Hyperspectral Image Classification Based on Cross-Scene Adaptive Learning

Aiming at few-shot classification in the field of hyperspectral remote sensing images, this paper proposes a classification method based on cross-scene adaptive learning. First, based on the unsupervised domain adaptive technology, cross-scene knowledge transfer learning is carried out to reduce the differences between source scene and target scene. At the same time, depthwise over-parameterized convolution is used in the deep embedding model to improve the convergence speed and feature extraction ability. Second, two symmetrical subnetworks are designed in the model to further reduce the differences between source scene and target scene. Then, Manhattan distance is learned in the Manhattan metric space in order to reduce the computational cost of the model. Finally, the weighted K-nearest neighbor is introduced for classification, in which the weighted Manhattan metric distance is assigned to the clustered samples to improve the processing ability to the imbalanced hyperspectral image data. The effectiveness of the proposed algorithm is verified on the Pavia and Indiana hyperspectral dataset. The overall classification accuracy is 90.90% and 65.01%. Compared with six other kinds of hyperspectral image classification methods, the proposed cross-scene method has better classification accuracy.

The Similarities and Distances of Growth Rates Related to COVID-19 Between Different Countries Based on Spectral Analysis

The COVID-19 pandemic has taken more than 1.78 million of lives across the globe. Identifying the underlying evolutive patterns between different countries would help us single out the mutated paths and behavior of this virus. I devise an orthonormal basis which would serve as the features to relate the evolution of one country's cases and deaths to others another's via coefficients from the inner product. Then I rank the coefficients measured by the inner product via the featured frequencies. The distances between these ranked vectors are evaluated by Manhattan metric. Afterwards, I associate each country with its nearest neighbor which shares the evolutive pattern via the distance matrix. Our research shows such patterns is are not random at all, i.e., the underlying pattern could be contributed to by some factors. In the end, I perform the typical cosine similarity on the time-series data. The comparison shows our mechanism differs from the typical one, but is also related to each it in some way. These findings reveal the underlying interaction between countries with respect to cases and deaths of COVID-19.

Attribution of authorship in instant messaging software applications, based on similarity measures of the stylometric features’ vector

This paper describes the issue of authorship attribution based on the content of conversations originating from instant messaging software applications. The results presented in the paper refer to the corpus of conversations conducted in Polish. On the basis of a standardised model of the corpus of conversations, stylometric features were extracted, which were divided into four groups: word and message length distributions, character frequencies, tf-idf matrix and features extracted on the basis of turns (conversational features). The vectors of users’ stylometric features were compared in pairs by using Euclidean, cosine and Manhattan metrics. CMC curves were used to analyse the significance of the feature groups and the effectiveness of the metrics for identifying similar speech styles. The best results were obtained by the group of features being the tf-idf matrix compared with the use of cosine distance and the group of features extracted on the basis of turns compared with the use of the Manhattan metric.

Clustering Kanker Serviks Berdasarkan Perbandingan Euclidean dan Manhattan Menggunakan Metode K-Means

K-means a fairly simple and commonly used cluster of clusters to partition datasets into multiple clusters. Distance calculations are used to find similar data objects that lead to developing powerful algorithms for datamining such as classification and grouping. Some studies apply k-means algorithms using distance calculations such as Euclidean, Manhattan and Minkowski. The study used datasets from gynecological patients with a total of 401 patients examined and as many as 205 patients detected cervical cancer, while 196 other patients did not have cervical cancer. The results were shown with the help of confusion matrix and ROC curve, accuracy value obtained by 79.30% with ROC 79.17% on K-Means Euclidean Metric while K-Means Manhattan Metric by 67.83% with ROC 65.94%. Thus it can be concluded that the Euclidean method is the best method to be applied in the K-Means Clustering algorithm on cervical cancer datasets.

Adaptations of Relief for continuous domains of bioinformatics

Relief occupies a niche among feature selection methods for data classification. Filters are faster, wrappers are much slower. Relief is feature-set-aware, same as wrappers. However, it is thought being able to deselect only irrelevant, but not redundant features, same as filters. Iterative Reliefs seek to increase the separation margin between classes in the anisotropic space defined by weighted features. Reliefs for continuous domains are much less developed than for categorical domains. The paper discusses a number of adaptations for continuous spaces with Euclidean or Manhattan metric. The ability of Relief to detect redundant features is demonstrated. A dramatic reduction of the feature-set is achieved in a health diagnostics problem.

Reducing Computational Complexity and Enhancing Performance of IKSD Algorithm for Encoded MIMO Systems

<p>The main challenge in MIMO systems is how to design the MIMO detection algorithms with lowest computational complexity and high performance that capable of accurately detecting the transmitted signals. In last valuable research results, it had been proved the Maximum Likelihood Detection (MLD) as the optimum one, but this algorithm has an exponential complexity especially with increasing of a number of transmit antennas and constellation size making it an impractical for implementation. However, there are alternative algorithms such as the K-best sphere detection (KSD) and Improved K-best sphere detection (IKSD) which can achieve a close to Maximum Likelihood (ML) performance and less computational complexity. In this paper, we have proposed an enhancing IKSD algorithm by adding the combining of column norm ordering (channel ordering) with Manhattan metric to enhance the performance and reduce the computational complexity. The simulation results show us that the channel ordering approach enhances the performance and reduces the complexity, and Manhattan metric alone can reduce the complexity. Therefore, the combined channel ordering approach with Manhattan metric enhances the performance and much reduces the complexity more than if we used the channel ordering approach alone. So our proposed algorithm can be considered a feasible complexity reduction scheme and suitable for practical implementation.</p>