Phenetic Research Articles

A Novel Similarity Measure of Single-Valued Neutrosophic Sets Based on Modified Manhattan Distance and Its Applications

A single-valued neutrosophic (SVN) set contains three parameters, which can well describe three aspects of an objective thing. However, most previous similarity measures of SVN sets often encounter some counter-intuitive examples. Manhattan distance is a well-known distance, which has been applied in pattern recognition, image analysis, ad-hoc wireless sensor networks, etc. In order to develop suitable distance measures, a new distance measure of SVN sets based on modified Manhattan distance is constructed, and a new distance-based similarity measure also is put forward. Then some applications of the proposed similarity measure are introduced. First, we introduce a pattern recognition algorithm. Then a multi-attribute decision-making method is proposed, in which a weighting method is developed by building an optimal model based on the proposed similarity measure. Furthermore, a clustering algorithm is also put forward. Some examples are also used to illustrate these methods.

Fuzzing-based hard-label black-box attacks against machine learning models

Machine learning models are vulnerable to adversarial examples. We study the most realistic hard-label black-box attacks in this paper. The main limitation of the existing attacks is that they need a large number of model queries, making them inefficient and even infeasible in practice. Inspired by the very successful fuzz testing approach in traditional software engineering and computer security domains, we propose fuzzing-based hard-label black-box attacks against machine learning models. We design an AdvFuzzer to explore multiple paths between a source image and a guidance image, and design a LocalFuzzer to explore the nearby space around a given input for identifying potential adversarial examples. We demonstrate that our fuzzing attacks are feasible and effective in generating successful adversarial examples with significantly reduced number of model queries and L0 distance. More interestingly, given a successful example generated by either our or other attacks, LocalFuzzer can immediately generate more successful adversarial examples even with smaller L2 distance from the source example.

Agglomerative Nesting (AGNES) Method and Divisive Analysis (DIANA) Method For Hierarchical Clustering On Some Distance Measurement Concepts

Clustering data through hierarchical approach could be performed by Agglomerative Nesting (AGNES) Method and Divisive Analysis (DIANA) Method. The objective of this research is to compare both the methods based on Euclid and Manhattan distance measurements. Of this research the clustering procedures of agglomerative method are conducted by exploring all techniques including single linkage, complete linkage, average linkage, and Ward. The data used are the National Socio-Economic Survey (SUSENAS) data which are selected specifically for the percentage of over 5 year old residents in each province, for both living in urban or rural, who access the internet in the last 3 months in 2017 but classified according purpose of accessing. By applying Mean Square Error (MSE) for 2 and 3 clusters, it can be concluded that the single linkage technique is the best performance of clustering procedure for both Euclidean and Manhattan distances.

Benchmarking with nonconvex production possibility set through data envelopment analysis: An application to China’s transportation system

Convex production possibility set (PPS) is an implicit assumption in previous benchmarking studies. Some scholars point out that the implicit assumption is inconsistent with the classical economic theory although it facilitates operational analysis. Hence, the present study develops a new benchmarking approach on the basis of technology that assumes convexity on consumption correspondence instead of imposing convexity on PPS, in which the closest benchmark with the least effort is identified. First, the correlation between two classical technologies in benchmarking (convex PPS and free disposal hull technology), and two nonconvex PPS (production correspondence and consumption correspondence), are identified. Next, the extreme efficient DMUs are identified to describe Pareto-efficient points on the frontier of the consumption correspondence. Then, the closest benchmark is determined by selecting a Pareto-efficient point that minimizes the distance between the evaluated unit and the efficient frontier of consumption correspondence. In addition, this study employs the Manhattan distance for benchmarking, and the proposed approach is flexible to accommodate other distances to derive different economic interpretations. Besides, the proposed approach introduces a new non-radial efficiency measure and conceptualizes a benchmark index by relaxing the convexity assumption on PPS. Finally, the proposed approach is applied to select benchmarks and measure performance for China’s transportation system. The proposed approach contributes to relaxing convexity assumption on PPS in benchmarking using DEA and provides a powerful managerial instrument to improve the performance of China’s transportation system.

Relationships between anthropometric characteristics, block settings, and block clearance technique during the sprint start

ABSTRACT This study aimed to identify how body dimensions interact with anteroposterior block distances to influence lower limb joint angles in the “set” position, how these angles relate to block clearance kinetic and kinematic parameters, and how these biomechanical parameters influence sprint start performance in sprinters of both sexes and of different ability levels. Seventy-eight sprinters performed six maximal-effort 10 m sprints. Joint angles in the “set” position were quantified through 2D video analysis, and the forces generated during block exit were measured by dynamometric starting blocks. Lower limb length was associated with the front block-starting line distance ([FB/SL], partial correlation [rPC] = 0.48) and was a significant predictor of FB/SL (R2 = 0.39). The FB/SL was associated with front hip angle (rPC = 0.38), which was consequently associated with numerous kinetic variables during block clearance (rPC from −0.41 to −0.61). Coaches should be encouraged to explore the interactions between individual lower limb lengths and the FB/SL distance in both male and female sprinters to manipulate the front hip angle in the “set” position in an attempt to achieve more favourable block clearance kinetics.

Clustering interval-valued data with adaptive Euclidean and City-Block distances

In several applications, data information is obtained in the form of intervals, such as the monthly temperature in a meteorological station or daily pollution levels in different locations. This paper proposes partitioning clustering algorithms for interval-valued data based on adaptive Euclidean and City-Block distances. Since some boundary variables may be more relevant for the clustering process, the proposals consider the joint weights of the relevance of the lower and upper boundaries of the interval-valued variables. Consequently, clusters of different shapes and sizes in some subspaces of the variables, even in specific boundaries of the interval-valued data, can be recognized. In addition, robust dissimilarity functions were introduced to reduce the influence of outliers in the data. The adaptive distances change at each iteration of the algorithms and can be different from one cluster to another. The methods optimize an objective function by alternating three steps for obtaining the representatives of each group, the cluster partition, and the relevance weights for the interval-valued variables. Experiments on synthetic and real data sets corroborate the robustness and usefulness of the proposed adaptive clustering methods.

Spatiotemporal Geostatistical Analysis of Groundwater Level in Aquifer Systems of Complex Hydrogeology

AbstractDirect interpolation of groundwater levels often leads to contour maps which are hydrogeological inconsistent since numerical algorithms do not consider changes in flowline patterns caused by hydrogeological heterogeneities and aquifer boundary conditions. In the present work, this issue is assessed by conducting a geostatistical analysis based on Gaussian process method, using space‐time groundwater level observations, to generate reliable spatial maps of groundwater level variability and to identify groundwater level patterns over the island of Crete, Greece. Besides, two innovative tools are employed in the process: the Manhattan distance metric to obtain spatial correlation where faults are present in the aquifer and the spatiotemporal Spartan covariance function to obtain spatiotemporal interdependence. The results show significant prediction improvement over convectional geostatistical methods. Useful information is obtained from the delivered map notifying areas under high risk of groundwater resources shortage. The developed approach could be applied to other areas with analogous hydrogeological properties. It will be especially valuable in semi‐arid areas prone to droughts, where groundwater represents the main source of water.

Application of an improved k‐means clustering algorithm in power user grouping

AbstractThe illegal act of stealing electricity has brought serious security risks to the operation of power transmission system. The electricity consumption law of power users has obvious characteristics, which can be classified by clustering method. However, the traditional k‐means clustering method has the characteristics of randomly selecting the initial cluster center, which leads to the instability of clustering results. Aiming at the shortcomings of traditional k‐means clustering algorithm, this paper proposes a density based k‐means clustering algorithm (Dk‐means clustering) to optimize the initial center selection. According to the characteristics of users' power load, the method of determining the number of clusters and the evaluation method of clustering effect are selected. Then, the traditional k‐means clustering algorithm and the improved Dk‐means clustering algorithm are used to analyze the power load data of a residential area, and six groups of characteristic curves are obtained. Based on the analysis of these curves, the power consumption characteristics of each group of users were evaluated. Finally, through the comparative analysis of Euclidean distance, Manhattan distance and correlation coefficient r, it is proved that Dk‐means algorithm has better clustering effect and is more accurate for the selection of suspicious power stealing users.

Improved Accuracy of Animal Skin Pattern retrieval with CNN Model and Distance Metrics

This study describes the retrieval of animal skins with very diverse shapes and textures. With so many variations in each animal skin pattern, an appropriate and supported CNN model is needed as well as an appropriate distance matrix method to support retrieval performance. This research was conducted on 6 types of animal skin images. In experimenting to obtain this animal skin image, 4 CNN models were used, namely EfficientnetB7, Inception V3, MobilenetV2, and Resnet50 V2, as well as 2 distance metrics methods, namely Euclidean and Manhattan. Based on the experiment, the average with 2 measuring distances is more than 90%. The CNN model has the highest retrieval accuracy in Inception V3 with an average of 97.2% MobilenetV2 with an average of 92.9%, Resnet50 V2 with an average of 95.5%, and EfficientNetB7 with an average of 96%. In the skin animal image retrieval accuracy which is more than 70%, the highest number on CNN model Inception V2 is 41 images and EfficientNetb7 is 40 images with Manhattan Distance. Some animal skin image patterns have a retrieval accuracy of up to 100% using 4 CNN models, but some patterns have an accuracy below 50%, so this is part of the continuation of research on animal skin patterns. Keywords-- Manhattan, Euclidean, CNN, EfficientnetB7, InceptionV3, Mobilenet V2, Resnet50 V2

Symmetry-based representation for registration of multimodal images.

We propose a new two-dimensional structural representation method for registration of multimodal images by using the local structural symmetry of images, which is similar at different modalities. The symmetry is measured in various orientations and the best is mapped and used for the representation image. The optimum performance is obtained when using only two different orientations, which is called binary dominant symmetry representation (BDSR). This representation is highly robust to noise and intensity non-uniformity. We also propose a new objective function based on L2 distance with low sensitivity to the overlapping region. Then, five different meta-heuristic algorithms are comparatively applied. Two of them have been used for the first time on image registration. BDSR remarkably outperforms the previous successful representations, such as entropy images, self-similarity context, and modality-independent local binary pattern, as well as mutual information-based registration, in terms of success rate, runtime, convergence error, and representation construction.

Integration of morpho-physico-biochemical traits with SSR and SRAP markers for characterization of castor genotypes of Indian origin

Castor (Ricinus communis L.) is an important tropical oilseed crop, whose oil has versatile, practical value, especially in industries. The present study aimed to estimate the nature and magnitude of variability in the castor germplasm concerning yield and its component traits and physico-biochemical characters. Seed yield per plant and oil content ranged from 80.90 ​g (ICS-165) to 248.30 ​g (RG-3216), and 34.7% (ICS-172) to 58.7% (JI-277), respectively. The iodine value of oil ranged from 76.36 (JI-370) to 89.84 (P2-135) with an average value of 83.02. The mean saponification value of oil was 182.24. The genotypic and phenotypic coefficients of variation were high for acid value, capsules on the main raceme, seed yield per plant, and total length of the main raceme. A positive association of porosity, average unit volume, and total length of the main raceme with seed yield per plant showed that these characters might be directly attributed to seed yield improvement. By Manhattan distances, the 30 genotypes were grouped into 3 clusters. Their genetic diversity was elucidated using SSR and SRAP markers. SRAP marker produced higher mean number of total bands (5.71), polymorphic bands (4.57), percentage polymorphism (83.10%), PIC (1.72), RP (5.90), mean RP (1.02), MR (5.71), EMR (4.57) and MI (1.44) values when compared to SSR (2.89, 2.11, 79.63%, 0.61, 1.90, 0.72, 2.89, 2.11 and 0.49, respectively) marker. The highest genetic distance (0.77) was between 48-1 and JI-370, which indicated that these genotypes could be used in biparental mating schemes, QTL map development, and hybridization programmes to increase oil content and quality for industrial purposes.

Analisa Distance Metric Algoritma K-Nearest Neighbor Pada Klasifikasi Kredit Macet

Data mining is a method that can classify data into different classes based on the features in the data. With data mining, non-performance loan categories can be classified based on data on lending from cooperatives to their members. This study uses K-Nearest Neighbor to classify non-performance loan categories with various distance metric variations such as Chebyshev, Euclidean, Mahalanobis, and Manhattan. The evaluation results using 10-fold cross-validation show that the Euclidean distance has the highest accuracy, precision, F1, and sensitivity values ​​compared to other distance metrics. Chebyshev distance has the lowest accuracy, precision, sensitivity, while Mahalanobis distance has the lowest F1 value. Euclidean and Manhattan distances have the highest reliability values ​​for true-positive and true-negative class classifications. Mahalanobis distance has the lowest reliability value for false-positive class classification, while Chebyshev distance has the lowest value for false-negative class classification

Consensus of Multi-agent Reinforcement Learning Systems: The Effect of Immediate Rewards

This paper studies the consensus problem of a leaderless, homogeneous, multi-agent reinforcement learning (MARL) system using actor-critic algorithms with and without malicious agents. The goal of each agent is to reach the consensus position with the maximum cumulative reward. Although the reward function converges in both scenarios, in the absence of the malicious agent, the cumulative reward is higher than with the malicious agent present. We consider here various immediate reward functions. First, we study the immediate reward function based on Manhattan distance. In addition to proposing three different immediate reward functions based on Euclidean, $n$-norm, and Chebyshev distances, we have rigorously shown which method has a better performance based on a cumulative reward for each agent and the entire team of agents. Finally, we present a combination of various immediate reward functions that yields a higher cumulative reward for each agent and the team of agents. By increasing the agents’ cumulative reward using the combined immediate reward function, we have demonstrated that the cumulative team reward in the presence of a malicious agent is comparable with the cumulative team reward in the absence of the malicious agent. The claims have been proven theoretically, and the simulation confirms theoretical findings.

Pneumonia identification based on lung texture analysis using modified k-nearest neighbour

Covid-19 is a virus that was first discovered in China, which has the impact of mild and severe respiratory infections such as pneumonia. Pneumonia is inflammation and consolidation of lung tissue due to infectious agents. Generally pneumonia has a high mortality rate, as do Covid-19 patients. For now, it is very difficult to distinguish between Pneumonia and Covid-19, due to the high similarity of X-Ray image results. The high similarity has an impact on the difficulty of difference between Pneumonia and Covid-19 patients. This research aims to be able to different Pneumonia and Covid-19 patients based on texture analysis of the Gray Level Co-Occurrence Matrix using Modified k-Nearest Neighbour as a classifier. The calculations used in the Gray Level Co-Occurrence Matrix method are Contrast, Correlation, Energy, and Homogeneity which will be input for the Modified k-Nearest Neighbour classifier. The results showed that the highest accuracy is when the value of K = 3 using Manhattan Distance and 80%:20% data percentage, which is 87.5%. For the values of K = 7 and K = 9 there is no change in accuracy, so it can be concluded that the value of K that affects accuracy only occurs at the values of K = 3 and K = 5. Then, the higher the K value, the lower the resulting accuracy.

Penggerombolan Kabupaten/Kota di Indonesia Berdasarkan Indikator Indeks Pembangunan Manusia Menggunakan Metode K-Means dan Fuzzy C-Means

The achievement of the human development index in Indonesia differs between regions with striking gaps occurring in the western and eastern parts of Indonesia. This difference in achievement can be seen more clearly by grouping regencies/municipalities in Indonesia based on the four indicators of the human development index. With this aim, this study uses the k-means and fuzzy c-means methods to determine the optimal cluster size with two distance approaches, namely the Euclidean and Manhattan distances on the human development index indicators data in 2020. In addition, this study also seeks to identify the distribution of regencies/municipalities based on the characteristics of the human development index indicators in the clustering result. The result is that the best distance measure is Euclidean distance with optimal cluster size is four for k-means and six for fuzzy c-means. In addition, the clustering results obtained by the k-means method are more optimal than the fuzzy c-means because the evaluation value is better. In general, the four clusters formed were in accordance with the grouping carried out by BPS with the percentage of conformity reaching 66,54%. In summary, most regencies/municipalities on the Island of Sumatera, Java, Borneo and Sulawesi have higher life expectancy and percapita expenditure than many regencies/municipalities in the Nusa Tenggara Islands (besides Bali), Moluccas and Papua. Very high achievement for each HDI indicators is dominated by the capital city of each province with unfavorable conditions occurring in most regencies/municipalities in Papua Province.

Heuristic Strategy of Service Function Chain Deployment Based on N-Base Continuous Digital Coding in Network Function Virtualization Environment

As a novel network architecture, network function virtualization (NFV) greatly improves the flexibility and scalability of service provision. Deploying the service function chain (SFC) in an NFV environment needs to coordinate the instantiation of the virtual network function descriptor, network function embedding, and traffic steering, which also improves the complexity of the problem. Although heuristic algorithms are widely used to optimize this problem, due to the lack of complete SFC deployment solution space and digital coding scheme, the time complexity of the algorithm is difficult to meet the requirements. Therefore, this paper studies the digital coding scheme of the heuristic SFC deployment to improve time efficiency without reducing performance. Firstly, we model the SFC deployment as a 0–1 integer linear programming, considering the above factors, and then design a continuous digital solution space construction scheme based on N-Base coding (NBACO-SS) to optimize the above problems. NBACO-SS uses integer size and carry to map the complex SFC deployment to simple digital coding, evaluates the continuity of solution space through the Manhattan distance, and optimizes the continuity through the ant colony algorithm. Based on NBACO-SS, we reconstruct two heuristic algorithms to solve the SFC deployment problem. Experimental results demonstrate that NBACO-SS can improve the time efficiency by 20% without reducing the total network service traffic.

Energy Efficient Distance Computing: Application to K-Means Clustering

Distance computation between two input vectors is a widely used computing unit in several pattern recognition, signal processing and neuromorphic applications. However, the implementation of such a functionality in conventional CMOS design requires expensive hardware and involves significant power consumption. Even power-efficient current-mode analog designs have proved to be slower and vulnerable to variations. In this paper, we propose an approximate mixed-signal design for the distance computing core by noting the fact that a vast majority of the signal processing applications involving this operation are resilient to small approximations in the distance computation. The proposed mixed-signal design is able to interface with external digital CMOS logic and simultaneously exhibit fast operating speeds. Another important feature of the proposed design is that the computing core is able to compute two variants of the distance metric, namely the (i) Euclidean distance squared (L22 norm) and (ii) Manhattan distance (L1 norm). The performance of the proposed design was evaluated on a standard K-means clustering algorithm on the “Iris flower dataset”. The results indicate a throughput of 6 ns per classification and ∼2.3× lower energy consumption in comparison to a synthesized digital CMOS design in commercial 45 nm CMOS technology.

Central limit theorem for subcopulas under the Manhattan distance

In this work, we prove a central limit theorem for the empirical subcopula processes by embedding the space of subcopulas in the space of Lebesgue integrable functions. The central limit theorem is then followed by the functional delta method. We also provide an example to demonstrate that convergence under the Chebyshev distance should not be expected.

A Scheme of Selecting Vehicles to Assist Download Based on WebGIS for VANET

The assisted download is an effective method solving the problem that the coverage range is insufficient when Wi-Fi access is used in VANET. For the low utilization of time-space resource within blind area and unbalanced download services in VANET, this paper proposes an approximate global optimum scheme to select vehicle based on WebGIS for assistance download. For WebGIS, this scheme uses a two-dimensional matrix to respectively define the time-space resource and the vehicle selecting behavior, and uses Markov Decision Process to solve the problem of time-space resource allocation within blind area, and utilizes the communication features of VANET to simplify the behavior space of vehicle selection so as to reduce the computing complexity. At the same time, Euclidean Distance(Metric) and Manhattan Distance are used as the basis of vehicle selection by the proposed scheme so that, in the case of possessing the balanced assisted download services, the target vehicles can increase effectively the total amount of user downloads. Experimental results show that because of the wider access range and platform independence of WebGIS, when user is in the case of relatively balanced download services, the total amount of downloads is increased by more than 20%. Moreover, WebGIS usually only needs to use Web browser (sometimes add some plug-ins) on the client side, so the system cost is greatly reduced.

PCA-Based Advanced Local Octa-Directional Pattern (ALODP-PCA): A Texture Feature Descriptor for Image Retrieval

This paper presents a novel feature descriptor termed principal component analysis (PCA)-based Advanced Local Octa-Directional Pattern (ALODP-PCA) for content-based image retrieval. The conventional approaches compare each pixel of an image with certain neighboring pixels providing discrete image information. The descriptor proposed in this work utilizes the local intensity of pixels in all eight directions of its neighborhood. The local octa-directional pattern results in two patterns, i.e., magnitude and directional, and each is quantized into a 40-bin histogram. A joint histogram is created by concatenating directional and magnitude histograms. To measure similarities between images, the Manhattan distance is used. Moreover, to maintain the computational cost, PCA is applied, which reduces the dimensionality. The proposed methodology is tested on a subset of a Multi-PIE face dataset. The dataset contains almost 800,000 images of over 300 people. These images carries different poses and have a wide range of facial expressions. Results were compared with state-of-the-art local patterns, namely, the local tri-directional pattern (LTriDP), local tetra directional pattern (LTetDP), and local ternary pattern (LTP). The results of the proposed model supersede the work of previously defined work in terms of precision, accuracy, and recall.