Global Transitions Proceedings最新文献

In the world of service-oriented businesses the deployment of services is of utmost importance. On the Web, the service-oriented business has a major impact through Web Application Program Interfaces (APIs). In the field of Web applications, majority of the applications run on the Web/Internet APIs. These APIs are dependent on architecture styles such as Representational State Transfer (REST) and Remote Procedure Call (RPC). Over a decade, REST is widely used for architectural style in API design followed by RPC. These styles are used extensively in various domains. This paper presents the survey of such APIs in the world of Software-Defined Networks (SDN) and Network Function Virtualization (NFV). The main contributions of the paper is to provide a ready reference for developing effective and efficient orchestration approaches towards micro services in SDN and NFV domains. The survey aims to help practitioners and researchers in this field to follow guidelines and standards for designing higher quality services.

The passage of a yield from the producer to the buyer is traced by a farming supply chain. A clear stage that permits organization members to farm together is a blockchain-based farming supply chain. This method eliminates the requirement for a centralized trusted authority, mediators, and business histories, growing production and security whereas sustaining extreme integrity, liability, and safety. According to the planned resolution, smart contracts are used to switch and handle all communications and dealings amongst all shareholders in the supply chain network. AgriBlockIoT is a block chain-based traceability key that comprises facts since IoT devices in the supply chain. It comprises an example of watching harvest from farmhouse to branch, as well as assessments of Ethereum and Hyperledger implementations.

EEG records the brain's electrical function. It displays the difference in voltages from distinct sites of the brain over a period of time. It plays a major role in understanding the brain. It also helps to diagnose disorders related to sleep, and find the epileptogenic foci in the brain. Recording accurate EEG signals, however, comes with its own limitations. Gels applied to the scalp are abrasive in nature. Gels dry up over time, and also irritate the skin. There are different kinds of electrodes that record EEG signals with appreciable accuracy, keeping comfort and practicality in mind. Some of them are: dry electrodes, capacitive electrodes, electrodes with nanomaterials. The electrodes mentioned above are discussed in this paper. It discusses the advancement in the field of EEG. This paper shows the ease at which signals can be captured, using the aforementioned electrodes. Cheaper, more accessible EEG electrodes are making it possible to bring affordable healthcare outside the confines of the hospitals and clinics.

Digital watermarking is one of the techniques used for copyright protection as well as authentication purpose. In this paper, digital watermark embedding and extraction techniques have been used in which a nested type of watermarking (a watermark inside another watermark) has been presented. The main purpose of using the nested watermarking method is, it increases the embedding capacity so that a large amount of information could be embedded. In this method, one watermark is embedded into another watermark. The resultant watermark is considered as main watermark. The main watermark is encrypted and then embedded into the main image. The main goal in encrypting the watermarks before embedding is increased safety. A5/1 encryption algorithm is used for the encryption and decryption purpose. Therefore, our research work focuses on two important things i.e., increased watermark embedding capacity and increased safety.

Communication in Wireless Sensor Networks (WSN) by its environment further disposed to danger attacks and it harms data. Secured information or maintain data confidentiality is a big challenge in today life, but main issue is which needs a security for the purpose of localization. Including to this one more complication now focused on sensation in acoustic motes which practices Particle Swarm Optimization (PSO) method for locality or Position Assessment. Here propose Contemporary Secured Target Locality (CSTL) algorithm mainly addresses security and target location issues, Sybil and on-off attack and wicked motes occurrence at altered stages of target locality at the end results are compared with existing method General Localization (GLS), Approximate Point in Triangle, Distance Vector hop localization and multi hop method with proposed method. Here outcome of simulation prove better accuracy and progressive reaction in attack recognition presently contributes in build a secured wireless sensor network.

Software reliability is one and only the standard used to calculate the excellence of a software system. In this situation, we suggest a method to forecast software reliability in the primary creation of any process growth phases since from the designing stage only. Software mobile application has remained a significant tool in our lifetime, and several small-medium scale software progress companies capitalize the substantial resources to mark accessible prospects. The landscape of mobile atmospheres varies as of server environments owing to various issues, by means of the net, liveliness, battery-operated, and compatibility. Estimating and forecasting mobile application reliability remain the actual contests as of the variety of the mobile situations in which the applications are used, and the absence of widely available fault data. In addition, error information is optionally given in to the end-users. Hence, in what way to estimate reliability of mobile software application has remained more and more significant. In this paper, site reliability engineering (SRE) for mobile application is proposed for the reliability of software and is taken as a case study. Their failure is copied back to the exact features of mobile application is useful and powerful.

The paper is introduced with a brief survey about existing causes for Knee ailments followed by conventional treatments for them. Studies show that ailments like Osteo Arthritis (OA) of the Knee and Knee related injuries cause chronic pain and stiffness to the knee joint. This affects the range of motion of the leg. The severity of this is highly dependent on the age and BMI (Body-mass Index) of the patient. Further, a contrast between conventional Physiotherapy Machines (CPM) and the proposed model is established. The paper proposes an alternative to the existing CPMs. A cost-effective system capable of diagnosing the severity of the knee using machine learning models and provide appropriate Automated physiotherapy. Using gyroscopic data and a predefined questionnaire, a 1D-CNN is trained. An accuracy of 90.21% was obtained from the machine learning model. The accuracy of the proposed model exceeded the accuracy of some state-of-the-art algorithms in determining the severity of the affected knee by utilizing gyroscopic parameters and with the least computational cost.

© 2019 The Authors. Published by Elsevier B.V.

This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/)

Peer-review under responsibility of the scientific committee of the 8th International Conference on Through-Life Engineering Service – TESConf 2019.

The main goal of this research work is to experimentally verify the importance of spoken Speech digit signal in person authentication in controlling applications. The motivation is based on the earlier work of demonstrating the feasibility of using spoken speech digit utterance signal for person security and controlling applications. This paper work also discusses the. Comparative analysis of the cepstral analysis with the mel frequency cepstral coefficient (MFCC) by using vector quantization feature matching technique. All digits speech digit from zero utterance to nine digit utterance data has been collected for 15 subjects in three different sessions. For the thus collected spoken speech digit data, the feature extraction techniques such as cepstral and MFCC were applied to extract the Cepstral and MFCC features. In the next stage of work vector quantization was used for feature matching for both Cepstral and MFCC features and performance were recorded for two different session data. By comparing the performance of Cepstral plus VQ with the MFCC plus VQ, we can conclude that feature extraction technique MFCC gives the better performance than cepstral feature for spoken digit utterance data.

The idea of sentimental analysis is getting attention for the last few years. The key challenges in a sentimental analysis are the collection of huge data from the sources, applying appropriate algorithms or techniques, and classifying them into different sentiments. In this fast-spreading internet world, social media provides a platform for individuals to express their sentiments. With the changing ways of things in different areas in our day-to-day life, the way of expressing one's view or opinion has also changed. People tend to express themselves in their regional language or in a way convenient to them. These individual reviews play an important role in decision-making. With the huge amount of data that is obtained on social media, it is of no use if the opinions are not classified based on their sentiments. This paper provides information about the tweets posted by the customer are positive, negative, or neutral. For this the proposed model first scrape the tweets from Twitter by using Twitter APIs, then later by using text blob, the customer reviews are given different sentiment scores and classify them as positive, negative, or neutral by using text classification model.

This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/)

Peer-review under responsibility of the scientific committee of the 8th International Conference on Through-Life Engineering Service – TESConf 2019.

The recognition of a person in society is based on the behaviour and socio-communicative skills. But some neurodevelopment illness like Autism Spectrum disorder (ASD) highly influences the behaviour and communication skill of an individual. Individuals with such illness need early detection for minimizing its effect. The ASD diagnosis is done by a mobile-based screening app to extract information from all individuals irrespective of age. The information is stored in publicly accessible authenticated research UCI Machine Learning (ML) repository and Kaggle. The proposed approach in this paper is investigated up on child data set gathered from UCI repository. The analysis is done for two distinct cases: complete and missing data via standardization by Mean Standard deviation approach followed by dimension reduction using Diffusion Mapping and finally classification of ASD class utilising Deep Neural Network Prediction and Classification (DNNPC) model. The performance of DNNPC classifier model is validated by distinct performance parameters.