Decision tree classifiers are regarded to be a standout of the most well-known methods to data classification representation of classifiers. Different researchers from various fields and backgrounds have considered the problem of extending a decision tree from available data, such as machine study, pattern recognition, and statistics. In various fields such as medical disease analysis, text classification, user smartphone classification, images, and many more the employment of Decision tree classifiers has been proposed in many ways. This paper provides a detailed approach to the decision trees. Furthermore, paper specifics, such as algorithms/approaches used, datasets, and outcomes achieved, are evaluated and outlined comprehensively. In addition, all of the approaches analyzed were discussed to illustrate the themes of the authors and identify the most accurate classifiers. As a result, the uses of different types of datasets are discussed and their findings are analyzed.

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Classification Based on Decision Tree Algorithm for Machine Learning

Purpose:
Recognising the high numbers of unbanked and financially excluded adults in Nigeria, this study positions chatbot as a digital transformation tool to radically change business model, improve customer experience, and enhance financial inclusion in emerging markets.

Methodology: The Search-Access-Test (S-A-T) model was adopted to understand how Nigerian banks are adopting chatbots.

Findings:
Majority of Nigerian banks now have chatbots which enhance customer engagement and financial inclusion. WhatsApp was the most frequently used platform. Chatbots were often branded and presented with female gender identification. The chatbots were less responsive beyond their pre-defined path. While Nigeria is a multilingual country with English being the original language, none of the chatbots used any of the Nigerian’s local languages.

Originality:
While many theoretically based model for investigating the adoption of digital technologies has often placed focus on users’ ability to engage, this study takes an alternative perspective; by using the Search-Access-Test (S-A-T) model, it lays the responsibilities on the banks and chatbot developer to ensure that their chatbots are secure, responsive and able to meet the needs of the customers.

Practical implications:
Brands needs to reevaluate their chatbots with regards to responsiveness, pre-defined questions, verification and privacy. There are also possibilities of branding the chatbot and developing content creation strategies for proper engagement. Beyond English, the integration of African languages into chatbot is essential for digital transformation. Digital literacy and skills, particularly in the field of science, technology, engineering and mathematics (STEM) should be supported to equip future developers and create more jobs.

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Digital transformation in financial services provision: A Nigerian perspective to the adoption of chatbot

Distributed machine learning (DML) techniques, such as federated learning, partitioned learning, and distributed reinforcement learning, have been increasingly applied to wireless communications. This is due to improved capabilities of terminal devices, explosively growing data volume, congestion in the radio interfaces, and increasing concern of data privacy. The unique features of wireless systems, such as large scale, geographically dispersed deployment, user mobility, and massive amount of data, give rise to new challenges in the design of DML techniques. There is a clear gap in the existing literature in that the DML techniques are yet to be systematically reviewed for their applicability to wireless systems. This survey bridges the gap by providing a contemporary and comprehensive survey of DML techniques with a focus on wireless networks. Specifically, we review the latest applications of DML in power control, spectrum management, user association, and edge cloud computing. The optimality, scalability, convergence rate, computation cost, and communication overhead of DML are analyzed. We also discuss the potential adversarial attacks faced by DML applications, and describe state-of-the-art countermeasures to preserve privacy and security. Last but not least, we point out a number of key issues yet to be addressed, and collate potentially interesting and challenging topics for future research.

Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications

Distributed machine learning (DML) techniques, such as federated learning, partitioned learning, and distributed reinforcement learning, have been increasingly applied to wireless communications. This is due to improved capabilities of terminal devices, explosively growing data volume, congestion in the radio interfaces, and increasing concern of data privacy. The unique features of wireless systems, such as large scale, geographically dispersed deployment, user mobility, and the massive amount of data, give rise to new challenges in the design of DML techniques. There is a clear gap in the existing literature that the DML techniques are yet to be systematically reviewed for their applicability to wireless systems. This survey bridges the gap by providing a contemporary and comprehensive survey of DML techniques with a focus on wireless networks. Specifically, we review the latest applications of DML in power control, spectrum management, user association, and edge cloud computing. The optimality, accuracy, convergence rate, computation cost, and communication overhead of DML are analyzed. We also discuss the potential adversarial attacks faced by DML applications, and describe state-of-the-art countermeasures to preserve privacy and security. Last but not least, we point out a number of key issues yet to be addressed, and collate potentially interesting and challenging topics for future research.

Terrorist attacks affect the confidence and security of citizens; it is a violent form of a political struggle that ends in the destruction of order. In the current decade, along with the growth of social networks, terrorist attacks around the world are still ongoing and have had potential growth in recent years. Consequently, it is necessary to identify where the attacks were committed and where is the possible area for an attack. The objective is to provide assertive solutions to these events. As a solution, this research focuses on one of the branches of artificial intelligence (AI), which is the Automatic Learning, also called Machine Learning. The idea is to use AI techniques to visualize and predict possible terrorist attacks using classification models, the decision trees, and the Random Forest. The input would be a database that has a systematic record of worldwide terrorist attacks from 1970 to the last recorded year, which is 2018. As a final result, it is necessary to know the number of terrorist attacks in the world, the most frequent types of attacks and the number of seizures caused by region; furthermore, to be able to predict what kind of terrorist attack will occur and in which areas of the world. Finally, this research aims to help the scientific community use artificial intelligence to provide various types of solutions related to global events.

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Machine Learning Techniques to Visualize and Predict Terrorist Attacks Worldwide using the Global Terrorism Database

Machine Learning is one of the finest fields of Computer Science world which has given the innumerable and invaluable solutions to the mankind to solve its complex problems. Decision Tree is one such modern solution to the decision making problems by learning the data from the problem domain and building a model which can be used for prediction supported by the systematic analytics. In order to build a model on a huge dataset Decision Tree algorithm needs to be transformed to manifest itself into distributed environment so that higher performance of training the model is achieved in terms of time, without compromising the accuracy of the Decision Tree built. In this paper, we have proposed an enhanced version of distributed decision tree algorithm to perform better in terms of model building time without compromising the accuracy.

Siddalingeshwar Patil

Papers

Accuracy Prediction for Distributed Decision Tree using Machine Learning approach