Women who have recovered from breast cancer (BC) always fear its recurrence. The fact that they have endured the painstaking treatment makes recurrence their greatest fear. However, with current advancements in technology, early recurrence prediction can help patients receive treatment earlier. The availability of extensive data and advanced methods make accurate and fast prediction possible. This research aims to compare the accuracy of a few existing data mining algorithms in predicting BC recurrence. It embeds a particle swarm optimization as feature selection into three renowned classifiers, namely, naive Bayes, K-nearest neighbor, and fast decision tree learner, with the objective of increasing the accuracy level of the prediction model.

COVID-19 has sparked a worldwide pandemic, with the number of infected cases and deaths rising on a regular basis. Along with recent advances in soft computing technology, researchers are now actively developing and enhancing different mathematical and machine-learning algorithms to forecast the future trend of this pandemic. Thus, if we can accurately forecast the trend of cases globally, the spread of the pandemic can be controlled. In this study, a hybrid CNN-LSTM model was developed on a time-series dataset to forecast the number of confirmed cases of COVID-19. The proposed model was evaluated and compared with 17 baseline models on test and forecast data. The primary finding of this research is that the proposed CNN-LSTM model outperformed them all, with the lowest average MAPE, RMSE, and RRMSE values on both test and forecast data. Conclusively, our experimental results show that, while standalone CNN and LSTM models provide acceptable and efficient forecasting performance for the confirmed COVID-19 cases time series, combining both models in the proposed CNN-LSTM encoder-decoder structure provides a significant boost in forecasting performance. Furthermore, we demonstrated that the suggested model produced satisfactory predicting results even with a small amount of data.

Malware development has significantly increased recently, posing a serious security risk to both consumers and businesses. Malware developers continually find new ways to circumvent security research’s ongoing efforts to guard against malware attacks. Malware Classification (MC) entails labeling a class of malware to a specific sample, while malware detection merely entails finding malware without identifying which kind of malware it is. There are two main reasons why the most popular MC techniques have a low classification rate. First, Finding and developing accurate features requires highly specialized domain expertise. Second, a data imbalance that makes it challenging to classify and correctly identify malware. Furthermore, the proposed malware classification (MC) method consists of the following five steps: (i) Dataset preparation: 2D malware images are created from the malware binary files; (ii) Visualized Malware Pre-processing: the visual malware images need to be scaled to fit the CNN model’s input size; (iii) Feature extraction: both hand-engineering (Tamura) and deep learning (GoogLeNet) techniques are used to extract the features in this step; (iv) Classification: to perform malware classification, we employed k-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Extreme Learning Machine (ELM). The proposed method is tested on a standard Malimg unbalanced dataset. The accuracy rate of the proposed method was extremely high, making it the most efficient option available. The proposed method’s accuracy rate was outperformed both the Hand-crafted feature and Deep Feature techniques, at 95.42 and 96.84 percent.

Early detection of stroke warning symptoms can help reduce the severity of ischemic stroke, the leading cause of mortality and disability worldwide. This study aims to develop a model to predict the disease by leveraging machine learning-based models. A model that concatenates a convolutional neural network and a long short-term memory was developed as the proposed model. Seven other classifiers were treated as the baseline models: logistic regression, random forest, extreme gradient boosting, k-nearest neighbor, artificial neural network, long short-term memory, and convolutional neural network. All models were trained using a healthcare dataset of 5110 patients’ health profiles. A synthetic minority oversampling technique was deployed to balance the data. Metrics such as accuracy, precision, F1-score, recall, area under the curve, and confusion metrics were used to evaluate the models’ performance. With a 95.9% accuracy, the proposed model outperformed the models employed in this study and improved the accuracy of prior studies that used the same dataset. The Shapley Additive Explanations method was applied to explain the result obtained by the best model. The proposed model was created to predict ischemic stroke. It considers each patient’s profile, allowing for personalized decision-making in resource-constrained settings.

Message queue telemetry transport (MQTT), a widely used messaging protocol in Internet of Things (IoT) applications, faces significant security challenges, particularly with denial-of-service (DoS) attacks and ensuring continuous, secure communication. Traditional security measures, like transport layer security (TLS)/secure socket layer (SSL), often introduce CPU overhead amd bandwidth issues and require complex certificate management. This article has two main objectives: first, to provide a comprehensive review of MQTT security, detailing how MQTT operates, the associated security vulnerabilities, and existing solutions; and second, to propose a framework called the stateful authentication framework (SAF). SAF enhances MQTT security through two key components: the SAF authentication protocol and a large language model (LLM)-based intrusion detection system (IDS). The SAF protocol uses client-state information to establish secure, stateful interactions between clients and brokers and incorporates a multifactor authentication mechanism to prevent replay attacks and unauthorized access. Our evaluation of SAF shows that it significantly improves MQTT security, with Scyther simulations confirming the robustness of the protocol against known generic attacks. Additionally, the LLM-based IDS that integrates SAF’s security policies provides an advanced approach for detecting intrusions. To our knowledge, this is the first integrated approach combining a state-based authentication protocol and LLM-based IDS to tackle MQTT security challenges.