besides Vector Vector

GitHub Repo https://github.com/Zhuosd/Density_peak_clustering-Semi-Supervision

Zhuosd/Density_peak_clustering-Semi-Supervision

Having a multitude of unlabeled data and few labeled ones is a common problem in many practical ap- plications. A successful methodology to tackle this problem is self-training semi-supervised classification. In this paper, we introduce a method to discover the structure of data space based on find of density peaks. Then, a framework for self-training semi-supervised classification, in which the structure of data space is integrated into the self-training iterative process to help train a better classifier, is proposed. A series of experiments on both artificial and real datasets are run to evaluate the performance of our proposed framework. Experimental results clearly demonstrate that our proposed framework has better performance than some previous works in general on both artificial and real datasets, especially when the distribution of data is non-spherical. Besides, we also find that the support vector machine is particularly suitable for our proposed framework to play the role of base classifier.

GitHub Repo https://github.com/eternal-vanguard/SpectralNET

eternal-vanguard/SpectralNET

Hyperspectral Image (HSI) classification using Convolutional Neural Networks (CNN) is widely found in the current literature. Approaches vary from using SVMs to 2D CNNs, 3D CNNs, 3D-2D CNNs. Besides 3D-2D CNNs and FuSENet, the other approaches do not consider both the spectral and spatial features together for HSI classification task, thereby resulting in poor performances. 3D CNNs are computationally heavy and are not widely used, while 2D CNNs do not consider multi-resolution processing of images, and only limits itself to the spatial features. Even though 3D-2D CNNs try to model the spectral and spatial features their performance seems limited when applied over multiple dataset. In this article, we propose SpectralNET, a wavelet CNN, which is a variation of 2D CNN for multi-resolution HSI classification. A wavelet CNN uses layers of wavelet transform to bring out spectral features. Computing a wavelet transform is lighter than computing 3D CNN. The spectral features extracted are then connected to the 2D CNN which bring out the spatial features, thereby creating a spatial-spectral feature vector for classification. Overall a better model is achieved that can classify multi-resolution HSI data with high accuracy. Experiments performed with SpectralNET on benchmark dataset, i.e. Indian Pines, University of Pavia, and Salinas Scenes confirm the superiority of proposed SpectralNET with respect to the state-of-the-art methods.

GitHub Repo https://github.com/XueQinliang/A-senior-calculator

XueQinliang/A-senior-calculator

this calculator has many strong function besides common function, for example matrix and vector calculation and defining a function.

GitHub Repo https://github.com/BahyMedhat/Vector-Drawing-Application

BahyMedhat/Vector-Drawing-Application

An OOP painting application with many Design Patterns and features like dynamic class loading, besides it reads and writes XML and JSON files.

GitHub Repo https://github.com/FellipeFrancoCouto/NLP-Affinity-Propagation-Clustering-for-words

FellipeFrancoCouto/NLP-Affinity-Propagation-Clustering-for-words

Application of an Affinity Propagation Clustering for word vectors. Besides that, a graph is plotted to represent the clusters and the Silhouette Test is performed. Check README for dataset references

GitHub Repo https://github.com/ZZoeGGo/student_performance

ZZoeGGo/student_performance

This brief report analyzes the "MathAchievement" data set associated with "Mathematics achievement scores" from MEMSS package. To the dependent variable "MathAch"(math achievement scores), besides two fixed effects: factor "Minority"(with level Yes or No) and variable "SES"(numeric vector of socio-economic status), the factor "School" is treated as a random effect. The reason for treating "School" as a random effect is we are interest in the differences between different schools whereas treating as fixed effects would be holding for it.

GitHub Repo https://github.com/mtheggi/sematic_search_DB

mtheggi/sematic_search_DB

This project objective is to have Sematic Search Database System that can create indexing on large amount of data, besides it is able to search in the created index for query vectors in a fast and accurate method

GitHub Repo https://github.com/masoudshab/OPTIMIZATION-of-the-APFs-Placement-Based-on-Instantaneous-Reactive-Power-Theory-by-GENETIC-ALGORITHM

masoudshab/OPTIMIZATION-of-the-APFs-Placement-Based-on-Instantaneous-Reactive-Power-Theory-by-GENETIC-ALGORITHM

In electrical distribution systems, a great amount of power are wasting across the lines, also nowadays power factors, voltage profiles and total harmonic distortions (THDs) of most loads are not as would be desired. So these important parameters of a system play highly important role in wasting money and energy, and besides both consumers and sources are suffering from a high rate of distortions and even instabilities. Active power filters (APFs) are innovative ideas for solving of this adversity which have recently used instantaneous reactive power theory. In this paper, a novel method is proposed to optimize the allocation of APFs. The introduced method is based on the instantaneous reactive power theory in vectorial representation. By use of this representation, it is possible to asses different compensation strategies. Also, APFs proper placement in the system plays a crucial role in either reducing the losses costs and power quality improvement. To optimize the APFs placement, a new objective function has been defined on the basis of five terms: total losses, power factor, voltage profile, THD and cost. Genetic algorithm has been used to solve the optimization problem. The results of applying this method to a distribution network illustrate the method advantages.

GitHub Repo https://github.com/SalahAbotaleb/sematic_search_DB

SalahAbotaleb/sematic_search_DB

This project objective is to have Sematic Search Database System that can create indexing on large amount of data, besides it is able to search in the created index for query vectors in a fast and accurate method

GitHub Repo https://github.com/hrishitelang/Breast-Cancer-Prediction

hrishitelang/Breast-Cancer-Prediction

Cancer is a collection of related diseases, in which some of the body’s cells begin to divide without stopping and spread into surrounding tissues. Regardless of the view of cancer may be, it is exaggerated and over-generalized. While a diagnosis of cancer may still leave patients feeling helpless and out of control, in many cases today there is cause for hope rather than a blinkered vision of survival. The basic aim of our project is to ensure that patients with a risk or borderline edge of getting cancer shall get themselves digitally scanned, that would eventually generate a report. This report shall achieve in alluding convoluted details regarding certain possible properties of tumours that could be sent for prediction so that they could immediately diagnose it if at all it is predicted to be malignant. The importance of classifying cancer patients into high or low-risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. Support vector machine has become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. Training a support vector machine requires the solution of a very large quadratic programming problem. Up to now, several approaches exist for circumventing the above shortcomings and work well with the dataset. And besides, till now the project has confined its attempt to diagnose breast cancer only. In this way, we can affirm that the prognosis of cancer can be achieved, and accordingly, we can produce outputs for the same.