Potholes Detection in Doppler Radar Signal’s Power Spectral Density using Decision Tree

Aim: The aim of the work is to evaluate the accuracy and precision in predicting heart disease using Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Decision Tree (DT) Classification algorithms. Materials and Methods: Classification algorithm is appealed on a heart dataset which consists of 180 records. A framework for heart disease prediction in the medical sector comparing Random forest, Logistic Regression, Decision Tree and SVM classifiers has been proposed and developed. The sample size was calculated as 55 in each group using G power 80%. Sample size was calculated using clincalc analysis, with alpha and beta values 0.05 and 0.5, 95% confidence, pretest power 80% and enrolment ratio 1. Results: The Novel Random Forest Algorithm (92.13%), Support Vector Machine (62.51%), Logistic Regression (84.89%), Decision Tree (86.25%) classifiers produce respectively. SVM, RF exists a statistically significant difference between the two groups (p=0.001,p=.004;p<0.05).LR, RF exists a statistically insignificant difference between the two groups (p=.103, P=.080;p>0.05) both with confidence interval 95%. Hence Random forest is better than SVM, RF, DT classifiers. Conclusion: The results show that the performance of RF is better when compared with SVM, LR and DT in terms of both precision and accuracy.

Software Defined Networking (SDN) is a dynamic architecture that employs a variety of applications for making networks more adaptable and centrally controlled. It is easy to attack the entire network in SDN because the control plane and data plane are separated. DDoS attack is major danger to SDN service providers because it can shut down the entire network and stop services to all customers at any time. One of the key flaws of most SDN architectures is lack of susceptibility to DDoS attacks with its types like TCP flooding, UDP flooding, SYN flooding, ICMP flooding and DHCP flooding for detecting those kinds of attacks. The machine learning algorithms are widely used in recent years to identify DDoS attacks. This research utilizes Decision Tree Classifier for detection and classification of DDoS attacks on SDN. The Forward Feature Selection technique is also used in the research to select the best features from the dataset and from that dataset the data are employed to train and test the model by Decision Tree Classifier Algorithm. The decision Tree Classifier technique is a supervised method used to forecast desired values of observations using rudimentary machine learning decision rules derived from training data. Based on the accuracy of decision tree techniques, in future, a hybrid learning model will be designed for detecting the Distributed Denial of Services in an SDN environment with high accuracy and a low false negative rate.

ML assisted enhanced recommendation system for reliable volunteering environment

To screen serum proteome biomarkers and establish predictive model with relation to the formation of portal vein tumor thrombi (PVTT) in hepatocellular carcinoma (HCC) patients. Serum samples were collected from 135 HCC patients, which were divided, into training set (including 33 HCC patients with PVTT and 62 HCC patients without PVTT) and blind testing set (including 18 HCC patients with PVTT and 22 HCC patients without PVTT). Special serum protein or peptide pattern was determined by SELDI-TOF-MS measurement after treating the sample onto WCX2 protein chip for each case. The obtained data were analyzed by BioMarker Wizard software to screen serum proteome biomarkers with relation to the formation of PVTT, while decision tree classification algorithm and blind validation were determined by Biomarker Patterns Software. Ranging from 1100 to 30 000 at the m/z value, 100 protein features were detected in the serum protein pattern stably. Among them, 6 protein peaks with the m/z value of 3478, 1314, 1744, 1725, 2022 and 3380 were upregulated, 10 proteins peaks with the m/z value of 8901, 9353, 9415, 8773, 2766, 2745, 8697, 7773, 8569 and 1373 were downregulated respectively in the group of HCC with PVTT. The 7 candidate protein peaks with the m/z value of 3478, 2022, 8901, 9415, 8773, 2766 and 2745 were selected to establish predictive model by BPS with a sensitivity of 75.8% (25/33) and specificity of 82.3% (51/62). An accuracy of 87.5% (35/40), sensitivity of 100% (18/18), specificity of 77.3% (17/22), positive predictive value of 78.3% (18/23), and negative predictive value of 100% (17/17) were validated in blind testing set. Sixteen candidate proteome biomarkers may be related with the formation of PVTT in HCC patients. Decision tree classification algorithm may have great clinical significance in predicting the formation of PVTT.

Many people today are unsure whether they have COVID-19. The frequent fever, dry cough, and sore throat are all signs and symptoms of COVID-19. If a person has signs or symptoms of coronavirus disease 2019 (COVID-19), he/she should see the doctor or go to a clinic as soon as possible. As a result, it's vital to learn and comprehend the fundamental differences. COVID-19 can cause a wide range of symptoms. The experiments were carried out using two Machine Learning Classification Algorithms, namely Decision Tree (DT) and Logistic Regression (LR). Both algorithms were written and analyzed using the Python program in Jupyter Notebook 6.4.5. From the results obtained in the experiments of covid symptoms dataset, on average, the DT model has obtained the best cross-validation average and the testing performance average compared to the LR machine learning models. For cross-validation results, the DT model has achieved an accuracy of 98.0%. For performance testing, the DT model has achieved an accuracy of 98.0%. The LR has obtained the second-best result on the average of cross-validation performance and the testing results. For cross-validation results, the LR model has achieved an accuracy of 96.0%. For performance testing, the LR model has achieved an accuracy of 97.0%. Consequently, the DT for the COVID-19 symptoms dataset is outperforming the LR for cross-validation and testing results.

Ultra-wideband radar signals have broad application prospects in ultra-close-range detection due to its high resolution, strong penetrability and strong anti-interference. Ultra-wideband radar is an ideal detection method in non-contact medical monitoring scenarios. Based on UWB radar, the time domain sliding window feature extraction method and decision tree classification algorithm are used to classify and recognize the human motion state behind the obstacle. The decision tree is generated by inputting the sliding window feature extraction result of the radar echo data into the decision tree algorithm. The experimental results show that the proposed feature extraction algorithm and decision tree algorithm can classify the motion state of the human target behind the obstacle, such as the human walking in the radial direction, walking in the tangential direction, and standing in the ground.

Fraud in financial transactions is a critical issue for businesses, governments, and consumers, causing substantial financial losses and eroding trust in financial systems. Rule-based systems and other conventional fraud detection techniques have trouble identifying complex fraudulent activity. This research applies various machine learning (ML) algorithms to detect fraud in financial transactions. The research compares the performance of supervised ML techniques, such as random forest, logistic regression, and decision tree classifier, using a publicly available Kaggle dataset. It evaluates the models based on accuracy, precision, recall, and F1 scores. To address data imbalance in both undersampling and oversampling techniques, with a focus on oversampling techniques such as Synthetic Minority Over-Sampling Method (SMOTE) to enhance model performance. Results show that the Decision Tree Classifier, when used with oversampling, outperforms other models, achieving a 99% accuracy in detecting fraudulent transactions. This highlights the effectiveness of using ensemble and hybrid models in combination with oversampling to enhance the identification of fraud. The findings emphasize the importance of using advanced ML techniques and robust data preprocessing for detection of financial fraud.

In this paper, we have investigated the real-world task of recognizing biological concepts in DNA sequences. Recognizing promoters in strings that represent nucleotides (one of A, G, T, or C) has been performed using a hybrid approach based on combining feature selection (FS), fuzzy weighted preprocessing, and C4.5 decision tree classifier (DCS). Dimensionality of E.coli Promoter Gene Sequences dataset has 57 attributes and 106 samples including 53 promoters and 53 non-promoters. The proposed approach consists of three stages. Firstly, we have used the FS process to reduce the dimensionality of E.coli Promoter Gene Sequences dataset that has 57 attributes. So the dimensionality of this dataset has been reduced to 4 attributes by means of FS process. Secondly, fuzzy weighted pre-processing has been used to weight E.coli Promoter Gene Sequences dataset that has 4 attributes in interval of [0,1]. Finally, C4.5 decision tree classifier algorithm has been run to estimation the E.coli Promoter Gene Sequences. In order to show the performance of the proposed system, we have used the predicton accuracy and 10-fold cross validation. 93.33% classification accuracy has been obtained by the proposed system using 10-fold cross validation. This success shows that the proposed system is a robust and effective system in the prediction of E.coli Promoter Gene Sequences.

In order to timely and properly analyse the customer credit assessment, and to speed up the decision-making speed, here introduced the application of data mining technology used in the bank on the users” evaluation, and establish a decision tree model of customer credit evaluation which is aimed at improving the credit rating quality . And introduced the basic process of decision tree algorithm. Bank customer’s credit is classified as background. Using the decision tree algorithm C4.5 as the most classic tools, specific studies of business understanding, data understanding, data preparation, modeling, evaluation and implementation of aspects of publishing have been done. Customer credit, using the decision tree classification algorithm, obtained a series of decision rules with which a bank to make the right decisions.

Distributed Denial of Service (DDoS) is a cyberattack in which the attacker makes a system or network resources unavailable to the intended audience temporarily or permanently. NetBIOS DDoS is a reflection based DDoS attack makes the victim system unavailable to communication other NetBIOS hosts. Service unavailable makes huge impact in terms of financially and reputational. So DDoS attack detection at early stage is more important. This study proposed machine learning algorithms for NetBIOS DDoS attack detection. Experiments are performed on NetBIOS_DrDoS dataset, which is collected from CIC-DDoS2019 evaluation dataset. In order to reduce computational overheads features are selected by Correlation methods. This study uses Pearson, spearman and Kendall correlation methods to select uncorrelated features. This study evaluated Logistic regression, Decision tree, Random forest, Ada Boost, Gradient Boost, K-Nearest Neighbour, Naive-Bayes and Multilayer perceptron classification algorithms with Pearson, Spearman and Kendall uncorrelated feature subsets in order to classify attack and benign class labels. Multilayer perceptron with Pearson uncorrelated feature subset gives the best performance for NetBIOS DDoS attack detection.

The aim of this study was to design an expert system to predict the Non-Dipping or Dipping pattern by using several basic clinical and laboratory data through an artificial intelligence algorithm. Data Mining is a technique which extracts information from data sets by using a combination of both statistical analysis methods and artificial intelligence algorithms. Also in this study, the decision tree and naivebayes classification algorithms of this technique were used. Firstly, sixty-five patients (mean age 51±7 years, 40 females,) were included in the study. Systolic and diastolic dipping were found in 13 and 15 % of the patients, respectively. In the advancing process of the experiment, the number of instances were reduced, because of some missing data of the patients. The data sets were tested using the J48 decision tree algorithm. This classification algorithm was implemented on 56 instances, and also the number of attributes was reduced from 35 to 23. 66 % of the instances (37) were reserved for training and 44 % of the instances (19) were reserved for testing. When the algorithm was run, the Non-Dipper/Dipper pattern of the instances were correctly predicted in a rate of 73.6842 %. Model was built in 0.02 seconds. This pilot study shows that a machine learning algorithm can help in the prediction of diurnal blood pressure pattern relying on some basic demographic, clinical and laboratory data, with a reasonable accuracy.

Documents classification is from most important fields for Natural language processing and text mining. There are many algorithms can be used for this task. In this paper, focuses on improving Text Classification by feature selection. This means determine some of the original features without affecting the accuracy of the work, where our work is a new feature selection method was suggested which can be a general formulation and mathematical model of Recursive Feature Elimination (RFE). The used method was compared with other two well-known feature selection methods: Chi-square and threshold. The results proved that the new method is comparable with the other methods, The best results were 83% when 60% of features used, 82% when 40% of features used, and 82% when 20% of features used. The tests were done with the Naïve Bayes (NB) and decision tree (DT) classification algorithms , where the used dataset is a well-known English data set “20 newsgroups text” consists of approximately 18846 files. The results showed that our suggested feature selection method is comparable with standard Like Chi-square.

A good and healthy life is the basic right of every human being. To achieve that, it is needed to make available better and cheaper medical facilities to everyone. There are a lot of disease a human life suffers with. Our focus in this paper is to study and analyze various classifiers algorithms to predict various liver diseases. We have compared the performance of Random Forest, Logistic Regression, Support Vector Machine, Decision Tree, Reduction Rule, Naive Bayes, and K-Nearest Neighbors classification algorithms. We have utilized an Irvine database of the University of California, namely the Indian Liver Patient Dataset (ILPD). The various characteristics of a patient such as gender, age, total bilirubins, direct bilirubins, albumins, globulin ratios, alkphos, sgot and sgpt were evaluated. The implementation of classification algorithms is done on the rapid miner tool along with Liver Patient dataset and comparison is done on the parameters such as training time, scoring time, accuracy, precision. The result of other parameters like classification error, recall is also compared on liver patient diseases dataset. We also proposed the use of a radial kernel function as a support vector machine, and it has observed that true prediction of class1 is about 81.30% having class recall is 96.15% and class2 is about 82.22% having class recall is 44.31%.

Decision tree is a data analysis method that can be used to predict the future behavior of individuals based on their past behavior. This technology is very effective in predicting the future employment situation of students, because it takes into account all relevant factors that affect a person's job prospects. In this paper, I will use decision tree classification algorithm the employment situation of college students based on their past educational background and work experience. This paper is based on the quality requirements of higher vocational education, based on the classification analysis algorithm in data mining and machine learning, and taking the enrollment, student status management and employment data of Yunnan Institute of Mechanical and Electrical Technology as samples, analyzes the employment factors of students through learning decision tree classifier, and obtains the scores of student enrollment, comprehensive quality evaluation The prediction model of the region and nature of the employment unit on the smooth employment and the type of the employment unit is designed to provide a scientific basis for the employment guidance and talent training program of higher vocational colleges, and has certain practical value.

Decision tree classification algorithms have significant potential for remote sensing data classification. This paper advances to adopt decision tree technologies to classify remote sensing images. First, this paper discussed the algorithms structure and the algorithms theory of decision tree. Second, C4.5 basic theory and boosting technology are explained. The decision tree technologies have several advantages for remote sensing application by virtue of their relatively simple, explicit and intuitive classification structure.