The use of social media networks is becoming a current phenomenon in the world today where people are sharing posts and tweets, connect with different groups, and share their opinions about things. This data is extremely heterogeneous and so it is hard to analyze and derive information from this data that is considered an indispensable source for decision-makers. New techniques are therefore needed to handle these huge amounts of data to find the hidden information thus improve the results of the analysis. We are developing a framework for the analysis of heterogeneous data using machine learning (ML) techniques. In contrast to most of the literature frameworks that focus on a specific type of heterogeneous data for evaluating the proposed framework, we have analyzed 15k tweets data from six American airlines. These tweets are collected from the open stream of Twitter, also predict, classify each tweet as a negative or positive review, and test the ability of deep learning (DL) algorithms by comparing it with traditional ML algorithms. The findings confirmed the validity of the proposed framework and helped to achieve the study objective by providing excellent analysis performance and provide insights into additional aspects of information extraction from heterogeneous data.

At present, millions of internet users are contributing a huge amount of data. This data is extremely heterogeneous, and so, it is hard to analyze and derive information from this data that is considered an indispensable source for decision-makers. Due to this massive growth, the classification of data and analysis has become an important research subject. Extracting information from this data has become a necessity. As a result, it was necessary to process these enormous volumes of data to uncover hidden information and therefore improve data analysis and, in turn, classification accuracy. In this paper, firstly, we focus on developing an ensemble machine-learning model based on active learning which identifies the most effective feature extraction strategy for heterogeneous data analysis, and compare it with traditional machine-learning algorithms. Secondly, we evaluate the proposed model during the experiments; five heterogeneous datasets from various domains were used, such as a Health Care Reform dataset, Sander Frandsen dataset, Financial Phrase Bank dataset, SMS Spam Collection dataset, and Textbook sales dataset. According to the results, the novel approach for data analysis performed better than conventional methods. Finally, the study’s findings confirmed the validity of the suggested technique, meeting the study’s goal of using ensemble methods with active learning to raise the model’s overall accuracy for effectively classifying and analyzing heterogeneous data, reducing the time and money spent training the model, and delivering superior analysis performance as well as insights into other elements of extracting information from heterogeneous data.

The integration of active learning (AL) and deep learning (DL) presents a promising avenue for enhancing the efficiency and performance of deep learning classifiers. This article introduces an approach that seamlessly integrates AL principles into the training process of DL models to build robust image classifiers. The proposed approach employs a unique methodology to select high-confidence unlabeled data points for immediate labeling, reducing the need for human annotation and minimizing annotation costs. Specifically, by combining uncertainty sampling with the pseudo-labeling of confident data, the proposed approach expands the training set efficiently. The proposed approach uses a hybrid active deep learning model that selects the most informative data points that need labeling based on an uncertainty measure. Then, it iteratively retrains a deep neural network classifier on the newly labeled samples. The model achieves high accuracy with fewer manually labeled samples than traditional supervised deep learning by selecting the most informative samples for labeling and retraining in a loop. Experiments on various image classification datasets demonstrate that the proposed model outperforms conventional approaches in terms of classification accuracy and reduced human annotation requirements. The proposed model achieved accuracy of 98.9% and 99.3% for the Cross-Age Celebrity and Caltech Image datasets compared to the conventional approach, which achieved 92.3% and 74.3%, respectively. In summary, this work presents a promising unified active deep learning approach to minimize the human effort in manually labeling data while maximizing classification accuracy by strategically labeling only the most valuable samples for the model.

Audio classification tasks like speech recognition and acoustic scene analysis require substantial labeled data, which is expensive. This work explores active learning to reduce annotation costs for a sound classification problem with rare target classes where existing datasets are insufficient. A deep convolutional recurrent neural network extracts spectro-temporal features and makes predictions. An uncertainty sampling strategy queries the most uncertain samples for manual labeling by experts and non-experts. A new alternating confidence sampling strategy and two other certainty-based strategies are proposed and evaluated. Experiments show significantly higher accuracy than passive learning baselines with the same labeling budget. Active learning generalizes well in a qualitative analysis of 20,000 unlabeled recordings. Overall, active learning with a novel sampling strategy minimizes the need for expensive labeled data in audio classification, successfully leveraging unlabeled data to improve accuracy with minimal supervision.

Background: The relationship between autoimmune thyroid disease and systemic lupus erythematosus (SLE) has been revealed but the prevalence of thyroid disease in lupus patients is controversial. The aim of this study is to assess thyroid dysfunction and the presence of anti-thyroid antibodies in patients with SLE, and its association with disease characteristics and disease activity. Methods: Sixty patients with SLE
18 years who satisfied the American College of Rheumatology (ACR) criteria and thirty age and sex matched normal volunteer were included, all underwent laboratory evaluation for serum free T3, free T4, TSH, Antithyroglobulin antibody (Ab TG) and Antithyroid peroxidase antibody (Ab TPO). Clinical and serological characteristics and disease activity of SLE were assessed; correlation with thyroid dysfunction was studied.Results: 2(3.33%) patients had subclinical hyperthyroidism, 24 (40%) were euthyroid, 12 (21.67%) had subclinical hypothyroidism and 22 (35%) had overt hypothyroidism. All the control group was euthyroid. The patients with thyroid dysfunction had more arthralgias, arthritis, changes of voice, bowel habits and weight, irregular menstruation, sleep disturbance, nervousness and tremors than the euthyroid lupus patients (p<0.05). The lupus patients with subclinical and overt hypothyroidism had statistically significant higher Ab TG than the euthyroid patients and patients with subclinical hyperthyroidism and control group (p<0.05) but no statistically significant difference between all groups as regard Ab TPO. SLE patients with subclinical & overt hypothyroidism had statistically significant higher ESR and SLAM score than theeuthyroid patients and patients with subclinical hyperthyroidism (p<0.05). There was a positive correlation between Ab TG levels and body mass index BMI, ESR and disease activity measures by SLAM score (p<0.05). Conclusion: The thyroid dysfunction is more frequent in SLE patients than control group. Subclinical and overt hypothyroidism are more likely to occur in SLE patients. There are a positive correlation between Ab TG level and disease activity and negative correlation with free T3 & free T4.

The rapid growth of user-generated textual content on the internet has intensified the need for accurate and scalable text classification methods. However, supervised learning approaches remain heavily constrained by the high cost and effort required for manual data annotation, particularly in large and heterogeneous datasets. To address this challenge, this paper proposes a novel hybrid active learning framework for efficient classification of unlabeled text data. The proposed approach integrates multiple classical machine learning classifiers—Support Vector Machines, Logistic Regression, Naive Bayes, and Random Forest—within a hybrid ensemble architecture, combined with a pool-based active learning strategy to iteratively select the most informative unlabeled instances for annotation. Textual data are transformed into numerical representations using several feature extraction techniques, including Bag-of-Words, TF-IDF, Word2Vec, and BERT-based embeddings, allowing for a comprehensive evaluation of representation effectiveness. Extensive experiments are conducted on four diverse benchmark datasets from healthcare, finance, spam detection, and e-commerce domains. The results consistently demonstrate that the proposed hybrid active learning model outperforms traditional ensemble classifiers across all datasets and evaluation metrics. In particular, TF-IDF-based hybrid ensembles achieve the highest gains in accuracy, precision, recall, and F1 score, while requiring substantially fewer labeled instances. Furthermore, the proposed framework exhibits strong robustness in imbalanced classification scenarios, significantly improving minority class detection. Overall, the findings confirm that combining hybrid ensemble learning with active learning offers an effective, lightweight, and cost-efficient alternative to purely transformer-based approaches, making it well-suited for real-world text classification tasks where labeled data are scarce or expensive.