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Engineering International
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  This is an Open Access Journal Open Access journal
ISSN (Print) 2409-3629
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  • Computer Vision: A Timely Opportunity

    • Authors: Takudzwa Fadziso
      Pages: 111 - 128
      Abstract: Different approaches to the study of computer vision have been taken into consideration. It begins with the collection of raw data and advances to methodologies and ideas that combine digital images, pattern recognition, machine learning, and computer graphics to produce new products, as well as new products themselves. In order to get crucial information, students can analyze images and videos to interpret events or descriptions, as well as discern patterns in the landscape, using computer vision. It makes use of a multi-spectrum application domain strategy in conjunction with a large amount of data in order to achieve its goals. In recent years, technological developments in computer vision have paved the way for the creation of novel agricultural applications. Precision yield forecasts for fruit and vegetable crops are particularly critical for improving harvesting, marketing, and logistics planning and execution. When a bridge is under stress or has a high volume of traffic, the geographical and temporal information provided by cars on the bridge reflects this. It is proposed to design a methodology for information gathering and dissemination by utilizing computer vision technology, which recognizes various items tracking and picture calibration via a quick regional neural convolution network, and a quick regional neural convolution network (Faster R-CNN). When dealing with small fish populations, it can be difficult to objectively assess the differences in behavior between individuals. The behavior of fish in aquaculture tanks has been studied with the use of a computer vision system that has been built in order to quantify these types of observations. Contained traffic load data is essential for bridge statistical analysis, security evaluation, and maintenance planning. This is particularly true for heavy trucks. From retail to agriculture, and across all industries, computer vision is having a big impact on organizations of all sizes and in all sectors. When a human eye is required to assess the situation, the significance of this becomes even more apparent. This paper provides information about computer vision technology, including short algorithms, issues, opportunities, and applications for computer vision in a range of fields in the year 2021, as well as information on computer vision in general. Information about computer vision applications in many fields is also included for the year 2021.
      PubDate: 2021-09-02
      DOI: 10.18034/ei.v9i2.571
      Issue No: Vol. 9, No. 2 (2021)
       
  • Strategic Growth of Everything-as-a-Service (XaaS) Business Model
           Transformation

    • Authors: Venkata Naga Satya Surendra Chimakurthi
      Pages: 129 - 140
      Abstract: The study presents the XaaS architecture and explores how this business model has strategically developed and how it has revolutionized the whole business industry. The study indicates how this particular business model allows companies to enhance the range of advantages of Internet Cloud model applications, particularly when it deals with process of media processing and customization of users. Its huge benefits with negligible disadvantages are mentioned as part of the presented research. This study will help to understand the services of XaaS model, challenges experiencing by it and its future opportunities. This will also assists new scholars of field to build a better understanding and better prospects regarding XaaS in future.
      PubDate: 2021-12-31
      DOI: 10.18034/ei.v9i2.589
      Issue No: Vol. 9, No. 2 (2021)
       
  • Quantum Computing in High Frequency Trading and Fraud Detection

    • Authors: Apoorva Ganapathy
      Pages: 61 - 72
      Abstract: ‘Quantum Computing in high-frequency trading and fraud detection is an analysis of quantum computing and how it can be used by the different industries especially finance. It is an evolution of computing from the traditional computing method. Quantum computing is a process that is concentrated on creating systems and technology based on quantum theory rules. Quantum theory describes the energy on atomic and subatomic levels. Quantum computing uses quantum bits (qubits) which are more advanced than the traditional bits used by traditional computers. This article focuses on deploying quantum computers in solving problems that cannot be efficiently solved using traditional computers. In the finance sector, such as banking, insurance, and high-frequency trading, quantum computers can help optimize service by providing targeting and predictive analytics to reduce risk, provide personalized customer service, and provide the needed security framework against fraud.
      PubDate: 2021-07-01
      DOI: 10.18034/ei.v9i2.549
      Issue No: Vol. 9, No. 1 (2021)
       
  • Significant of Gradient Boosting Algorithm in Data Management System

    • Authors: Md Saikat Hosen, Ruhul Amin
      Pages: 85 - 100
      Abstract: Gradient boosting machines, the learning process successively fits fresh prototypes to offer a more precise approximation of the response parameter. The principle notion associated with this algorithm is that a fresh base-learner construct to be extremely correlated with the “negative gradient of the loss function” related to the entire ensemble. The loss function's usefulness can be random, nonetheless, for a clearer understanding of this subject, if the “error function is the model squared-error loss”, then the learning process would end up in sequential error-fitting. This study is aimed at delineating the significance of the gradient boosting algorithm in data management systems. The article will dwell much the significance of gradient boosting algorithm in text classification as well as the limitations of this model. The basic methodology as well as the basic-learning algorithm of the gradient boosting algorithms originally formulated by Friedman, is presented in this study. This may serve as an introduction to gradient boosting algorithms. This article has displayed the approach of gradient boosting algorithms. Both the hypothetical system and the plan choices were depicted and outlined. We have examined all the basic stages of planning a specific demonstration for one’s experimental needs. Elucidation issues have been tended to and displayed as a basic portion of the investigation. The capabilities of the gradient boosting algorithms were examined on a set of real-world down-to-earth applications such as text classification.
      PubDate: 2021-07-20
      DOI: 10.18034/ei.v9i2.559
      Issue No: Vol. 9, No. 1 (2021)
       
  • Wave Structures for Nonlinear Schrodinger Types Fractional Partial
           Differential Equations Arise in Physical Sciences

    • Authors: Mst. Nasrin Nahar, Md. Tarikul Islam, Diganta Broto Kar
      Pages: 101 - 110
      Abstract: Nonlinear partial differential equations are mostly renowned for depicting the underlying behavior of nonlinear phenomena relating to the nature of the real world. In this paper, we discuss analytic solutions of fractional-order nonlinear Schrodinger types equations such as the space-time fractional nonlinear Schrodinger equation and the (2+1)-dimensional time-fractional Schrodinger equation. The considered equations are converted into ordinary differential equations with the help of wave variable transformation and then the recently established rational ( )-expansion method is employed to construct the exact solutions. The obtained solutions have appeared in the forms of a trigonometric function, hyperbolic function, and rational function which are compared with those of literature and claimed to be different. The graphical representations of the solutions are finally brought out for their physical appearances. The applied method is seemed to be efficient, concise, and productive which might be used for further research. Mathematics Subject Classifications: 35C08, 35R11
      PubDate: 2021-07-30
      DOI: 10.18034/ei.v9i2.560
      Issue No: Vol. 9, No. 1 (2021)
       
 
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