Crop production can be greatly reduced due to various diseases, which seriously endangers
food security. Thus, detecting plant diseases accurately is necessary and urgent. Traditional …

In recent years, the healthcare industry has faced new challenges around staffing, human
interaction, and the adoption of telehealth. Technological innovations can improve …

In this paper, we proposed a novel plant leaf disease identification model based on a deep
convolutional neural network (Deep CNN). The Deep CNN model is trained using an open …

Day-ahead residential load forecasting is crucial for electricity dispatch and demand
response in power systems. Electrical loads are characterized by volatility and uncertainty …

Predicting crop yield based on the environmental, soil, water and crop parameters has been
a potential research topic. Deep-learning-based models are broadly used to extract …

Phishing has become an increasing concern and captured the attention of end-users as well
as security experts. Existing phishing detection techniques still suffer from the deficiency in …

This paper describes the development of a fault detection and diagnosis method to
automatically identify different fault conditions of a hydraulic blade pitch system in a spar …

In this research, we employed a deep convolutional neural network, often known as a Deep
CNN, to propose a novel approach to the detection of illnesses in the leaves of plants. In …

Addressing issues related to multi-label classification is relevant in many fields of
applications. In this work. We present a multi-label classification architecture based on Multi …

Deep learning (DL) is one of the branches of artificial intelligence that has seen exponential
growth in recent years. The scientific community has focused its attention on DL due to its …