Chronic kidney disease (CKD) refers to the gradual decline of kidney function over months or years. Early detection of CKD is crucial and significantly affects a patient’s decreasing health progression through several methods, including pharmacological intervention in mild cases or hemodialysis and kidney transportation in severe cases. In the recent past, machine learning (ML) and deep learning (DL) models have become important in the medical diagnosis domain due to their high prediction accuracy. The performance of the developed model mainly depends on choosing the appropriate features and suitable algorithms. Accordingly, the paper aims to introduce a novel ensemble DL approach to detect CKD; multiple methods of feature selection were used to select the optimal selected features. Moreover, we study the effect of the optimal features chosen on CKD from the medical side. The proposed ensemble model integrates pretrained DL models with the support vector machine (SVM) as the metalearner model. Extensive experiments were conducted by using 400 patients from the UCI machine learning repository. The results demonstrate the efficiency of the proposed model in CKD prediction compared to other models. The proposed model with selected features using mutual_info_classi obtained the highest performance.

Due to the global pandemic (COVID-19) currently facing humanity, a new environment that promotes teaching–learning is now emerging. This environment that challenges traditional teaching practices created an opportunity for the technology industry to capitalize on by developing creative e-learning platforms that empowers the teaching–learning process, during this ‘emergency’ situation. E-learning scenario is an important element to be considered, as it offers a host of benefits, such as reducing costs, allowing for education on-demand, etc., to its adopters. However, the application of this could bring some challenges, as some of the existing online platforms are not conducive to support clear communication among academic staff. This paper describes a report identifying the main problems faced by teachers and students from different countries in Latin America, when using e-learning platforms in a lockdown scenario, reporting the importance to include aspects related with emotions and awareness.

Colon cancer is the third most common cancer type worldwide in 2020, almost two million cases were diagnosed. As a result, providing new, highly accurate techniques in detecting colon cancer leads to early and successful treatment of this disease. This paper aims to propose a heterogenic stacking deep learning model to predict colon cancer. Stacking deep learning is integrated with pretrained convolutional neural network (CNN) models with a metalearner to enhance colon cancer prediction performance. The proposed model is compared with VGG16, InceptionV3, Resnet50, and DenseNet121 using different evaluation metrics. Furthermore, the proposed models are evaluated using the LC25000 and WCE binary and muticlassified colon cancer image datasets. The results show that the stacking models recorded the highest performance for the two datasets. For the LC25000 dataset, the stacked model recorded the highest performance accuracy, recall, precision, and F1 score (100). For the WCE colon image dataset, the stacked model recorded the highest performance accuracy, recall, precision, and F1 score (98). Stacking-SVM achieved the highest performed compared to existing models (VGG16, InceptionV3, Resnet50, and DenseNet121) because it combines the output of multiple single models and trains and evaluates a metalearner using the output to produce better predictive results than any single model. Black-box deep learning models are represented using explainable AI (XAI).

Segmenting a breast ultrasound image is still challenging due to the presence of speckle noise, dependency on the operator, and the variation of image quality. This paper presents the UltraSegNet architecture that addresses these challenges through three key technical innovations: This work adds three things: (1) a changed ResNet-50 backbone with sequential 3 convolutions to keep fine anatomical details that are needed for finding lesion boundaries; (2) a computationally efficient regional attention mechanism that works on high-resolution features without using a transformer’s extra memory; and (3) an adaptive feature fusion strategy that changes local and global features based on how the image is being used. Extensive evaluation on two distinct datasets demonstrates UltraSegNet’s superior performance: On the BUSI dataset, it obtains a precision of 0.915, a recall of 0.908, and an F1 score of 0.911. In the UDAIT dataset, it achieves robust performance across the board, with a precision of 0.901 and recall of 0.894. Importantly, these improvements are achieved at clinically feasible computation times, taking 235 ms per image on standard GPU hardware. Notably, UltraSegNet does amazingly well on difficult small lesions (less than 10 mm), achieving a detection accuracy of 0.891. This is a huge improvement over traditional methods that have a hard time with small-scale features, as standard models can only achieve 0.63–0.71 accuracy. This improvement in small lesion detection is particularly crucial for early-stage breast cancer identification. Results from this work demonstrate that UltraSegNet can be practically deployable in clinical workflows to improve breast cancer screening accuracy.

Postpartum depression (PPD) is becoming increasingly prevalent worldwide, often manifesting in new mothers due to a complex interplay of physical, behavioral, and emotional transformations post-childbirth. The primary aim of our research is to analyze the contributory factors leading to PPD, including familial, social, and other maternal health-related aspects, and to devise a predictive model that can accurately assess the risk of PPD. In this research, we analyzed a benchmark dataset of 1,503 entries gathered from a medical institution, where the data was compiled through questionnaires disseminated using a digital Google Forms platform. We deployed eleven advanced machine-learning algorithms for comparison. We proposed a novel MDKR model, a meta-learner designed to excel in predicting PPD. Questionnaire data is initially processed in the proposed MDKR through the decision tree, k-nearest classifier, and random forest models. Subsequently, the outputs from these models are fed into a meta-learner multi-layer perceptron for the final prediction. Compared to state-of-the-art studies, the proposed MDKR model surfaced as the most proficient, with an exemplary accuracy of 99% in detecting PPD. In addition, we have confirmed the performance using k-fold validation and tuning hyperparameters. In the comparative assessment of all the models concerning their ability to predict PPD risk levels, MDKR emerged as the superior model. This meta-learning model has significantly contributed to identifying pivotal factors influencing PPD, enhancing the predictive framework within maternal healthcare domains.

Over the past two decades, cloud computing has experienced exponential growth, becoming a critical resource for organizations and individuals alike. However, this rapid adoption has introduced significant security challenges, particularly in intrusion detection, where traditional systems often struggle with low detection accuracy and high processing times. To address these limitations, this research proposes an optimized Intrusion Detection System (IDS) that leverages Graph Neural Networks and the Leader K-means clustering algorithm. The primary aim of the study is to enhance both the accuracy and efficiency of intrusion detection within cloud environments. Key contributions of this work include the integration of the Leader K-means algorithm for effective data clustering, improving the IDS’s ability to differentiate between normal and malicious activities. Additionally, the study introduces an optimized Grasshopper Optimization algorithm, which enhances the performance of the Optimal Neural Network, further refining detection accuracy. For added data security, the system incorporates Advanced Encryption Standard encryption and steganography, ensuring robust protection of sensitive information. The proposed solution has been implemented on the Java platform with CloudSim support, and the findings demonstrate a significant improvement in both detection accuracy and processing efficiency compared to existing methods. This research presents a comprehensive solution to the ongoing security challenges in cloud computing, offering a valuable contribution to the field.