With the current continuous increase in the usage of communication technologies and data transmission amount, the available web services and mobile applications are used to access and complete several online activities such as online education, e-commerce, and online financial transactions. The needs for a strong and secure access control authentication solution of users’ data and the available online resources are crucial. Authentication can play a major rule in protection by implementing a solution, which allows users to prove their identities and provide access permission to all legitimate users only. This paper proposes two-factor authentication scheme based on the usage of a modified copy of the Schnorr Signcryption algorithm. The proposed scheme achieves the target goal of this paper, which provides the users with an efficient solution that can be implemented in any authentication solution as the second factor, with no special requirement and can be used to authenticate the users and control recourses access permission. Moreover, Java-based application is developed in order to examine the proposed scheme in a client/server architecture where it is found that the solution can successfully authenticate authorised users and reject any unauthorised access request with an adequate security protection level and acceptable performance.

In equestrian sports and veterinary medicine, horse welfare is paramount. Horse tiredness, lameness, colic, and anemia can be identified and classified using deep learning (DL) models. These technologies analyze horse images and videos to help vets and researchers find symptoms and trends that are hard to see. Early detection and better treatment of certain disorders can improve horses’ health. DL models can also improve with new data, improving diagnosis accuracy and efficiency. This study comprehensively evaluates three convolutional neural network (CNN) models to distinguish normal and abnormal horses using the generated horse dataset. For this study, a unique dataset of horse breeds and their normal and abnormal states was collected. The dataset includes mobility patterns from this study’s initial data collection. DL models like CNNs and transfer learning (TL) models (visual geometry group (VGG)16, InceptionV3) were employed for categorization. The InceptionV3 model outperformed CNN and VGG16 with over 97% accuracy. Its depth and multi-level structure allow the InceptionV3 model to recognize characteristics in images of varied scales and complexities, explaining its excellent performance.

An increase in population causes loopholes in controlling law and order situations. One of the threatening aspects of peace is the availability of weapons to the general public. As a result, many dangerous situations arise, most notably street crimes. Traditional methods are not sufficient to deal with such situations. Consequently, the police and other security concerns need serious technological reforms to prevent such situations. In modern technology tools, deep learning has made great improvements in various areas of daily life, especially in object detection. This paper presents an efficient technique for detecting weapons from closed-circuit television (CCTV) cameras, videos, or images. Upon the detection of the weapon, the concerned person is automatically informed to take the necessary action; without human intervention. For the first time, RetinaNet has been employed to detect weapons in real-time scenarios. RetinaNet has shown remarkable improvement in this domain, by achieving an average of 90% accuracy in real-time scenarios. With the emergence of quantum computing, many computer environments saw a revolution. Thus, we have also utilized quantum computing technology for real-time weapons detection using quantum deep learning. In this paper, quantum inspired RetinaNet (QIR-Net) is developed for weapons detection and amazing results are observed.