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...

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...

The practice of cultivating the soil, producing crops, and keeping livestock is referred to as farming. Agriculture is critical to a country's economic development. Nearly 58 percent of a country's primary source of livelihood is farming. Farmers till date had adopted conventional farming...

Deep neural networks (DNNs) have been applied to predict the cycle times of jobs in manufacturing accurately. However, the prediction mechanism of a DNN is complex and difficult to communicate. This limits its acceptability (or practicability) in real-world applications. An explainable...

Artificial Intelligence (AI) uses systems and machines to simulate human intelligence and solve common real-world problems. Machine learning and deep learning are Artificial intelligence technologies that use algorithms to predict outcomes more accurately without relying on human intervention....

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...