Gene microarray classification problems are considered a challenge task since the datasets contain few number of samples with high number of genes (features). The genes subset selection in microarray data play an important role for minimizing the computational load and solving classification problems. In this paper, the Correlation-based Feature Selection (CFS) algorithm is utilized in the feature selection process to reduce the dimensionality of data and finding a set of discriminatory genes. Then, the Decision Table, JRip, and OneR are employed for classification process. The proposed approach of gene selection and classification is tested on 11 microarray datasets and the performances of the filtered datasets are compared with the original datasets. The experimental results showed that CFS can effectively screen irrelevant, redundant, and noisy features. In addition, the results for all datasets proved that the proposed approach with a small number of genes can achieve high prediction accuracy and fast computational speed. Considering the average accuracy for all the analysis of microarray data, the JRip achieved the best result as compared to Decision Table, and OneR classifier. The proposed approach has a remarkable impact on the classification accuracy especially when the data is complicated with multiple classes and high number of genes.

Numerous studies have highlighted the significance of artificial intelligence (AI) in breast cancer diagnosis. However, systematic reviews of AI applications in this field often lack cohesion, with each study adopting a unique approach. The aim of this study is to provide a detailed examination of AI's role in breast cancer diagnosis through citation analysis, helping to categorize the key areas that attract academic attention. It also includes a thematic analysis to identify the specific research topics within each category. A total of 30,200 studies related to breast cancer and AI, published between 2015 and 2024, were sourced from databases such as IEEE, Scopus, PubMed, Springer, and Google Scholar. After applying inclusion and exclusion criteria, 32 relevant studies were identified. Most of these studies utilized classification models for breast cancer prediction, with high accuracy being the most commonly reported performance metric. Convolutional Neural Networks (CNN) emerged as the preferred model in many studies. The findings indicate that both the quantity and quality of AI-based algorithms in breast cancer diagnosis are increases in the given years. AI is increasingly seen as a complement to healthcare sector and clinical expertise, with the target of enhancing the accessibility and affordability of quality healthcare worldwide.

Detection and management of diabetes at an early stage is essential since it is rapidly becoming a global health crisis in many countries. Predictions of diabetes using machine learning algorithms have been promising. In this work, we use data collected from the Pima Indians to assess the performance of multiple machine-learning approaches to diabetes prediction. Ages, body mass indexes, and glucose levels for 768 patients are included in the data set. The methods evaluated are Logistic Regression, Decision Tree, Random Forest, k-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting, and Neural Network. The findings indicate that the Logistic Regression and Neural Network models perform the best on most criteria when considering all classes together. The SVM, Random Forest, and Naive Bayes models also receive moderate to high scores, suggesting their strength as classification models. However, the kNN and Tree models show poorer scores on most criteria across all classes, making them less favorable choices for this dataset. The SGD, AdaBoost, and CN2 rule inducer models perform the poorest when comparing all models using a weighted average of class scores. The results of the study suggest that machine learning algorithms may help predict the onset of diabetes and for detecting the disease at an early stage.

Diabetes is a chronic disease that affects millions of people worldwide. Early diagnosis and effective management are crucial for reducing its complications. Diabetes is the fourth-highest cause of mortality due to its association with various comorbidities, including heart disease, nerve damage, blood vessel damage, and blindness. The potential of machine learning algorithms in predicting Diabetes and related conditions is significant, and mining diabetes data is an efficient method for extracting new insights.The primary objective of this study is to develop an enhanced ensemble model to predict Diabetes with improved accuracy by leveraging various machine learning algorithms.This study tested several popular machine learning algorithms commonly used in diabetes prediction, including Naive Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Fast Large Margin (FLM), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), Gradient Boosted Trees (GBT), and Support Vector Machine (SVM). The performance of these algorithms was compared, and two different ensemble techniques—stacking and voting—were used to build a more accurate predictive model.The top three algorithms based on accuracy were Deep Learning, Naive Bayes, and Gradient Boosted Trees. The machine learning algorithms revealed that individuals with Diabetes are significantly affected by the number of chronic conditions they have, as well as their gender and age. The ensemble models, particularly the stacking method, provided higher accuracy than individual algorithms. The stacking ensemble model achieved a slightly better accuracy of 99.94% compared to 99.34% for the voting method.Building an ensemble model significantly increased the accuracy of predicting Diabetes and related conditions. The stacking ensemble model, in particular, demonstrated superior performance, highlighting the importance of combining multiple machine learning approaches to enhance predictive accuracy

In this study, we evaluated the performance of various machine-learning models on multiple datasets labeled GR1, GR2, GR3, GR4, and GR5. We assessed the models using a range of evaluation metrics, including AUC, CA, F1, precision, recall, MCC, specificity, and log loss. The models examined were logistic regression, decision tree, kNN, random forest, gradient boosting, neural network, AdaBoost, and stochastic gradient descent. The results indicate that all models consistently demonstrated outstanding performance across all datasets, with most achieving perfect scores in all metrics. The models exhibited high accuracy and effectiveness in accurately classifying instances. Although random forests displayed slightly lower scores in some metrics, theyi still maintained an overall high level of accuracy. The findings highlight the models’ ability to effectively learn the underlying patterns within the data and make accurate predictions. The low log loss values further confirmed the models’ precise estimation of probabilities. Consequently, these models possess strong potential for practical applications in various domains, offering reliable and robust classification capabilities.

In today’s interconnected world, the transmission of both lengthy and concise text messages is ubiquitous across diverse communication platforms. With the proliferation of sensitive and specialized information being exchanged, safeguarding these messages from potential threats such as intruders, abusers, and data hackers becomes imperative. This study introduces an innovative approach aimed at streamlining data protection procedures while concurrently thwarting hacking attempts. At the core of this method lies the utilization of a sophisticated variable content private key designed to facilitate ease of alteration without compromising the integrity of encryption and decryption operations. The pivotal aspect involves leveraging a covert color image to generate this private key, mandating both the sender and receiver to securely retain this image. By employing a selected color image, the private key can seamlessly adapt to match the length of the confidential message. To fortify the level of security, the message is recommended to be segmented into blocks of predetermined sizes, with the sender and receiver jointly establishing these parameters. Subsequently, the bytes within each block are consolidated into a singular vector, which undergoes a left rotation by a predetermined number of bits as specified by the communicating parties. The proposed methodology is empirically validated through the implementation of various color images and text messages. Comparative analyses against existing methods underscore the efficacy and robustness of the proposed approach, substantiating its significant advancements in data protection paradigms.