Search

Smart health care will be a major application in future smart cities. Timely and precise delivery of patients' data to their medical consultant, to allow the necessary actions, is one of the basic needs in health care systems. Blockchain technology, with the provisioning of recording and tracking of data blocks, guarantees secure and error-free data delivery. The vital sign data from patients' sensors are placed in different data blocks. To become a part of the blockchain, the block must contain a valid key, based on a hash function. Mining nodes with high processing capabilities generate the required key using a 32-bit number, known as a nonce, which is changed for every new block. Finding a nonce that meets the hash function requirements is a time-intensive process in blockchain technology and is performed by several fog mining nodes. However, an efficient resource allocation that results in the fair placement of data in these fog mining nodes, while maintaining the priority and sensitivity of patients' data, is a challenge. This work proposes two algorithms for the resource allocation of mining nodes. The first algorithm uses a load balancing technique to distribute the load of nonce computing tasks. The second algorithm utilizes the knapsack algorithm to allocate the caching space of the mining nodes. The simulation results highlighted that the proposed resource allocation techniques outperformed the existing techniques, in terms of quick mining of the most sensitive patient data blocks. [ABSTRACT FROM AUTHOR]

This paper proposes two novel approaches to measure the similarity of co-cited authors for the task of document clustering, a) paragraph-level content-based author co-citation analysis (PCACA) and b) section-level content-based author co-citation analysis (SCACA), by mining the textual cited reference at the paragraph and the section level within a given scientific publication, respectively. Using over 2000 full-text publications, indexed in the field of Computer and Information Sciences, indexed in PLOS.org, we extract the useful information from a full-text publication such as citing sentences, location of citing sentences, cited first author name, and title of cited. We show that our proposed SCACA method of clustering outperforms existing clustering methods by exhibiting more optimal clusters with minimum graph density and an average degree in SCACA, i.e., 0.327 and 27.143, respectively. Finally, we show that SCACA produces the optimum number of clusters that comprehensively explains sub-disciplines of co-cited author pairs. [ABSTRACT FROM AUTHOR]

Contemporarily numerous analysts labored in the field of Vehicle detection which improves Intelligent Transport System (ITS) and reduces road accidents. The major obstacles in automatic detection of tiny vehicles are due to occlusion, environmental conditions, illumination, view angles and variation in size of objects. This research centers on tiny and partially occluded vehicle detection and identification in challenging scene specifically in crowed area. In this paper we present comprehensive methodology of tiny vehicle detection using Deep Neural Networks (DNN) namely CenterNet. Substantially DNN disregards objects that are small in size 5 pixels and more false positives likely to happen in crowded area. Primarily there are two categories of deep learning models single-step and two-step. A single forward pass model is the one in which detection is performed directly to possible location over dense sampling, wherein two-step models incorporated by Region proposals followed by object detection. We in this research scrutinize one-step State of the art (SOTA) model CenteNet as proposed recently with three different feature extractor ResNet-50, HourGlass-104 and ResNet-101 one by one. We train our model on challenging KITTI dataset which outperforms in comparison with SOTA single-step technique MSSD300* which depicts performance improvement by 20.2%mAPandSMOKEby with 13.2% mAP respectively. Effectiveness of CenterNet can be justified through the huge improved performance. The performance of our model is evaluated on KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) benchmark dataset with different backbones such as ResNet-50 gives 62.3% mAP ResNet-101 82.5% mAP, last but not the least HourGlass-104 outperforms with 98.2% mAP CenterNet-HourGlass-104 achieved high mAP among above mentioned feature extractors. We also compare our model with other SOTA techniques. [ABSTRACT FROM AUTHOR]

The current cyber-attack environment has put even the most protected systems at risk as the hackers are now modifying technologies to exploit even the tiniest of weaknesses and infiltrate networks. In this situation, it's critical to design and construct software that is both secure and long-lasting. While security is the most well-defined aspect of health information software systems, it is equally significant to prioritise sustainability because any health information software system will be more effective if it provides both security and sustainability to the customers at the same time. In this league, it is crucial to determine those characteristics in the systems that can help in the accurate assessment of the sustainable-security of the health information software during the development stage. This research work employed the Fuzzy Analytic Network Process (Fuzzy ANP) to estimate the impact of the overall sustainable-security of health information software systems and their characteristics in order to achieve a high level of sustainable-security. Furthermore, the study validates the efficacy of the Fuzzy ANP procedure by testing it on five different versions of a health information software system through Fuzzy Technique for Order of Preference by Similarity to Ideal Solutions (Fuzzy TOPSIS). Despite the sensitivity of the health information software systems, this study employed multiple versions of health information software system. When compared with the existing procedures for testing the sustainable-security of health information software systems, the outcomes were conclusive and significantly more effective. Besides saving time and resources, the mechanism suggested in this research work aims to establish an outline that software practitioners can follow to enhance the sustainable-security of health information software systems. [ABSTRACT FROM AUTHOR]

Security is an important component in the process of developing healthcare web applications. We need to ensure security maintenance; therefore the analysis of healthcare web application's security risk is of utmost importance. Properties must be considered to minimise the security risk. Additionally, security risk management activities are revised, prepared, implemented, tracked, and regularly set up efficiently to design the security of healthcare web applications. Managing the security risk of a healthcare web application must be considered as the key component. Security is, in specific, seen as an add-on during the development process of healthcare web applications, but not as the key problem. Researchers must ensure that security is taken into account right from the earlier developmental stages of the healthcare web application. In this row, the authors of this study have used the hesitant fuzzy-based AHP-TOPSIS technique to estimate the risks of various healthcare web applications for improving security-durability. This approach would help to design and incorporate security features in healthcare web applications that would be able to battle threats on their own, and not depend solely on the external security of healthcare web applications. Furthermore, in terms of healthcare web application's security-durability, the security risk variable is measured, and vice versa. Hence, the findings of our study will also be useful in improving the durability of several web applications in healthcare. [ABSTRACT FROM AUTHOR]

Source location privacy (SLP) is a serious issue in wireless sensor networks (WSN) since Eavesdroppers tries to determine the source location. Hunting Animals in Forest is considered as an example for SLP. Many conventional schemes have been proposed for SLP in WSN, namely, Random Walk Routing, and Fake Messages Transmission, which cause critical issues (less safety period, packet delivery latency, and high energy consumption). Furthermore, the security analysis is not properly investigated in any previous work. In this paper, we propose a new model called the circular chessboard-based secure source location privacy model (C2S2-LOOP) with the following tasks: key generation, network topology management (clustering), intercluster routing (travel plan), and data packets encryption. All sensor nodes are deployed in a circular chessboard (Circular Field) and the key generation ( P U K , S E K ) is invoked using elliptic curve cryptography (ECC) with Ant Lion Optimization algorithm, which mitigate the issues of conventional ECC. Then, the network topology is managed using clustering where residual energy of the nodes is used for Cluster Head (CH) selection. Intercluster routing is implemented using packet traversing using clockwise and anticlockwise directions, which are mainly concerned with establishing a secure route between the source to the destination node. To ensure data security, we present the Chaotic Artificial Neural Network (C-ANN) in which encryption is executed. Assume that CH near to the source node has a high trust value, then it traverses (clock-wise) real packets towards sink node and similarly in the left side region (anticlockwise), fake packets are transmitted. Network simulations (OMNeT++) are evaluated and compared with the previous approaches, and finally, our proposed scheme concludes that it maintains not only source node location privacy (large safety period) and also reduces energy consumption by more than 40% and latency by more than 35%. [ABSTRACT FROM AUTHOR]