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.

In this article, a standalone solar tracking system is intended and successfully synchronized using proteus 7.6 ISIS. Best at instantly aligning a solar panel with the position of the sun, resulting in a 40% increase in efficiency. A low-cost solar tracking detector is being used to rotate the solar panel paired to the motor at such a specific angle to rotate 180°s in 8 steps per day with better accuracy. A microcontroller (Atmega3285P) is used as a control unit, and ADC ports are used to interconnect sensor units. To interconnect a solar array, the motor driver ULN 293D is also used to rotate the solar panel at the highest solar power angle. The modeling is performed in two ways, using four LDRs and eight LDRs, and the results of both are measured. The simulation and analysis results show that the maximum current drawn by the solar tracking sensor is less than 0.5 mA. “Proteus 7.6 ISIS” and “Arduino UNO” and “Code Vision AVR” are used to write the program code and to burn over Atmega3285P for parameters estimation.

With the massive use of electronic gadgets and the developing fame of web-based media, a great deal of text information is being produced at the rate never observed. It is not feasible for people to pursue all information produced and discover what is being investigated in their area of interest. To determine topics in large textual documents Topic modeling is used. Topic Modeling Algorithms are Unsupervised Machine Learning approaches which are widely used and have proven to be successful in the area of Aspect-based Opinion Mining to extract ‘latent’ topics, which are aspects of interest. In this paper, the approaches that are widely used for topic modeling are examined and compared to find their importance in detecting topics based on metrics such as Perplexity and Coherence. As a result, Latent Dirichlet Allocation is a good topic modeling algorithm compared to Latent Semantic Analysis and Hierarchical Dirichlet Process for aspect extraction process in aspect-based opinion mining. Also, we have proposed an unsupervised aspect extraction algorithm based on topic models for Aspect-based Opinion mining.

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.

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.

During this covid pandemic it is clearer than ever how much health misinformation effects. It is much easier now to publish health related articles online without validation, these articles are shared across social media contributing to the spread of health fake news. This Health fake news are spread with intent to damage image of person or product, to increase sells of a product or to promote a product. In recent research papers, many useful health misinformation detection models use BERT (Bidirectional Encoder Representations from Transformers) which is pretrained on unlabeled data extracted from English Wikipedia and book corpus and are mostly dealt with health misinformation on social media. Therefore, a self - ensemble SCIBERT (Scientific BERT) based model that makes use of domain specific word embeddings is proposed for detection of health misinformation specifically in news which is less explored and a dataset combining existing FakeHealth dataset and custom dataset that contains health articles scraped from news fact checking website Snopes.com. Classification results exhibits that the proposed model provides weighted F1 score of 0.715.