mRMR Feature Selection Algorithm Research Articles

Detection of acute lymphocytic leukemia (ALL) with a pre-trained deep learning model

Acute Lymphocytic Leukemia (ALL) is a type of cancer caused by immature lymphocytes inthe bone marrow. Acute Leukemia is common in both children and adults. It can also cause death if leftuntreated. Hematologists diagnose ALL by examining the blood and bone marrow. This method used isslow and takes more time. In this study, the diagnosis and classification of the disease was carried out usingperipheral smear images with the proposed method. In the proposed method, 99.80% accuracy was obtainedby using the DarkNet19 pre-trained model. Then, 1000 features were obtained from Darknet19. 521 of theobtained features were selected with Mrmr feature selection algorithm. The selected features are classifiedwith support vector machines. An accuracy of 99.94% was achieved with the proposed method. The resultsshow that the proposed method can be used as a tool that will certainly assist pathologists in diagnosingALL and its subtypes.

Application of terahertz spectroscopy combined with feature improvement algorithm for the identification of adulterated rice seeds

In order to combat the adulteration of rice seeds for earning illegal huge profits, this paper presents a terahertz time-domain spectroscopy-based pattern recognition method for adulterated rice seeds. Based on the collected terahertz time-domain spectral data, the Relief algorithm, random forest (RF) algorithm and maximum correlation minimum redundancy (mRMR) algorithm are developed to select the characteristic frequencies, followed by two types of signal processing methods, Hilbert transform, Butterworth Low-Pass Filter, to process the spectral data and fuse them with the original spectral data. Finally, two machine learning models, support vector machine (SVM) model and extreme learning machine (ELM) model, are used to classify the spectral data samples after the feature processing. The results show that the sample spectral data processed by mRMR feature selection algorithm and Hilbert transform have the best recognition effect on the ELM model with an accuracy of 100%. This study has some reference significance for detecting adulterated rice seeds.

Automated gender‐Parkinson's disease detection at the same time via a hybrid deep model using human voice

SummaryGender and Parkinson disease (PD) identifications are critical parts to be noted from a given in human voice. Numerous artificial intelligence based methods have been proposed to detect gender and PD easily in literature. It is purposed to build an effective and a dependable simultaneously gender and PD recognition system based on feature extraction and feature selection methods in this study. First, CNN structure is used for obtaining deeper features from TQWT applied data and acoustic deep parameters are obtained by it. Later, these deep features are subjected to mRMR feature selection algorithm that increase the performance efficiency of the classifiers. As a result, the crucial features obtained by this hybrid structure and significant success rate 98.9% is obtained with the k‐NN classifier. Thus, gender and PD are detected at the same time. Also, this work is multiclass problem so, the other success parameters are calculated separately.

RECOMMENDATION SYSTEM FOR TOURISTS

This project uses statistics from previous tourism experiences to recommend travel areas to travelers. To recommend a traveling area, we use the C4.5 decision tree algorithm with some specific criteria. To promote new locations, all existing algorithms, such as collaboration algorithms and content filtering algorithms, employ the most recent user experience data. If the present user does not have any previous experience data, these algorithms will fail. Another solution to the problem is to utilize the C4.5 decision tree method to build the model based on prior users' experiences. When a new user enters a requirement the decision tree predicts the best and most accurate location. The decision tree does not require any additional user experience data. To use the decision tree model, you'll need a dataset. Pre-processing techniques can be used to eliminate empty or wrong values from this dataset, which can hurt the decision tree model. It is possible to remove the value. To predict or build the model, it may not be necessary to use all of the column values in the dataset. To remove unwanted features a feature selection algorithm is used. The MRMR feature selection algorithm is used to remove extraneous attributes, minimize the building model's execution time, and increase the system's correctness.

Stacked Autoencoder based Feature Compression for Optimal Classification of Parkinson Disease from Vocal Feature Vectors using Immune Algorithms

Parkinson’s disease (PD) is a neurological progressive disorder and is most common among people who are above 60 years old. It affects the brain nerve cells due to the deficiency of dopamine secretion. Dopamine acts as a neurotransmitter and helps in the movement of the body parts. Once brain cells/neurons start dying due to aging, then it will lead to a decrease in dopamine levels. The symptoms of Parkinson’s are difficultly in doing regular/habitual movements, uncontrollable shaking of hands and limbs may encounter memory loss, stiff muscles, sudden temporary loss of control, etc. The severity of the disease will be worse if not diagnosed and treated at the early stages. This paper concentrates on developing Parkinson’s disease diagnosing system using machine learning techniques and algorithms. Machine Learning is an integral part of artificial intelligence it takes huge data as input and train by making use of existing algorithms to understand the pattern of the data. Based on the recognized pattern, the machine will act accordingly without any human intervention. In this work, two major approaches have been employed to diagnose PD. Initially, 26 vocal data of PD affected and healthy individual datasets are obtained from the UCI Machine Learning data repository, are taken as initial raw data/features. In pre-processing, the mRMR feature selection algorithm is employed to minimize the feature count and increase the accuracy rate. The selected features will further be extracted using the Stacked Autoencoder technique to improve and increase the accuracy rate and quality of classification with reduced run time. K-fold cross-validation is used to evaluate the predictive capability of the model and the effectiveness of the extracted features. Artificial Immune Recognition System – Parallel (AIRS-P), an immune inspired algorithm is employed to classify the data from the extracted features. The proposed system attained 97% accuracy, outperforms the benchmarked algorithms and proved its significance on PD classification.

Enhanced Optimal Feature Selection Techniques for Parkinson’s disease Detection using Machine Learning Algorithms

Parkinson disease is a common mass measurement problem in public health. Machine-based learning is used to differentiate between the stable and Parkinson's disease people. This paper provides a comprehensive review of the Parkinson disease buying estimate using machine-based learning approaches. A brief introduction is given to various methods of artificial intelligence, focused on strategies used to predict Parkinson disease. This paper also offers a study of the results obtained by using MRMR feature selection algorithms with four classifications for Parkinson’s disease detection using python

Enhanced Optimal Feature Selection Techniques for Fetal Risk Prediction using Machine Learning Algorithms

Cardiotocography (CTG) records fetal heart rate (FHR) and uterine contractions (UC) simultaneously. The CTG,*which is one of the*most common*diagnostic techniques used during pregnancy and before delivery to evaluate maternal and fetal well-being. Doctors can understand the state of the fetus by observing the*Cardiotocography trace patterns. There are several techniques for interpreting a typical cardiotocography data based on signal processing and computer programming. Only a few decades after cardiotocography has been implemented into clinical*practice, the predictive potential of these approaches remains controversial and still unreliable This paper presents MRMR feature selection algorithms with four classification for Fetal risk prediction using python.

Voiceless Bangla vowel recognition using sEMG signal.

Some people cannot produce sound although their facial muscles work properly due to having problem in their vocal cords. Therefore, recognition of alphabets as well as sentences uttered by these voiceless people is a complex task. This paper proposes a novel method to solve this problem using non-invasive surface Electromyogram (sEMG). Firstly, eleven Bangla vowels are pronounced and sEMG signals are recorded at the same time. Different features are extracted and mRMR feature selection algorithm is then applied to select prominent feature subset from the large feature vector. After that, these prominent features subset is applied in the Artificial Neural Network for vowel classification. This novel Bangla vowel classification method can offer a significant contribution in voice synthesis as well as in speech communication. The result of this experiment shows an overall accuracy of 82.3 % with fewer features compared to other studies in different languages.

Computer-Aided Diagnosis of Parkinson's Disease Using Complex-Valued Neural Networks and mRMR Feature Selection Algorithm.

Parkinson's disease (PD) is a neurological disorder which has a significant social and economic impact. PD is diagnosed by clinical observation and evaluations, coupled with a PD rating scale. However, these methods may be insufficient, especially in the initial phase of the disease. The processes are tedious and time-consuming, and hence systems that can automatically offer a diagnosis are needed. In this study, a novel method for the diagnosis of PD is proposed. Biomedical sound measurements obtained from continuous phonation samples were used as attributes. First, a minimum redundancy maximum relevance (mRMR) attribute selection algorithm was applied for the identification of the effective attributes. After conversion to a complex number, the resulting attributes are presented as input data to the complex-valued artificial neural network (CVANN). The proposed novel system might be a powerful tool for effective diagnosis of PD.