scholarly journals Automated detection of colon cancer using genomic signal processing

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Safaa M. Naeem ◽  
Mai S. Mabrouk ◽  
Mohamed A. Eldosoky ◽  
Ahmed Y. Sayed
Keyword(s):  
Colon Cancer ◽  
Nearest Neighbor ◽  
Haar Wavelet ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Colon Cells ◽  
Genomic Signal Processing ◽  
Dna Mutations ◽  
Representation Method

Abstract Background Disorders in deoxyribonucleic acid (DNA) mutations are the common cause of colon cancer. Detection of these mutations is the first step in colon cancer diagnosis. Differentiation among normal and cancerous colon gene sequences is a method used for mutation identification. Early detection of this type of disease can avoid complications that can lead to death. In this study, 55 healthy and 55 cancerous genes for colon cells obtained from the national center for biotechnology information GenBank are used. After applying the electron–ion interaction pseudopotential (EIIP) numbering representation method for the sequences, single-level discrete wavelet transform (DWT) is applied using Haar wavelet. Then, some statistical features are obtained from the wavelet domain. These features are mean, variance, standard deviation, autocorrelation, entropy, skewness, and kurtosis. The resulting values are applied to the k-nearest neighbor (KNN) and support vector machine (SVM) algorithms to obtain satisfactory classification results. Results Four important parameters are calculated to evaluate the performance of the classifiers. Accuracy (ACC), F1 score, and Matthews correlation coefficient (MCC) are 95%, 94.74%, and 0.9045%, respectively, for SVM and 97.5%, 97.44%, and 0.9512%, respectively, for KNN. Conclusion This study has created a novel successful system for colorectal cancer classification and detection with the well-satisfied results. The K-nearest network results are the best with low error for the generated classification system, even though the results of the SVM network are acceptable.

scholarly journals Enhanced Performance of PCG Signal using Effective Feature Extraction Method

2020 ◽  
Vol 9 (2) ◽  
pp. 809-813
Keyword(s):  
Nearest Neighbor ◽  
Heart Sound ◽  
Biomedical Signal Processing ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Feature Extraction Method ◽  
Signal Features ◽  
Heart Auscultation ◽  
Noisy Background

Phonocardiography (PCG) is the realistic portrayal of sounds created in the heart auscultation. PCG is an improvement for ECG. Particularly in observing of patient and biomedical research, these signals need to do the diagnosis. This paper deals with the processing of heart sound signals i.e., Phonocardiography (PCG) Signals. The primary goal of analyzing these heart sound signals is to separate the signals from the noisy background and to extract some parameters which are used for patient monitoring and for other researches. Various momentum explore ventures are going on biomedical signal processing and its applications. The performance of the PCG signal will comprise of sectioning the signal into S1 and S2 and then compare, whether the PCG is normal or abnormal. In the previous framework the different change approaches are utilized to break down the PCG signal.In the primary stage, for include extraction; acquired heart sound signals were isolated to its subgroups utilizing discrete wavelet change with Level-1 to Level-10. This upgraded strategy proposes a best component for Heart Signal Features, which are removed and changed in to other area to arrange signals. This enhanced method proposes a best feature for Heart Signal Features, which are extracted and transformed in to other domain to classify signals. In the proposed strategy the Wavelet is utilized for highlight extraction and different Statistical strategies are utilized. InformationGain (IG), Mutual Information (MI) and so on. Feature selection techniques are compared using classifiers like kNN(k-Nearest Neighbor), Naïve Bayes, C4.5 and Support Vector Machines (SVMs). MATLAB & WEKA Soft wares are used for analysis Purpose. In this paper, coiffelet technique is utilized to analyze the synthetic PCG and the classifier parameters are compared with one another.

scholarly journals A Novel Machine-Learning Framework-based on LBP and GLCM Approaches for CBIR System

2021 ◽  
Vol 18 (3) ◽  
Author(s):  
Meenakshi Garg ◽  
Manisha Malhotra ◽  
Harpal Singh
Keyword(s):  
Wavelet Transforms ◽  
Nearest Neighbor ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Texture Image Classification ◽  
Definition Of ◽  
Occurrence Matrix ◽  
Experimental Findings

This paper presents a Multiple-features extraction and reduction-based approaches for Content-Based Image Retrieval (CBIR). Discrete Wavelet Transforms (DWT) on colored channels is used to decompose the image at multiple stages. The Gray Level Co-occurrence Matrix (GLCM) concept is used to extract statistical characteristics for texture image classification. The definition of shared knowledge is used to classify the most common features for all COREL dataset groups. These are also fed into a feature selector based on the particle swarm optimization which reduces the number of features that can be used during the classification stage. Three classifiers, called the Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Decision Tree (DT), are trained and tested, in which SVM give high classification accuracy and precise rates. In several of the COREL dataset types, experimental findings have demonstrated above 94 percent precision and 0.80 to 0.90 precision values.

scholarly journals A Review Study for Electrocardiogram Signal Classification

2020 ◽  
Vol 4 (1) ◽  
pp. 103
Author(s):  
Lana Abdulrazaq Abdulla ◽  
Muzhir Shaban Al-Ani
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Electrocardiogram Signal ◽  
Review Study ◽  
Electrocardiogram Ecg

An electrocardiogram (ECG) signal is a recording of the electrical activity generated by the heart. The analysis of the ECG signal has been interested in more than a decade to build a model to make automatic ECG classification. The main goal of this work is to study and review an overview of utilizing the classification methods that have been recently used such as Artificial Neural Network, Convolution Neural Network (CNN), discrete wavelet transform, Support Vector Machine (SVM), and K-Nearest Neighbor. Efficient comparisons are shown in the result in terms of classification methods, features extraction technique, dataset, contribution, and some other aspects. The result also shows that the CNN has been most widely used for ECG classification as it can obtain a higher success rate than the rest of the classification approaches.

scholarly journals Recognition of Consumer Preference by Analysis and Classification EEG Signals

2021 ◽  
Vol 14 ◽  
Author(s):  
Mashael Aldayel ◽  
Mourad Ykhlef ◽  
Abeer Al-Nafjan
Keyword(s):  
Feature Extraction ◽  
Nearest Neighbor ◽  
Consumer Preference ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Detection Systems ◽  
Power Spectral ◽  
Selection Of

Neuromarketing has gained attention to bridge the gap between conventional marketing studies and electroencephalography (EEG)-based brain-computer interface (BCI) research. It determines what customers actually want through preference prediction. The performance of EEG-based preference detection systems depends on a suitable selection of feature extraction techniques and machine learning algorithms. In this study, We examined preference detection of neuromarketing dataset using different feature combinations of EEG indices and different algorithms for feature extraction and classification. For EEG feature extraction, we employed discrete wavelet transform (DWT) and power spectral density (PSD), which were utilized to measure the EEG-based preference indices that enhance the accuracy of preference detection. Moreover, we compared deep learning with other traditional classifiers, such as k-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF). We also studied the effect of preference indicators on the performance of classification algorithms. Through rigorous offline analysis, we investigated the computational intelligence for preference detection and classification. The performance of the proposed deep neural network (DNN) outperforms KNN and SVM in accuracy, precision, and recall; however, RF achieved results similar to those of the DNN for the same dataset.

scholarly journals Machine Learning-Based Fault Diagnosis of Self-Aligning Bearings for Rotating Machinery Using Infrared Thermography

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ankush Mehta ◽  
Deepam Goyal ◽  
Anurag Choudhary ◽  
B. S. Pabla ◽  
Safya Belghith
Keyword(s):  
Fault Diagnosis ◽  
Infrared Thermography ◽  
Condition Monitoring ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Mechanical Equipment ◽  
K Nearest Neighbor ◽  
Linear Discriminant

Bearings are considered as indispensable and critical components of mechanical equipment, which support the basic forces and dynamic loads. Across different condition monitoring (CM) techniques, infrared thermography (IRT) has gained the limelight due to its noncontact nature, high accuracy, and reliability. This article presents the use of IRT for the bearing fault diagnosis. A two-dimensional discrete wavelet transform (2D-DWT) has been applied for the decomposition of the thermal image. Principal component analysis (PCA) has been used for the reduction of dimensionality of extracted features, and thereafter the most relevant features are accomplished. Furthermore, support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbor (KNN) as the classifiers were considered for classification of faults and performance assessment. The results reveal that the SVM outperformed LDA as well as KNN. Noncontact condition monitoring shows a great potential to be implemented in determining the health of machine. The utilization of noncontact thermal imaging-based instruments has enormous potential in anticipating the maintenance and increased machine availability.

Optimizing Error Rate in Intrusion Detection System Using Artificial Neural Network Algorithm

2018 ◽  
Vol 6 (9) ◽  
pp. 152
Author(s):  
S. Vijaya Rani ◽  
G. N. K. Suresh Babu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Error Rate ◽  
Learning Process ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Artificial Neural

The illegal hackers  penetrate the servers and networks of corporate and financial institutions to gain money and extract vital information. The hacking varies from one computing system to many system. They gain access by sending malicious packets in the network through virus, worms, Trojan horses etc. The hackers scan a network through various tools and collect information of network and host. Hence it is very much essential to detect the attacks as they enter into a network. The methods  available for intrusion detection are Naive Bayes, Decision tree, Support Vector Machine, K-Nearest Neighbor, Artificial Neural Networks. A neural network consists of processing units in complex manner and able to store information and make it functional for use. It acts like human brain and takes knowledge from the environment through training and learning process. Many algorithms are available for learning process This work carry out research on analysis of malicious packets and predicting the error rate in detection of injured packets through artificial neural network algorithms.

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

2019 ◽  
Vol 20 (5) ◽  
pp. 488-500 ◽  
Author(s):  
Yan Hu ◽  
Yi Lu ◽  
Shuo Wang ◽  
Mengying Zhang ◽  
Xiaosheng Qu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Anticancer Drugs ◽  
Nearest Neighbor ◽  
Cost Effective ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Activity Prediction ◽  
Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world&#039;s highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. </P><P> Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. </P><P> Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. </P><P> Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm

2021 ◽  
pp. 1-17
Author(s):  
Ahmed Al-Tarawneh ◽  
Ja’afer Al-Saraireh
Keyword(s):  
Machine Learning ◽  
Complex Networks ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Efficient Detection ◽  
Suggested Keywords

Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks technique with adapted machine learning algorithms. This work presents a methodology that collects a list of users with their followers who are sharing their posts that have similar interests from a hackers’ community on Twitter. The list is built based on a set of suggested keywords that are the commonly used terms by hackers in their tweets. After that, a complex network is generated for all users to find relations among them in terms of network centrality, closeness, and betweenness. After extracting these values, a dataset of the most influential users in the hacker community is assembled. Subsequently, tweets belonging to users in the extracted dataset are gathered and classified into positive and negative classes. The output of this process is utilized with a machine learning process by applying different algorithms. This research build and investigate an accurate dataset containing real users who belong to a hackers’ community. Correctly, classified instances were measured for accuracy using the average values of K-nearest neighbor, Naive Bayes, Random Tree, and the support vector machine techniques, demonstrating about 90% and 88% accuracy for cross-validation and percentage split respectively. Consequently, the proposed network cyber Twitter model is able to detect hackers, and determine if tweets pose a risk to future institutions and individuals to provide early warning of possible attacks.

scholarly journals Multiclass emotion prediction using heart rate and virtual reality stimuli

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Aaron Frederick Bulagang ◽  
James Mountstephens ◽  
Jason Teo
Keyword(s):  
Heart Rate ◽  
Virtual Reality ◽  
Nearest Neighbor ◽  
Subject Classification ◽  
Support Vector ◽  
Display Device ◽  
K Nearest Neighbor ◽  
Potential Applications ◽  
Health Rehabilitation ◽  
Emotion Model

Abstract Background Emotion prediction is a method that recognizes the human emotion derived from the subject’s psychological data. The problem in question is the limited use of heart rate (HR) as the prediction feature through the use of common classifiers such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) in emotion prediction. This paper aims to investigate whether HR signals can be utilized to classify four-class emotions using the emotion model from Russell’s in a virtual reality (VR) environment using machine learning. Method An experiment was conducted using the Empatica E4 wristband to acquire the participant’s HR, a VR headset as the display device for participants to view the 360° emotional videos, and the Empatica E4 real-time application was used during the experiment to extract and process the participant's recorded heart rate. Findings For intra-subject classification, all three classifiers SVM, KNN, and RF achieved 100% as the highest accuracy while inter-subject classification achieved 46.7% for SVM, 42.9% for KNN and 43.3% for RF. Conclusion The results demonstrate the potential of SVM, KNN and RF classifiers to classify HR as a feature to be used in emotion prediction in four distinct emotion classes in a virtual reality environment. The potential applications include interactive gaming, affective entertainment, and VR health rehabilitation.

scholarly journals PlncRNA-HDeep: plant long noncoding RNA prediction using hybrid deep learning based on two encoding styles

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Jun Meng ◽  
Qiang Kang ◽  
Zheng Chang ◽  
Yushi Luan
Keyword(s):  
Deep Learning ◽  
Noncoding Rna ◽  
Nearest Neighbor ◽  
Short Term Memory ◽  
Biological Activities ◽  
Support Vector ◽  
Multiple Perspectives ◽  
K Nearest Neighbor ◽  
Rna Sequences ◽  
Deep Learning Model

Abstract Background Long noncoding RNAs (lncRNAs) play an important role in regulating biological activities and their prediction is significant for exploring biological processes. Long short-term memory (LSTM) and convolutional neural network (CNN) can automatically extract and learn the abstract information from the encoded RNA sequences to avoid complex feature engineering. An ensemble model learns the information from multiple perspectives and shows better performance than a single model. It is feasible and interesting that the RNA sequence is considered as sentence and image to train LSTM and CNN respectively, and then the trained models are hybridized to predict lncRNAs. Up to present, there are various predictors for lncRNAs, but few of them are proposed for plant. A reliable and powerful predictor for plant lncRNAs is necessary. Results To boost the performance of predicting lncRNAs, this paper proposes a hybrid deep learning model based on two encoding styles (PlncRNA-HDeep), which does not require prior knowledge and only uses RNA sequences to train the models for predicting plant lncRNAs. It not only learns the diversified information from RNA sequences encoded by p-nucleotide and one-hot encodings, but also takes advantages of lncRNA-LSTM proposed in our previous study and CNN. The parameters are adjusted and three hybrid strategies are tested to maximize its performance. Experiment results show that PlncRNA-HDeep is more effective than lncRNA-LSTM and CNN and obtains 97.9% sensitivity, 95.1% precision, 96.5% accuracy and 96.5% F1 score on Zea mays dataset which are better than those of several shallow machine learning methods (support vector machine, random forest, k-nearest neighbor, decision tree, naive Bayes and logistic regression) and some existing tools (CNCI, PLEK, CPC2, LncADeep and lncRNAnet). Conclusions PlncRNA-HDeep is feasible and obtains the credible predictive results. It may also provide valuable references for other related research.

Sign in / Sign up

Export Citation Format

Share Document