In recent years, the need for a fast, efficient and a reliable wireless network has increased dramatically. Numerous 5G networks have already been tested while a few are in the early stages of deployment. In non-cooperative communication scenarios, the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over them. The recent advancements in both Machine Learning (ML) and Deep Learning (DL) models demand the development of effective modulation recognition models with self-learning capability. In this background, the current research article designs a Deep Learning enabled Intelligent Modulation Recognition of Communication Signal (DLIMR-CS) technique for next-generation networks. The aim of the proposed DLIMR-CS technique is to classify different kinds of digitally-modulated signals. In addition, the fractal feature extraction process is applied with the help of the Sevcik Fractal Dimension (SFD) approach. Then, the extracted features are fed into the Deep Variational Autoencoder (DVAE) model for the classification of the modulated signals. In order to improve the classification performance ofthe DVAE model, the Tunicate Swarm Algorithm (TSA) is used to fine-tune the hyperparameters involved in DVAE model. A wide range of simulations was conducted to establish the enhanced performance of the proposed DLIMR-CS model. The experimental outcomes confirmed the superior recognition rate of the DLIMR-CS model over recent state-of-the-art methods under different evaluation parameters.

There is a drastic increase experienced in the production of vehicles in recent years across the globe. In this scenario, vehicle classification system plays a vital part in designing Intelligent Transportation Systems (ITS) for automatic highway toll collection, autonomous driving, and traffic management. Recently, computer vision and pattern recognition models are useful in designing effective vehicle classification systems. But these models are trained using a small number of hand-engineered features derived from small datasets. So, such models cannot be applied for real-time road traffic conditions. Recent developments in Deep Learning (DL)-enabled vehicle classification models are highly helpful in resolving the issues that exist in traditional models. In this background, the current study develops a Lightning Search Algorithm with Deep Transfer Learning-based Vehicle Classification Model for ITS, named LSADTL-VCITS model. The key objective of the presented LSADTL-VCITS model is to automatically detect and classify the types of vehicles. To accomplish this, the presented LSADTL-VCITS model initially employs You Only Look Once (YOLO)-v5 object detector with Capsule Network (CapsNet) as baseline model. In addition, the proposed LSADTL-VCITS model applies LSA with Multilayer Perceptron (MLP) for detection and classification of the vehicles. The performance of the proposed LSADTL-VCITS model was experimentally validated using benchmark dataset and the outcomes were examined under several measures. The experimental outcomes established the superiority of the proposed LSADTL-VCITS model compared to existing approaches.

In the software landscape, understanding component impacts on system reliability is pivotal, especially given the unique complexities of modern software systems. This paper presents a model tailored for software reliability assessment. Our approach introduces the “component influence” to measure a single component’s effect on overall system reliability. Additionally, we adapt a state transition model to cater to the diverse architectures of software systems. Using a discrete-time Markov chain, we predict software reliability. We test our model on an actual software system, finding it notably accurate and superior to existing methods. Our work offers a promising direction for those venturing into software reliability enhancement.