope was divided according to some concentric circles and radials with some radius and angle step lengths. Intersections of the circles and radials were candidate centers for circularity evaluation under the minimum zone criteria. An optimal center with minimum circularity error value was picked out as a new location of the search scope. The distance from the old location was the radius of the new search scope. Further it was divided and the intersections were evaluated until the optimal solution was met. Some examples in the literature were used to verify the validity of this method. The results are the same as or better than those minimum zone solutions adopted from previous work, and computation time is no more than 0.07 s implementing on an IBM ThinkPad R40 laptop for all examples. The computation and comparison show that the proposed INSA is a precise, fast, convergent and simple approach which solved circularity evaluation problems effectively.

Tabu search algorithms for water network optimization

In this paper we propose a tabu search algorithm to find the least-cost design of looped water distribution networks. The mathematical nature of this optimization problem, a nonlinear mixed integer problem, is at the origin of a multitude of contributions to the literature in the last 25 years. In fact, exact optimization methods have not been found for this type of problem, and, in the past, classical optimization methods, like linear and nonlinear programming, were tried at the cost of drastic simplifications. Tabu search is a valuable heuristic technique for solving problems cast in combinatorial form. This is based on the human memory process and uses an iterative neighborhood search procedure in an attempt to avoid becoming trapped in local optima. The use of such a heuristic procedure to solve the aforementioned problem needs particular tailoring to produce high quality solutions. In this paper we present the essential features of the algorithm and the results obtained when it is applied to some of the classical water distribution network case studies appearing in the literature. The results are very promising and demonstrate the usefulness of tabu search algorithms in solving this kind of optimization problem.

Evolutionary relationships among proteins probed by an iterative neighborhood cluster analysis (INCA). Alignment of bacteriorhodopsins with the yeast sequence YRO2.

Searching the existing databases for homologous sequences is essential to understanding a protein's structure and function. For a query sequence, its nearest neighbors can be identified by BLAST (basic local alignment search tool). However, a single query sequence is sufficient to define the entire neighborhood of related sequences, and multiple BLAST queries are needed. We describe here a program which permits automated and iterative BLAST analysis of an entire neighborhood of sequences and apply this to search for homologs of the bacteriorhodopsins outside the archaea phylum. We have developed a Java program, 'Iterative Neighborhood Cluster Analysis' (INCA), which performs iterative BLAST searches, beginning with a single starter sequence, and proceeding with any other sequence achieving a predefined minimum alignment score. This results in a cluster of sequences where each sequence is related to at least one other sequence by the cutoff score, additional lists of more distantly related sequences for each member of cluster. Bacteriorhodopsins had not been previously aligned with any other protein family with scores indicative of probable homology. Using INCA, we identified a probable homolog in yeast, YRO2_YEAST, also containing seven putative transmembrane domains. A finding of probable homology was supported by additional alignment strategies. INCA is a useful tool to assess complete protein neighborhoods. With an increasing database, INCA can serve to detect the emergence of evolutionary links between even the most distantly related protein families. Identifying a homolog of the bacteriorhodopsins in yeast illustrates this approach but at the same time highlights the vast evolutionary distances between polytopic membrane proteins, such as the bacteriorhodopsins.