Blockchain Application on the Internet of Vehicles (IoV)

With the rapid development of the Internet of Things (IoT) and its potential integration with the traditional Vehicular Ad-Hoc Networks (VANETs), we have witnessed the emergence of the Internet of Vehicles (IoV), which promises to seamlessly integrate into smart transportation systems. However, the key characteristics of IoV, such as high-speed mobility and frequent disconnections make it difficult to manage its security and privacy. The Blockchain, as a distributed tamper-resistant ledge, has been proposed as an innovative solution that guarantees privacy-preserving yet secure schemes. In this paper, we review recent literature on the application of blockchain to IoV, in particular, and intelligent transportation systems in general

Effect of serialized messaging on web services performance

Message serialization is a format of messaging leveraging Web services to exchange data over the network. Serialized messages are processed at the server and sent as objects over the network to the client to be consumed. While, serialization process minimizes network bandwidth requirement but then incurs overhead at the communicating ends. This research contributes to the study of message exchange using HTTP across communication systems. The research identified the fundamental effect of serializing high-volume messages across network and the sources for the effects at the communication endpoints. The study utilized server - client SOAP Web services to identify the fundamental effect of serialization in the communication endpoints. SOAP messages were exchanged as XML messages over HTTP. Payload sizes (1MB-22MB) for serialized and normal messages were exchanged through the services. The message payload, overhead, and response time were monitored and measured. The overall result indicated that is more beneficial to serialized large payload than smaller one. Generally, the serialization and deserialization cost incurred at individual ends are slightly constant irrespective of the payload size. Also, the serialization and deserialization process is insignificant to the overall transaction as it delay is below 3% of the total overhead

HMM-based Arabic handwritten word recognition via zone segmentation

This paper presents a novel approach towards Arabic handwritten word recognition using the zone-wise material. Due to complex nature of the Arabic characters involving issues of overlapping and related issues like touching, the segmentation and recognition is a monotonous main occupation of in Arabic cursive (e.g. Naskha, Riqaa and other comparable scripts written for Holy Quran). To solve the issues of this character segmentation in such cursive, HMM founded on sequence modelling relying on the holistic way. This paper proposes an efficient framework word recognition by segmenting the handwritten word features horizontally into three zones (upper, middle and lower) and then recognise the corresponding zones. The aim of this zone is to minimise the quantity of distinct component classes associated to the total a number of classes in Arabic cursive. As an outcome of this proposed approach is to enhance the recognition performance of the system. The elements of segmentation zone especially in middle zone (baseline), where characters are frequently tender, are recognised using HMM. After the recognition of middle zone, HMM Based in Viterbi forced Alignment is performed to mark the right and left characters in conjoint zones. Next, the residue components, if any, in upper and lower zones are highlighted in a character boundary then the Components are joint with the morphology of the character to achieve the whole word level recognition. Water reservoir- created the main properties that had integrated into the framework to increase the performance of the zone segmentation especially for the upper zone for the character to determine the boundary detection imperfections in segmentation stage. A new sliding window-based feature, named hierarchical Histogram OF-Oriented Gradient (PHOG) is suggested for lower and upper zone recognition. The comparison study with other similar PHOG features and found robust for Arabic handwriting script recognition. An exhaustive experiment is performed of other handwriting using different dataset such IFN / IFNT to evaluate the rate and the recognition performance. The outcome of this experiment, it has been renowned that proposed zone-wise recognition increases accuracy

Face Recognition and Age Estimation Implications of Changes in Facial Features: A Critical Review Study

Facial features are considered as one of the important personal characteristics. This can be used in many applications, such as face recognition and age estimation. The value of these applications depends in several areas, such as security applications, law enforcement applications, and attendance systems. In addition, facial features are particularly the key usage in the finding of lost child. Present applications have achieved a high level of accuracy. This paper provides a survey of face recognition, including the age estimation, which was discussed. Moreover, the research outlines several challenges faced in face recognition area that had been explored. The research also provides a landscape mapping based on integrating into a critical and coherent taxonomy. In the methodology sections, the exploration the accomplished via a deep focused in every single article in 'Face Recognition', then 'Age Estimation', and later in 'Facial Features'. The 'Articles extraction' is mining from diverse sources, such as Web of Science, ACM, IEEE, Science Direct, and Springer databases. The research covers overall 72 articles; 32/72 articles were face recognition. Moreover, 39/72 of the articles were for age estimation. A comparison based on the objectives of the approaches is presented to underline the taxonomy. Ending by research conclusion on face techniques contributes to the understanding of the recognition approaches, which can be used in future researches. The research concluded that face techniques' performance is distinct from one data set to another. This paper contributes to display gaps for other researchers to join this line of research

Image pattern recognition in big data: taxonomy and open challenges: survey

Image pattern recognition in the field of big data has gained increasing importance and attention from researchers and practitioners in many domains of science and technology. This paper focuses on the usage of image pattern recognition for big data applications. In this context, the taxonomy of image pattern recognition and big data is revealed. The applications of image pattern recognition for big data, including multimedia, biometrics, and biology/biomedical, are also highlighted. Moreover, the significance of using pattern-based feature reduction in big data is discussed, and machine-learning techniques in pattern recognition applications are presented. A comparison based on the objectives of the approaches is presented to underline the taxonomy. This paper provides a novel review in exploring image recognition approaches for big data, which can be used in future research