Leukemia is a form of cancer that affects the bone marrow and lymphatic system, and it requires complex treatment strategies that vary with each subtype. Due to the subtle morphological differences among these types, monitoring gene expressions is crucial for accurate classification. Manual or pathological testing can be time-consuming and expensive. Therefore, data-driven methods and machine learning algorithms offer an efficient alternative for leukemia classification. This study introduced a novel super learning model that leverages heterogeneous machine learning models to analyze gene expression data and classify leukemia cells. The proposed approach incorporates an entropy-based feature importance technique to identify the gene profiles most significant to the labeling process. The strength of this super learning model lies in its final super learner, Random Forest, which effectively classifies cross-validated data from the candidate learners. Validation on a gene expression monitoring dataset demonstrates that this model outperforms other state-of-the-art models in predictive accuracy. The study contributes to the knowledge regarding the use of advanced machine learning techniques to improve the accuracy and reliability of leukemia classification using gene expression data, addressing the challenges of traditional methods that rely on clinical features and morphological examination.

Automatic synthesis of realistic images is challenging, and even state-of-the-art artificial intelligence and machine learning algorithms suffer from not fulfilling this expectation. However, the emergence of image processing has allowed operations on an image to enhance or extract information from it and synthesize pictures from textual descriptions, which has become an active research area in recent times. The already-developed model by OpenAI surprised the world after its launch. However, everything worthwhile has a price. Further study is necessary since the model could not account for problems including gender prejudice, stereotypes, language structure, viewpoint, writing, symbolism, and the delivery of explicit material. This survey report aims to supplement past studies using different image processing techniques to create synthetic images. This article critically assesses current approaches to assess text-to-image synthesis models, draws attention to the existing architectures' limitations, and identifies new research areas. To further advance research in the field, improvement of the architectural design and model training is needed. This can be achieved by developing better datasets and evaluation metrics.

A everyday battery survival pattern with very long testing periods, no contact estimate programs, or other devices will be useful in modern businesses. This abstract focuses on employing a super learner technique to estimate the remaining useful lifespan (RUL) for NMC-LCO 18650 batteries. The super learning algorithm has become a type of ensemble machine learning that combines several different basic models to increase the accuracy of predictions. In this research, a set of features from the batteries were extracted, which includes current, temperature, voltage, and battery cycle, and the RUL was calculated utilizing a super learner algorithm. The linear regression model, the random forest, and two deep learning models used as basis models for the super learner algorithm. Mean absolute error (MAE), which as well as root mean squared error were two metrics used to assess the efficacy of this super learner algorithm. (RMSE). The outcomes demonstrate that with regard to of prediction accuracy, the super learner strategy exceeds the separate base models. In order to improve battery performance and lower maintenance costs, it is essential to be able to predict the of the RUL for NMC-LCO 18650 batteries. The suggested method is capable of being utilized as an accurate and dependable way for doing this.