Zahid Ullah

Muhammad Umar Farooq, Zahid Ullah, Jeonghwan Gwak

To improve the recognition ability of computer-aided breast mass classification among mammographic images, in this work we explore the state-of-the-art classification networks to develop an ensemble mechanism. First, the regions of interest (ROIs) are obtained from the original dataset, and then three models, i.e., XceptionNet, DenseNet, and EfficientNet, are trained individually. After training, we ensemble the mechanism by summing the probabilities outputted from each network which enhances the performance up to 5%. The scheme has been validated on a public dataset and we achieved accuracy, precision, and recall 88%, 85%, and 76% respectively.

Sulaiman Khan, Suleman Khan, Yasir Ali et al.

In the current era, the next-generation networks like 5th generation (5G) and 6th generation (6G) networks require high security, low latency with a high reliable standards and capacity. In these networks, reconfigurable wireless network slicing is considered as one of the key elements for 5G and 6G networks. A reconfigurable slicing allows the operators to run various instances of the network using a single infrastructure for a better quality of services (QoS). The QoS can be achieved by reconfiguring and optimizing these networks using Artificial intelligence and machine learning algorithms. To develop a smart decision-making mechanism for network management and restricting network slice failures, machine learning-enabled reconfigurable wireless network solutions are required. In this paper, we propose a hybrid deep learning model that consists of a convolution neural network (CNN) and long short term memory (LSTM). The CNN performs resource allocation, network reconfiguration, and slice selection while the LSTM is used for statistical information (load balancing, error rate etc.) regarding network slices. The applicability of the proposed model is validated by using multiple unknown devices, slice failure, and overloading conditions. The overall accuracy of 95.17% is achieved by the proposed model that reflects its applicability.

Inayat Ali, Sonia Sabir, Zahid Ullah

The Internet of Things (IoT) is one of the emerging technologies that has grabbed the attention of researchers from academia and industry. The idea behind Internet of things is the interconnection of internet enabled things or devices to each other and to humans, to achieve some common goals. In near future IoT is expected to be seamlessly integrated into our environment and human will be wholly solely dependent on this technology for comfort and easy life style. Any security compromise of the system will directly affect human life. Therefore security and privacy of this technology is foremost important issue to resolve. In this paper we present a thorough study of security problems in IoT and classify possible cyberattacks on each layer of IoT architecture. We also discuss challenges to traditional security solutions such as cryptographic solutions, authentication mechanisms and key management in IoT. Device authentication and access controls is an essential area of IoT security, which is not surveyed so far. We spent our efforts to bring the state of the art device authentication and access control techniques on a single paper.