This study introduces and investigates the truncated unit exponentiated Ailamujia (TUEA) distribution within the framework of a progressive Type–Ⅱ censoring scheme. By incorporating truncation on the unit interval, the proposed model extends the unit exponentiated Ailamujia distribution, significantly enhancing its flexibility for modeling bounded lifetime data. We derive the fundamental mathematical properties of the TUEA model, including the probability density function, the cumulative distribution function, reliability measures, and hazard rate functions. Statistical inference for the model parameters is developed within both frequentist and Bayesian frameworks using progressive Type–Ⅱ censored data. The maximum likelihood estimates are computed through the Newton–Raphson iterative algorithm, whereas Bayesian inference is carried out under symmetric squared error and asymmetric LINEX loss functions. Point estimates and highest posterior density credible intervals are obtained via Markov chain Monte Carlo (MCMC) sampling procedures. In addition, a comprehensive Monte Carlo simulation study is conducted to investigate the finite sample performance of the new estimators in terms of bias, mean square error, and confidence interval coverage probability. In addition, the practical usefulness of the TUEA distribution is shown via an analysis of a real-life data sets, where the new model exhibits a better fit than competing models.

There are millions of people suffering from thyroid disease all over the world. For thyroid cancer to be effectively treated and managed, a correct diagnosis is necessary. In this article, we suggest an innovative approach for diagnosing thyroid disease that combines an adaptive synthetic sampling method with weighted average voting (WAV) ensemble of two distinct super learners (SLs). Resampling techniques are used in the suggested methodology to correct the class imbalance in the datasets and a group of two SLs made up of various base estimators and meta-estimators is used to increase the accuracy of thyroid cancer identification. To assess the effectiveness of our suggested methodology, we used two publicly accessible datasets: the KEEL thyroid illness (Dataset1) and the hypothyroid dataset (Dataset2) from the UCI repository. The findings of using the adaptive synthetic (ADASYN) sampling technique in both datasets revealed considerable gains in accuracy, precision, recall and F1-score. The WAV ensemble of the two distinct SLs that were deployed exhibited improved performance when compared to prior existing studies on identical datasets and produced higher prediction accuracy than any individual model alone. The suggested methodology has the potential to increase the accuracy of thyroid cancer categorization and could assist with patient diagnosis and treatment. The WAV ensemble strategy computational complexity and the ideal choice of base estimators in SLs continue to be constraints of this study that call for further investigation.

Recently, there have been several uses for digital image processing. Image fusion has become a prominent application in the domain of imaging processing. To create one final image that proves more informative and helpful compared to the original input images, image fusion merges two or more initial images of the same item. Image fusion aims to produce, enhance, and transform significant elements of the source images into combined images for the sake of human visual perception. Image fusion is commonly employed for feature extraction in smart robots, clinical imaging, audiovisual camera integration, manufacturing process monitoring, electronic circuit design, advanced device diagnostics, and intelligent assembly line robots, with image quality varying depending on application. The research paper presents various methods for merging images in spatial and frequency domains, including a blend of stable and curvelet transformations, everage Max-Min, weighted principal component analysis (PCA), HIS (Hue, Intensity, Saturation), wavelet transform, discrete cosine transform (DCT), dual-tree Complex Wavelet Transform (CWT), and multiple wavelet transform. Image fusion methods integrate data from several source images of an identical target, thereby enhancing information in an extremely efficient manner. More precisely, in imaging techniques, the depth of field constraint precludes images from focusing on every object, leading to the exclusion of certain characteristics. To tackle thess challanges, a very efficient multi-focus wavelet decomposition and recomposition method is proposed. The use of these wavelet decomposition and recomposition techniques enables this method to make use of existing optimized wavelet code and filter choice. The simulated outcomes provide evidence that the suggested approach initially extracts particular characteristics from images in order to accurately reflect the level of clarity portrayed in the original images. This study enhances the performance of the eXtreme Gradient Boosting (XGBoost) algorithm in detecting brain malignancies with greater precision through the integration of computational image analysis and feature selection. The performance of images is improved by segmenting them employing the K-Means algorithm. The segmentation method aids in identifying specific regions of interest, using Particle Swarm Optimization (PCA) for trait selection and XGBoost for data classification. Extensive trials confirm the model’s exceptional visual performance, achieving an accuracy of up to 97.067% and providing good objective indicators.

The growing usage of Android smartphones has led to a significant rise in incidents of Android malware and privacy breaches. This escalating security concern necessitates the development of advanced technologies capable of automatically detecting and mitigating malicious activities in Android applications (apps). Such technologies are crucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital world. Current methods employed to detect sensitive data leaks in Android apps are hampered by two major limitations they require substantial computational resources and are prone to a high frequency of false positives. This means that while attempting to identify security breaches, these methods often consume considerable processing power and mistakenly flag benign activities as malicious, leading to inefficiencies and reduced reliability in malware detection. The proposed approach includes a data preprocessing step that removes duplicate samples, manages unbalanced datasets, corrects inconsistencies, and imputes missing values to ensure data accuracy. The Minimax method is then used to normalize numerical data, followed by feature vector extraction using the Gain ratio and Chi-squared test to identify and extract the most significant characteristics using an appropriate prediction model. This study focuses on extracting a subset of attributes best suited for the task and recommending a predictive model based on domain expert opinion. The proposed method is evaluated using Drebin and TUANDROMD datasets containing 15,036 and 4,464 benign and malicious samples, respectively. The empirical result shows that the Random Forest (RF) and Support Vector Machine (SVC) classifiers achieved impressive accuracy rates of 98.9% and 98.8%, respectively, in detecting unknown Android malware. A sensitivity analysis experiment was also carried out on all three ML-based classifiers based on MAE, MSE, R², and sensitivity parameters, resulting in a flawless performance for both datasets. This approach has substantial potential for real-world applications and can serve as a valuable tool for preventing the spread of Android malware and enhancing mobile device security.

Large language models (LLMs) and natural language processing (NLP) have significant promise to improve efficiency and refine healthcare decision-making and clinical results. Numerous domains, including healthcare, are rapidly adopting LLMs for the classification of biomedical textual data in medical research. The LLM can derive insights from intricate, extensive, unstructured training data. Variants need to be accurately identified and classified to advance genetic research, provide individualized treatment, and assist physicians in making better choices. However, the sophisticated and perplexing language of medical reports is often beyond the capabilities of the devices we now utilize. Such an approach may result in incorrect diagnoses, which could affect a patient’s prognosis and course of therapy. This study evaluated the efficacy of the proposed model by looking at publicly accessible textual clinical data. We have cleaned the clinical textual data using various text preprocessing methods, including stemming, tokenization, and stop word removal. The important features are extracted using Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TFIDF) feature engineering methods. The important motive of this study is to predict the genetic variants based on the clinical evidence using a novel method with minimal error. According to the experimental results, the random forest model achieved 61% accuracy with 67% precision for class 9 using TFIDF features and 63% accuracy and a 73% F1 score for class 9 using Bag of Words features. The accuracy of the proposed BERT (Bidirectional Encoder Representations from Transformers) model was 70% with 5-fold cross-validation and 71% with 10-fold cross-validation. The research results provide a comprehensive overview of current LLM methods in healthcare, benefiting academics as well as professionals in the discipline.