International Association of Engineers

About: International Association of Engineers is a nonprofit organization based out in Hong Kong, Hong Kong, China. It is known for research contribution in the topics: The Internet & Cloud computing. The organization has 7 authors who have published 14 publications receiving 56 citations.

Building panoptic change segmentation with the use of uncertainty estimation in squeeze-and-attention CNN and remote sensing observations

Abstract: In conventional frameworks, the building change prediction of the deep model can be just blindly assumed accurate, but sometimes the truth is not. In this study, a new deep convolutional neural net...

A Hybrid Technique Based on a Genetic Algorithm for Fuzzy Multiobjective Problems in 5G, Internet of Things, and Mobile Edge Computing

Abstract: Emerging commucation technologies, such as mobile edge computing (MEC), Internet of Things (IoT), and fifth-generation (5G) broadband cellular networks, have recently drawn a great deal of interest. Therefore, numerous multiobjective optimization problems (MOOP) associated with the aforementioned technologies have arisen, for example, energy consumption, cost-effective edge user allocation (EUA), and efficient scheduling. Accordingly, the formularization of these problems through fuzzy relation equations (FRE) should be taken into consideration as a capable approach to achieving an optimized solution. In this paper, a modified technique based on a genetic algorithm (GA) to solve MOOPs, which are formulated by fuzzy relation constraints with - norm, is proposed. In this method, firstly, some techniques are utilized to reduce the size of the problem, so that the reduced problem can be solved easily. The proposed GA-based technique is then applied to solve the reduced problem locally. The most important advantage of this method is to solve a wide variety of MOOPs in the field of IoT, EC, and 5G. Furthermore, some numerical experiments are conducted to show the capability of the proposed technique. Not only does this method overcome the weaknesses of conventional methods owing to its potentials in the nonconvex feasible domain, but it also is useful to model complex systems.

A hybrid neural network cybernetic system for quantifying cross-market dynamics and business forecasting

Abstract: The internal structure of a complex system can manifest itself with correlations among its components. In global business, the interactions between different markets cause collective lead–lag behavior having special statistical properties which reflect the underlying dynamics. In this work, a cybernetic system of combining the vector autoregression (VAR) and genetic algorithm (GA) with neural network (NN) is proposed to take advantage of the lead–lag dynamics, to make the NN forecasting process more transparent and to improve the NN’s prediction capability. Two business case studies are carried out to demonstrate the advantages of our proposed system. The first one is the tourism demand forecasting for the Hong Kong market. Another business case study is the modeling and forecasting of Asian Pacific stock markets. The multivariable time series data is investigated with the VAR analysis, and then the NN is fed with the relevant variables determined by the VAR analysis for forecasting. Lastly, GA is used to cope with the time-dependent nature of the co-relationships among the variables. Experimental results show that our system is more robust and makes more accurate prediction than the benchmark NN. The contribution of this paper lies in the novel application of the forecasting modules and the high degree of transparency of the forecasting process.

Smart delivery of multifaceted services through connected governance

Abstract: Advancement of Information and Communication Technology have enabled delivery of electronic communication services to the doorstep of Citizen. To deliver these communication services efficiently w.r.t time, cost and output, service providers have to rely upon the publicly available communication channel like Internet. Since Internet is an open communication channel, attackers may mount cyber-attacks to compel either the service provider, or the user or both the communicating parties to agree on compromising terms. Since these electronic communication transmits bulk amount of sensitive information, its privacy and consistency must be ensured with maximum priority. To ensure privacy of these sensitive information using software tools, cryptographic security protocols may be installed using the unique parameters of user. Furthermore, to enhance these security parameters, hardware devices may be installed within the connected system. Since Government act as the primary actor for delivery of any type of electronic services under its jurisdiction, Citizen centric multifaceted smart card based E-Governance system has been already proposed. For management of huge amount of sensitive information, it has been enhanced to its cloud architecture to design a connected governance model. Finally, the viability of enhanced model is shown during Cloud Banking transaction through a connected governance environment.

Improving weed segmentation in sugar beet fields using potentials of multispectral unmanned aerial vehicle images and lightweight deep learning

Abstract: In our study, a real-world application using the latest unmanned aerial vehicle (UAV) functionality is presented. Sugar beet is an important industrial crop in many countries. According to estimates, weeds in sugar beet fields dramatically reduce both the quantity and quality of sugar beet crops. Due to the spectral similarities between weeds and sugar beet seedlings, visual identification of weeds is extremely difficult in the sugar beet fields. In the present study, a lightweight, end-to-end trainable guided feature-based deep learning method, called DeepMultiFuse, has been developed to improve the weed segmentation performance using multispectral UAV images that aim to fulfill these requirements (to identify weed in sugar beet fields). The proposed architecture is composed of five basic concepts, including guided features, fusion module, dilation convolution operation, modified inception module, and gated encoder–decoder network extracting the object-level image representation for different scenes. The proposed network was trained on the generated dataset, including four multispectral orthomosaic reflectance maps using the RedEdge-M sensor in Rheinbach and three multispectral orthomosaic reflectance maps applying Sequoia sensor in Eschikon for mapping weed segmentation on the field. Experimental results demonstrated that the proposed network taking advantage of the feature fusion module-rich features outperforms state-of-the-art networks.