Energy Efficiency Prediction using Artificial Neural Network

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%

A case study of predicting banking customers behaviour by using data mining

Data Mining (DM) is a technique that examines information stored in large database or data warehouse and find the patterns or trends in the data that are not yet known or suspected. DM techniques have been applied to a variety of different domains including Customer Relationship Management CRM). In this research, a new Customer Knowledge Management (CKM) framework based on data mining is proposed. The proposed data mining framework in this study manages relationships between banking organizations and their customers. Two typical data mining techniques - Neural Network and Association Rules - are applied to predict the behavior of customers and to increase the decision-making processes for recalling valued customers in banking industries. The experiments on the real world dataset are conducted and the different metrics are used to evaluate the performances of the two data mining models. The results indicate that the Neural Network model achieves better accuracy but takes longer time to train the model

New Trends in Development of Services in the Modern Economy

The services sector strategic development unites a multitude of economic and managerial aspects and is one of the most important problems of economic management. Many researches devoted to this industry study are available. Most of them are performed in the traditional aspect of the voluminous calendar approach to strategic management, characteristic of the national scientific school. Such an approach seems archaic, forming false strategic benchmarks. The services sector is of special scientific interest in this context due to the fact that the social production structure to the services development model attraction in many countries suggests transition to postindustrial economy type where the services sector is a system-supporting sector of the economy. Actively influencing the economy, the services sector in the developed countries dominates in the GDP formation, primary capital accumulation, labor, households final consumption and, finally, citizens comfort of living. However, a clear understanding of the services sector as a hyper-sector permeating all spheres of human activity has not yet been fully developed, although interest in this issue continues to grow among many authors. Target of strategic management of the industry development setting requires substantive content and the services sector target value assessment

Emerging Technology in Business and Finance

In the globalized scenario where technologies are developing continuously with time, these novel methods are affecting the business and finance in the significant way. In this chapter we are going to discuss about the major emerging technologies in the field of entrepreneurship, application development, finance, and business. The authors are going to start with the introduction about the business, finance, entrepreneurship and application development, and the effect of the emerging technologies on these fields and the way in which technologies are developing from time to time, about adoption of these technologies by industries. The changes in the technologies with special reference to developed and developing country will also be the part of this chapter. Moving ahead we are discussing about these technologies in prevailing businesses as well as upcoming business. Some of the technologies we are going to discuss are Embedded Business Intelligence, Amplified Visual Presentation, Augmented Analytics, Cloud Management. Beside these technologies, we are going to cover about the growing automation in the finance sector such as Cloud banking, Robotic process automation, Blockchain, Internet of things, etc. This chapter will cover all the technologies while getting the complete knowledge about what, why, where, when and how it is changing in the present finance and business scenario. Just like the two opposite faces of the coin, one side these emerging technologies are boon for the business and finances then on the other side there are certain risks involved in these technologies, which can be a great threat to our business as well as in our routine life. So, we also discuss about the potential risks associated with these technologies. We will end our chapter by giving our conclusion, precautions, and suggestions on these technologies

Artificial intelligence – a key success factor for wealth management industry

The Private Banking & Wealth Management (PWM) industry is generally seen as embodying traditional, old- fashioned and even archaic values. Upheld for centuries, its business model, which is based on intensive, comprehensive and discreet personal interactions between financial advisors and wealthy clients, is put to the test today. In today's dynamic and highly connected world, a large number of HNWIs (High Net Worth Individuals) want faster and more convenient value propositions and a cutting-edge digital experience – a trend that the pandemic has amplified many times over. In order to meet the increased expectations of this clientele, private banks and other institutions in the sector are increasingly investing in a number of new technologies and tools, artificial intelligence (AI) taking a leading place among them. In addition to enabling a more complete and qualitative satisfaction of user needs, AI promises benefits for PWM companies in a number of other areas: risk management, compliance, cost reduction, etc

AI, Robotics, and the Future of Jobs

This report is the latest in a sustained effort throughout 2014 by the Pew Research Center's Internet Project to mark the 25th anniversary of the creation of the World Wide Web by Sir Tim Berners-Lee (The Web at 25).The report covers experts' views about advances in artificial intelligence (AI) and robotics, and their impact on jobs and employment

The transformation of traditional banking activity in digital

Purpose: This article investigates specifics of the transformation of banking activity in the conditions of digitalization of the economy. In the light of penetration of digital technologies into all the spheres of our life, the rapid development of financial technologies and their active implementation in the banking sector of the economy, digital financial innovations are formed at the intersection of the concepts of "financial technologies" and "financial innovations". Design/Methodology/Approach: In order to investigate the process of transformation of the banking sector in the context of digitalization, it is necessary to consider this issue from three points of view: 1) theoretical understanding of the concept of "financial technologies"; 2) the need to ensure the efficiency and sustainability of the banking sector; 3) the change in the IT- architecture of banking activities and the formation of the digital ecosystem with banks in the center. It is also reasonable to analyze promising areas of implementation of financial technologies into the banking sector. Findings: The main directions of the development of financial technologies in the banking sector, aimed at further transformation of traditional banking services through digital technologies. Practical Implications: The results of the study can be applied in the development of the legislative regulation of the FinTech industry in Russia. Originality/Value: The main contribution of this study is to determine the prospects for the development of the domestic banking sector in the context of digitalization, the need to transform in order not only to improve the competitiveness and efficiency of functioning, but also to stay in the banking business.peer-reviewe

Adoption state of artificial intelligence: a saas perspective

The following thesis will focus on the general topic of Artificial Intelligence (AI). The main purpose of this work is to investigate how generally AI is being implemented and developed in modern times. Artificial Intelligence is critical in the SaaS industry. The study aims to get an overview of the state of adoption of Artificial Intelligence with particular attention to how it is in the SaaS industry and what it may indicate for the future. The author compares secondary data analysis with interviews of SaaS experts to better understand of how the SaaS industry differentiates from the general market