This paper proposes Simultaneous Trainings of Identical Neural Networks (STNN) that aims to predict when sufficient data is not available for training neural networks (NN). While predictive applications of neural networks are growing, a common assumption in the NN algorithms is to have a...

The generation of huge data with high velocity creates alteration in the distribution of the stream data, which is defined as the concept of drifts. The concept drifts negatively influence the classification accuracy and the stability of the data streams. Numerous machine learning-based models...

An Autoencoder (AE) is an independent feature extractor from data samples and a deep network can be obtained by stacking several AEs. This paper presents a novel hybrid stacked Autoencoder-based Deep Kernel-based Random Vector Functional Link Network (DKRVFLN-AE) for forecasting and trend...

Spam emails are unwanted and unsolicited messages. The major problems in email spam detection methods are low detection rates and a high likelihood of false alarms. This study proposes a hybrid correlation-based deep learning model for email spam classification using a fuzzy inference system....

Multimodal image fusion combines information from multiple modalities to generate a composite image containing complementary information. Multimodal image fusion is challenging due to the heterogeneous nature of data, misalignment and nonlinear relationships between input data, or incomplete...

Coronary Heart Disease (CHD) is a life-threatening public health problem. Many chronic CHDs and health risks can be avoided, reversed, and reduced with proper risk assessment. Medical professionals find it challenging to anticipate heart attacks and heart failures since it is a complex process...

Assistant Conversational Agents (chatbots) are becoming increasingly complex and sophisticated since they are enabled to understand and respond to human language. Chatbots have varying organizational functions, from customer support to personal assistance, and have dominated the digital market...

Training a Convolutional Neural Network (CNN) from scratch is time-consuming and expensive. In this study, we propose implementing the DenseNet architecture for classification of AD in three classes. Our approach leverages transfer learning architectures as the base model and showcases superior...

Malaria represents a potentially fatal communicable illness triggered by the Plasmodium parasite. This disease is transmitted to humans through the bites of Anopheles mosquitoes that carry the infection. This disease has significant and devastating consequences on the health systems of fragile...

Artificial intelligence (AI) systems can assist in analyzing medical images and aiding in the early detection of diseases. AI can also ensure the quality of services by avoiding misdiagnosis caused by human errors. This study proposes a deep neural network (DNN) model with fine-tuned training...