Data Mining Methods for Extracting Rumors Using Social Analysis Tools

This paper discusses a novel hybrid approach to text classification that integrates a machine learning algorithm along with DistilBERT, a pre-trained deep learning framework for natural language processing, offers a base model fine-tuned on Indian Banking News-Events with a rule-based method that is used by filtering false positives and dealing with false negatives to enhance the results given by the previous classifier. The major benefit is that by incorporating unique rules for certain chaotic or overlapping categories that have not been effectively trained, the system can be quickly fine-tuned. This research also compares the effectiveness of state-of-art deep contextual language representation DistilBERT used in our proposed hybrid model with the most preferred context independent language representation TFIDF, on supervised learning of the classification of multiclass Banking News-Events. Both representations are fed into the machine learning classifiers, Logistic Regression, Linear SVC, Decision Tree, and Random Forest. The findings indicate that DistilBERT can better transfer generic domain knowledge to other domains as compared to the baseline TFIDF and results into higher accuracy with Random Forest. The result is further fed into the Rules based method as per our proposed model. And, our proposed hybrid model resulted into improved accuracy.

Accurate classification of viral DNA sequences is essential for tracking mutations, understanding viral evolution, and enabling timely public health responses. Traditional alignment‐based methods are often computationally intensive and less effective for highly mutating viruses. This article presents a machine learning framework for classifying DNA sequences of COVID‐19 variants using K‐mer‐based tokenization and vectorization techniques inspired by Natural Language Processing (NLP). DNA sequences corresponding to Alpha, Beta, Gamma, and Omicron variants are obtained from the Global Initiative on Sharing All Influenza Data (GISAID) database and encoded into feature vectors. Multiple classifiers, including Extra Trees, Random Forest, Support Vector Classifier (SVC), Decision Tree, Logistic Regression, Naive Bayes, K‐Nearest Neighbor (KNN), Ridge Classifier, Stochastic Gradient Descent (SGD), and XGBoost, are evaluated based on accuracy, precision, recall, and F1‐score. The Extra Trees model achieved the highest accuracy of 93.10% 0.42, followed by Random Forest with 92.60% 0.38, both demonstrating robust and balanced performance. Statistical significance tests confirmed the robustness of the results. The results validate the effectiveness of K‐mer‐based encoding combined with traditional machine learning models in classifying COVID‐19 variants, offering a scalable and efficient solution for genomic surveillance.

Gesture recognition is a topic in computer science and language technology that aims to interpret human gestures with computer programs and many different algorithms. It can be seen as the way computers can understand human body language. Today, the main interaction tools between computers and humans are still the keyboard and mouse. Gesture recognition can be used as a tool for communication with the machine and interaction without any mechanical device such as a keyboard or mouse. In this paper, we present the results of a comparison of eight different machine learning (ML) classifiers in the task of human hand gesture recognition and classification to explore how to efficiently implement one or more tested ML algorithms on an 8-bit AVR microcontroller for on-line human gesture recognition with the intention to gesturally control the mobile robot. The 8-bit AVR microcontrollers are still widely used in the industry, but due to their lack of computational power and limited memory, it is a challenging task to efficiently implement ML algorithms on them for on-line classification. Gestures were recorded by using inertial sensors, gyroscopes, and accelerometers placed at the wrist and index finger. One thousand and eight hundred (1800) hand gestures were recorded and labelled. Six important features were defined for the identification of nine different hand gestures using eight different machine learning classifiers: Decision Tree (DT), Random Forests (RF), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) with linear kernel, Naïve Bayes classifier (NB), K-Nearest Neighbours (KNN), and Stochastic Gradient Descent (SGD). All tested algorithms were ranged according to Precision, Recall, and F1-score (abb.: P-R-F1). The best algorithms were SVM (P-R-F1: 0.9865, 0.9861, and 0.0863), and RF (P-R-F1: 0.9863, 0.9861, and 0.0862), but their main disadvantage is their unusability for on-line implementations in 8-bit AVR microcontrollers, as proven in the paper. The next best algorithms have had only slightly poorer performance than SVM and RF: KNN (P-R-F1: 0.9835, 0.9833, and 0.9834) and LR (P-R-F1: 0.9810, 0.9810, and 0.9810). Regarding the implementation on 8-bit microcontrollers, KNN has proven to be inadequate, like SVM and RF. However, the analysis for LR has proved that this classifier could be efficiently implemented on targeted microcontrollers. Having in mind its high F1-score (comparable to SVM, RF, and KNN), this leads to the conclusion that the LR is the most suitable classifier among tested for on-line applications in resource-constrained environments, such as embedded devices based on 8-bit AVR microcontrollers, due to its lower computational complexity in comparison with other tested algorithms.

Machine learning (ML) models are cost-effective tools that can establish a prognosis by considering clinical, genomic, and histological data from breast cancer patients. In this study, we evaluate the performance of 12 ML models in predicting the overall survival of breast cancer patients from “The Cancer Genome Atlas-Breast Cancer” database at 60 months of follow-up. The models evaluated were Logistic Regression (LR), Ridge Classifier (RC), Least Absolute Shrinkage and Selection Operator (LASSO), K-Nearest Neighbors (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Decision Tree (DT), Multilayer Perceptron (MP), Stochastic Gradient Descent (SGD), Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB). The most accurate models were NB (86.76%) and SGD (85.29%). The models with the highest sensitivity were SGD (93.75%), MP (91.67%), and NB (89.89%), while the models with the highest specificity were KNN (95.0%), DT (90.0%), and LASSO (85.0%). The models with the highest area under the ROC curve were LDA (90.83%), LASSO (90.1%), and LR (89.79%). The most relevant variables were “Previous Diagnosis of Cancer,” “Presence of Tumor,” “Ancillary Therapy,” and “Histology.” Our findings demonstrate that AI tools for predicting patient outcomes are a viable option when expensive prognostic genomic tools are not available. According to our results, NB and LASSO show promising performance in predicting the prognosis of breast cancer patients. Further studies with data from developing countries are needed to improve the performance of these tools.

Text mining is a special case of data mining which explore unstructured or semi-structured text documents, to establish valuable patterns and rules that indicate trends and significant features about specific topics. Text mining has been in pattern recognition, predictive studies, sentiment analysis and statistical theories in many areas of research, medicine, financial analysis, social life analysis, and business intelligence. Text mining uses concept of natural language processing and machine learning. Machine learning algorithms have been used and reported to give great results, but their performance of machine learning algorithms is affected by factors such as dataset domain, number of classes, length of the corpus, and feature selection techniques used. Redundant attribute affects the performance of the classification algorithm, but this can be reduced by using different feature selection techniques and dimensionality reduction techniques. Feature selection is a data preprocessing step that chooses a subset of input variable while eliminating features with little or no predictive information. Feature selection techniques are Information gain, Term Frequency, Term Frequency-Inverse document frequency, Mutual Information, and Chi-Square, which can use a filters, wrappers, or embedded approaches. To get the most value from machine learning, pairing the best algorithms with the right tools and processes is necessary. Little research has been done on the effect of feature selection techniques on classification accuracy for pairing of these algorithms with the best feature selection techniques for optimal results. In this research, a text classification experiment was conducted using incident management dataset, where incidents were classified into their resolver groups. Support vector machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB) and Decision tree (DT) machine learning algorithms were examined. Filtering approach was used on the feature selection techniques, with different ranking indices applied for optimal feature set and classification accuracy results analyzed. The classification accuracy results obtained using TF were, 88% for SVM, 70% for NB, 79% for Decision tree, and KNN had 55%, while Boolean registered 90%, 83%, 82% and 75%, for SVM, NB, DT, and KNN respectively. TF-IDF, had 91%, 83%, 76%, and 56% for SVM, NB, DT, and KNN respectively. The results showed that algorithm performance is affected by feature selection technique applied. SVM performed best, followed by DT, KNN and finally NB. In conclusion, presence of noisy data leads to poor learning performance and increases the computational time. The classifiers performed differently depending on the feature selection technique applied. For optimal results, the classifier that performed best together with the feature selection technique with the best feature subset should be applied for all types of data for accurate classification performance. Keywords: Text Classification, Supervised Machine Learning, Feature Selection DOI : 10.7176/JIEA/9-3-06 Publication date :May 31 st 2019

Chapter 9 - An intelligent deep learning approach for colon cancer diagnosis

Student’s mental health problems have been explored previously in higher education literature in various contexts including empirical work involving quantitative and qualitative methods. Nevertheless, comparatively few research could be found, aiming for computational methods that learn information directly from data without relying on set parameters for a predetermined equation as an analytical method. This study aims to investigate the performance of Machine learning (ML) models used in higher education. ML models considered are Naïve Bayes, Support Vector Machine, K-Nearest Neighbor, Logistic Regression, Stochastic Gradient Descent, Decision Tree, Random Forest, XGBoost (Extreme Gradient Boosting Decision Tree), and NGBoost (Natural) algorithm. Considering the factors of mental health illness among students, we follow three phases of data processing: segmentation, feature extraction, and classification. We evaluate these ML models against classification performance metrics such as accuracy, precision, recall, F1 score, and predicted run time. The empirical analysis includes two contributions: 1. It examines the performance of various ML models on a survey-based educational dataset, inferring a significant classification performance by a tree-based XGBoost algorithm; 2. It explores the feature importance [variables] from the datasets to infer the significant importance of social support, learning environment, and childhood adversities on a student’s mental health illness.

The increasing number of untruths on social media has become a critical concern, affecting public sentiment and confidence. The broad spread of misleading information on the Internet and other social platforms presents a substantial barrier, exerting an impact on public sentiment, influencing political discussions, and eroding the reliability of information sources. Identifying false information on the X platform, previously known as Twitter, is an intricate task because of the network's attributes, such as conciseness, swift spread, and varied user engagements. Extracting crucial information from brief texts, such as tweets, is challenging, even with precise labeling. This study focuses on recognizing misinformation on social media platforms. The CIC Truth-Seeker Dataset 2023, one of the most extensive datasets in its category, contains over 134,000 labeled tweets. The study introduces novel methods in the field of short text classification, incorporating machine learning and natural language processing techniques (NLP). These techniques involve feature extraction using the term frequency-inverse document frequency (TF-IDF) algorithm after the dataset is preprocessed. The study then tests a number of machine learning models, including Random Forest RF, K-Nearest Neighbor KNN, Decision Tree DT, Logistic Regression LR, Naive Bayes NB, and stochastic gradient descent SGD, to see which ones can tell the most accurate difference between real and fake tweets. The findings demonstrated significant advancements in models designed to handle short text effectively, effectively addressing a practical issue such as automatically identifying fake content on social media platforms. Furthermore, we have achieved a significant advantage over previous research on the same dataset. When implementing the models on the news data, the random forest method attained the utmost accuracy at 93%, while the K-Nearest Neighbor strategy yielded a lower accuracy of 68%. This research paper aims to offer helpful information and practical answers to recognizing and reducing false news on social media platforms, specifically focusing on the X platform. Through a Truth-Seeker dataset, we will utilize machine learning methods to enhance previous text classification models.

This study’s main purpose is to provide helpful information using blood samples from COVID-19 patients as a non-medical approach for helping healthcare systems during the pandemic. Also, this paper aims to evaluate machine learning algorithms for predicting the survival or death of COVID-19 patients. We use a blood sample dataset of 306 infected patients in Wuhan, China, compiled by Tangji Hospital. The dataset consists of blood’s clinical indicators and information about whether patients are recovering or not. The used methods include K-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), support vector machine (SVM), random forest (RF), stochastic gradient descent (SGD), bagging classifier (BC), and adaptive boosting (AdaBoost). We compare the performance of machine learning algorithms using statistical hypothesis testing. The results show that the most critical feature is age, and there is a high correlation between LD and CRP, and leukocytes and CRP. Furthermore, RF, SVM, DT, AdaBoost, DT, and KNN outperform other machine learning algorithms in predicting the survival or death of COVID-19 patients.

The proliferation of fake news across online media platforms poses significant challenges to public trust,societal harmony,and information integrity.Many of the things that people read online, particularly on social media, seem true, but they are frequently not. Fake news is defined as news, articles, or hoaxes that are purposefully created to mislead or deceive readers..This study develops a machine learning-based system for detecting fake news on digital platforms. Utilizing algorithms such as K-Nearest Neighbors (KNN), Naive Bayes, Logistic Regression, Random Forest, Gradient Boosting and Support Vector Machines (SVM), the system processes textual data using natural language processing (NLP) techniques like term frequency-inverse document frequency (TF-IDF) and bag-of- words for feature extraction.The performance of each algorithm is evaluated using metrics such as accuracy, precision, recall, and F1-score to identify the most effective model. By identifying the most effective machine learning model, this research aims to provide a scalable solution that can help reduce misinformation and enhance the credibility of online information.By distinguishing the best A1 model,this expectation expects to give a versatile arrangement that can assist with lessening deception and improve the believability of online data. Keywords- News and Fake,Natural Language Processing,Feature Extraction,a classification and Model Evaluation

Parkinson’s disease (PD) is a nervous system disorder first described as a neurological condition in 1817. It is one of the more prevalent diseases in the elderly, and Alzheimer’s is the second most common neurodegenerative illness. It impacts the patient’s movement. Symptoms start gradually with tremors, stiffness in movement, and speech and voice disorders. Researches proved that 89% of patients with Parkinson’s has speech disorder including uncertain articulation, hoarse and breathy voice and monotone pitch. The cause behind this voice change is the reduction of dopamine due to damage of neurons in the substantia nigra responsible for dopamine production. In this work, Parkinson’s disease is classified with the help of human voice signals. Six different machine learning (ML) algorithms are used in the classification: Stochastic Gradient Descent (SGD) Classifier, Extreme Gradient Boosting (XGB) Classifier, Logistic Regression Classifier, Random Forest Classifier, K-Nearest Neighbour (KNN) Classifier, and Decision Tree (DT) Classifier. This research aims to classify Parkinson’s disease using human voice signals and extract essential features to reduce the complexity of the dataset. Then, human voice signals are analyzed to check the voice intensity and spectrum for PD patients. Then, machine learning classifiers are applied to classify the PD patients based on the extracted features. The results show that SGD-Classifier has 91% accuracy, XGB-Classifier has 95% accuracy, Logistic Regression has 91% accuracy, Random Forest shows 97% accuracy, KNN shows 95% accuracy, and Decision Tree has 95% accuracy. Hence, Random Forest has the highest accuracy. The disease can be studied more by looking for more characteristics of PD patients to enhance its proper use in the medical field.

Prediction of cause of death from forensic autopsy reports using text classification techniques: A comparative study

Text classifications have been popular in recent years. To classify the text, the first step that needs to be done is to convert the text into some value. Some values that can be used, such as Term Frequencies, Inverse Document Frequencies, Term Frequencies – Inverse Document Frequencies, and Frequency of the word itself. This study aims to get which metric value is best in text classification. The method used is Naïve Bayes, Logistic Regression, and Random Forest. The evaluation score that is used is accuracy and Area Under Curve value. It comes out that some metric values produce similar evaluation scores. Another finding is that Random Forest is the best method among others, also the best metric for text classification is Term Frequencies – Inverse Document Frequencies.

The classification of autism spectrum disorder by machine learning methods on multiple datasets for four age groups

Most previous studies have neglected the potential of integrating structured data and unstructured workplace injury reports to perform a predictive analysis of occupational injury severity. This study proposes an optimized integrated approach for occupational injury severity prediction using multimodal machine and deep learning techniques. We used 66,405 data points gathered from the US OSHA Severe Injury Reports from January 2015 to July 2021. Structured labeled data are preprocessed and normalized, whereas unstructured injury reports undergo text cleaning using Natural Language Processing techniques, tokenized, and text representation using Term Frequency-Inverse Document Frequency (TF-IDF) and Global Vector (GloVe) to convert them into numerical representations. Both modalities, in the form of vector representations, were concatenated and fed as input features for the proposed models. Seven sets of classifiers, namely Naïve Bayes, Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbors, Long Short-Term Memory, and Bidirectional Long Short-Term Memory, were employed to learn the multimodal representations. The algorithm with superior performance was further optimized using the proposed feature importance and hyperparameter optimization techniques. Our findings revealed that the proposed optimized-Bi-LSTM architecture outperformed other classifiers in learning multimodal data to predict the likelihood of hospitalization and amputation with higher accuracies of 0.93 and 0.99, respectively. Consequently, the proposed approach enhances the performance by significantly improving the model processing time. This performance prediction provides a convincing benchmark for the successful execution of multimodal deep learning in occupational injury research. Therefore, the proposed multimodal occupational injury severity prediction model enhances the early screening and identification of at-risk workers with severe occupational injury outcomes, as well as, provides valuable information to improve the workplace safety, health, and well-being of the workers.