Passive Aggressive Classifier Research Articles

Enhancing brain cancer type prediction through machine learning algorithms and feature selection techniques

Abstract The manual identification of brain cancer types is often fraught with inaccuracies, leading to potential delays in diagnosis and treatment planning. This study presents a novel approach to predict brain cancer types using advanced machine learning (ML) algorithms integrated with sophisticated feature selection techniques. A multi-class classification framework was developed and evaluated, incorporating six ML models: Bernoulli Naive Bayes, K-nearest neighbors classifier, decision tree classifier, Gaussian process classifier (GPC), passive aggressive classifier, and perceptron. To enhance model performance, feature selection methods including the Gini index, mutual information, and principal component analysis (PCA) were employed. A comprehensive case study was conducted to assess the predictive accuracy of these models. The GPC, when trained and validated on features derived via PCA, outperformed other models in terms of predictive accuracy and generalization. Specifically, the dimensions identified by PCA (d1, d2, d3, and d4) were most effective in distinguishing between different brain cancer types. This methodology resulted in a significant improvement across various performance metrics. Compared to the baseline GPC model using all original features, the PCA-enhanced GPC achieved remarkable increases in Accuracy, Precision, Recall, and F1 Score by 294.31%, 22.14%, 294.31%, and 878.18%, respectively. These findings underscore the potential of combining ML algorithms with targeted feature selection techniques to advance the accuracy of brain cancer type prediction, offering substantial benefits for clinical decision-making and patient outcomes.

A Predictive Model for Benchmarking the Performance of Algorithms for Fake and Counterfeit News Classification in Global Networks.

The pervasive spread of fake news in online social media has emerged as a critical threat to societal integrity and democratic processes. To address this pressing issue, this research harnesses the power of supervised AI algorithms aimed at classifying fake news with selected algorithms. Algorithms such as Passive Aggressive Classifier, perceptron, and decision stump undergo meticulous refinement for text classification tasks, leveraging 29 models trained on diverse social media datasets. Sensors can be utilized for data collection. Data preprocessing involves rigorous cleansing and feature vector generation using TF-IDF and Count Vectorizers. The models' efficacy in classifying genuine news from falsified or exaggerated content is evaluated using metrics like accuracy, precision, recall, and more. In order to obtain the best-performing algorithm from each of the datasets, a predictive model was developed, through which SG with 0.681190 performs best in Dataset 1, BernoulliRBM has 0.933789 in Dataset 2, LinearSVC has 0.689180 in Dataset 3, and BernoulliRBM has 0.026346 in Dataset 4. This research illuminates strategies for classifying fake news, offering potential solutions to ensure information integrity and democratic discourse, thus carrying profound implications for academia and real-world applications. This work also suggests the strength of sensors for data collection in IoT environments, big data analytics for smart cities, and sensor applications which contribute to maintaining the integrity of information within urban environments.

Predictive Value of Machine Learning Models in Mortality of Coronavirus Disease 2019 (COVID-19) Pneumonia

The coronavirus disease 2019 (COVID-19) became the most spread and lethal disease in the last 3 years. Early predictions could optimize the decision-making process, healthcare outcomes, and effective usage of healthcare resources during peaks. This study set out to predict the mortality risk of COVID-19 patients by investigating 14 machine learning (ML) models using extensive clinical, laboratory, and image-based features. Additionally, feature importances in each model and the influences of features in the mortality prediction of ML models have been evaluated in this study. Data from 252 patients during the 5th peak of the COVID-19 pandemic (July 2021-September 2021) with 42 features were used for the training of ML models. Fourteen ML models were created using the fivefold cross-validation method. Each model was trained using a training-validation dataset with its own optimized parameters. The performance of models has been evaluated by metric parameters of accuracy, precision, sensitivity, specificity, AUC, and F1 score. The highest values of accuracy (87.30%), precision (100%), sensitivity (77.27%), specificity (100%), AUC (91.90%), and F1 score (77.99%) were observed for the linear discriminant analysis (LDA), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), KNN, Passive Aggressive Classifier (PAC), and LDA models, respectively, when training was performed with all 42 features. By using feature selection techniques, the support vector classifier (SVC) model with 10 features showed the most AUC of 93.40%. The features of mechanical ventilation, consolidation, fatigue, malignancy, dry cough, level of consciousness (LOC), gender, diarrhea, O2 therapy, and SpO2 are potential predictors of mortality rates in COVID-19 patients.

An intelligent deep augmented model for detection of banana leaves diseases.

One of the most popular fruits worldwide is the banana. Accurate identification and categorization of banana diseases is essential for maintaining global fruits security and stakeholder profitability. Four different types of banana leaves exist Healthy, Cordana, Sigatoka, and Pestalotiopsis. These types can be analyzed using four types of vision: RGB, night vision, infrared vision, and thermal vision. This paper presents an intelligent deep augmented learning model composed of VGG19 and passive aggressive classifier (PAC) to classify the four diseases types of bananas under each type of vision. Each vision consisted of 1600 images with a size of (224 × 224). The training-testing approach was used to evaluate the performance of the hybrid model on Kaggle dataset, which was justified by various methods and metrics. The proposed model achieved a remarkable mean accuracy rate of 99.16% for RGB vision, 98.02% for night vision, 96.05% for infrared vision, and 96.10% for thermal vision for training and testing data. Microscopy employed in this research as a validation tool. The microscopic examination of leaves confirmed the presence and extent of the disease, providing ground truth data to validate and refine the proposed model. RESEARCH HIGHLIGHTS: The model can be helpful for internet of things -based drones to identify the large scale of banana leaf-disease detection using drones for images acquisition. Proposed an intelligent deep augmented learning model composed of VGG19 and passive aggressive classifier (PAC) to classify the four diseases types of bananas under each type of vision. The model detected banana leaf disease with a 99.16% accuracy rate for RGB vision, 98.02% accuracy rate for night vision, 96.05% accuracy rate for infrared vision, and 96.10% accuracy rate for thermal vision The model will provide a facility for early disease detection which minimizes crop loss, enhances crop quality, timely decision making, cost saving, risk mitigation, technology adoption, and helps in increasing the yield.

A Multi-Model Approach for Disaster-Related Tweets

This research centers around utilizing Natural Language Processing (NLP) techniques to analyze disaster-related tweets. The rising impact of global temperature shifts, leading to irregular weather patterns and increased water levels, has amplified the susceptibility to natural disasters. NLP offers a method for quickly identifying tweets about disasters, extracting crucial information, and identifying the types, locations, intensities, and effects of each type of disaster. This study uses a range of machine learning and neural network models and does a thorough comparison analysis to determine the best effective method for catastrophe recognition. Three well-known techniques, in-cluding the Multinomial Naive Bayes Classifier, the Passive Aggressive Classi-fier, and BERT (Bidirectional Encoder Representations from Transformers) were carefully examined with the ultimate goal of discovering the best strategy for correctly recognising disasters within the context of tweets. Among the three models, BERT achieved the highest performance in analyzing disaster-related tweets with an accuracy of 94.75%.

Analysis of Patient Condition Classification Methods Utilizing Drug Reviews

Nowadays, a new area of marketing and communication has emerged thanks to internet reviews, bridging the gap between conventional word-of-mouth and a viral feedback loop that can sway consumers' perceptions. Reviews of specific medications, however, are much more important in the medical industry because they may be used to track side effects and determine how consumers feel about a medication overall. This paper's main goal is to use medication reviews to categorize patient conditions, specifically Type 2 Diabetes, High Blood Pressure, and Depression. The goal is to study and understand the effectiveness of drugs for specific conditions and their potential side effects by analysing patient reviews, ratings, and useful counts. The insights gained from this analysis can be used to recommend suitable drugs for patients based on their condition and the experiences of other patients with similar conditions. Key Words: TF-IDF, BOW, Passive Aggressive Classifier, stop word.

Semi-automated seizure detection using interpretable machine learning models.

Despite the vast number of seizure detection publications there are no validated open-source tools for automating seizure detection based on electrographic recordings. Researchers instead rely on manual curation of seizure detection that is highly laborious, inefficient, error prone, and heavily biased. Here we developed an open-source software called SeizyML that uses sensitive machine learning models coupled with manual validation of detected events reducing bias and promoting efficient and accurate detection of electrographic seizures. We compared the validity of four interpretable machine learning models (decision tree, gaussian naïve bayes, passive aggressive classifier, and stochastic gradient descent classifier) on an extensive electrographic seizure dataset that we collected from chronically epileptic mice. We find that the gaussian naïve bayes and stochastic gradient descent models achieved the highest precision and f1 scores, while also detecting all seizures in our mouse dataset and only require a small amount of data to train the model and achieve good performance. Further, we demonstrate the utility of this approach to detect electrographic seizures in a human EEG dataset. This approach has the potential to be a transformative research tool overcoming the analysis bottleneck that slows research progress.

Deceptive Content Detection Using Machine Learning

Deceptive content, such as fake news, poses a significant challenge in today's information landscape, influencing public opinion and decision-making processes. This paper presents an innovative approach for the detection of deceptive content using machine learning techniques. The proposed system leverages a combination of natural language processing and supervised learning algorithms to identify patterns indicative of misinformation in textual data Our approach leverages term frequency-inverse document frequency (TF-IDF) of bag-of-words and n-grams as feature extraction methods, complemented by the utilization of Support Vector Machine (SVM) as a classifier. Additionally, we introduce a meticulously curated dataset comprising both fake and genuine news articles to train and evaluate our proposed system. Our findings underscore the efficacy of the developed framework, demonstrating its capability in discerning between fake and authentic news articles. Keywords — Deceptive Content, TfidfVectorizer, Natural Language Processing, Text Classification, Passive-Aggressive Classifier, Machine Learning

Deceptive Content Detection Using Machine Learning

Deceptive Content Detection Using Machine Learning

Detecting health misinformation: A comparative analysis of machine learning and graph convolutional networks in classification tasks

In the digital age, the proliferation of health-related information online has heightened the risk of misinformation, posing substantial threats to public well-being. This research conducts a meticulous comparative analysis of classification models, focusing on detecting health misinformation. The study evaluates the performance of traditional machine learning models and advanced graph convolutional networks (GCN) across critical algorithmic metrics. The results comprehensively understand each algorithm's effectiveness in identifying health misinformation and provide valuable insights for combating the pervasive spread of false health information in the digital landscape. GCN with TF-IDF gives the best result, as shown in the result section.•The research method involves a comparative analysis of classification algorithms to detect health misinformation, exploring traditional machine learning models and graph convolutional networks.•This research used algorithms such as Passive Aggressive Classifier, Random Forest, Decision Tree, Logistic Regression, Light GBM, GCN, GCN with BERT, GCN with TF-IDF, and GCN with Word2Vec were employed. Performance Metrics: Accuracy: for Passive Aggressive Classifier: 85.75 %, Random Forest: 86 %, Decision Tree: 81.30 %, Light BGM: 83.29 %, normal GCN: 84.53 %, GCN with BERT: 85.00 %, GCN with TR-IDF: 93.86 % and GCN with word2Vec: 81.00 %•Algorithmic performance metrics, including accuracy, precision, recall, and F1-score, were systematically evaluated to assess the efficacy of each model in detecting health misinformation, focusing on understanding the strengths and limitations of different approaches. The superior performance of Graph Convolutional Networks (GCNs) with TF-IDF embedding, achieving an accuracy of 93.86 %

An Intelligent Model for Predictions of PIWI-Interacting RNAs and Their Functions

A documented class of short non-coding RNA molecules is known as PIWI interacting RNA (PiRNA). The creation of new drugs and the identification of various tumor types are linked to the PiRNA molecules. Additionally, it is related to controlling transcription of genes, squelching transposons, and preserving genomic stability. The discovery of piRNAs and their functionality has grown to be a significant research topic in bioinformatics because of the crucial influence that piRNAs play in biological processes. The 2L-piRNA-ML predictor is a strong two-layer predictor that is suggested in this research to enhance the prediction of PiRNA and their functionality. The suggested model uses Quadratic Discriminant Analysis Classifier, Linear Discriminant Analysis, Passive Aggressive Classifier, Extra Tree Classifier, Logistic Regression, Random Forest, Ridge Classifier CV for classification. It also employs DNC and TNC for extraction of features. The suggested model is created using a two-layer construction strategy. The 1st layer makes a prediction about a given sequence whether it is PiRNA or not, and the 2nd layer makes a prediction about a given PiRNA sequence whether it is having the function of instructing target mRNA deadenylation or not. Proposed model achieved 95.65 % accuracy at the first layer and 92.30% accuracy at the second layer.

Comparative Analysis of Machine Learning Algorithms for Detecting Fake News: Efficacy and Accuracy in the Modern Information Ecosystem

In an era where the spread of fake news poses a significant threat to the integrity of the information landscape, the need for effective detection tools is paramount. This study evaluates the efficacy of three machine learning algorithms—Multinomial Naive Bayes, Passive Aggressive Classifier, and Logistic Regression—in distinguishing fake news from genuine articles. Leveraging a balanced dataset, meticulously processed and vectorized through Term Frequency-Inverse Document Frequency (TF-IDF), we subjected each algorithm to a rigorous classification process. The algorithms were evaluated on metrics such as precision, recall, and F1-score, with the Passive Aggressive Classifier outperforming others, achieving a remarkable 0.99 in both precision and recall. Logistic Regression followed with an accuracy of 0.98, while Multinomial Naive Bayes displayed robust recall at 1.00 but lower precision at 0.91, resulting in an accuracy of 0.95. These metrics underscored the nuanced capabilities of each algorithm in correctly identifying fake and real news, with the Passive Aggressive Classifier demonstrating superior balance in performance. The study's findings highlight the potential of employing machine learning techniques in the fight against fake news, with the Passive Aggressive Classifier showing promise due to its high accuracy and balanced precision-recall trade-off. These insights contribute to the ongoing efforts in digital media to develop advanced, ethical, and accurate tools for maintaining information veracity. Future research should continue to refine these models, ensuring their applicability in diverse and evolving news ecosystems.

Analysis of Traffic Accidents Based on the Integration Model

To enhance the safety of road traffic operations, this paper proposed a model based on stacking integrated learning utilizing American road traffic accident statistics. Initially, the process involved data cleaning, transformation, and normalization. Subsequently, various classification models were constructed, including logistic regression, k-nearest neighbors, gradient boosting, decision trees, AdaBoost, and extra trees models. Evaluation metrics such as accuracy, precision, recall, F1 score, and Hamming loss were employed. Upon analysis, the passive-aggressive classifier model exhibited superior comprehensive indices compared to other models. Based on the model’s output results, an in-depth examination of the factors influencing traffic accidents was conducted. Additionally, measures and suggestions aimed at reducing the incidence of severe traffic accidents were presented. These findings served as a valuable reference for mitigating the occurrence of traffic accidents.

Fake News Identification for Web Scrapped Data

Majority of the people get affected with misleading stories spread through different posts on social media and forward them assuming that it is a fact. Nowadays, Social media is used as a weapon to create havoc in the society by spreading fake news. Such havoc can be controlled by using machine-learning algorithms. Various methods of machine learning and deep learning techniques are used to identify false stories. There is a need for identification and controlling of fake news posts that have increased in alarming rate. Here we use Passive-Aggressive Classifier for fake news identification. Two datasets, Kaggle fake news dataset and as well as dynamically web scrapped dataset from politifact.com website. We achieved 88.66% accuracy using Passive Aggressive Classifier.

Deep Dive into Fake News Detection: Feature-Centric Classification with Ensemble and Deep Learning Methods

The online spread of fake news on various platforms has emerged as a significant concern, posing threats to public opinion, political stability, and the dissemination of reliable information. Researchers have turned to advanced technologies, including machine learning (ML) and deep learning (DL) techniques, to detect and classify fake news to address this issue. This research study explores fake news classification using diverse ML and DL approaches. We utilized a well-known “Fake News” dataset sourced from Kaggle, encompassing a labelled news collection. We implemented diverse ML models, including multinomial naïve bayes (MNB), gaussian naïve bayes (GNB), Bernoulli naïve Bayes (BNB), logistic regression (LR), and passive aggressive classifier (PAC). Additionally, we explored DL models, such as long short-term memory (LSTM), convolutional neural networks (CNN), and CNN-LSTM. We compared the performance of these models based on key evaluation metrics, such as accuracy, precision, recall, and the F1 score. Additionally, we conducted cross-validation and hyperparameter tuning to ensure optimal performance. The results provide valuable insights into the strengths and weaknesses of each model in classifying fake news. We observed that DL models, particularly LSTM and CNN-LSTM, showed better performance compared to traditional ML models. These models achieved higher accuracy and demonstrated robustness in classification tasks. These findings emphasize the potential of DL models to tackle the spread of fake news effectively and highlight the importance of utilizing advanced techniques to address this challenging problem.

DETECTING DECEPTIVE REVIEWS: AN INTEGRATED MACHINE LEARNING APPROACH

In recent years, online reviews have become a crucial factor in promoting products and services. However, the rise of fake reviews has posed a significant challenge. Businesses, marketers, and advertisers often resort to embedding fake reviews to attract customers or undermine their competitors. Deceptive reviews have become a common practice, as they serve as a means of promoting one''''s own business or tarnishing the reputation of rivals. Consequently, the identification of deceptive reviews has emerged as a critical and ongoing research area. This research paper presents a machine learning model approach to detect deceptive reviews. The study focuses on experiments conducted using a deceptive opinion spam corpus dataset, specifically targeting restaurant reviews. An n-gram model combined with max features is developed to identify deceptive content, with a particular emphasis on fake reviews. Additionally, a benchmark study is conducted to explore the performance of two different feature extraction techniques and their application in five machine learning classification techniques. The experimental findings demonstrate that the passive aggressive classifier outperforms other algorithms, achieving the highest accuracy not only in text classification but also in identifying fake reviews. Moreover, the research delves into the identification of deceptive reviews and explores diverse feature extraction and machine learning techniques to improve the model''''s accuracy.

Sentiment Analysis Based on Performance of Linear Support Vector Machine and Multinomial Naïve Bayes Using Movie Reviews with Baseline Techniques

Movies are the better source of entertainment. Every year, a great percentage of movies are released. People comment on movies in the form of reviews after watching them. Since it is difficult to read all of the reviews for a movie, summarizing all of the reviews will help make this decision without wasting time in reading all of the reviews. Opinion mining also known as sentiment analysis is the process of extracting subjective information from textual data. Opinion mining involves identifying and extracting the opinions of individuals, which can be positive, neutral, or negative. The task of opinion mining also called sentiment analysis is performed to understand people’s emotions and attitudes in movie reviews. Movie reviews are an important source of opinion data because they provide insight into the general public’s opinions about a particular movie. The summary of all reviews can give a general idea about the movie. This study compares baseline techniques, Logistic Regression, Random Forest Classifier, Decision Tree, K-Nearest Neighbor, Gradient Boosting Classifier, and Passive Aggressive Classifier with Linear Support Vector Machines and Multinomial Naïve Bayes on the IMDB Dataset of 50K reviews and Sentiment Polarity Dataset Version 2.0. Before applying these classifiers, in pre-processing both datasets are cleaned, duplicate data is dropped and chat words are treated for better results. On the IMDB Dataset of 50K reviews, Linear Support Vector Machines achieve the highest accuracy of 89.48%, and after hyperparameter tuning, the Passive Aggressive Classifier achieves the highest accuracy of 90.27%, while Multinomial Nave Bayes achieves the highest accuracy of 70.69% and 71.04% after hyperparameter tuning on the Sentiment Polarity Dataset Version 2.0. This study highlights the importance of sentiment analysis as a tool for understanding the emotions and attitudes in movie reviews and predicts the performance of a movie based on the average sentiment of all the reviews.

Detection of fake news using Naive Bayes and Passive Aggressive Classifier

Detection of fake news using Naive Bayes and Passive Aggressive Classifier

Clinically adaptable machine learning model to identify early appreciable features of diabetes

Objective Diabetes mellitus is a serious disease where the body of affected patients are failed to produce enough insulin that causes an abnormality of blood sugar. This disease happens for a number of reasons including modern lifestyle, lethargic attitude, unhealthy food consumption, family history, age, overweight, etc. The aim of this study was to propose a machine learning based prediction model that detected diabetes at the beginning.Methods In this work, we collected 520 patients records from the University of California, Irvine (UCI) machine learning repository of Sylhet Diabetes Hospital, Sylhet. Then, a similar questionnaire of that hospital was followed and assembled 558 patients records from all over Bangladesh through this questionnaire. However, we accumulated patient records of these two datasets. In the next step, these datasets were cleaned and applied thirty five state-of-arts classifiers such as logistic regression (LR), K nearest neighbors (KNN), support vector classifier (SVC), Nave Byes (NB), decision tree (DT), random forest (RF), stochastic gradient descent (SGD), Perceptron, AdaBoost, XGBoost, passive aggressive classifier (PAC), ridge classifier (RC), Nu-support vector classifier (Nu-SVC), linear support vector classifier (LSVC), calibrated classifier CV (CCCV), nearest centroid (NC), Gaussian process classifier (GPC), multinomial NB (MNB), complement NB, Bernoulli NB (BNB), categorical NB, Bagging, extra tree(ET), gradiant boosting classifier (GBC), Hist gradiant boosting classifier (HGBC), one vs rest classifier (OVsRC), multi-layer perceptron (MLP), label propagation (LP), label spreading (LS), stacking, ridge classifier CV (RCCV), logistic regression CV (LRCV), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and light gradient boosting machine (LGBM) to explore best stable predictive model. The performance of the classifiers has been measured using five metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic. Finally, these outcomes were interpreted using Shapley additive explanations methods and identified relevant features for happening diabetes.Results In this work, different classifiers were shown their performance where ET outperformed any other classifiers with 97.11% accuracy for the Sylhet Diabetes Hospital dataset (SDHD) and MLP shows the best accuracy (96.42%) for the collected dataset. Subsequently, HGBC and LGBM provide the highest 94.90% accuracy for the combined datasets individually.Conclusion LGBM, stacking, HGBC, RF, ET, bagging, and GBC might represent more stable prediction results for each dataset.

A Highly Accurate Internet-Based Fake Information Detection Tool for Indonesian Twitter

The dissemination of fake information through social media has several harmful effects on the social life of a nation. Indonesia has been afflicted by the dissemination of erroneous information regarding the negative health consequences of vaccination, resulting in widespread unwillingness to undergo immunization. Therefore, it is necessary to combat such misleading information. We developed a web application using machine learning technologies to identify bogus information flowing on Indonesian Twitter. A Passive-Aggressive Classifier and n-gram tokenization are used to handle data. The application test results indicate that the detection accuracy, precision, and recall for 1-3 grams of tokenization are higher than 90%. In addition, the black box approach yields reliable findings for all application functionalities.