Distance Criterion Research Articles

Fuzzy Regression Model Based on Geometric Centroid and Incentre Points and Application to Performance Evaluation

Fuzzy regression model is developed to construct the relationship between independent variable and dependent variable in a fuzzy environment. In order to increase the explanatory performance of fuzzy regression model, the least-squares method usually is applied to determine the numeric coefficients based on the concept of distance. In this paper, we consider the fuzzy linear regression model with fuzzy input, fuzzy output and crisp parameters and introduce a new distance based on the geometric centroid and incentre points (GCIP) of triangular fuzzy number, merge least-squares method with the new GCIP distance and propose least-squares GCIP distance method. Finally, an example of employee job performance is given to illustrate the effectiveness and feasibility of the method. Comparisons with existing methods show that total estimation error using the same distance criterion, the explanatory performance of the GCIP method is satisfactory, and the calculation is relatively simple.

Treyler Üreten Bir İşletmede Bulanık AHP, ELECTRE ve VIKOR Yöntemi ile Catering Firma Seçimi

It is very important to choose a catering company for all kinds of businesses where there is a need for bulk meals. Catering companies are often preferred because they offer a more practical and more economical food solution at workplaces or other community locations. Therefore, people are looking for food companies to meet their expectations. In order to make a selection that is meaningful and meets your expectations, it is necessary to choose a company that can provide this service in a complete way. The food break and quality are important for the employees to continue their daily life and get away from the work stress in a busy working environment. The food break and quality are important for the employees to continue their daily life and get away from the work stress in a busy working environment. Among the issues that employees complain to the human resources department in the companies is the fact that the selected catering companies cannot meet the expectations. It has been observed that companies have difficulty in choosing food supplier in order to meet the expectation of increase in people's awareness. While choosing a catering company, there are other important issues as much as the catering menus. There are some criteria to consider when working with a good catering firm. In this study, a catering company was selected by using multi-criteria decision making methods (MCDM) for a firm making a trailer. In choosing a catering company, quality, price, distance and service criteria are taken into consideration. The weight of the criteria was determined by the Fuzzy Analytic Hierarchy Process (FAHP), and alternatives were selected by ELECTRE (ELimination and Choice Expressing REality) and VIKOR methods.

Ranking-based triplet loss function with intra-class mean and variance for fine-grained classification tasks

This paper proposed a deep ranking model for triplet selection to efficiently learn similarity metric from top ranked images. A modified distance criterion described in the current work leverages the intra-category variance in metric learning of a triplet network by learning a local sample structure. A multicolumn fusion architecture is used to capture different levels of variance, which when incorporated in the loss function strengthens it and optimizes the objective of the triplet networks. This enables a fine-grained classification strategy. State-of-the-art techniques use a group-sensitive triplet sampling to deal with this issue. However, these have the disadvantage of increased group sampling computations. Experiments are conducted over a variety of benchmark datasets including Model40, PatternNet, and In-Shop Clothing. The main purpose of these experiments are to verify whether the triplet learning technique can be applied over different kinds of data. Results demonstrate that the current work provides superior results in most cases. These results can further be improved with specific parameter tunings and ensembling techniques wherever applicable.

Optimal planning of timber extraction methods using analytic hierarchy process

Timber extraction is one of the most difficult work stages of forest operations in Turkey. Inappropriately, planned timber extraction operations can be very time-consuming activities and may also cause serious damage to residual trees, saplings, and forest soil. Thus, an optimal timber extraction method should be determined considering both economic and ecological constraints. In this study, commonly implemented timber extraction methods including chute system, portable winch, and farm tractor were evaluated with respect to their productivity and potential damages on residual trees. In the solution process, one of the well-known multi-criteria decision analysis (MCDA) techniques, the analytic hierarchy process (AHP), was used to determine optimal timber extraction planning of priority scenarios. Specific timber extraction methods were identified and evaluated based on slope, volume, and distance criteria. A total of five priority scenarios were examined: (1) optimal extraction with the highest productivity, (2) optimal extraction with the lowest stand damage, (3) productivity and stand damage receive equal importance, (4) productivity is more important than stand damage, and (5) stand damage is more important than productivity. The AHP results indicated that the optimal timber extraction method was the farm tractor winching with 30 m skidding distance in Scenario I. On the other hand, the portable winch with 20 m skidding distance was found to be the optimal method with the least stand damage. In Scenario III, the portable winch, with an average skidding distance of 40 m, was found to be the most suitable extraction method. The overall results indicated that implementing MCDA techniques for optimal planning of timber extraction will help provide important contributions to conduct economic, environmental, and site-specific operations during logging stages.

Evaluation of Adaptive Planning of Lung Cases based on Cone Beam CT Images

The purpose of this study was to evaluate the accuracy of dose calculation based on Cone Beam Computed Tomography (CBCT) as adaptive planning for lung cancer. Dose calculation based on CBCT images was compared to Computed Tomography (CT) simulator images as references. Treatment planning was generated for 7 patients of lung using Intensity Modulated Radiotherapy (IMRT) technique and Volumetric Arc Therapy (VMAT) technique. Eclipse v13.6 Treatment Planning System (TPS) with Analytical Anisotropic Algorithm (AAA) and Acuros External Beam (AXB) algorithm were used to calculate the dose. This study was divided into three major parts: (1) Hounsfield Unit (HU) calibration for CBCT images by using CIRS 002LFC phantom; (2) analysis of Dose Volume Histogram (DVH); (3) analysis of Passing Rate Gamma with criteria Dose Difference (DD) 2%/Distance To Agreement (DTA) 2mm and DD 3%/DTA 3mm using Electronic Portal Imaging Device (EPID). The DVH analysis for D98%, D50% and D2% deviation was evaluated and compared to the DVH for CT images with AAA algorithm as reference. The deviation of D98%, D50% and D2% for lung cancer was less than 2%. The range of conformity index based on CBCT images for all planning was 0,923-0,999 and the homogeneity index was in the range of 0,042-0,197. The gamma criteria (DD/DTA) of dose difference and distance to agreement for 2%/2mm are 87% - 94% and for 3%/3mm were 96% -98% for IMRT techniques. Passing Rate Gamma for VMAT with 2%/2mm criteria were 87% -96% and 3%/3mm criteria were 96% - 98%. The results of this study showed that dose calculation based on CBCT image was similar to dose calculation using CT image with descrepancy less than 2%.

Isolated Command Recognition Using MFCC and Clustering Algorithm

People who cannot walk by themselves need someone to carry them around on a wheelchair. A voice recognition system was developed to recognize a set of commands used by people with disability to control their wheelchair and devices around them. Voice samples of various commands from 8 different speakers were collected. The features of the collected samples were extracted using MFCC feature extraction technique. The MFCC (Mel-frequency cepstral coefficient) feature extraction technique involves pre-emphasis, framing, windowing, FFT, Mel-scale transformation operations. The major reason for choosing MFCC extraction is that the human ear has a response of a logarithmic scale and not a linear scale. Hence, all the framed voice samples are transformed to the Mel-scale with a logarithmic response and stored in the form of K-means clusters. Features extracted from test data are applied to the models developed for commands, and based on the minimum distance criterion, the model is selected to be closely associated with the respective voice command. The voice recognition system was developed for an 8-command model using MATLAB.

Graph-based description of tertiary lymphoid organs at single-cell level.

Our aim is to complement observer-dependent approaches of immune cell evaluation in microscopy images with reproducible measures for spatial composition of lymphocytic infiltrates. Analyzing such patterns of inflammation is becoming increasingly important for therapeutic decisions, for example in transplantation medicine or cancer immunology. We developed a graph-based assessment of lymphocyte clustering in full whole slide images. Based on cell coordinates detected in the full image, a Delaunay triangulation and distance criteria are used to build neighborhood graphs. The composition of nodes and edges are used for classification, e.g. using a support vector machine. We describe the variability of these infiltrates on CD3/CD20 duplex staining in renal biopsies of long-term functioning allografts, in breast cancer cases, and in lung tissue of cystic fibrosis patients. The assessment includes automated cell detection, identification of regions of interest, and classification of lymphocytic clusters according to their degree of organization. We propose a neighborhood feature which considers the occurrence of edges with a certain type in the graph to distinguish between phenotypically different immune infiltrates. Our work addresses a medical need and provides a scalable framework that can be easily adjusted to the requirements of different research questions.

A novel quantum steganography-Steganalysis system for audio signals

As a substitute for classical solutions, quantum information hiding techniques have become an essential issue in the field of quantum communications by utilizing the inherent features of quantum mechanics and creating more secured communications for the more reliable exchange of digital media within the context of quantum communications networks. Quantum steganography has been considered as one of these approaches in recent years, but the importance of investigating and discovering these hidden communications within the context of quantum communication networks that require the use of quantum steganalysis methods has not been addressed so far. Therefore, in this paper, a novel quantum steganography-steganalysis system for digital audio signals is proposed, which can accurately detect audio steganography methods in the context of quantum communication networks. The proposed model consists of two separate sections: steganography and steganalysis; in the steganography part, to minimize the impacts of the embedding process and increasing the Signal to Noise Ratio (SNR), the embedding operation is carried out within the Least Significant Fractional Qubit (LSFQ) of the amplitude information of the audio signal samples. Then, a universal steganalyzer in the steganalysis part distinguishes the stego audio signals using the extracted statistical features from the audio signals. The universal steganalyzer consists of a mean feature extraction module to extract features from the audio signal frames and the quantum circuits for implementing the K-Nearest Neighbor (KNN) algorithm and the Hamming distance criterion. The simulation-based quantum circuits of the proposed system tested and evaluated using different audio files. Over 80% accuracy in detecting stego audio signals indicates high accuracy and efficiency of the proposed scheme and its applicability in quantum communication networks. Along with the higher efficiency and security of quantum steganography methods when compared with the classical one, the results show that the proposed quantum steganography-steganalysis scheme is also capable of competing with classical methods in terms of accurately detecting steganography methods.

Synthesis of highly nonlinear S-boxes satisfying higher order propagation criterion

The S-box is the main component of modern block cryptographic algorithms and hash functions. One of the main criteria for the cryptographic quality of an S-boxes is the criterion of a high nonlinearity distance, as well as a propagation criterion of order m. One of the effective methods for constructing S-boxes is the method of their construction on the basis of a set of Boolean functions with a given level of cryptographic quality. This paper discusses the spectral classes (obtained on the basis of the classification of the Walsh-Hadamard transform coefficient vectors) of Boolean functions of 1 … 5 variables, for each of which the numbers of Boolean functions that satisfy the propagation criterion of order m were found. Using the constructive method among the full set of Boolean functions of 5 variables, a subclass of 12 balanced maximally nonlinear Boolean functions satisfying the propagation criterion PC(4) were found. It is shown that on the basis of this set of Boolean functions, bijective cryptographic S-boxes can be synthesized, which are the best among the entire set of S-boxes of length N = 32, from the point of view of the criterion of maximum nonlinearity distance and propagation criterion.

Evaluation of a facility location for a food assistance supply chain. The case of Bienestarina in Colombia

The evaluation of the strategic supply chain configuration is considered one of the strategic logistics decisions, especially in food assistance supply chains focused on generating better nutritional conditions in vulnerable populations. In Colombia, there is a social program called Bienestarina, which aims to promote food and nutritional security in a vulnerable population. Although the government supports the program for improving nutritional support, there are currently inconsistencies in freight flows, lack of coverage in some areas, and delivery delays. Therefore, this work aims to evaluate the current configuration of the supply chain and propose improvements related to the facility location. Such advances would enable the increase in the efficacy of the network and the reduction of malnutrition in the country. For this purpose, a mixed-integer mathematical programming model is presented, which considers the weighted distance criterion for different demand scenarios and supports the location-allocation decision in a social assistance supply chain. The current network configuration was compared with the optimal proposed structure. The comparisons show highly potential improvements in freight flow allocation, suggests several variations in the existing warehouses emplacement, and generates public policy implications to reduce the logistic cost in the system, prioritizing in turn the demand covering.

Hybrid Methodology to Improve Health Status Utility Values Derivation Using EQ-5D-5L and Advanced Multi-Criteria Techniques.

This paper presented a new approach to the calculation of quality-adjusted life years (QALY) based on multi-criteria decision-making (MCDM) methods and using the EQ-5D-5L questionnaire. The health status utility values were calculated through a hybrid methodology. We combined the analytic hierarchy process (AHP), the AHP with a D-number extended fuzzy preference relation (D-AHP), the fuzzy analytic hierarchy process (F-AHP), and the technique for order preference by similarity to the ideal solution (TOPSIS) to obtain individual and aggregated utility values. The preference data were elicited using a sample of individuals from a Colombian university. In all tested methods, the ordinal preferences were consistent, and the weights were compared using the Euclidean distance criterion (EDC). We identified F-AHP-TOPSIS as the optimal method; its benefits were associated with modeling the response options of the EQ-5D in linguistic terms, it gave the best approximation to the initial preferences according to EDC, and it could be used as an alternative to the known prioritization method. This hybrid methodology was particularly useful in certain medical decisions concerned with understanding how a specific person values his or her current health or possible health outcomes from different interventions in small population samples and studies carried out in low- and middle-low-income countries.

Evaluation of Pb concentrations in selected vegetables and portable drinking water, and intelligent quotients of school children in Ishiagu-a Pb mining community: health risk assessment using predictive modelling.

This study evaluates the concentrations of lead (Pb) in 6 selected vegetables and drinking water samples taken from an agricultural/mining town Ishiagu. This evaluation is important because these vegetables and water are major gateway of lead exposure through ingestion, especially children in the Pb mining environment. Pb at even very low concentrations has been shown to have adverse effect on developing brain and hence children's intellectual ability. The impact of lead-contaminated food/water intake on the cognitive function was focused on school children whose parents have lived in the Pb mining town for over 25years before they were born. Non-invasive, "target risk quotient" (TRQ) methodology, based on the principle of predictive toxicology was adopted for our analysis. Samples of these vegetables harvested in July and August 2015, and water taken from homes at 4 different villages in Ishiagu town and neighbouring community Akaeze (control), were subjected to appropriate chemical treatment/digestion procedures and the concentrations of Pb determined using AA-700 Shimadzu model atomic absorption spectrophotometer. From 642 structured questionnaire administered to the teachers/children, the daily vegetable ingestion rates for each vegetable (mg/child/day) and estimated daily intakes (EDI) of lead were obtained. The results show that the concentrations of Pb in water samples and the 6 vegetables harvested from the lead mining town vary as distances increase from the mining sites while the total target hazard quotients (TTHQs) for the vegetable crops were greater than one (˃1). The cognitive functions of 160 school children (aged 6-8years), sampled from 265 families based on their meeting the criteria for distances away from the mining site, were evaluated using Raven's Standard Progressive Matrices and psychometrics. The data generated were analysed using (SPSS) version 21.0 and results expressed as mean ± standard deviation of intelligent quotient (IQ). Students' t tests for independent samples were used to compare the IQ results for children in the lead mining area and non-mining area. A model based on predictive toxicology paradigm which can show a relationship between concentrations of lead in vegetables/water and cognitive function was developed. This model shows that there is a positive correlation between total lead concentrations in vegetables/water and children's cognitive function.

Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

The main aim of this paper is to solve a path planning problem for an autonomous mobile robot in static and dynamic environments. The problem is solved by determining the collision-free path that satisfies the chosen criteria for shortest distance and path smoothness. The proposed path planning algorithm mimics the real world by adding the actual size of the mobile robot to that of the obstacles and formulating the problem as a moving point in the free-space. The proposed algorithm consists of three modules. The first module forms an optimized path by conducting a hybridized Particle Swarm Optimization-Modified Frequency Bat (PSO-MFB) algorithm that minimizes distance and follows path smoothness criteria. The second module detects any infeasible points generated by the proposed hybrid PSO-MFB Algorithm by a novel Local Search (LS) algorithm integrated with the hybrid PSO-MFB algorithm to be converted into feasible solutions. The third module features obstacle detection and avoidance (ODA), which is triggered when the mobile robot detects obstacles within its sensing region, allowing it to avoid collision with obstacles. The simulation results indicate that this method generates an optimal feasible path even in complex dynamic environments and thus overcomes the shortcomings of conventional approaches such as grid methods. Moreover, compared to recent path planning techniques, simulation results show that the proposed hybrid PSO-MFB algorithm is highly competitive in terms of path optimality.

A Novel Quantum Audio Steganography–Steganalysis Approach Using LSFQ-Based Embedding and QKNN-Based Classifier

In this paper, a steganography–steganalysis model for quantum audio signals is presented. This model consists of two separate parts of steganography and steganalysis. In the steganography part, to increase the imperceptibility and enhance the undetectability of the least significant bit method, first, the quantum host audio signal is divided into quantum frames of the specified length, and then, using the triangular number sequence, some samples of each frame are selected for the embedding operation. Finally, the embedding process is carried out within the least significant fractional qubit of the amplitude information of the selected samples. The steganalysis part also includes a quantum universal steganalyzer for detecting the embedding operations of the steganography part and a feature extraction module for calculating the power feature of quantum audio signal frames. The recognition operation is based on the quantum K-nearest neighbor algorithm and the Hamming distance criterion. All quantum circuits of the steganography and steganalysis parts of the proposed model were simulated and then tested and evaluated using different audio files. Detection accuracy of over 90% indicates the accuracy and efficiency of the proposed method in quantum audio steganography detection in the context of quantum communication networks.

Can We Safely Obtain Formal Oxidation States from Centroids of Localized Orbitals?

The use of centroids of localized orbitals as a method to derive oxidation states (OS) from first-principles is critically analyzed. We explore the performance of the closest-atom distance criterion to assign electrons for a number of challenging systems, including high-valent transition metal compounds, π-adducts, and transition metal (TM) carbenes. Here, we also introduce a mixed approach that combines the position of the centroids with Bader’s atomic basins as an alternative criterion for electron assignment. The closest-atom criterion performs reasonably well for the challenging systems, but wrongly considers O-H and N-H bonds as hydrides. The new criterion fixes this problem, but underperforms in the case of TM carbenes. Moreover, the OS assignment in dubious cases exhibit undesirable dependence on the particular choice for orbital localization.

Metrizable preferences over preferences

A hyper-preference is a weak order over all linear orders defined over a finite set A of alternatives. An extension rule associates with each linear order p over A a hyper-preference. The well-known Kemeny extension rule ranks all linear orders over A according to their Kemeny distance to p. More generally, an extension rule is metrizable iff it extends p to a hyper-preference consistent with a distance criterion. We characterize the class of metrizable extension rules by means of two properties, namely self-consistency and acyclicity across orders. Moreover, we provide a characterization of neutral and metrizable extension rules, based on a simpler formulation of acyclicity across orders. Furthermore, we establish the logical incompatibility between neutrality, metrizability and strictness. However, we show that these three conditions are pairwise logically compatible.

An Enhanced Spatial Intuitionistic Fuzzy C-means Clustering for Image Segmentation

Intuitionistic based Fuzzy clustering is a popular method in the field of image segmentation. The widely used Intuitionistic Fuzzy C-means (IFCM) based image segmentation is sensitive to noise since it uses only distance criterion in the feature space to segment the images. To overcome this, an enhanced spatial intuitionistic fuzzy c-means clustering algorithm is proposed that uses:- (i) an intuitionistic fuzzification of image to simplify the representation of the image (ii) an improved method to calculate the hesitation degree in the images. (iii) the spatial property of an image in order to make segmentation more robust and effective. The performance of the proposed method is evaluated for synthetic and real images. The result indicates the effectiveness of the proposed methodology over existing methodologies.

Sparse ℓ1 and ℓ2 Center Classifiers

The nearest-centroid classifier is a simple linear-time classifier based on computing the centroids of the data classes in the training phase, and then assigning a new datum to the class corresponding to its nearest centroid. Thanks to its very low computational cost, the nearest-centroid classifier is still widely used in machine learning, despite the development of many other more sophisticated classification methods. In this paper, we propose two sparse variants of the nearest-centroid classifier, based respectively on ℓ1 and ℓ2 distance criteria. The proposed sparse classifiers perform simultaneous classification and feature selection, by detecting the features that are most relevant for the classification purpose. We show that training of the proposed sparse models, with both distance criteria, can be performed exactly (i.e., the globally optimal set of features is selected) and at a quasi-linear computational cost. The experimental results show that the proposed methods are competitive in accuracy with state-of-the-art feature selection techniques, while having a significantly lower computational cost.

Multilevel High-Rate Coding With Twofold Concatenation for IoTs in Wireless Mesh Networks

Recent studies have shown that wireless mesh networks (WMNs) can be cheap, reliable, and efficient solutions for Internet of Things (IoTs) applications and connected devices. However, the increase in the size of the WMNs could lead to a degradation in performance. This makes the network vulnerable to high error rates over noisy and fading channels. This has increased the demand for more efficient channel coding schemes that can provide reliability, high data rate, high coding gain, energy efficiency, and minimum decoding complexity especially for low-cost and special purpose WMNs. In this research, an original multilevel coding with twofold concatenation scheme is designed based on high-rate space-time block codes (HRSTBCs) to cope with this increasing demand, particularly in MIMO-based WMNs. First, a multilevel coding scheme is implemented by correlating HRSTBCs with the uniquely constructed set-partitioning of the transmission matrix, based on the coding gain distance (CGD) criterion. This generates a new scheme, namely multilevel high-rate space-time block code (MHRSTBC), to provide a high transmission rate. Second, a twofold concatenation coding scheme is introduced by concatenating Reed-Solomon (RS) code, has been used for its energy efficiency and optimality in the mobile environment, with the inner code of the associated MHRSTBC. This has formed a new scheme that reaches the maximum coding gains, called the Reed-Solomon multilevel high-rate space-time block code (RS-MHRSTBC). The efficiency of the RS-MHRSTBC is verified by a computer simulation over a Rayleigh flat-fading and additive white Gaussian noise (AWGN) channel. The RS-MHRSTBC is compared with the classical schemes of Alamouti STBC and Multilevel STBC over uncoded QPSK, and the RS-MHRSTBC shows significant high coding gains reached of 47.79 dB, while Alamouti STBC and Multilevel STBC reached 38.12 dB and 44.30 dB at a BER of 10−6 respectively. RS-MHRSTBC also shows a reasonable decoding complexity while maintaining full transmission diversity at spectral efficiency of 2 bits/s/Hz.

Phase equilibrium modelling for multi-component mixtures using highly accurate Helmholtz energy equation of state

Phase stability of a mixture at given conditions is studied. The modelling of equilibrium phase compositions is considered when the original phase of the mixture is unstable. The tangent plane distance criterion is used for stability analysis and the Gibbs energy minimisation is employed for phase equilibrium calculation when the successive substitution does not work well. With the Helmholtz free energy equation of state, thermodynamic properties in single phase and two-phase equilibrium were calculated for methane-ethane mixtures. Phase envelopes were also calculated.