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A systematic review of Explainable Artificial Intelligence models and applications : recent developments and future trends

Saranya, A.; Subhashini, R. - In: Decision analytics journal 7 (2023), pp. 1-14

, trustworthiness and accountability of AI system, especially when they are used for high-stakes application such as healthcare, finance … finance. We investigated the Scopus, Web of Science, IEEE Xplore and PubMed databases, to find the pertinent publications …

Persistent link: https://www.econbiz.de/10014436688

A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning

Bansal, Malti; Goyal, Apoorva; Choudhary, Apoorva - In: Decision analytics journal 3 (2022), pp. 1-21

makes predictions for the new data using the past data and knowledge. Lately, it has been adopted for text detection, hate …

Persistent link: https://www.econbiz.de/10013339256

Power genesis in social networks : an entropy-driven decision support model with conditional data

Rödder, Wilhelm; Dellnitz, Andreas; Reucher, Elmar - In: Decision analytics journal 1 (2021)

Power is a central phenomenon in human societies. Sociologists, politicians, philosophers in all cultures try to understand emergence and change processes of power. What was missing for a long time is measurement of power. First attempts were made in the last decades. This contribution...

Persistent link: https://www.econbiz.de/10013163022

A predictive analytics model for COVID-19 pandemic using artificial neural networks

Kuvvetli, Yusuf; Deveci, Muhammet; Paksoy, Turan; Garg, … - In: Decision analytics journal 1 (2021)

The COVID-19 pandemic spread rapidly around the world and is currently one of the most leading causes of death and heath disaster in the world. Turkey, like most of the countries, has been negatively affected by COVID-19. The aim of this study is to design a predictive model based on artificial...

Persistent link: https://www.econbiz.de/10013163050

The applications of artificial neural networks, support vector machines, and long-short term memory for stock market prediction

Chhajer, Parshv; Shah, Manan; Kshirsagar, Ameya - In: Decision analytics journal 2 (2022)

The future is unknown and uncertain, but there are ways to predict future events and reap the rewards safely. One such opportunity is the application of machine learning and artificial intelligence for stock market prediction. The stock market is turbulent, yet using artificial intelligence to...

Persistent link: https://www.econbiz.de/10013163098

A new in-form and role-based Deep Player Performance Index for player evaluation in T20 Cricket

Prakash, C. Deep; Verma, Sanjay - In: Decision analytics journal 2 (2022)

Sports analytics has benefited immensely from the growth and popularity of Machine Learning algorithms. Machine Learning and Data Mining advances have enabled sports analysts to evaluate a player’s performance more effectively. A review of existing literature on player performance evaluation...

Persistent link: https://www.econbiz.de/10013163119

A study of artificial intelligence impacts on human resource digitalization in industry 4.0

Murugesan, Umasankar; Subramanian, Padmavathy; … - In: Decision analytics journal 7 (2023), pp. 1-9

Artificial Intelligence (AI) has opened up tremendous opportunities in the workplace through robotics innovation, which envelops both AI and the Internet of Things (IoT). Precision, Efficiency, and Flexibility are considered the potential benefits of Industry 4.0. The implementation of Industry...

Persistent link: https://www.econbiz.de/10014505208

A novel application of machine learning and zero-shot classification methods for automated abstract screening in systematic reviews

Moreno-Garcia, Carlos Francisco; Jayne, Chrisina; … - In: Decision analytics journal 6 (2023), pp. 1-9

Zero-shot classification refers to assigning a label to a text (sentence, paragraph, whole paper) without prior … training. This is possible by teaching the system how to codify a question and find its answer in the text. In many domains …

Persistent link: https://www.econbiz.de/10014505380

A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions

Afriyie, Jonathan Kwaku; Tawiah, Kassim; Pels, … - In: Decision analytics journal 6 (2023), pp. 1-12

Fraudsters are now more active in their attacks on credit card transactions than ever before. With the advancement in data science and machine learning, various algorithms have been developed to determine whether a transaction is fraudulent. We study the performance of three different machine...

Persistent link: https://www.econbiz.de/10014505835

A comparative assessment of machine learning methods for predicting housing prices using Bayesian optimization

Lahmiri, Salim; Bekiros, Stelios; Avdoulas, Christos - In: Decision analytics journal 6 (2023), pp. 1-8

The valuation of house prices is drawing noteworthy attention due to worldwide financial and real estate crises in the last decade. Therefore, there is an immediate need to design more effective predictive systems of house prices. Indeed, investors, creditors, and governments are all interested...

Persistent link: https://www.econbiz.de/10014505865