Khan Muhammad

Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust due to their black-box nature. Usually, it is essential to understand the reasoning behind an AI model’s decision-making. Thus, the need for eXplainable AI (XAI) methods for improving trust in AI models has arisen. XAI has become a popular research subject within the AI field in recent years. Existing survey papers have

No system entity within a contemporary distributed cyber system can be entirely trusted. Hence, the classic centralized trust management method cannot be directly applied to it. Blockchain technology is essential to achieving decentralized trust management, its consensus mechanism is useful in addressing large-scale data sharing and data consensus challenges. Herein, an n-party quantum detectable Byzantine agreement (DBA) based on the GHZ state to realize the data consensus in a quantum blockcha

Vehicle license plate images are often low resolution and blurry because of the large distance and relative motion between the vision sensor and vehicle, making license plate identification arduous. The extensive use of expensive, high-quality vision sensors is uneconomical in most cases; thus, images are initially captured and then translated from low resolution to high resolution. For this purpose, several traditional techniques such as bilinear, bicubic, super-resolution convolutional neural

Nowadays, the use of public transportation is reducing and people prefer to use private transport because of its low cost, comfortable ride, and personal preferences. However, personal transport causes numerous real-world road accidents due to the conditions of the drivers’ state such as drowsiness, stress, tiredness, and age during driving. In such cases, driver fatigue detection is mandatory to avoid road accidents and ensure a comfortable journey. To date, several complex systems have been pr

Currently, real-time assessment of surface damage to bridges is crucial for ensuring infrastructure safety. Unfortunately, existing methods often present a challenge: overly complex computational models are incompatible with systems that have limited resources, while lightweight models struggle to achieve sufficient detection accuracy. This task is further complicated by the diverse nature of bridge damages, such as cracks, exposed reinforcement, and efflorescence, as well as the challenges of d