Machine Learning: The New Language for Applications

Padala, Venkatsai Siddesh; Gandhi, Kathan; Pushpalatha, D.V.

doi:10.11591/ijai.v8.i4.pp411-421

Cited by 12 publications

(11 citation statements)

References 12 publications

(18 reference statements)

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“…The training set assists the model by “teaching it” patterns for prediction or classification, while the testing set is used to gauge its capacity in completing those tasks. The process is named “supervised learning” [ 16 , 17 , 18 ].…”

Section: Background and Criteriamentioning

confidence: 99%

“…It allows the model to generate patterns for recognition on its own. This learning technique is used in more complicated tasks, such as misinformation detection (due to its large number of features) [ 15 , 16 , 17 ]. Regarding unsupervised machine learning models, this paper makes references to the following: Neural Networks: A collection of interconnected nodes (neurons), modelled to imitate neurons in a human brain.…”

Section: Background and Criteriamentioning

confidence: 99%

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Misinformation vs. Situational Awareness: The Art of Deception and the Need for Cross-Domain Detection

Xarhoulacos

Anagnostopoulou

Stergiopoulos

et al. 2021

Sensors

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The world has been afflicted by the rise of misinformation. The sheer volume of news produced daily necessitates the development of automated methods for separating fact from fiction. To tackle this issue, the computer science community has produced a plethora of approaches, documented in a number of surveys. However, these surveys primarily rely on one-dimensional solutions, i.e., deception detection approaches that focus on a specific aspect of misinformation, such as a particular topic, language, or source. Misinformation is considered a major obstacle for situational awareness, including cyber, both from a company and a societal point of view. This paper explores the evolving field of misinformation detection and analytics on information published in news articles, with an emphasis on methodologies that handle multiple dimensions of the fake news detection conundrum. We analyze and compare existing research on cross-dimensional methodologies. Our evaluation process is based on a set of criteria, including a predefined set of performance metrics, data pre-processing features, and domains of implementation. Furthermore, we assess the adaptability of each methodology in detecting misinformation in real-world news and thoroughly analyze our findings. Specifically, survey insights demonstrate that when a detection approach focuses on several dimensions (e.g., languages and topics, languages and sources, etc.), its performance improves, and it becomes more flexible in detecting false information across different contexts. Finally, we propose a set of research directions that could aid in furthering the development of more advanced and accurate models in this field.

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Section: Background and Criteriamentioning

confidence: 99%

Section: Background and Criteriamentioning

confidence: 99%

Misinformation vs. Situational Awareness: The Art of Deception and the Need for Cross-Domain Detection

Xarhoulacos

Anagnostopoulou

Stergiopoulos

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…We decided to use only these classical modeling techniques [23] Moreover, we disregarded more complex algorithms such as multilayer neural networks since the number of observations available for modeling is quite limited. In total, the used datasets [11] contain measurements of 300 odor samples.…”

Section: Classification Modelingmentioning

confidence: 99%

On data collection time by an electronic nose

Borowik

Adamowicz

Tarakowski

et al. 2021

IJECE

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<p>We use electronic nose data of odor measurements to build machine learning classiﬁcation models. The presented analysis focused on determining the optimal time of measurement, leading to the best model performance. We observe that the most valuable information for classiﬁcation is available in data collected at the beginning of adsorption and the beginning of the desorption phase of measurement. We demonstrated that the usage of complex features extracted from the sensors’ response gives better classiﬁcation performance than use as features only raw values of sensors’ response, normalized by baseline. We use a group shufﬂing cross-validation approach for determining the reported models’ average accuracy and standard deviation.</p>

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“…Motivated by the limitation, this paper attempts to fill the gap by developing model to predict tax avoidance strategies. In the recent Industrial 4.0 era, many recent studies have demonstrated that machine learning and big data mining approaches are effective tools for many problems [6][7][8][9][10] and also for the detection of financial fraud including tax fraud [11]. Despite the wiser used machine learning in many applications, there is limited literature on the development of related to tax avoidance.Therefore, this study has been initiated to fill the gap by looking at the experimental methods of developing tax avoidance classification model based on machine learning.…”

Section: Introductionmentioning

confidence: 99%

An application of machine learning on corporate tax avoidance detection model

Rahman

Masrom

Omar

et al. 2020

IJ-AI

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Corporate tax avoidance reduces government revenues which could limit country development plans. Thus, the main objectives of this study is to establish a rigorous and effective model to detect corporate tax avoidance to assist government to prevent such practice. This paper presents the fundamental knowledge on the design and implementation of machine learning model based on five selected algorithms tested on the real dataset of 3,365 Malaysian companies listed on bursa Malaysia from 2005 to 2015. The performance of each machine learning algorithms on the tested dataset has been observed based on two approaches of training. The accuracy score for each algorithm is better with the cross-validation training approach. Additionationally, with the cross-validation training approach, the performances of each machine learning algorithm were tested on different group of features selection namely industry, governance, year and firm characteristics. The findings indicated that the machine learning models present better reliability with industry, governance and firm characteristics features rather than single year determinant mainly with the Random Forest and Logistic Regression algorithms.

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Machine Learning: The New Language for Applications

Cited by 12 publications

References 12 publications

Misinformation vs. Situational Awareness: The Art of Deception and the Need for Cross-Domain Detection

Misinformation vs. Situational Awareness: The Art of Deception and the Need for Cross-Domain Detection

On data collection time by an electronic nose

An application of machine learning on corporate tax avoidance detection model

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