scholarly journals Real Estate MSc Curriculum in the New Era of Artificial Intelligence

2020 ◽  
Author(s):  
Hajnal István
Keyword(s):  
Artificial Intelligence ◽  
Real Estate ◽  
New Era

scholarly journals How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

2021 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Oliwia Koteluk ◽  
Adrian Wartecki ◽  
Sylwia Mazurek ◽  
Iga Kołodziejczak ◽  
Andrzej Mackiewicz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Medical Data ◽  
General Process ◽  
New Era ◽  
Automated Evaluation ◽  
Machine Learning Applications ◽  
And Training ◽  
Current Standards

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

scholarly journals Comparative study of cloud platforms to develop a Chatbot

2017 ◽  
Vol 6 (3) ◽  
pp. 57 ◽  
Author(s):  
Amit Patil ◽  
Marimuthu K ◽  
Nagaraja Rao A ◽  
Niranchana R
Keyword(s):  
Artificial Intelligence ◽  
Comparative Study ◽  
Time Complexity ◽  
Completion Time ◽  
Setup Time ◽  
Human Being ◽  
Cloud Platform ◽  
New Era ◽  
Ibm Watson ◽  
Aws Lambda

Before chatbots there were simply bots: The invention of a chatbot brought us to the new era of technology, the era of conversation service. A chatbot is a virtual person that can effectively talk to any human being with the help of interactive conversion textual skill. Now a days there are many cloud-based platforms available for developing and deploying the chatbot such as Microsoft bot framework, IBM Watson, Kore, AWS lambda, Microsoft Azure bot service, Chatfuel, Heroku and many more but all those techniques has some drawbacks such as built-in Artificial Intelligence, NLP, conversion service, programming etc. This paper represents the comparison between all cloud-based chatbot technologies with some constraint such as built-in AI, setup time, completion time, complexity etc. Finally, by the comparison, we will get to know that which cloud platform is efficient and suitable for developing chatbot.

scholarly journals Performance Evaluation of Artificial Intelligence on Soil Property Detection

2018 ◽  
Vol 4 (10) ◽  
pp. 5
Author(s):  
Smriti Singhatiya ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Artificial Intelligence ◽  
Precision Agriculture ◽  
Crop Production ◽  
Nutrient Status ◽  
Soil Testing ◽  
Support Vector ◽  
Soil Test ◽  
New Era ◽  
Agriculture Soil ◽  
Historical Role

Now-a-days there is a need to study the nutrient status in lower horizons of the soil. Soil testing has played historical role in evaluating soil fertility maintenance and in sustainable agriculture. Soil testing shall also play its crucial role in precision agriculture. At present there is a need to develop basic inventory as per soil test basis and necessary information has to be built into the system for translating the results of soil test to achieve the crop production goal in new era. To achieve this goal artificial intelligence approach is used for predicting the soil properties.  In this paper for analysing these properties support vector regression (SVR), ensembled regression (ER) and neural network (NN) are used. The performance is evaluated with respect to MSE and RMSE and it is observed that ER outperforms better with respect to SVR and NN.

scholarly journals Civil Engineering Inspection for Real Estate Evaluation with the Use of Artificial Learning Algorithms and Fuzzy Logic

2020 ◽  
Vol 43 (338) ◽  
pp. 75-82
Author(s):  
Vladimir Surgelas ◽  
Irina Arhipova ◽  
Vivita Pukite
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Real Estate ◽  
Civil Engineering ◽  
Market Value ◽  
The State ◽  
The Real ◽  
The Real Estate ◽  
Technical Inspection ◽  
Physical Conditions

AbstractThe technical inspection of a building carried out by an expert in civil engineering can identify and classify the physical conditions of the real estate; this generates relevant information for the protection and safety of users. Given the real conditions of the property, and for the real estate valuation universe, using artificial intelligence and fuzzy logic, it is possible to obtain the market price associated with the physical conditions of the building. The objective of this experiment is to develop a property evaluation model using a civil engineering inspection form associated with artificial intelligence, and fuzzy logic, and also compare with market value to verify the applicability of this inspection form. Therefore, the methodology used is based on technical inspection of civil engineering regarding the state of conservation of properties according to the model used in Portugal and adapted to the reality of Latvia. Artificial intelligence is applied after obtaining data from that report. From this, association rules are obtained, which are used in the diffuse logic to obtain the price of the apartment per square meter, and for comparison with the market value. For this purpose, 48 samples of residential apartments located in the city of Jelgava in Latvia are used, with an inspection carried out from October to December 2019. The main result is the 9% error metric, which demonstrates the possibility of applying the method proposed in this experiment. Thus, for each apartment sample consulted, it resulted in the state of conservation and a market value associated.

scholarly journals STATE CADASTRAL ASSESSMENT OF REAL ESTATE AND NATURAL RESOURCES IN THE DIGITAL ECONOMY OF RUSSIA (DEFINITION OF ECONOMIC STANDARDS FOR THE RATIONAL USE OF REAL ESTATE PROPERTIES AND NATURAL RESOURCES)

2021 ◽  
Vol 3 (1) ◽  
pp. 269-276
Author(s):  
Alexander D. Vlasov
Keyword(s):  
Artificial Intelligence ◽  
Real Estate ◽  
Natural Resources ◽  
Digital Economy ◽  
Rational Use ◽  
Definition Of ◽  
Organizational Problems

Methodological and organizational problems of accounting, appraisal of real estate objects and natural resources of Russia are posed. The technology of accounting and determination of economic standards for the rational use of real estate and natural resources in the digital economy of Russia based on artificial intelligence is proposed.

scholarly journals Artificial Intelligence for Modeling Real Estate Price Using Call Detail Records and Hybrid Machine Learning Approach

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1421
Author(s):  
Gergo Pinter ◽  
Amir Mosavi ◽  
Imre Felde
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Estate ◽  
Model Performance ◽  
Machine Learning Method ◽  
Learning Method ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Price ◽  
Call Detail Records

Advancement of accurate models for predicting real estate price is of utmost importance for urban development and several critical economic functions. Due to the significant uncertainties and dynamic variables, modeling real estate has been studied as complex systems. In this study, a novel machine learning method is proposed to tackle real estate modeling complexity. Call detail records (CDR) provides excellent opportunities for in-depth investigation of the mobility characterization. This study explores the CDR potential for predicting the real estate price with the aid of artificial intelligence (AI). Several essential mobility entropy factors, including dweller entropy, dweller gyration, workers’ entropy, worker gyration, dwellers’ work distance, and workers’ home distance, are used as input variables. The prediction model is developed using the machine learning method of multi-layered perceptron (MLP) trained with the evolutionary algorithm of particle swarm optimization (PSO). Model performance is evaluated using mean square error (MSE), sustainability index (SI), and Willmott’s index (WI). The proposed model showed promising results revealing that the workers’ entropy and the dwellers’ work distances directly influence the real estate price. However, the dweller gyration, dweller entropy, workers’ gyration, and the workers’ home had a minimum effect on the price. Furthermore, it is shown that the flow of activities and entropy of mobility are often associated with the regions with lower real estate prices.

scholarly journals Artificial Intelligence & Popular Music: SKYGGE, Flow Machines, and the Audio Uncanny Valley

Arts ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 130 ◽  
Author(s):  
Melissa Avdeeff
Keyword(s):  
Artificial Intelligence ◽  
Popular Music ◽  
Markov Models ◽  
Uncanny Valley ◽  
New Era ◽  
Music Production ◽  
Music Generation

This article presents an overview of the first AI-human collaborated album, Hello World, by SKYGGE, which utilizes Sony’s Flow Machines technologies. This case study is situated within a review of current and emerging uses of AI in popular music production, and connects those uses with myths and fears that have circulated in discourses concerning the use of AI in general, and how these fears connect to the idea of an audio uncanny valley. By proposing the concept of an audio uncanny valley in relation to AIPM (artificial intelligence popular music), this article offers a lens through which to examine the more novel and unusual melodies and harmonization made possible through AI music generation, and questions how this content relates to wider speculations about posthumanism, sincerity, and authenticity in both popular music, and broader assumptions of anthropocentric creativity. In its documentation of the emergence of a new era of popular music, the AI era, this article surveys: (1) The current landscape of artificial intelligence popular music focusing on the use of Markov models for generative purposes; (2) posthumanist creativity and the potential for an audio uncanny valley; and (3) issues of perceived authenticity in the technologically mediated “voice”.

Artificial intelligence and real estate - not just an evolution, a real game changer!

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marcelo Cajias
Keyword(s):  
Artificial Intelligence ◽  
Real Estate ◽  
New Technologies ◽  
Current Status ◽  
Real Estate Industry ◽  
Expiry Date ◽  
Content Type ◽  
The Real ◽  
The Real Estate ◽  
Analyse Data

PurposeDigitalisation and AI are the most intensively discussed topics in the real estate industry. The subject aims at increasing the efficiency of existing processes and the institutional side of the industry is really interested. And in some ways, this is a breakthrough. This article elaborates on the current status quo and future path of the industry.Design/methodology/approachThe real estate industry is evolving, and parts of the business are increasingly being conquered by “proptechs” and “fintechs”. They have come into real estate to stay not because they discovered inefficiencies in the way one manages and does business with real estate, but because they come with an arsenal of new technologies that can change the whole game. The article discusses a path for changing the game in real estate.Findings“location, location, location” has now evolved to “data, data, data”. However, there is one essential aspect that must be considered before the latter can become the real value creator: the ability of market players to analyse data. And this does not mean being an excellent Excel user. The near future sees a solution called Explainable Artificial Intelligence (XAI) meaning that the econometric world constructed decades ago has an expiry date.Originality/valueOne needs to delete two myths from their mind: data quantity is proportional to accurate insights and that bringing your data to a cloud will deliver you with all the insights your business needs almost immediately.

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