A Machine Learning Approach for Micro-Credit Scoring

Ampountolas, Apostolos; Nde, Titus Nyarko; Date, Paresh; Constantinescu, Corina

doi:10.3390/risks9030050

Cited by 37 publications

(15 citation statements)

References 39 publications

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“…Mobile Microlearning. In the era of "Internet +," the Internet is becoming more and more popular, and mobile intelligent terminals such as smart phones and tablet computers allow us to use the Internet to obtain and transmit information anytime, anywhere, and become a new mode of information acquisition[15]. Mobile microlearning is the continuous development of new mobile technology, a microlearning model that facilitates learning with the aid of mobile terminals.…”

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confidence: 99%

Online English Teaching System Based on Internet of Things Technology

Tao¹

2022

Journal of Sensors

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In order to better improve students’ English performance and adapt to the progress in the age of science and technology faster, the author proposes an online English teaching system method based on Internet of Things technology. The author studies the English SPOC teaching mode and constructs a multimedia teaching system based on the Internet of Things technology, improve the teaching system, and improve and learn the teaching mode, to achieve the improvement of the quality of English teaching. Experimental results show that under the author’s method, students’ scores on both the written and oral exams are about 10 points higher than those in the traditional teaching method. Conclusion. The online English teaching system based on Internet of Things technology can effectively improve students’ English performance and allow students to better control their own progress.

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confidence: 99%

Online English Teaching System Based on Internet of Things Technology

Tao¹

2022

Journal of Sensors

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“…The ROC curve along a 45• angle is equivalent to a random classifier. On the other hand, the closer the AUC is to 1, the machine learning system approaches perfect behaviour [25]. The micro-average measure is a weighted average that accounts for each risk class's contribution.…”

Section: Metricsmentioning

confidence: 99%

MLIDS22- IDS Design by Applying Hybrid CNN-LSTM model on Mixed-Datasets

Abdulmajeed¹,

Husien²

2022

IJCAI

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The intrusion detection system (IDS) is an essential part of cyber security which captures and investigates traffic to distinguish between legitimate and malicious activities and determines the type of attack. The selection of the dataset used in training the machine learning-based IDS is crucial in ensuring that IDS performs accurately in cyber-attack classification. When utilizing multiple datasets in the training process, the metrics will relate numerically between the ML algorithm and a particular dataset. Previous research concluded a major decline in metrics when using inter-datasets evaluation. This research thoroughly investigates the use of the most modern and comprehensive IDS datasets, CIC-IDS2017 and CSE-CIC-IDS2018, to design and evaluate machine learning-based IDS systems using hybrid CNN-LSTM architecture. The new approach followed is to generate a new dataset which is the output of mixing both datasets. The experimental testing showed superior metrics values yielded when training with the mixture dataset against the use of individual datasets, especially when performing the inter-datasets evaluation, which overcomes the generalization problem.Povzetek: S kombiniranjem učnih množic postopek doseže boljše zaznavanje kibernetskega napada. Literature reviewThe protection of businesses from cyberattacks is a crucial issue nowadays, and a difficult subject since it

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“…Furthermore, works such as [46] examine various machine learning algorithms to classify borrowers into various credit categories. However, the authors had to rebalance the dataset using a SMOTENC algorithm.…”

Section: A Additional Related Workmentioning

confidence: 99%

A Learning and Control Perspective for Microfinance

Kurniawan¹,

Deng²,

Chakraborty³

et al. 2022

Preprint

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Microfinance in developing areas such as Africa has been proven to improve the local economy significantly. However, many applicants in developing areas cannot provide adequate information required by the financial institution to make a lending decision. As a result, it is challenging for microfinance institutions to assign credit properly based on conventional policies. In this paper, we formulate the decision-making of microfinance into a rigorous optimization-based framework involving learning and control. We propose an algorithm to explore and learn the optimal policy to approve or reject applicants. We provide the conditions under which the algorithms are guaranteed to converge to an optimal one. The proposed algorithm can naturally deal with missing information and systematically tradeoff multiple objectives such as profit maximization, financial inclusion, social benefits, and economic development. Through extensive simulation of both real and synthetic microfinance datasets, we showed our proposed algorithm is superior to existing benchmarks. To the best of our knowledge, this paper is the first to make a connection between microfinance and control and use control-theoretic tools to optimize the policy with a provable guarantee.Preprint. Under review.

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A Machine Learning Approach for Micro-Credit Scoring

Cited by 37 publications

References 39 publications

Online English Teaching System Based on Internet of Things Technology

Online English Teaching System Based on Internet of Things Technology

MLIDS22- IDS Design by Applying Hybrid CNN-LSTM model on Mixed-Datasets

A Learning and Control Perspective for Microfinance

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