Volume 9 | Issue 6 |

 Abstract

Fog Computing is a paradigm that expands Distributed computing (cloud computing) and administrations to the edge of the network. Comparable to Cloud, Fog gives information, process, stockpiling, and application administrations to end-clients. The inspiration of Fog processing lies in an arrangement of genuine situations, like Smart Grid, keen traffic signals in vehicular networks and programming characterized networks. Nowadays usage of cloud computing has increased broadly but there are some issues which have not been solved due to inherent problems of cloud computing like unreliable latency, no mobility support and location awareness. Fog computing is also known as edge computing . This can resolve all those problems by providing elastic resources and services to the end users. This paper discusses the definition of fog computing and its applications in IOT (Internet of Things). It also gives some advantages and challenges of fog computing.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Fog computing, Edge computing, IOT (Internet of Things), Cloud computing

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In recent times, Heart Stroke prediction is one of the most complicated tasks in medical field. In the modern era, approximately one person dies per minute due to heart Stroke. Data science plays a crucial role in processing huge amount of data in the field of healthcare. As heart stroke prediction is a complex task, there is a need to automate the prediction process to avoid risks associated with it and alert the patient well in advance. This paper makes use of heart stroke dataset. The proposed work predicts the chances of Heart Stroke and classifies patient's risk level by implementing different data mining techniques such as KNN, Decision Tree and Random Forest. Thus, this paper presents a comparative study by analyzing the performance of different machine learning algorithms. The trial results verify that Random Forest algorithm has achieved the highest accuracy of 99.17% compared to other ML algorithms implemented.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

KNN, Decision Tree, Random Forest, Heart Stroke Prediction

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

accidents, application

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

ACCESS TO JUSTICE AND LOCUS STANDI

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In injection molding cycle, packing phase predominate part quality and responsible for defect free part. The packing pressure, melt temperature, mould temperature, plastic materials, and the configuration of the runner and the gate are all factors that influence the production of components in injection moulding during the packing process. It's very tough only by trial and error to identify them. For packing analysis, Moldex3D is a valuable tool for dealing with this issue. Packing phase comprises different parameters which contribute in packing formation and part quality. These parameter has been studied and analyzed in conventional and conformal cooling channels so the effect of conformal cooling channels on packing parameter can be optimized and analyzed where ever it is needed.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Injection Molding, Packing Phase, Packing Analysis, Conventional Cooling Channels, Conformal Cooling Channels, Packing Parameter

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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Two element mono-pole antenna, FR-4 substrate, micro strip feed, HFSS software.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In the service sector, Airline Industry is the biggest transportation industry in worldwide. To achieve customer loyalty quality service plays avital role, so this industry needs to focus on unique service strategy to attract and retain its customers. Service quality and price both are the most influential factors of this industry.This investigation looks at the basic powers of service quality impacts on the airline travelers. In the study the researchers have tried to inspect which measurements affect service quality and which measurements have the most and least significant effect on service quality in worldwide air travel, via carrier travelers. The sample size of the study is 106. This investigation broke down the information from travelers of two classes, economy, and business. The outcomes propose that there are various components of in-flight service quality that are significant as indicated by the client and class. The dimensionality of service quality in universal air travel was investigated and three measurements were distinguished. These measurements remember for flight service, in-flight computerized administration and service and back-office activities. The discoveries uncover that these three measurements are emphatically identified with see administration and service quality in universal air travel and of these measurements, Cuisines gave, seat comfort wellbeing are the most significant measurement in-flight administration service quality. Individual amusement is the most significant measurement as seen via aircraft travelers In-flight advanced help quality. Online ticket booking is another measurement in back-office tasks. Moreover, the discoveries demonstrate that travelers" fulfillment on various aircraft organizations on premise of the services conveyed.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Service quality, customer satisfaction, airline passengers, Aviation Industry

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The method of identifying the handwritten text using machine interface is known as handwritten character recognition. Handwritten characters present challenges because they differ from one writer to the other; even though the same author writes the same character, the form, size, and location of the character vary. Handwritten Character Recognition has been one of the challenging areas in the field of image processing. It has numerous applications which include, bank cheques and conversion of any handwritten document into digital text form. There is a prominent need for storing information to desktop storage from the information available in handwritten images to later reprocess this information utilizing computers. But to re-process this information, it would be very difficult to read the other information from these image files. Therefore, a technique to automatically resolve and store information, in particular text, from image files is needed. This paper aims to classify an individual handwritten word so that handwritten text for English alphabets can be translated to a typed or editable form. We used the Convolutional Neural Networks to accomplish this task. We used this architecture to train a model that can accurately predict words. Trends suggest that the market value for such applications is going to increase exponentially in the future.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Convolutional Network Layer, Architecture of CNN, English Words, image files.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Shipping Industry, Approaching, Customer Relationship Management, Communication, Liner Shipping.

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Creative Commons Attribution 4.0 and The Open Definition