Explore the vast range of titles available.

Medical care has become one of the most indispensable parts of human lives, leading to a dramatic increase in medical big data. To streamline the diagnosis and treatment process, healthcare professionals are now adopting Internet of Things (IoT)-based wearable technology. Recent years have witnessed billions of sensors, devices, and vehicles being connected through the Internet. One such technology—remote patient monitoring—is common nowadays for the treatment and care of patients. However, these technologies also pose grave privacy risks and security concerns about the data transfer and the logging of data transactions. These security and privacy problems of medical data could result from a delay in treatment progress, even endangering the patient’s life. We propose the use of a blockchain to provide secure management and analysis of healthcare big data. However, blockchains are computationally expensive, demand high bandwidth and extra computational power, and are therefore not completely suitable for most resource-constrained IoT devices meant for smart cities. In this work, we try to resolve the above-mentioned issues of using blockchain with IoT devices. We propose a novel framework of modified blockchain models suitable for IoT devices that rely on their distributed nature and other additional privacy and security properties of the network. These additional privacy and security properties in our model are based on advanced cryptographic primitives. The solutions given here make IoT application data and transactions more secure and anonymous over a blockchain-based network.

The top priority of today’s healthcare system is delivering medicine directly from the manufacturer to end-user. The pharmaceutical supply chain involves some level of commingling of a collection of stakeholders such as distributors, manufacturers, wholesalers, and customers. The biggest challenge associated with this supply chain is temperature monitoring as well as counterfeit drug prevention. Many drugs and vaccines remain viable within a specific range of temperatures. If exposed beyond this temperature range, the medicine no longer works as intended. In this paper, an Internet of Things (IoT) sensor-based blockchain framework is proposed that tracks and traces drugs as they pass slowly through the entire supply chain. On the one hand, these new technologies of blockchain and IoT sensors play an essential role in supply chain management. On the other hand, they also pose new challenges of security for resource-constrained IoT devices and blockchain scalability issues to handle this IoT sensor-based information. In this paper, our primary focus is on improving classic blockchain systems to make it suitable for IoT based supply chain management, and as a secondary focus, applying these new promising technologies to enable a viable smart healthcare ecosystem through a drug supply chain.

In social network big data scheduling, it is easy for target data to conflict in the same data node. Of the different kinds of entropy measures, this paper focuses on the optimization of target entropy. Therefore, this paper presents an optimized method for the scheduling of big data in social networks and also takes into account each task’s amount of data communication during target data transmission to construct a big data scheduling model. Firstly, the task scheduling model is constructed to solve the problem of conflicting target data in the same data node. Next, the necessary conditions for the scheduling of tasks are analyzed. Then, the a periodic task distribution function is calculated. Finally, tasks are scheduled based on the minimum product of the corresponding resource level and the minimum execution time of each task is calculated. Experimental results show that our optimized scheduling model quickly optimizes the scheduling of social network data and solves the problem of strong data collision.

An important area of computer vision is real-time object tracking, which is now widely used in intelligent transportation and smart industry technologies. Although the correlation filter object tracking methods have a good real-time tracking effect, it still faces many challenges such as scale variation, occlusion, and boundary effects. Many scholars have continuously improved existing methods for better efficiency and tracking performance in some aspects. To provide a comprehensive understanding of the background, key technologies and algorithms of single object tracking, this article focuses on the correlation filter-based object tracking algorithms. Specifically, the background and current advancement of the object tracking methodologies, as well as the presentation of the main datasets are introduced. All kinds of methods are summarized to present tracking results in various vision problems, and a visual tracking method based on reliability is observed.

The 21st century has seen rapid changes in technology, industry, and social patterns. Most industries have moved towards automation, and human intervention has decreased, which has led to a revolution in industries, named the fourth industrial revolution (Industry 4.0). Industry 4.0 or the fourth industrial revolution (IR 4.0) relies heavily on the Internet of Things (IoT) and wireless sensor networks (WSN). IoT and WSN are used in various control systems, including environmental monitoring, home automation, and chemical/biological attack detection. IoT devices and applications are used to process extracted data from WSN devices and transmit them to remote locations. This systematic literature review offers a wide range of information on Industry 4.0, finds research gaps, and recommends future directions. Seven research questions are addressed in this article: (i) What are the contributions of WSN in IR 4.0? (ii) What are the contributions of IoT in IR 4.0? (iii) What are the types of WSN coverage areas for IR 4.0? (iv) What are the major types of network intruders in WSN and IoT systems? (v) What are the prominent network security attacks in WSN and IoT? (vi) What are the significant issues in IoT and WSN frameworks? and (vii) What are the limitations and research gaps in the existing work? This study mainly focuses on research solutions and new techniques to automate Industry 4.0. In this research, we analyzed over 130 articles from 2014 until 2021. This paper covers several aspects of Industry 4.0, from the designing phase to security needs, from the deployment stage to the classification of the network, the difficulties, challenges, and future directions.

During the process of fine segmentation of medical images, although a single threshold can improve the efficiency of processing, there will be the problem of fuzzy features and non-convergence of threshold in denoising of details such as contour extraction. To extract contour information of medical images, a method based on multi-threshold optimization is proposed. This paper analyzes the influence of contour wave transformation on gray correlation degree and noise intensity of different medical images and improves the Bayesian threshold. The middle threshold function was improved by correlation characteristics of contour wave coefficients, and contour features of medical images were constrained by multiple thresholds. Based on the above, the dimension of the medical image was reduced by the wavelet multi-resolution analysis method, and the corresponding threshold search space was obtained. A genetic algorithm was used to find the best quasi threshold in the search space. Through this value, the attribute histogram of the medical image was established, the best feature extraction threshold of the medical image was obtained by the golden section method, and contour feature information of the medical image was extracted. The experimental results show that the proposed method can achieve the fast extraction of the contour feature information of running image, get an ideal feature extraction effect, and has high efficiency of feature extraction.

Image reconstruction is a mathematical process, where the image is compressed into a small representation and derived from this form. The general use of the reconstruction technique finds a place in noise removal from images obtained in medicine or other areas of life. In this paper, we propose a heuristic validation mechanism for training different types of neural networks in the problem of image reconstruction. The main idea is based on finding some important areas on image by heuristic algorithm and train network until a certain level of entropy of these areas is achieved. The mathematical model of this technique is described and supported by experimental results on different datasets with complex analysis of different heuristics. Proposed approach shows that it can reduce the average time of training process using convolutional neural networks.

Image watermarking techniques are used to provide copyright protection and verify ownership of media/entities. This technique refers to the concept of embedding of secret data/information of an owner in a given media/entity for determining any ownership conflicts that can arise. Many watermarking approaches have been offered by various authors in the last few years. However, there are not enough studies and comparisons of watermarking techniques in soft computing environments. Nowadays, soft computing techniques are used to improve the performance of watermarking algorithms. This paper surveys soft computing-based image watermarking for several applications. We first elaborate on novel applications, watermark characteristics and different kinds of watermarking systems. Then, soft computing based watermarking approaches providing robustness, imperceptibility and good embedding capacity are compared systematically. Furthermore, major issues and potential solutions for soft computing-based watermarking are also discussed to encourage further research in this area. Thus, this survey paper will be helpful for researchers to implement an optimized watermarking scheme for several applications.

Because of the hierarchical significance of traffic sign images, the traditional methods do not effectively control and extract the brightness and features of layered images. Therefore, an automatic recognition algorithm for traffic signs based on a convolution neural network is proposed in this paper. First, the histogram equalization method is used to pre-process the traffic sign images, with details of the images being enhanced and contrast of the images improved. Then, the traffic sign images are recognized by a convolution neural network and the large scale structure of information in the traffic sign images are obtained by using a hierarchical significance detection method based on graphical models. Next, the area of interest in the traffic sign images are extracted by using the hierarchical significance model. Finally, the Softmax classifier is selected to classify the input feature images to realize the automatic recognition of traffic signs. Experimental results show that the proposed algorithm can control the brightness of traffic sign images, which can accurately extract image regions of interest and complete the automatic recognition of traffic signs.

DNA (Deoxyribonucleic Acid) Cryptography has revolutionized information security by combining rigorous biological and mathematical concepts to encode original information in terms of a DNA sequence. Such schemes are crucially dependent on corresponding DNA-based cryptographic keys. However, owing to the redundancy or observable patterns, some of the keys are rendered weak as they are prone to intrusions. This paper proposes a Genetic Algorithm inspired method to strengthen weak keys obtained from Random DNA-based Key Generators instead of completely discarding them. Fitness functions and the application of genetic operators have been chosen and modified to suit DNA cryptography fundamentals in contrast to fitness functions for traditional cryptographic schemes. The crossover and mutation rates are reducing with each new population as more keys are passing fitness tests and need not be strengthened. Moreover, with the increasing size of the initial key population, the key space is getting highly exhaustive and less prone to Brute Force attacks. The paper demonstrates that out of an initial 25 × 25 population of DNA Keys, 14 keys are rendered weak. Complete results and calculations of how each weak key can be strengthened by generating 4 new populations are illustrated. The analysis of the proposed scheme for different initial populations shows that a maximum of 8 new populations has to be generated to strengthen all 500 weak keys of a 500 × 500 initial population.