Along with the increasingly serious electromagnetic interference (EMI) pollution, it is in
urgent need of exploiting high-performance EMI shielding materials. However, the …

Deep neural networks (DNNs) having multiple hidden layers are very efficient to learn large
volume datasets and applied in a wide range of applications. The DNNs are trained on …

Amidst the tumultuous storms and challenging weather conditions that have engulfed
Northern Ireland at the end of 2023 into 2024, highlights the demand of Unmanned Aerial …

The parameter gamma, which is used in distance-based knowledge graph embedding to
distinguish positive and negative samples, plays an important role in model performance …

For deep model training, an optimization technique is required that minimizes loss and
maximizes accuracy. The development of an effective optimization method is one of the most …

Colorectal cancer (CRC), ranking third most prevalent cancer type, can be diagnosed with
the detection of polyps in the colon and rectum through endoscopic procedures facilitating …

Pothole detection is an essential aspect of road maintenance and safety. This paper
introduces a novel method for pothole classification using the EfficientNet architecture within …

Deep learning [1, 2] simulates the way the human brain makes decisions, creates patterns,
and processes data using artificial neural network (ANN) to analyze the data. To accomplish …

The development of the power system cannot be separated from real-time monitoring,
analysis, and control of the transmission network, and online monitoring is an important part …

For deep neural networks, gradient descent (GD) is the backbone. Slow convergence is an
issue with GD. Using momentum is the well-known method of overcoming delayed …