Выбор оптимального количества бинов для расчета взаимной информации между сигналами ЭЭГ и кардиоритмограммы

В роботі розглянуто метод визначення взаємної інформації для оцінки взаємозв’язку між сигналами ЕЕГ та кардіоритмограми. У розробленому методі кількість бінів вибирається базуючись на значеннях взаємної інформації, які розраховані на проміжку значень бінів. При застосуванні методу до сигналів ЕЕГ та КРГ було знайдено, що метод може бути застосований при аналізі взаємозв’язку між сигналами ЕЕГ в каналах, що розташовані поряд або симетрично, згідно з системою 10-20. Для взаємної інформації між сигналами ЕЕГ та КРГ, що пов’язані у значно меншій мірі, метод не може бути застосований для малого обсягу вибірки.In the present work the problem of optimal bin number selection for equidistant Mutual Information (MI) estimator between electroencephalogram (EEG) and cardiorhythmogram (CRG) is addressed. In the previously developed method the bin number selected based on the finding an optimal bin number on the MI values on the range of bin numbers. With application to the real raw EEG and CRG signals it was found that for closely placed or symmetrical channels of EEG data the method can be applied, and the true value of MI value can be found with proposed method. In application to MI calculation between raw EEG and CRG signals that are not significantly coupled, true MI value cannot be estimated with proposed method for small sample size.В работе рассмотрен метод определения взаимной информации для оценки взаимосвязи между сигналами ЭЭГ и кардиоритмограммы. В разработанном методе количество бинов выбирается на промежутке значений бинов. При применении метода к сигналам ЭЭГ и КРГ было найдено, что метод может быть применим при анализе взаимосвязи между сигналами ЭЭГ в каналах, что расположены рядом или симметрично, в соответствии с системой 10-20. Для взаимной информации между сигналами ЭЭГ и КРГ, что связаны в значительно меньшей мере, метод не может применяться для малого значения выборки

Intelligent Assessment of Sun Flower Seeds Using Machine Learning Approaches

Pakistan is an agricultural country. Sun flower is the major crop of Pakistan which is being sowing in many areas of country. It fulfills the requirement of edible oil. In this paper we are trying to identify the best quality from different sun flowers seeds verities by using machine learning approaches. We take the images of four kinds of sunflower seeds which names Top sun(A), High Sun(B),US666(C) and Seji(D) for classification. We get eight different images of each kind of sunflower. In this paper sun flowers seeds varieties were categorized by using Computer vision image processing tool (CVIP). The experience and knowledge of inspectors are required to perfectly perform this assessment process. We use the RST-Invariant Features, Histogram Features, Texture Features, and Pattern Classification and also use the nearest neighbor and k-nearest neighbor algorithms for final classification. We achieved the final results of four kinds of sunflower using nearest neighbor on distance one and two 89% and 72% average and on k-nearest neighbor 89% and 73% average percentage. These are the best percentage results using these algorithms for classification. In this way we can easily classify the sunflower seeds and also these methods provide opportunity to farmer and other people for identify and select the different better and healthy sunflower seeds for better benefits. Keywords: RST-Invariant Features, Histogram Features, Texture Features, Classification Algorithm

Systematic Review on Missing Data Imputation Techniques with Machine Learning Algorithms for Healthcare

Missing data is one of the most common issues encountered in data cleaning process especially when dealing with medical dataset. A real collected dataset is prone to be incomplete, inconsistent, noisy and redundant due to potential reasons such as human errors, instrumental failures, and adverse death. Therefore, to accurately deal with incomplete data, a sophisticated algorithm is proposed to impute those missing values. Many machine learning algorithms have been applied to impute missing data with plausible values. However, among all machine learning imputation algorithms, KNN algorithm has been widely adopted as an imputation for missing data due to its robustness and simplicity and it is also a promising method to outperform other machine learning methods. This paper provides a comprehensive review of different imputation techniques used to replace the missing data. The goal of the review paper is to bring specific attention to potential improvements to existing methods and provide readers with a better grasps of imputation technique trends

K-nearest neighbours based on mutual information for incomplete data classification

Incomplete data is a common drawback that machine learning techniques need to deal with when solving real-life classification tasks. One of the most popular procedures for solving this kind of problems is the K-nearest neighbours (KNN) algorithm. In this paper, we present a weighted KNN approach using mutual information to impute and classify incomplete input data. Numerical results on both artificial and real data are given to demonstrate the effectiveness of the proposed method