Vector Classification Research Articles

Intratumoral and peritumoral radiomics for forecasting microsatellite status in gastric cancer: a multicenter study

ObjectiveThis investigation attempted to examine the effectiveness of CT-derived peritumoral and intratumoral radiomics in forecasting microsatellite instability (MSI) status preoperatively among gastric cancer (GC) patients.MethodsA retrospective analysis was performed on GC patients from February 2019 to December 2023 across three healthcare institutions. 364 patients (including 41 microsatellite instability-high (MSI-H) and 323 microsatellite instability-low/stable (MSI-L/S)) were stratified into a training set (n = 202), an internal validation set (n = 84), and an external validation set (n = 78). Radiomics features were obtained from both the intratumoral region (IR) and the intratumoral plus 3-mm peritumoral region (IPR) on preoperative contrast-enhanced CT images. After standardizing and reducing the dimensionality of these features, six radiomic models were constructed utilizing three machine learning techniques: Support Vector Machine (SVM), Linear Support Vector Classification (LinearSVC), and Logistic Regression (LR). The optimal model was determined by evaluating the Receiver Operating Characteristic (ROC) curve's Area Under the Curve (AUC), and the radiomics score (Radscore) was computed. A clinical model was developed using clinical characteristics and CT semantic features, with the Radscore integrated to create a combined model. Used ROC curves, calibration plots, and Decision Curve Analysis (DCA) to assess the performance of radiomics, clinical, and combined models.ResultsThe LinearSVC model using the IPR achieved the highest AUC of 0.802 in the external validation set. The combined model yielded superior AUCs in internal and external validation sets (0.891 and 0.856) in comparison to clinical model [(0.724, P = 0.193) and (0.655, P = 0.072)] and radiomics model [(0.826, P = 0.160) and (0.802, P = 0.068)]. Furthermore, results from calibration and DCA underscored the model's suitability and clinical relevance.ConclusionThe combined model, which integrates IPR radiomics with clinical characteristics, accurately predicts MSI status and supports the development of personalized treatment strategies.

Enhancing fake profile detection through supervised and hybrid machine learning: a comparative analysis

In modern times, social networks have become ubiquitous platforms facilitating widespread information dissemination, resulting in significant daily data generation. This increase in data production encompasses a wide range of user-generated content, which in turn promotes the proliferation of fraudulent users creating fake profiles and engaging in deceptive activities. This article aims to address this challenge by employing machine learning algorithms to accurately identify fake profiles. The research involves a thorough analysis of various user behaviors, engagement metrics, and content attributes within social platforms. The primary goal is to develop robust models capable of effectively detecting deceptive profiles by meticulously examining user activities and content characteristics. The study explores the application of robust methodologies such as K-means and K-medoids clustering, alongside supervised machine learning classifiers including K-nearest neighbors (KNN), support vector machine (SVM), Bernoulli Naïve Bayes (NB), logistic regression, and linear support vector classification (SVC), specifically tailored for the detection of fake profiles.

Political sentiment analysis using natural language processing on social media

In this contemporary era , social media has become an essential component of daily life as a result of the extensive use of the internet. This paper explores sentiment analysis of political topics through social media comments. We collected a large dataset of over 14,000 political comments and applied advanced machine learning models such as logistic regression , linear support vector classification , random forest, decision tree classification , and naive bayes to evaluate expressed sentiments. Performance metrics , including accuracy , precision , recall , and F1 scores , were utilized to assess these models , with Linear SVC achieving the highest accuracy at 91.18% , closely followed by Logistic Regression at 90%. This research not only evaluates model performance on political sentiment data but also addresses data imbalance, presenting actionable insights into each algorithm’s suitability. Our study introduces a refined approach to political sentiment analysis by optimizing model selection for high accuracy and robustness, thus setting a foundation for effective political sentiment understanding on social media platforms.

A Comparative Analysis of Machine Learning Techniques to Explore Factors Affecting Mathematics Success in Developing Countries: Turkey, Mexico, Thailand, And Bulgaria Case Studies

This study explores factors influencing mathematics achievement in Turkey, Bulgaria, Mexico, and Thailand using PISA 2018 data and machine learning models, comparing their performance. Both classification and regression models were utilized: linear regression, support vector machine, decision tree, and random forest for regression; logistic regression, support vector, decision tree, and random forest for classification. Additionally, XGBoost identified key predictors of math achievement, and K-Means filled missing data. According to results, key contributing factors across all countries included students' economic, social, and cultural status, study materials at home, sense of ownership, and family welfare. Regarding model success, random forests outperformed other models in both regression and classification, with Random Forest Regression achieving the highest R-square values (71%-84%) while linear regression has the lowest (22%-43%). In addition, the classification algorithms were analyzed in terms of binary and ternary classification, binary classification proved more successful than ternary, with RF accuracy scores ranging from 73% to 83% across countries. The study's findings offer valuable insights for selecting optimal algorithms for predicting math achievement, aiding decision-makers in enhancing educational outcomes.

Application of Online Anomaly Detection Using One-Class Classification to the Z24 Bridge.

The usage of anomaly detection is of critical importance to numerous domains, including structural health monitoring (SHM). In this study, we examine an online setting for damage detection in the Z24 bridge. We evaluate and compare the performance of the elliptic envelope, incremental one-class support vector classification, local outlier factor, half-space trees, and entropy-guided envelopes. Our findings demonstrate that XGBoost exhibits enhanced performance in identifying a limited set of significant features. Additionally, we present a novel approach to manage drift through the application of entropy measures to structural state instances. The study is the first to assess the applicability of one-class classification for anomaly detection on the short-term structural health data of the Z24 bridge.

A XGBoost-Based Prediction Method for Meat Sheep Transport Stress Using Wearable Photoelectric Sensors and Infrared Thermometry.

Transportation pressure poses a serious threat to the health of live sheep and the quality of their meat. So, the edible Hu sheep was chosen as the research object for meat sheep. We constructed a systematic biosignal detecting, processing, and modeling method. The biosignal sensing was performed with wearable sensors (photoelectric sensor and infrared temperature measurement) for physiological dynamic sensing and continuous monitoring of the transport environment of meat sheep. Core waveform extraction and modern spectral estimation methods are used to determine and strip out the target signal waveform from it for the purpose of accurate sensing and the acquisition of key transport parameters. Subsequently, we built a qualitative stress assessment method based on external manifestations with reference to the Karolinska drowsiness scale to establish stage classification rules for monitoring data in the transportation environment of meat sheep. Finally, machine learning algorithms such as Gaussian Naive Bayes (GaussianNB), Passive-Aggressive Aggregative Classifier (PAC), Nearest Centroid (NC), K-Nearest Neighbor Classification (KNN), Random Forest (RF), Support Vector Classification (SVC), Gradient Boosting Decision Tree (GBDT), and eXtreme Gradient Boosting (XGB) were established to predict the classification models of transportation stress in meat sheep. Their classification results were compared. The results show that SVC and GBDT algorithms are more effective and the overall model classification accuracy reached 86.44% and 91.53%. XGB has the best results. The accuracy of the assessment of the transport stress state of meat sheep after the optimization of three parameters was 100%, 90.91%, and 93.33%, and the classification accuracy of the overall model reached 94.92%. The final results achieved improve transport reliability, reduce transport risk, and solve the problems of inefficient meat sheep transport supervision and quality control.

Predicting Financial Distress in Indonesian Companies using Machine Learning

Predicting financial distress is essential in Indonesia's rapidly evolving economy, characterized by diverse business environments and regulatory challenges. This study evaluates four machine learning classifiers, XGBoost, Random Forest (RF), Support Vector Classification (SVC), and Long Short-Term Memory (LSTM), to predict financial distress among Indonesian companies. Two sampling methods, Random Under-Sampling (RUS) and Synthetic Minority Over-Sampling Technique (SMOTE), were used to address class imbalance. Empirical results indicate that the RF model trained with SMOTE sampling was the most effective, achieving an F1 score of 0.9632 and an accuracy of 0.96, while the XGBoost classifier with RUS sampling achieved a precision of 0.9716. These findings provide valuable insights into Indonesia's financial sector, guiding the selection of appropriate models for timely financial distress prediction and intervention.

A significant features vector for internet traffic classification based on multi-features selection techniques and ranker, voting filters

A significant features vector for internet traffic classification based on multi-features selection techniques and ranker, voting filters

Classification method of seabed sonar image substrate based on ELM-AdaBoost

The results of sediment classification are affected by the measurement environment. The water depth has a significant impact on the sound propagation, and the acoustic characteristics will be different under different water depths. In addition, seabed topography, sediment types and their distribution, acoustic frequency, equipment types and settings, and suspended substances in water will all affect the original sonar data. In order to obtain accurate classification results, a seabed sonar image classification method based on Elm AdaBoost is proposed. The original sound intensity data is compensated for gain and the anomaly is eliminated. After preprocessing the multi beam sonar data, the image texture feature is extracted from the gray level co-occurrence matrix as the feature vector for classification, and the elm classifier is constructed. The elm is enhanced by AdaBoost. The calculated feature is input into the elm AdaBoost network for sediment classification, and the result is output. Simulation results show that the proposed method effectively improves the accuracy of sediment classification, and verifies the feasibility of this method.

Diagnostic artificial intelligence model predicts lymph node status in non-small cell lung cancer using simplified examination.

Artificial intelligence (AI) technology was introduced in medical data area and applied disease prediction models. This study aimed to establish an AI model for predicting lymph node metastasis based on simple medical examinations in patients with non-small cell lung cancer (NSCLC). We retrospectively analyzed 988 patients with NSCLC who underwent radical pulmonary resection with mediastinal lymph node dissection between January 2011 and October 2022. We collected clinical characteristics including age, sex, smoking history, tumor marker levels, tumor side, segment location, total tumor size, solid tumor size and consolidation-to-tumor ratio, obtainable from medical interview, blood tests and plain computed tomography (CT) of the chest. All patients were randomly classified into a training set (n=790) and a validation set (n=198). Six algorithms including Support Vector Classification (SVC), k-nearest neighbor algorithm (k-NN), logistic regression (LR), random forest (RF), gradient boosting (GB) and multilayer perceptron (MLP) were created to decide the lymph node metastasis. The GB model showed the best diagnostic performance, with 80.0% accuracy, 95.6% specificity and an area under the curve (AUC) of 0.75. An AI model showed high specificity and accuracy for predicting lymph node metastasis. These models have potential to categorize suitable surgical procedures for NSCLC patients without needing contrast-enhanced CT or positron emission tomography.

Predicting abnormal C-reactive protein level for improving utilization by deep neural network model

BackgroundC-reactive protein (CRP) is an inflammatory biomarker frequently used in clinical practice. However, insufficient evidence-based ordering inevitably results in its overuse or underuse. This study aims to predict its normal and abnormal levels using the deep neural network (DNN) models, helping clinicians order this item more appropriately and intelligently. MethodsWe considered complete blood count (CBC) parameters as feature vectors and 10 mg/L as a cutoff value for CRP. Several models, including linear support vector classification, logistic regression, decision trees, random forests, and DNN, were developed based on a dataset of 53834 medical records to predict binary output. We externally validated DNN models on independent 20723 samples through discrimination, calibration curve, and decision curve analysis. ResultsDNN models has the best area under the receiver operating characteristic curves (AUC). Learning curves revealed that models’ AUC, balanced accuracy, and F1 score do not significantly and continuously improve following increasing data volume. In internal validation, the AUC, balanced accuracy, and the F1 score of 10 models were 0.818 (0.95 CI: 0.812-0.824), 0.741 (0.95 CI: 0.736-0.747), and 0.649 (0.95 CI: 0.643-0.656), respectively. These metrics were 0.817 (0.95 CI: 0.816-0.817), 0.741 (0.95 CI: 0.740-0.742), and 0.641 (0.95 CI: 0.640-0.642), respectively, in external validation. AUC and balanced accuracy shown no significant difference (P-values were 0.106 and 0.339). CRP10-C2 model has the lowest Brier score of 0.154, AUC of 0.818, and calibration curve formula of y=1.001x-0.010, which was identified as a target model to deploy in the app. ConclusionsDNN models obtained moderate performance, surpassing baseline indices in distinguishing binary CRP levels. They are good generalizations and well-calibrated. The CRP-C2 model can enhance CRP utilization by informing the orders appropriately and can contribute to inflammatory diagnostics in primary health care where CBC is available, but the CRP test is inaccessible.

Machine learning for real-time reservoir operation simulation: comparing input variables and algorithms for the Sirikit Reservoir, Thailand

ABSTRACT Machine learning (ML) models offer advantages over process-based models for real-time reservoir operation modelling, yet the impact of input variable selection (IVS) and data pre-processing on model performance remains underexplored. This study investigates various input variables for simulating daily reservoir outflow, using the Sirikit reservoir in Thailand as a case study. The datasets include daily Sirikit storage and inflow, outflow of Bhumibol (neighbouring reservoir), downstream discharge, and temporal factors (month and day of the week). Time series decomposition and correlation analyses were used to assess data relationships. We tested seven ML models: multiple linear regression, support vector machine, K-nearest neighbour, classification and regression tree, random forest, multi-layer perceptron, and recurrent neural network (RNN). The optimal input set comprised the previous day’s storage, inflow from 2 days before to 2 days after, and month. With these inputs, all ML models simulated outflow adequately (KGEtraining = 0.42–1.0 and KGEtesting = 0.46–0.56), with RNN showing the most potential for improvement. Input scaling significantly enhanced model performance, reducing RMSEtraining by 44 m3 s-1 and RMSEtesting by 14 m3 s-1. This study’s novelty lies in its comprehensive insights of IVS and data scaling, highlighting their critical roles in enhancing ML model application for operational reservoir simulations.

Applications of remote sensing and GIS techniques for identifying of the plastic waste from space: Evidence from Khulna city corporation in Bangladesh

Plastic waste poses a significant threat to the environment, public health, and aquatic life. Several methods are now under development in many studies to monitor plastic waste through earth observation satellites. These methods were successfully applied to monitor plastic litter and debris. Due to the special optical signature of plastic, it is easy to identify it in aquatic environments. But in the case of identifying plastic waste on land or in a terrestrial environment, it is very difficult as different types of land cover have their own special optical signature. Conducting field surveys could be a possible solution for monitoring plastic waste on land, but it’s costly and time-consuming. To tackle this problem, remote sensing-based observation can make a sustainable contribution. This study aims to identify plastic waste on land by the combination of Sentinel-2 imagery and two supervised classification algorithms: (1) maximum likelihood classification and (2) support vector classification. Two locations where plastic waste was recycled were considered for conducting this study by field observations. A total of 60 samples have been taken in this study, out of which 80% (48) have been taken as training samples and the remaining 20% (12) have been taken as testing samples, and the entire process was done using ArcGIS 10.8. This analysis revealed that algorithms used in this study successfully identify plastic waste on land, and between two algorithms, support vector classification achieves the highest accuracy (93%). Bands 6, 7, and 8 show higher spectral reflectance for plastic. The finding suggests that supervised algorithms can be used to identify plastic waste on land. Other algorithms, high-resolution satellite imagery, and a larger dataset are necessary to identify smaller plastic waste on land. This study will help policymakers and decision-makers at national and local levels to identify and manage plastic waste in a sustainable way.

Spatiotemporal Dynamics and Driving Factors of Soil Salinization: A Case Study of the Yutian Oasis, Xinjiang, China

Soil salinization is a critical global environmental issue, exacerbated by climatic and anthropogenic factors, and posing significant threats to agricultural productivity and ecological stability in arid regions. Therefore, remote sensing-based dynamic monitoring of soil salinization is crucial for timely assessment and effective mitigation strategies. This study used Landsat imagery from 2001 to 2021 to evaluate the potential of support vector machine (SVM) and classification and regression tree (CART) models for monitoring soil salinization, enabling the spatiotemporal mapping of soil salinity in the Yutian Oasis. In addition, the land use transfer matrix and spatial overlay analysis were employed to comprehensively analyze the spatiotemporal trends of soil salinization. The geographical detector (Geo Detector) tool was used to explore the driving factors of the spatiotemporal evolution of salinization. The results indicated that the CART model achieved 5.3% higher classification accuracy than the SVM, effectively mapping the distribution of soil salinization and showing a 26.76% decrease in salinized areas from 2001 to 2021. Improvements in secondary salinization and increased vegetation coverage were the primary contributors to this reduction. Geo Detector analysis highlighted vegetation (NDVI) as the dominant factor, and its interaction with soil moisture (NDWI) has a significant impact on the spatial and temporal distribution of soil salinity. This study provides a robust method for monitoring soil salinization, offering critical insights for effective salinization management and sustainable agricultural practices in arid regions.

The Effect of Regularized Regression and Tree-Based Missing Data Imputation Methods on Classification Performance in High Dimensional Data

Missing data is an important problem in the analysis and classification of high dimensional data. The aim of this study is to compare the effects of four different missing data imputation methods on classification performance in high dimensional data. In this study, missing data imputation methods were evaluated using data sets, whose independent variables between mixed correlated with each other, for binary dependent variable, p=500 independent variables, n=150 units and 1000 times running simulation. Missing data structures were created according to different missing rates. Different datasets were obtained by imputing the missing values using different methods. Regularized regression methods such as least absolute shrinkage and selection operator (lasso) and elastic net regression were used for imputation, as well as tree-based methods such as support vector machine and classification and regression trees. At the end of simulation, the classification scores of the methods were obtained by gradient boosting machine and the missing data prediction performances were evaluated according to the distance of these scores from the reference. Our simulation demonstrates that regularized regression methods outperform tree-based methods in classifying high dimensional datasets. Additionally, it was found that the increase in the amount of missing values reduced the classification performance of the methods in high dimensional data.

Utilizing Artificial Intelligence for Microbiome Decision-Making: Autism Spectrum Disorder in Children from Bosnia and Herzegovina.

The study of microbiome composition shows positive indications for application in the diagnosis and treatment of many conditions and diseases. One such condition is autism spectrum disorder (ASD). We aimed to analyze gut microbiome samples from children in Bosnia and Herzegovina to identify microbial differences between neurotypical children and those with ASD. Additionally, we developed machine learning classifiers to differentiate between the two groups using microbial abundance and predicted functional pathways. A total of 60 gut microbiome samples (16S rRNA sequences) were analyzed, with 44 from children with ASD and 16 from neurotypical children. Four machine learning algorithms (Random Forest, Support Vector Classification, Gradient Boosting, and Extremely Randomized Tree Classifier) were applied to create eight classification models based on bacterial abundance at the genus level and KEGG pathways. Model accuracy was evaluated, and an external dataset was introduced to test model generalizability. The highest classification accuracy (80%) was achieved with Random Forest and Extremely Randomized Tree Classifier using genus-level taxa. The Random Forest model also performed well (78%) with KEGG pathways. When tested on an independent dataset, the model maintained high accuracy (79%), confirming its generalizability. This study identified significant microbial differences between neurotypical children and children with ASD. Machine learning classifiers, particularly Random Forest and Extremely Randomized Tree Classifier, achieved strong accuracy. Validation with external data demonstrated that the models could generalize across different datasets, highlighting their potential use.

Detectability of multi-dimensional movement and behaviour in cattle using sensor data and machine learning algorithms: Study on a Charolais bull

The development of motion sensors for monitoring cattle behaviour has enabled farmers to predict the state of their cattle's welfare more efficiently. While most studies work with one dimensional output with disjunct behaviour categories, more accurate prediction can still be achieved by including complex movements and enriching the sensor algorithm to detect multi-dimensional movements, i.e., more than one movement occurring simultaneously. This paper presents such a machine-learning method for analysing overlapping independent movements. The output of the method consists of automatically recognized complex behaviour patterns that can be used for measuring animal welfare, predicting calving, or detecting early signs of diseases. This study combines automated motion sensors (i.e., halter and pedometer) for ruminants known as RumiWatch mounted on a Charolais fattening bull and camera observation. Fourteen types of complex movements were identified, i.e., defecating-urinating, eating, drinking, getting up, head movement, licking, lying down, lying, playing-aggression, rubbing, ruminating, sleeping, standing, and stepping. As multiple parallel binary classificators were used, the system was able to recognize parallel behavioural patterns with high fidelity. Two types of machine learning, i.e., Support Vector Classification (SVC) and RandomForest were used to recognize different general and non-general forms of movement. Results from these two supervised learning systems were compared. A continuous forty-eight hours of video were annotated to train the systems and validate their predictions. The success rate of both classifiers in recognizing special movements from both sensors or separately in different settings (i.e., window and padding) was examined. Although the two classifiers produced different results, the ideal settings showed that all forms of movement in the subject animal were successfully recognized with high accuracy. More studies using more individual animals and different ruminants would increase our knowledge on enhancing the system's performance and accuracy.

Coupling Different Machine Learning and Meta-Heuristic Optimization Techniques to Generate the Snow Avalanche Susceptibility Map in the French Alps

The focus of this study is to introduce a hybrid predictive framework encompassing different meta-heuristic optimization and machine learning techniques to identify the regions susceptible to snow avalanches. To accomplish this aim, the present research sought to acquire the best-performed model among nine different hybrid scenarios encompassing three different meta-heuristics, namely particle swarm optimization (PSO), gravitational search algorithm (GSA), and Cuckoo Search (CS), and three different ML approaches, i.e., support vector classification (SVC), stochastic gradient boosting (SGB), and k-nearest neighbors (KNN), pertaining to different predictive families. According to diligent analysis performed with regard to the blinded testing set, the PSO-SGB illustrated the most satisfactory predictive performance with an accuracy of 0.815, while the precision and recall were found to be 0.824 and 0.821, respectively. The F1-score of the predictions was found to be 0.821, and the area under the receiver operating curve (AUC) was obtained to be 0.9. Despite attaining similar predictive success via the CS-SGB model, the time-efficiency analysis underscored the PSO-SGB, as the corresponding process consumed considerably less computational time compared to its counterpart. The SHapley Additive exPlanations (SHAP) implementation further informed that slope, elevation, and wind speed are the most contributing attributes to detecting snow avalanche susceptibility in the French Alps.

Hybrid Learning Model for intrusion detection system: A combination of parametric and non-parametric classifiers

The growing digital transformation has increased the need for effective intrusion detection systems. Traditional intrusion detection systems face challenges in accurately classifying complex patterns. To address this issue, this study proposed a Hybrid Learning Model (HLM) that combines both parametric and non-parametric classifiers. The proposed HLM consist of two stages: the first stage employs a non-parametric Base Learner (np-BL) to analyze the data patterns and the second stage involves meta-modelling to generalize the overall performance of the model, named the Parametric Meta-Learning (PML) model. The proposed HLM blends the outcomes of np-BL and PML models using a stacking ensemble. As a base learning model K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), and Support Vector Classification with Radial Basis Function (SVC-RBF), are adopted from a non-parametric classifier group. The parametric classifiers Logistic Regression (LR), Naïve Bayes Classifier (NBC), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machine with linear kernel (Linear SVM) were used as meta-models. The HLM, as proposed, enhances the adaptability and robustness of the model by combining non-parametric and parametric models. To evaluate the competence of the proposed HLM, a performance analysis was conducted using the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets. The effectiveness was assessed using various metrics, including classification accuracy, precision, recall, F1-Score (F1), Receiver Operating Characteristic (ROC) curve, Detection Rate (DR), and False Alarm Rate (FAR). The proposed HLM achieves a better accuracy rate across different datasets when compared with the existing models. The achieved accuracies are 99.02 %, 99.98 % and 99.63 % for the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets respectively. Furthermore, the HLM gave a significant reduction in FAR, with values of 0.0126, 0.0001, and 0.0016 for the above-mentioned datasets.

Performance Comparison between Deep Neural Network and Machine Learning based Classifiers for Huntington Disease Prediction from Human DNA Sequence.

Huntington Disease (HD) is a type of neurodegenerative disorder which causes problems like psychiatric disturbances, movement problem, weight loss and problem in sleep. It needs to be addressed in earlier stage of human life. Nowadays Deep Learning (DL) based system could help physicians provide second opinion in treating patient's disease. In this work, human Deoxyribo Nucleic Acid (DNA) sequence is analyzed using Deep Neural Network (DNN) algorithm to predict the HD disease. The main objective of this work is to identify whether the human DNA is affected by HD or not. Human DNA sequences are collected from National Center for Biotechnology Information (NCBI) and synthetic human DNA data are also constructed for process. Then numerical conversion of human DNA sequence data is done by Chaos Game Representation (CGR) method. After that, numerical values of DNA data are used for feature extraction. Mean, median, standard deviation, entropy, contrast, correlation, energy and homogeneity are extracted. Additionally, the following features such as counts of adenine, thymine, guanine and cytosine are extracted from the DNA sequence data itself. The extracted features are used as input to the DNN classifier and other machine learning based classifiers such as NN (Neural Network), Support Vector Machine (SVM), Random Forest (RF) and Classification Tree with Forward Pruning (CTWFP). Six performance measures are used such as Accuracy, Sensitivity, Specificity, Precision, F1 score and Mathew Correlation Co-efficient (MCC). The study concludes DNN, NN, SVM, RF achieve 100% accuracy and CTWFP achieves accuracy of 87%.