Phenetic Research Articles

Swim start and performance in 50 m freestyle in different age categories of competitive swimmers

Background and Study Aim. In international races, the winners are decided by hundredths of a second, which is why the swim start plays an important role, especially in the sprint disciplines. The aim of the study is to reveal the differences in kinematic parameters of start and performance in the sprint 50 m freestyle discipline based on gender in different age categories of competitive swimmers at international competitions organized in Slovakia. Material and Methods. The sample consisted of 180 females and 189 males who were divided into age categories (K1, K2, K3). SwimPro cameras and Dartgish software were used to monitor kinematic parameters. The parameters monitored were - block time (BT), time (FT) and distance (FD) of flight, time (UWT) and distance (UWD) underwater, time to 15 m (T15), 25 m (T25) and 50 m (T50). Data were tested by Shapiro-Wilk, Kurskal-Wallis ANOVA and Mann-Whitney U test in Statistica 13.5. Results. In the phase above water level, there were greater differences (p<0.01) in females than in males. Inter-sex differences (p<0.01) were evident in FT in K3, K2 and in FD across all categories. In the underwater phase, differences (p<0.01) were evident in both sexes. Inter-sex differences were more evident in UWT (p<0.01) than UWD (p<0.05). There were inter-sex differences (p<0.01) in ST and SD between all categories except K3. At T15, T25 and T50, differences (p<0.01) were most pronounced between K3 and K2, K1 in females and between all categories in males. Inter-sex differences (p<0.01) were also evident across all categories. Conclusions. The study highlighted differences in 50m freestyle start and performance between age groups and gender, so coaches are advised to design training sessions for swimmers separately.

A Low-Power Analog Integrated Euclidean Distance Radial Basis Function Classifier

This study introduces a low-power analog integrated Euclidean distance radial basis function classifier. The high-level architecture is composed of several Manhattan distance circuits in connection with a current comparator circuit. Notably, each implementation was designed with modularity and scalability in mind, effectively accommodating variations in the classification parameters. The proposed classifier’s operational principles are meticulously detailed, tailored for low-power, low-voltage, and fully tunable implementations, specifically targeting biomedical applications. This design methodology materialized within a 90 nm CMOS process, utilizing the Cadence IC Suite for the comprehensive management of both the schematic and layout design aspects. During the verification phase, post-layout simulation results were meticulously cross-referenced with software-based classifier implementations. Also, a comparison study with related analog classifiers is provided. Through the simulation results and comparative study, the design architecture’s accuracy and sensitivity were effectively validated and confirmed.

Structural performance‐based anomaly detection for velocity pulse

AbstractPulse‐like ground motion can cause extreme damage to long‐period structures. An automatic algorithm is proposed to identify pulse‐like ground motions, in which improved anomaly detection is applied and the structural performance is considered. To characterize the intrinsic pulse‐like features, the distance‐based anomaly detection algorithm is improved, and the relative cumulative energy is added to the Manhattan distance. The first application of the anomaly detection algorithm performs well in pulse‐like motion identification. The pulse‐like ground motions are identified according to the performance of typical long‐period engineering structures in Hazus by the improved capacity spectrum method. The method is applied to 214 strong ground motions in the Next Generation Attenuation West 2 database. Comprehensive comparisons of the proposed approach with the wavelet analysis and the energy method are presented to illustrate the significance of the structural performance on the pulse‐like identification method.

Formula omitted]-adic distance and [formula omitted]-Nearest Neighbor classification

The k-Nearest Neighbor (k-NN) is a well-known supervised learning algorithm. The effect of the distance used in the analysis on the k-NN performance is very important. According to Ostrowski’s theorem, there are only two nontrivial absolute values on the field of rational numbers, Q, which are the usual absolute value and the p-adic absolute value for a prime p. In view of this theorem, the p-adic absolute value motivates us to calculate the p-adic distance between two samples for the k-NN algorithm. In this study, the p-adic distance on Q was coupled with the k-NN algorithm and was applied to 10 well-known public datasets containing categorical, numerical, and mixed (both categorical and numerical) type predictive attributes. Moreover, the p-adic distance performance was compared with Euclidean, Manhattan, Chebyshev, and Cosine distances. It was seen that the average accuracy obtained from the p-adic distance ranks first in 5 out of 10 datasets. Especially, in mixed datasets, the p-adic distance gave better results than other distances. For r=1,2,3, the effect of the r-decimal values of the number for the p-adic calculation was examined on numerical and mixed datasets. In addition, the p parameter of the p-adic distance was tested with prime numbers less than 29, and it was found that the average accuracy obtained for each p was very close to each other, especially in categorical and mixed datasets. Also, it can be concluded that k-NN with the p-adic distance may be more suitable for binary classification than multi-class classification.

Group of All Taxicab Isometries: A Combinatorial Approach

In this work, we give a more thorough and exhaustive proof of the set of all isometries in taxicab geometry using a combinatorial approach. We show that isometries preserving taxicab distance while leaving the origin fixed are uniquely determined by how they permute the vertices of circles. Then, we use this principle to identify all isometries in taxicab geometry.

A Simple Preprocessing Method Enhances Machine Learning Application to EEG Data for Differential Diagnosis of Autism

A new pre-processing approach of EEG data to detect topological EEG features has been applied to a continuous segment of artifact-free EEG data lasting 10 minutes in ASCII format derived from 50 ASD children and 50 children with other Neuro-Psychiatric Disorders (NPD), matched for age and male/female ratios. Each EEG is transformed in a triangular matrix of 171 values expressing all reciprocal Manhattan distances among the 19 electrodes of to the international 10-20 system. From this matrix, the minimum spanning tree (MST) is calculated. Electrode identification serial codes sorted according to the decreasing number of links in MST, and the number of links in MST are taken as input vectors for machine learning systems. Machine learning systems have been applied to build up a predictive model to distinguish between the two diagnostic classes (autism vs NPD) following a rigorous validation protocol. The best machine learning system (KNN algorithm) obtained a global accuracy of 93.2% (92.37 % sensitivity and 94.03 % specificity) in differentiating ASD subjects from NPD subjects. The results obtained in this study suggest that, thanks to the new pre-processing method introduced, there is the possibility to discriminate subjects with autism from subjects affected by other psychiatric disorders with a modest computational time reducing the information to 38 figures.

Retracted: Research on Fast Compensation Algorithm for Interframe Motion of Multimedia Video Based on Manhattan Distance

Retracted: Research on Fast Compensation Algorithm for Interframe Motion of Multimedia Video Based on Manhattan Distance

A transcriptomic approach to explore the immune landscape of patient with pancreatic ductal adenocarcinoma with prognostic impact.

681 Background: Pancreatic ductal adenocarcinoma (PDAC) is characterized by its immunologically cold tumor microenvironment (TME) with scarce T cell infiltration and few molecular signatures of immune activation. To date, the immunotherapies including the immune checkpoint blockade of PD-1, and CTLA-4 either single or in combination have shown modest results in PDAC tumors. Recently, the Lymphocyte Activation Gene (LAG3) has emerged as a promising checkpoint target as indicated by preclinical and clinical data. The aim of this study is to explore the immune profile of a series of PDAC and to evaluate its prognostic impact. Methods: A 5-mm thick section of Formalin-Fixed Paraffin-Embedded (FFPE) tissue from a retrospective cohort of 28 PDAC cases were analyzed using the Precision Immunooncology panel (PIP, HTG Molecular Diagnostics) on a NextSeq 550 sequencer (Illumina). The RNA expression of 1392 immune-related genes were analyzed. Principal Components (PC) and Manhattan distance were studied for data visualization and clustering. Maxstat algorithm (maxstat v0.7-25) was used to establish optimal cut-offs for variable categorization. Log-rank and Cox regression were used for both univariate and multivariate for survival. All tests were two-tailed. The statistical analysis was performed using R studio (R version 4.0.3). Lastly, immunohistochemistry (IHC) for CD4, CD8, CD20, PD1, PD-L1, and LAG-3 proteins was performed on tissue microarrays (TMAs) to assess their correlation with RNA expression. Results: Unsupervised analysis of the gene expression identified two clusters of patients with differentially prognostic information. These two groups were defined with a logistic regression model of 14 genes ( BTLA, CXCR5, DLX6, KLHDC9, KRT13, LAG3, LGSN, MICB, SIGLEC5, SPINK5, SPN, TBX21, UPK2, ZIC5) showing for cluster1 and cluster 2 a median survival of 13 (0-97.583) and 57 (0.67-256.08) months, respectively (p-value = 0.0088). This predictive model was independently validated in the PDAC dataset of the Cancer Genome Atlas (TCGA) (p<0.0001). Interestingly, cluster 1, was characterized by the significant overexpression of LAG3 which has recently been proposed as the next immune checkpoint (IC) receptor target. Moreover, Cluster 1, exhibited the overexpression of other ICs such as PD1, PD-L1, IDO1, LIF and CTLA4, defining cluster 1 as an immunologically hot tumor. IHC for LAG3 and other ICs showed a moderate correlation with HTG results. Conclusions: In this study, a prognostic transcriptomic-based signature of 14 genes has been defined and validated for PDAC. This signature clearly identifies two prognostic groups that could constitute the basis for tailored immunotherapy with specific IC inhibitors. LAG-3 is a promising target for immunotherapy in PDAC patients.

Minimal extension for the $\alpha$-Manhattan norm

Let \partial\mathcal{Q} be the boundary of a convex polygon in \mathbb{R}^2 , e_\alpha=(\cos\alpha,\sin\alpha) and e_{\alpha}^{\bot}=(-\sin\alpha,\cos\alpha) a basis of \mathbb{R}^2 for some \alpha\in[0,2\pi) and \varphi:\partial\mathcal{Q}\to\mathbb{R}^2 a continuous, finitely piecewise linear injective map. We construct a finitely piecewise affine homeomorphism v:\mathcal{Q}\to\mathbb{R}^2 coinciding with \varphi on \partial\mathcal{Q} such that the following property holds: |\langle Dv, e_{\alpha}\rangle|(\mathcal{Q}) (resp., \langle Dv, e_{\alpha}^{\bot}\rangle|(\mathcal{Q}) ) is as close as we want to \inf|\langle Du, e_{\alpha}\rangle|(\mathcal{Q}) (resp., \inf|\langle Du, e_{\alpha}^{\bot}\rangle|(\mathcal{Q}) ) where the infimum is meant over the class of all BV homeomorphisms u extending \varphi inside \mathcal{Q} . This result extends that already proven by Pratelli and the third author in [Atti Accad. Naz. Lincei Rend. Lincei Mat. Appl. 29 (2018), no. 3, 511–555] in the shape of the domain.

Research on Rejoining Bone Stick Fragment Images: A Method Based on Multi-Scale Feature Fusion Siamese Network Guided by Edge Contour

The rejoining of bone sticks holds significant importance in studying the historical and cultural aspects of the Han Dynasty. Currently, the rejoining work of bone inscriptions heavily relies on manual efforts by experts, demanding a considerable amount of time and energy. This paper introduces a multi-scale feature fusion Siamese network guided by edge contour (MFS-GC) model. Constructing a Siamese network framework, it first uses a residual network to extract features of bone sticks, which is followed by computing the L2 distance for similarity measurement. During the extraction of feature vectors using the residual network, the BN layer tends to lose contour detail information, resulting in less conspicuous feature extraction, especially along fractured edges. To address this issue, the Spatially Adaptive DEnormalization (SPADE) model is employed to guide the normalization of contour images of bone sticks. This ensures that the network can learn multi-scale boundary contour features at each layer. Finally, the extracted multi-scale fused features undergo similarity measurement for local matching of bone stick fragment images. Additionally, a Conjugable Bone Stick Dataset (CBSD) is constructed. In the experimental validation phase, the MFS-GC algorithm is compared with classical similarity calculation methods in terms of precision, recall, and miss detection rate. The experiments demonstrate that the MFS-GC algorithm achieves an average accuracy of 95.5% in the Top-15 on the CBSD. The findings of this research can contribute to solving the rejoining issues of bone sticks.

Selection of forage grasses for cultivation under water-limited conditions using Manhattan distance and TOPSIS.

Extreme weather events, such as severe droughts, pose a threat to the sustainability of beef cattle by limiting the growth and development of forage plants and reducing the available pasture for animals. Thus, the search for forage species that are more tolerant and adapted to soil water deficit conditions is an important strategy to improve food supply. In this study, we propose utilizing the mathematical concept of the Manhattan distance to assess the variations in the morphological variables of tropical forage grasses under water-limited conditions. This study aimed to select genotypes of tropical forage grasses under different water stress levels (moderate or severe) at this distance and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Nine varieties from five species were examined. Forage grasses were grown in 12-L pots under three soil irrigation regimes [100% pot capacity-PC (well-irrigated control), 60% PC (moderate drought stress), and 25% PC (severe drought stress)] with four replicates. Drought stress treatments were applied for 25 days during the forage grass tillering and stalk elongation phases. After exposure to drought stress, the growth and morphological traits of forage plants were evaluated. The results show that the use of the Manhattan distance combined with TOPSIS helps in the genotypic selection of more stable tropical forage grass varieties when comparing plants exposed to moderate and severe drought conditions in relation to the nonstressful environment (control). The 'ADR 300', 'Pojuca', 'Marandu', and 'Xaraés' varieties show greater stability when grown in a greenhouse and subjected to water stress environments. The selected forage varieties can be used as parents in plant breeding programs, allowing us to obtain new drought-resistant genotypes.

Link prediction based on spectral analysis.

Link prediction in complex network is an important issue in network science. Recently, various structure-based similarity methods have been proposed. Most of algorithms are used to analyze the topology of the network, and to judge whether there is any connection between nodes by calculating the similarity of two nodes. However, it is necessary to get the extra attribute information of the node in advance, which is very difficult. Compared to the difficulty in obtaining the attribute information of the node itself, the topology of the network is easy to obtain, and the structure of the network is an inherent attribute of the network and is more reliable. The proposed method measures kinds of similarity between nodes based on non-trivial eigenvectors of Laplacian Matrix of the network, such as Euclidean distance, Manhattan distance and Angular distance. Then the classical machine learning algorithm can be used for classification prediction (two classification in this case), so as to achieve the purpose of link prediction. Based on this process, a spectral analysis-based link prediction algorithm is proposed, and named it LPbSA (Link Prediction based on Spectral Analysis). The experimental results on seven real-world networks demonstrated that LPbSA has better performance on Accuracy, Precision, Receiver Operating Curve(ROC), area under the ROC curve(AUC), Precision and Recall curve(PR curve) and balanced F Score(F-score curve) evaluation metrics than other ten classic methods.

Application of multi-objective fruit fly optimisation algorithm based on population Manhattan distance in distribution network reconfiguration

Application of multi-objective fruit fly optimisation algorithm based on population Manhattan distance in distribution network reconfiguration

Customized K-nearest neighbors’ algorithm for malware detection

The security and integrity of computer systems and networks highly depend on malware detection. In the realm of malware detection, the K-Nearest Neighbors (KNN) algorithm is a well-liked and successful machine learning algorithm. However, the choice of an acceptable distance metric parameter has a significant impact on the KNN algorithm's performance. This study tries to improve malware detection by adjusting the KNN algorithm's distance metric parameter. The distance metric greatly influences the similarity or dissimilarity between instances in the feature space. The KNN algorithm for malware detection can be more accurate and effective by carefully choosing or modifying the distance metric. This paper analyzes multiple distance metrics, including Minkowski distance, Manhattan distance, and Euclidean distance. These metrics account for the traits of malware samples while capturing various aspects of similarity. The effectiveness of the KNN algorithm is evaluated using the MalMem-2022 malware dataset, and the results are broken down into these three-distance metrics. The experimental findings show that, among the three distance metric parameters, the Euclidean and Minkowski distance metric parameters considerably produced the best outcomes with binary classification. While with multiclass classification, the KNN algorithm has achieved the highest outcomes using Manhattan distance.

Optimal scheduling strategy of electric vehicle based on improved NSGA-III algorithm.

Aiming at the problem of load increase in distribution network and low satisfaction of vehicle owners caused by disorderly charging of electric vehicles, an optimal scheduling model of electric vehicles considering the comprehensive satisfaction of vehicle owners is proposed. In this model, the dynamic electricity price and charging and discharging state of electric vehicles are taken as decision variables, and the income of electric vehicle charging stations, the comprehensive satisfaction of vehicle owners considering economic benefits and the load fluctuation of electric vehicles are taken as optimization objectives. The improved NSGA-III algorithm (DJM-NSGA-III) based on dynamic opposition-based learning strategy, Jaya algorithm and Manhattan distance is used to solve the problems of low initial population quality, easy to fall into local optimal solution and ignoring potential optimal solution when NSGA-III algorithm is used to solve the multi-objective and high-dimensional scheduling model. The experimental results show that the proposed method can improve the owner's satisfaction while improving the income of the charging station, effectively alleviate the conflict of interest between the two, and maintain the safe and stable operation of the distribution network.

Batik classification using KNN algorithm and GLCM features extraction

Batik is one of the Indonesian cultures that has been recognized by UNESCO as an intellectual right of Indonesia. The popularity of batik internationally raises concerns about the Indonesian people’s understanding of batik if Indonesian people only refer to all types of batik just as ‘batik’. By utilizing K-Nearest Neighbour (KNN) algorithm which is a simple classification algorithm, then a system can be created that can classify batik types. The first step of KNN is training, which stores each training pattern. The second step is classification, whenever classifying a pattern, KNN examine all training patterns to determine the K closest patterns using certain calculations such as Euclidean Distance and Manhattan Distance. Before classification, a characteristic that represents a pattern is needed. Gray-Level Co-Occurrence Matrix (GLCM) is an algorithm that has proven to be very powerful as a feature descriptor in representing the texture characteristic of an image. This research experiments with the value of K in KNN = 1, 3, 5, and 7 with the distance calculation using Euclidean and Manhattan. The GLCM characteristic used are Entropy, Energy, Contrast, Homogeneity, Dissimilarity, Correlation, ASM, and the average of each characteristic. From the research that has been done, the system created obtained the highest accuracy of 75% with the combination of parameters; pixel distance = 7, K value = 1, 1st fold as test data and 2nd and 3rd fold as training data, and by using StandardScaler. But despite getting decent accuracy, there is still a need for further research to improve the accuracy of batik classification.

SBiLM: Siamese Bi-LSTM model for handling imbalance in fake review detection

The growing significance of online reviews in e-commerce platforms has a profound impact on consumers' purchase decisions. Unfortunately, dishonest sellers exploit this system by engaging in deceptive practices and manipulating reviews. These fake reviews are very hard to be detected by humans. Hence, the issue of detecting fake online reviews has gained significant attention due to their prevalence. Typically, the datasets available have much higher number of real reviews as compared to fake reviews, which leads to data imbalance problem. Machine learning algorithms struggle to learn from these datasets and give biased results. However, existing studies on this topic have not adequately addressed the challenges posed by imbalanced data. They have mostly used under-sampling or over-sampling to handle imbalance in datasets, which leads to potential information loss or overfitting due to replication. Hence, to handle this class imbalance problem without using sampling techniques, this paper proposes a novel SBiLM model using Siamese Neural Network with Bidirectional LSTM and Manhattan Distance. SBiLM takes pair of reviews as inputs and computes similarity between them using distance metric. The outcomes indicate that the SBiLM model exhibits better performance in terms of both accuracy and loss, when compared with traditional deep learning models on a highly imbalanced restaurant review dataset. This presents a promising strategy for tackling the issues related to data imbalance in the domain of fake review detection.

SVM-RLF-DNN: A DNN with reliefF and SVM for automatic identification of COVID from chest X-ray and CT images.

To develop an advanced determination technology for detecting COVID-19 patterns from chest X-ray and CT-scan films with distinct applications of deep learning and machine learning methods. The newly enhanced proposed hybrid classification network (SVM-RLF-DNN) comprises of three phases: feature extraction, selection and classification. The in-depth features are extracted from a series of 3×3 convolution, 2×2 max polling operations followed by a flattened and fully connected layer of the deep neural network (DNN). ReLU activation function and Adam optimizer are used in the model. The ReliefF is an improved feature selection algorithm of Relief that uses Manhattan distance instead of Euclidean distance. Based on the significance of the feature, the ReliefF assigns weight to each extracted feature received from a fully connected layer. The weight to each feature is the average of k closest hits and misses in each class for a neighbouring instance pair in multiclass problems. The ReliefF eliminates lower-weight features by setting the node value to zero. The higher weights of the features are kept to obtain the feature selection. At the last layer of the neural network, the multiclass Support Vector Machine (SVM) is used to classify the patterns of COVID-19, viral pneumonia and healthy cases. The three classes with three binary SVM classifiers use linear kernel function for each binary SVM following a one-versus-all approach. The hinge loss function and L2-norm regularization are selected for more stable results. The proposed method is assessed on publicly available chest X-ray and CT-scan image databases from Kaggle and GitHub. The performance of the proposed classification model has comparable training, validation, and test accuracy, as well as sensitivity, specificity, and confusion matrix for quantitative evaluation on five-fold cross-validation. Our proposed network has achieved test accuracy of 98.48% and 95.34% on 2-class X-rays and CT. More importantly, the proposed model's test accuracy, sensitivity, and specificity are 87.9%, 86.32%, and 90.25% for 3-class classification (COVID-19, Pneumonia, Normal) on chest X-rays. The proposed model provides the test accuracy, sensitivity, and specificity of 95.34%, 94.12%, and 96.15% for 2-class classification (COVID-19, Non-COVID) on chest CT. Our proposed classification network experimental results indicate competitiveness with existing neural networks. The proposed neural network assists clinicians in determining and surveilling the disease.

Clustering Performance Analysis of the K-Medoids Algorithm for Improved Fingerprint-Based Localization

Fingerprint-based localization, which uses received signal strength (RSS) measurements from spatially deployed wireless access points (APs), is a popular technique for indoor positioning. The size of the fingerprint database has a significant impact on the accuracy of localization. The higher the density of the fingerprint database, the more accurate the localization, but the longer the localization time. Clustering is one of the techniques used such systems to improve localization accuracy and reduce localization time. To cluster fingerprints, the majority of clustering techniques employ a distance-based fingerprint similarity metric. However, the choice of distance metric has a significant impact on the performance of the clustering algorithm. Using four publicly available RSS-based fingerprint databases, this paper investigates the clustering performance of the k-medoids algorithm using six distance metrics, namely Euclidean, Manhattan, cosine, Mahalanobis, Chebyshev, and Canberra distance. Using the silhouette score as a performance metric, the cosine and Euclidean distance metrics outperform the others, with the highest silhouette score values of about 0.38, 0.43, 0.34, and 0.31 on the SEUG_IndoorLoc, IIRC_IndoorLoc, MSI_IndoorLoc, and IPIN_2019_PIEP_UM databases, respectively. It demonstrates that on these four databases, using Euclidean distance as well as the angle between fingerprint measurement vectors is the best option for generating efficient clusters that will result in high localization accuracy and low localization time.

Accelerating dynamic time warping for speech recognition with SSE

This study presents a significant enhancement to the Dynamic Time Warping (DTW) algorithm for real-time applications like speech recognition. Through integration of SIMD (Single Instruction Multiple Data) instructions to distance function, the research demonstrates how SSE accelerates DTW, markedly reducing computation time. The paper not only explores the theoretical aspects of DTW and this optimization but also provides empirical evidence of its effectiveness. Diverse dataset of 18 voice command classes was assembled, recorded in controlled settings to ensure audio quality. The audio signal of each speech sample was segmented into frames for detailed analysis of temporal dynamics. DTW search was performed on features set based on Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC), combined with delta features. A comprehensive set of 27 features was extracted from each frame to capture critical speech characteristics. The core of the study involved applying traditional DTW as a baseline for performance comparison with the SSE-optimized DTW. The evaluation, focusing on computational time, included measurements like minimum, maximum, average, and total computation times for both standard and SSE-optimized implementations. Experimental results, conducted on datasets ranging from 5 to 60 WAV files per class, revealed that the SSE-optimized DTW significantly outperformed the standard implementation across all dataset sizes. Particularly noteworthy was the consistent speed of the SSE-optimized Manhattan and Euclidean distance functions, which is crucial for real-time applications. The SSE-optimized DTW maintained a low average time, demonstrating remarkable stability and efficiency, especially with larger datasets. The study illustrates the potential of SSE optimizations in speech recognition, emphasizing the SSE-optimized DTW's capability to efficiently process large datasets.