Decision Tree Learning Algorithm Research Articles

A novel ensemble approach for Twitter sentiment classification with ML and LSTM algorithms for real-time tweets analysis

Social media sentiment classification was an essential consideration in natural language processing (NLP) for evaluating normal people’s perspectives on a given topic. With Twitter’s massive rise in popularity in recent years, the capacity to extract information about public sentiment from tweets became a major focus. This paper not only analyzed public sentiment through data from Twitter but introduced a novel ensemble approach in the methods employed for Twitter sentiment classification. Real-time tweets on various topics, including “covid,” “crime,” “spam,” “flipkart,” “migraine,” and “airlines,” were extracted and thoroughly examined to gain insight into public opinions. Leveraging the Twitter API for real-time tweet extraction, natural language processing techniques were applied to clean the tweet data. Subsequently, we applied several machine learning (ML) algorithms Naïve Bayes, decision tree (DT), random forest (RF), logistic regression (LGR), and deep learning (DL) algorithms recurrent neural network (RNN), LSTM, and GRU individually. Later, we proposed a novel ensemble of ML and DL algorithms for sentiment classification, with a novel emphasis on ensemble techniques and enhanced the accuracy with a significance compared to individual ML or DL model applied. The experimental results demonstrated that our novel ensemble approach achieved high accuracy when compared to existing work.

High-performance grating-like SERS substrate based on machine learning for ultrasensitive detection of Zexie-Baizhu decoction

Rapid, universal and accurate identification of chemical composition changes in multi-component traditional Chinese medicine (TCM) decoction is a necessary condition for elucidating the effectiveness and mechanism of pharmacodynamic substances in TCM. In this paper, SERS technology, combined with grating-like SERS substrate and machine learning method, was used to establish an efficient and sensitive method for the detection of TCM decoction. Firstly, the grating-like substrate prepared by magnetron sputtering technology was served as a reliable SERS sensor for the identification of TCM decoction. The enhancement factor (EF) of 4-ATP probe molecules was as high as 1.90 × 107 and the limit of detection (LOD) was as low as 1 × 10−10 M. Then, SERS technology combined with support vector machine (SVM), decision tree (DT), Naive Bayes (NB) and other machine learning algorithms were used to classify and identify the three TCM decoctions, and the classification accuracy rate was as high as 97.78 %. In summary, it is expected that the proposed method combining SERS and machine learning method will have a high development in the practical application of multi-component analytes in TCM.

Multiple Disease Prediction Using Machine Learning

Machine learning, which is a type of computer technology, has changed healthcare a lot. It helps doctors predict diseases better and faster. In healthcare, using machine learning algorithms decision tree (DT), logistic regression (LR), support vector machine (SVM) that can help predict lots of different diseases at the same time. This helps doctors find and treat illnesses early, which makes patients better and saves money on healthcare. This paper looks at how we can use computer programs that learn from data to predict many diseases. It talks about why this is good, what problems we might face, and where we might go next with it. We give a summary of the several machine learning models and information sources that are often employed in illness prediction. We also go over the significance of feature selection, model assessment, and combining several data modalities for improved illness prediction. We give a summary of the several machine learning models and information sources that are often employed in illness prediction. We also go over the significance of feature selection, model assessment, and combining several data modalities for improved illness prediction. The research shows that using machine learning algorithms to predict many diseases at once could really help public health. Again, we use a machine learning model to determine whether or not an individual is impacted by a few diseases. This training model trains itself to predict illness using sample data.

Analysis of Emerging Variants of Turkey Reovirus using Machine Learning.

Avian reoviruses continue to cause disease in turkeys with varied pathogenicity and tissue tropism. Turkey enteric reovirus has been identified as a causative agent of enteritis or inapparent infections in turkeys. The new emerging variants of turkey reovirus, tentatively named turkey arthritis reovirus (TARV) and turkey hepatitis reovirus (THRV), are linked to tenosynovitis/arthritis and hepatitis, respectively. Turkey arthritis and hepatitis reoviruses are causing significant economic losses to the turkey industry. These infections can lead to poor weight gain, uneven growth, poor feed conversion, increased morbidity and mortality and reduced marketability of commercial turkeys. To combat these issues, detecting and classifying the types of reoviruses in turkey populations is essential. This research aims to employ clustering methods, specifically K-means and Hierarchical clustering, to differentiate three types of turkey reoviruses and identify novel emerging variants. Additionally, it focuses on classifying variants of turkey reoviruses by leveraging various machine learning algorithms such as Support Vector Machines, Naive Bayes, Random Forest, Decision Tree, and deep learning algorithms, including convolutional neural networks (CNNs). The experiments use real turkey reovirus sequence data, allowing for robust analysis and evaluation of the proposed methods. The results indicate that machine learning methods achieve an average accuracy of 92%, F1-Macro of 93% and F1-Weighted of 92% scores in classifying reovirus types. In contrast, the CNN model demonstrates an average accuracy of 85%, F1-Macro of 71% and F1-Weighted of 84% scores in the same classification task. The superior performance of the machine learning classifiers provides valuable insights into reovirus evolution and mutation, aiding in detecting emerging variants of pathogenic TARVs and THRVs.

CT Image-Based Radiomic Analysis for Detecting PD-L1 Expression Status in Bladder Cancer Patients

Rationale and ObjectivesThe role of Programmed death-ligand 1 (PD-L1) expression is crucial in guiding immunotherapy selection. This study aims to develop and evaluate a radiomic model, leveraging Computed Tomography (CT) imaging, with the objective of predicting PD-L1 expression status in patients afflicted with bladder cancer. Materials and MethodsThe study encompassed 183 subjects diagnosed with histologically confirmed bladder cancer, among which the PD-L1(+) cohort constituted 60.1% of the total population. Stratified random sampling was utilized at a 7:3 ratio. We employed five diverse machine learning algorithms—Decision Tree, Random Forest, Linear Support Vector Classification, Support Vector Machine, and Logistic Regression—to establish radiomic models on the training dataset. These models endeavored to predict PD-L1 expression status premised on radiomic features derived from region-of-interest (ROI) segmentation. Subsequent to this, the predictive performance of these models was examined on a validation set employing the receiver operating characteristic (ROC) curve. The DeLong test was utilized to contrast ROC curves, thereby pinpointing the model with superior predictive accuracy. ResultsSixteen features were chosen for the model construction. All five models revealed strong performance in the training set (AUC, 0.920-1) and commendable predictive ability in the validation set (AUC, 0.753-0.766). As per the DeLong test, no statistically significant disparities were observed among any of the models (P>0.05) in the validation set. Additional verification through the calibration curve and decision curve analysis indicated that the Logistic Regression model exhibited extraordinary precision and practicality. ConclusionOur machine learning model, grounded on radiomic features, demonstrated its proficiency in accurately distinguishing bladder cancer patients with high PD-L1 expression. Future research, incorporating more exhaustive datasets, could potentially augment the predictive efficiency of radiomic algorithms, thereby advancing their clinical utility.

Evaluating trustworthiness of decision tree learning algorithms based on equivalence checking

Evaluating trustworthiness of decision tree learning algorithms based on equivalence checking

Active intrusion detection and prediction based on temporal big data analytics

Computer security consists in protecting access and manipulating system data by several mechanisms. However, conventional protection technologies are ineffective against current attacks. Thus, new tools have appeared, such as the intrusion detection and prediction systems which are important defense elements for network security since they detect the ongoing intrusions and predict the upcoming attacks. Besides, most of conventional protection technologies remain insufficient in terms of actions since they are all passive systems, unable to provide recommendations in order to block or stop the attacks. In this paper, a distributed detection and prediction system, composed of three major parts, is proposed. The first part deals with the detection of intrusions based on the decision tree learning algorithm. The second part deals with intrusions prediction using the chronicle algorithm. The third part proposes an expert system for security recommendations in response to detected intrusions, able to provide appropriate recommendations to stop the attacks. The proposed system gives good results in terms of accuracy and precision in detecting and predicting attacks, and efficiency in proposing the right recommendations to stop the attacks.

Application of regression decision tree and machine learning algorithms to examine students’ online learning preferences during COVID-19 pandemic

The emergence of the novel coronavirus (COVID-19) profoundly disrupted the field of education, ushering in an era of widespread online learning adoption. This research paper seeks to investigate the multifaceted factors influencing students' preferences for online learning. Employing data exploration techniques and machine learning algorithms, the study aimed to identify the pivotal variables affecting students' willingness and performance in online educational environments. The research encompassed data collection through designated questionnaires and the application of decision tree-based machine learning algorithms to analyze these diverse factors. Through this approach, seven specific prerequisites were derived, employing multiple linear regression analysis within the decision tree framework, to illuminate the relationships between these factors. Key aspects considered in these prerequisites included factors such as "internet connectivity issues," "COVID-19 pandemic-induced stress," "COVID-19 vaccination status," and "close relatives' COVID-19 infections". Foremost among the reasons for students' reluctance to embrace online learning was the presence of "internet difficulties," including issues like slow connections and frequent disruptions. From the results of this research, it can be concluded that basic computer and internet courses can be beneficial for encouraging online education. Findings of this study underscore the potential benefits of offering basic computer and internet courses as a means to encourage and facilitate effective online education, particularly in the context of the COVID-19 pandemic.

Two-level optimization by differential evolution in decision tree learning algorithm

Decision tree learning algorithms have a long history, but they are still popular due to their efficiency. Tree construction starts at the root and continues to each leaf node, creating a “near-optimal” tree. One of the key steps in creating a decision tree is the selection of a feature to split at each root node, which affects the classification accuracy. This process can be quite labor intensive. The article proposes a new approach to constructing decision trees based on the use of differential evolution for two-level tree optimization. Differential evolution works in two stages: at the first level, a feature is selected for separation, and at the second level, the threshold value for this feature is optimized. The results of the work were tested on several examples of classification problems.

Analyzing electric vehicle battery health performance using supervised machine learning

Lithium-ion batteries having high energy and power densities, fast depleting cost, and multifaceted technological improvement lead to the first choice for electric transportation systems. A noble supervised K nearest neighbors (KNN), support vector regressor (SVR), decision tree (DT), and random forest (RF) regressors machine learning algorithm are developed with different accuracy and usefulness for the state of health estimation from direct measurable indices of voltage, current, and temperature without inherited electrochemical characteristics like voltage hysteresis, aging, degradation level, operational, and environmental effect. Based on the battery dataset from Sandia National Laboratories, the proposed technique is validated. The comparison characteristics are provided with the help of mean absolute percent error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) techniques. The MAPE error at 15 °C for different regression algorithms SVR, DT, KNN, and RF are 2.791E-02, 1.607E-03, 9.957E-04, and 4.323E-03 respectively. The MAE error at 25 °C for different regression algorithms SVR, DT, KNN, and RF are 5.593E-02, 2.379E-03, 2.429E-03, and 5.073E-03 respectively. The RMSE error at 35 °C for different regression algorithms SVR, DT, KNN, and RF are 5.062E-02, 6.862E-03, 6.333E-03, and 1.275E-02 respectively. The MSE error percentages for the testing sample at 35 °C for different regression algorithms are 0.37 %, 0.01 %, 0.01 %, and 0.02 % respectively. The results indicate that KNN and DT exhibit higher precision when applied to lithium-ion batteries for electric vehicles. Collaborating with hardware manufacturers to exchange data has the potential to foster the creation of innovative battery health monitoring systems.

Enhancing LAN Failure Predictions with Decision Trees and SVMs: Methodology and Implementation

Predicting Local Area Network (LAN) equipment failure is of utmost importance to ensure the uninterrupted operation of modern communication networks. This study explores the use of machine learning algorithms to enhance the accuracy of equipment failure prediction in LAN environments. Using these algorithms to enhance LAN failure predictions involves collecting and analyzing network data, such as packet loss rates and latency, to identify patterns and anomalies. These algorithms can then predict potential LAN failures by recognizing early warning signs and deviations from normal network behavior. By leveraging machine learning, network administrators can proactively address issues, reduce downtime, and improve overall network reliability. In our study, two powerful machine learning algorithms—decision tree and support vector machine (SVM)—are used. To evaluate the effectiveness of the proposed models, a comprehensive dataset comprising various LAN equipment parameters and corresponding failure instances is utilized. The dataset is pre-processed to handle missing values and normalize features, ensuring the algorithms’ optimal performance. Performance metrics, such as accuracy, precision, recall, and F1-score, are employed to assess the predictive capabilities of the models. The excremental results of our study lead to more reliable and stable network operations by allowing early detection of potential issues and preventive maintenance. This leads to reduced downtime, improved network performance, and enhanced overall user satisfaction. They demonstrate the efficacy of both decision tree and SVM algorithms in accurately predicting LAN equipment failure.

Examining the distance education process in terms of professional competencies of maritime students

ABSTRACT Measures were implemented between 2020 and 2022 to encourage people to stay at home in the context of the COVID-19 pandemic. One of these measures was the transition to remote and virtual education in many countries’ educational institutions. This study aims to predict the professional competencies of final-year undergraduate students selected from five maritime faculties in the spring semester of 2020-2021. To make this prediction, a survey was conducted with 52 inexperienced deck officers, and the data regarding their self-efficacy beliefs were collected. The officer's questionnaire answers were entered into the Orange data-mining program, and the decision tree learning algorithm was obtained to quantitatively evaluate the pupils’ professional competencies. The student's future professional performance was predicted thanks to the algorithm that was developed. As a result of the study, it has been determined that the most challenging competencies are Cargo Handling, Maritime English and Ship Handling. Furthermore, according to the decision tree learning algorithm, the distance education process has been determined to influence students’ future professional performance negatively. As a result of the study, a unique method application has been brought to the literature for evaluating self-efficacy belief levels. Finally, suggestions have been made regarding the deficiencies observed in education.

A Predictive Performance Analysis of Vitamin D Deficiency Using a Decision Tree model

Background. HBV infection is a major health problem which may be life-threatening. Vitamin D (VD) is involved in various pathophysiological mechanisms in a plethora of diseases. And also, there is a strong demand for the prediction of its severity using different methods. The study aims to evaluate performance of DT as one of the machine learning models in the prediction of severity in vitamin D deficiency. Methods. In total, data containing serum VD levels were collected from 292 CHB patients. The independent characteristics such as: age, sex, weight, height, zinc, BMI, body fat, sunlight exposure, and milk consumption were used for prediction of VD deficiency. 60% of them were allocated to a training dataset randomly. To evaluate the performance of decision-tree the remaining 40% were used as the testing dataset. The validation of the model was evaluated by ROC curve. Results. The prevalence of VD deficiency was high among patients (63.0%). The final experimentation results showed that DT classifier achieves better accuracy of 96 % and outperforms well on training and testing of VD dataset. Also, the areas under the ROC curve AUC is 0.78, when we applied DT algorithm with the significant variables by cross validation, the values of AUC = 0.78 and 85.3% accuracy were obtained. Conclusion. We concluded that the serum level of Zn is an important associated risk factor for identifying cases with vitamin D deficiency. Also, the risk of VD deficiency could be predicted with high accuracy using decision tree learning algorithm that could be used for antiviral therapy in CHB patients.

Exclusive Biosynthesis of Pullulan Using Taguchi's Approach and Decision Tree Learning Algorithm by a Novel Endophytic Aureobasidium pullulans Strain.

Pullulan is a biodegradable, renewable, and environmentally friendly hydrogel biopolymer, with potential uses in food, medicine, and cosmetics. New endophytic Aureobasidium pullulans (accession number; OP924554) was used for the biosynthesis of pullulan. Innovatively, the fermentation process was optimized using both Taguchi's approach and the decision tree learning algorithm for the determination of important variables for pullulan biosynthesis. The relative importance of the seven tested variables that were obtained by Taguchi and the decision tree model was accurate and followed each other's, confirming the accuracy of the experimental design. The decision tree model was more economical by reducing the quantity of medium sucrose content by 33% without a negative reduction in the biosynthesis of pullulan. The optimum nutritional conditions (g/L) were sucrose (60 or 40), K2HPO4 (6.0), NaCl (1.5), MgSO4 (0.3), and yeast extract (1.0) at pH 5.5, and short incubation time (48 h), yielding 7.23% pullulan. The spectroscopic characterization (FT-IR and 1H-NMR spectroscopy) confirmed the structure of the obtained pullulan. This is the first report on using Taguchi and the decision tree for pullulan production by a new endophyte. Further research is encouraged for additional studies on using artificial intelligence to maximize fermentation conditions.

Machine Learning for Detecting the WestRock Ransomware Attack Using BGP Routing Records

Border Gateway Protocol (BGP) enables Internet data routing. Hence, its anomalies affect Internet connectivity and cause routing discon-nections, route flaps, and oscillations. Detection of anomalous BGP routing dynamics is a topic of great interest in cybersecurity. In this article, we survey machine learning algorithms for detecting BGP anomalies and intrusions. Gradient boosting decision tree and deep learning algorithms are evaluated by creating models using collected routing records during the WestRock ransomware event. BCPGuard, a BGP anomaly detection tool, has been developed to integrate various stages of the anomaly detection process.

A Novel Assistive Glove to Convert Arabic Sign Language into Speech

People with speech disorders often communicate through special gestures and sign language gestures. However, other people around them might not understand the meaning of those gestures. The research described in this article is aimed at providing an assistive device to help those people communicate with others by translating their gestures into a spoken voice that others can understand. The proposed device includes an electronic glove that is worn on the hand. It employs an MPU6050 accelerometer/gyro with 6 degrees of freedom to continuously monitor hand orientation and movement, plus a potentiometer for each finger, to monitor changes in finger posture. The signals from the MPU6050 and the potentiometers are routed to an Arduino board, where they are processed to determine the meaning of each gesture, which is then voiced using the audio streams stored in an SD memory card. The audio output drives a speaker, allowing the listener to understand the meaning of each gesture. We built a database with the help of 10 deaf people who cannot speak. We asked them to wear the glove while performing a set of 40 Arabic sign language words and recorded the resulting data stream from the glove. That data was then used to train seven different learning algorithms. The results showed that the Decision Tree learning algorithm achieved the highest accuracy of 98%. A usability study was then conducted to determine the usefulness of the assistive device in real-time.

Cytogenetic Abnormalities for Predicting the Risk of Metastases in Choroidal and Ciliary Body Melanoma.

Choroidal melanoma (CM) and ciliary body melanoma (CBM) are the two most common subtypes of uveal melanoma. Starting from the observation that CBM tends to have a higher metastatic potential than CM, we hypothesized that specific cytogenetic abnormalities could be associated with tumor location - reflecting distinct genetic signatures that would drive the risk of distant spread. Chromosomal alterations were investigated by molecular cytogenetic techniques in 217 and 97 patients with CM and CBM, respectively. Cox proportional hazards regression analysis was used to identify the independent predictors of distant metastasis. Patients with CBM had larger tumor sizes (P < 0.001), higher disease stages (P < 0.001), and more frequently showed distant metastasis (P = 0.002) than those with CM. On analyzing the entire study cohort, we found that specific chromosomal alterations - including chromosome 8p loss (P < 0.001), 1p loss (P < 0.001), and monosomy 3 (P < 0.005) - were independent predictors of distant metastasis. Based on a decision-tree learning algorithm, we identified three specific subgroups of patients with uveal melanoma at high risk of distant spread. Monosomy 3 occurred significantly more frequently in patients with T3 CBM tumors. Specific cytogenetic abnormalities - including chromosome 8p loss, 1p loss, and monosomy 3 - are independent risk factors for distant metastasis in uveal melanoma. Larger tumor size at presentation and monosomy 3 contribute to a higher metastatic risk in patients with CBM.

Fuzzy Gaussian Decision Tree

The Decision Tree algorithm is one of the first machine learning algorithms developed. It is used both as a standalone model and as an ensemble of many cooperating trees like Random Forest, AdaBoost, Gradient Boosted Trees, or XGBoost. In this work, a new version of the Decision Tree was developed for classifying real-world signals using Gaussian distribution functions and a fuzzy decision process. The research was carried out on Power Quality Classification Dataset hosted on the platform kaggle.com for accessing the algorithm’s classification. The proposed algorithm modification produces a sparse tree of multidimensional Gaussian kernels performing fuzzy, proximity-based division of solution space instead of a typical Decision Tree performing definite space restrictions. The machine learning model can cluster samples based on the common pattern and evaluate the input’s similarity in a fuzzified fashion. The studies were conducted for the prediction of 6 classes of current. The proposed modification achieved the best accuracy and F1 score compared to the default Decision Tree and other machine learning algorithms.

Violence Detection with Machine Learning: A Sociodemographic Approach

This study suggests that by implementing machine learning methods on a sociodemographic data set can be helpful in preventing domestic violence. This approach is important in predicting high-risk factors that an offender may cause and it offers treatment, and financial or mental health aids in order to prevent domestic violence. In this sense, this proposal is critical at a personal and social level in creating a secure and healthy environment as well as empowering an equal society. In our study, we use k-nearest neighbor (k-nn), support vector machine (SVM), decision tree (DT), and Gaussian Naive Bayes (GNB) machine learning algorithms for the prediction analysis. We provide the comparison of the classifiers with precision, recall, F1 score, and accuracy performance measures. According to our analysis, the decision tree (DT) performs the best performance in terms of accuracy.

Rural Landscape Design Data Analysis Based on Multi-Media, Multi-Dimensional Information Based on a Decision Tree Learning Algorithm

Rural Landscape Design Data Analysis Based on Multi-Media, Multi-Dimensional Information Based on a Decision Tree Learning Algorithm