Machine Learning Approach for Text Classification in Cybercrime

Kumari, Swati; Saquib, Zia; Pawar, Sanjay

doi:10.1109/iccubea.2018.8697442

Cited by 7 publications

(1 citation statement)

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“…For the decision-making support system in ML, we explored datasets where topic classes are already labeled [42]. We selected the average of these measurements as they were calculated per class.…”

Section: Evaluations Of Decision-making Support System 71 Machine-learning Results In Dmssmentioning

confidence: 99%

Real-Time AI-Based Informational Decision-Making Support System Utilizing Dynamic Text Sources

Islam

Chang

2021

Applied Sciences

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Unstructured data from the internet constitute large sources of information, which need to be formatted in a user-friendly way. This research develops a model that classifies unstructured data from data mining into labeled data, and builds an informational and decision-making support system (DMSS). We often have assortments of information collected by mining data from various sources, where the key challenge is to extract valuable information. We observe substantial classification accuracy enhancement for our datasets with both machine learning and deep learning algorithms. The highest classification accuracy (99% in training, 96% in testing) was achieved from a Covid corpus which is processed by using a long short-term memory (LSTM). Furthermore, we conducted tests on large datasets relevant to the Disaster corpus, with an LSTM classification accuracy of 98%. In addition, random forest (RF), a machine learning algorithm, provides a reasonable 84% accuracy. This research’s main objective is to increase the application’s robustness by integrating intelligence into the developed DMSS, which provides insight into the user’s intent, despite dealing with a noisy dataset. Our designed model selects the random forest and stochastic gradient descent (SGD) algorithms’ F1 score, where the RF method outperforms by improving accuracy by 2% (to 83% from 81%) compared with a conventional method.

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Section: Evaluations Of Decision-making Support System 71 Machine-learning Results In Dmssmentioning

confidence: 99%