Context : Quantum Cryptography (QC) revolutionizes network communication – harnessing principles of quantum mechanics to enable the exchange of encrypted messages – for enabling secure data transmission in an era of quantum information processing. With a significant rise in quantum computing research and development efforts, there is an increasing interest in exploring QC (e.g., Quantum Key Distribution (QKD) and Quantum Secured Encryption (QSE)) against a multitude of security threats in futuristic networks for quantum information processing. Objectives and Method : The objective of this study is to review the existing research i.e., consolidating the published evidence, that streamlines and documents the predominant challenges, recurring solutions, security threats, and their counter-measures against the outlined research questions in the context of QC. To conduct this study, we followed the guidelines and method of Systematic Literature Reviews (SLRs) to answer seven research questions. These questions investigate the proposed solutions for state-of-the-art QC and its impact on future network security. Results and Implications : Based on the seven (7) outlined research questions, this study systematically selected and reviewed one hundred and thirty four (134) research studies published from 2016 to 2023 with a focus on QC for quantum information processing. The results of this SLR establish a knowledge base for modern QC applications to guarantee network security in quantum-enabled network communications. The review reveals that though still in the phase of its inception, the research on QC is progressing rapidly, highlighting the necessity for network protocol and frameworks to cater for quantum network security. The SLR also highlights the challenges encountered while designing or implementing the QC systems, pinpointing the significance of keeping abreast of QKD networks and addressing possible ramifications for internet security in the future. The SLR provides theoretical foundations and evidence-based guidelines to tackle emerging and futuristic challenges of security in the context of QC and QKD for quantum information processing.

Driver fatigue is a major cause of traffic accidents. Automatic vision-based driver fatigue recognition is one of the most prospective commercial applications based on facial expression analysis technology. Generally, factors such as noise, illumination effects, image scaling, and redundant data affect the performance of facial expression recognition systems. In this paper, we have proposed an efficient algorithm, which is not only capable of working with multi-scale images but also able to overcome the mentioned obstacles. The proposed framework can be divided into three main phases. In the first step, the input image is converted into four sub-band images by applying a discrete wavelet transform, which preserves the important information of face image. Also, the original image is down-sampled to obtain the image of different sizes. Based on entropy analysis, each image is then further divided into a number of blocks classified as either informative or non-informative blocks. In the second step, the high variance features are selected in a zigzag manner using discrete cosine transform. In the final step, classifiers are trained and tested to accurately classify the expressions into seven generic expression classes. The empirical results suggest that the proposed framework not only effectively utilizes the multi-scale images but also outperforms other similar techniques in terms of classification accuracy rate.

Breast cancer is a highly prevalent disease in females that may lead to mortality in severe cases. The mortality can be subsided if breast cancer is diagnosed at an early stage. The focus of this study is to detect breast malignancy through computer-aided diagnosis (CADx). In the first phase of this work, Hilbert transform is employed to reconstruct B-mode images from the raw data followed by the marker-controlled watershed transformation to segment the lesion. The methods based only on texture analysis are quite sensitive to speckle noise and other artifacts. Therefore, a hybrid feature set is developed after the extraction of shape-based and texture features from the breast lesion. Decision tree, k-nearest neighbor (KNN), and ensemble decision tree model via random under-sampling with Boost (RUSBoost) are utilized to segregate the cancerous lesions from the benign ones. The proposed technique is tested on OASBUD (Open Access Series of Breast Ultrasonic Data) and breast ultrasound (BUS) images collected at Baheya Hospital Egypt (BHE). The OASBUD dataset contains raw ultrasound data obtained from 100 patients containing 52 malignant and 48 benign lesions. The dataset collected at BHE contains 210 malignant and 437 benign images. The proposed system achieved promising accuracy of 97% with confidence interval (CI) of 91.48% to 99.38% for OASBUD and 96.6% accuracy with CI of 94.90% to 97.86% for the BHE dataset using ensemble method.

Fleet maintenance management requires adequate data and timely information regarding vehicle systems so that early diagnostics can be performed to avoid unscheduled maintenance and breakdowns which affects fleet productivity and performance. In this paper, we present a fleet maintenance system called Car e-Talk that uses Internet-of-Things technology and cloud computing to monitor vehicle health and report any anomalies along with information about the nearest maintenance center. Different sensors are attached to the vehicle for monitoring the vehicle’s health. Data from sensors are received on the driver’s smartphone through a microcontroller and, after processing, useful information is displayed on the driver’s mobile screen. The same information is uploaded to a cloud server, where a history of the system is maintained and analyzed for predictive maintenance. The advantages of our system are that it is able to monitor real-time vehicle health statistics, predict fleet health and maintenance, improve vehicle diagnostics, and perform automatic reporting, thus increasing the usable life of the vehicle, fleet productivity, and performance.