Innovations in Artificial Intelligence using Data Mining and Big Data

Submission Deadline: 30 November 2021 (closed) View: 164

Guest Editors

Dr. Mohammad Tabrez Quasim, University of Bisha, Saudi Arabia.
Dr. Surbhi Bhatia, King Faisal University, Saudi Arabia.
Prof. Swati Chandna, SRH University Heidelberg, Germany.

Summary

The two growing fields named as Data Mining and Big Data fascinate ideas and resources from varied disciplines along with machine learning, high computational techniques, and other statistical methods. The dynamic area combining different disciplines will help in extracting useful information for generating useful patterns with the aim of providing knowledge as a holistic view for diversified communities. Artificial Intelligence using data mining and big data theories with its applications will provide a comprehensive introduction to relevant innovations in the digital era and real-time applications. Artificial neural networks are a gross simplification of real networks of neurons. The paradigm of neural networks with data mining and big data could be a new and chop-chop growing field. The convergence will help in addressing the problems describing both theoretical and practical evaluations by directing useful knowledge with the power of Artificial Intelligence.

Keywords

Topics include but are not limited to:
Artificial Intelligence for Engineering Application
Machine Learning for Data Science
Soft Computing for Emerging Applications
Optimization Algorithms
Data Mining
Big Data Analytics
Opinion Mining
Deep Learning
Computational techniques

Published Papers

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Open Access

ARTICLE

Dm-Health App: Diabetes Diagnosis Using Machine Learning with Smartphone
Elias Hossain, Mohammed Alshehri, Sultan Almakdi, Hanan Halawani, Md. Mizanur Rahman, Wahidur Rahman, Sabila Al Jannat, Nadim Kaysar, Shishir Mia
CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1713-1746, 2022, DOI:10.32604/cmc.2022.024822
（This article belongs to the Special Issue: Innovations in Artificial Intelligence using Data Mining and Big Data)
Abstract Diabetes Mellitus is one of the most severe diseases, and many studies have been conducted to anticipate diabetes. This research aimed to develop an intelligent mobile application based on machine learning to determine the diabetic, pre-diabetic, or non-diabetic without the assistance of any physician or medical tests. This study's methodology was classified into two the Diabetes Prediction Approach and the Proposed System Architecture Design. The Diabetes Prediction Approach uses a novel approach, Light Gradient Boosting Machine (LightGBM), to ensure a faster diagnosis. The Proposed System Architecture Design has been combined into seven modules; the Answering… More >

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ARTICLE

An Improved Optimized Model for Invisible Backdoor Attack Creation Using Steganography
Daniyal M. Alghazzawi, Osama Bassam J. Rabie, Surbhi Bhatia, Syed Hamid Hasan
CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1173-1193, 2022, DOI:10.32604/cmc.2022.022748
（This article belongs to the Special Issue: Innovations in Artificial Intelligence using Data Mining and Big Data)
Abstract The Deep Neural Networks (DNN) training process is widely affected by backdoor attacks. The backdoor attack is excellent at concealing its identity in the DNN by performing well on regular samples and displaying malicious behavior with data poisoning triggers. The state-of-art backdoor attacks mainly follow a certain assumption that the trigger is sample-agnostic and different poisoned samples use the same trigger. To overcome this problem, in this work we are creating a backdoor attack to check their strength to withstand complex defense strategies, and in order to achieve this objective, we are developing an improved… More >

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ARTICLE

Attribute Weighted Naïve Bayes Classifier
Lee-Kien Foo, Sook-Ling Chua, Neveen Ibrahim
CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1945-1957, 2022, DOI:10.32604/cmc.2022.022011
（This article belongs to the Special Issue: Innovations in Artificial Intelligence using Data Mining and Big Data)
Abstract The naïve Bayes classifier is one of the commonly used data mining methods for classification. Despite its simplicity, naïve Bayes is effective and computationally efficient. Although the strong attribute independence assumption in the naïve Bayes classifier makes it a tractable method for learning, this assumption may not hold in real-world applications. Many enhancements to the basic algorithm have been proposed in order to alleviate the violation of attribute independence assumption. While these methods improve the classification performance, they do not necessarily retain the mathematical structure of the naïve Bayes model and some at the expense… More >

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ARTICLE

A Novel Cryptocurrency Prediction Method Using Optimum CNN
Syed H. Hasan, Syeda Huyam Hasan, Mohammed Salih Ahmed, Syed Hamid Hasan
CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1051-1063, 2022, DOI:10.32604/cmc.2022.020823
（This article belongs to the Special Issue: Innovations in Artificial Intelligence using Data Mining and Big Data)
Abstract In recent years, cryptocurrency has become gradually more significant in economic regions worldwide. In cryptocurrencies, records are stored using a cryptographic algorithm. The main aim of this research was to develop an optimal solution for predicting the price of cryptocurrencies based on user opinions from social media. Twitter is used as a marketing tool for cryptoanalysis owing to the unrestricted conversations on cryptocurrencies that take place on social media channels. Therefore, this work focuses on extracting Tweets and gathering data from different sources to classify them into positive, negative, and neutral categories, and further examining More >

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ARTICLE

Fusion of Infrared and Visible Images Using Fuzzy Based Siamese Convolutional Network
Kanika Bhalla, Deepika Koundal, Surbhi Bhatia, Mohammad Khalid Imam Rahmani, Muhammad Tahir
CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5503-5518, 2022, DOI:10.32604/cmc.2022.021125
（This article belongs to the Special Issue: Innovations in Artificial Intelligence using Data Mining and Big Data)
Abstract Traditional techniques based on image fusion are arduous in integrating complementary or heterogeneous infrared (IR)/visible (VS) images. Dissimilarities in various kind of features in these images are vital to preserve in the single fused image. Hence, simultaneous preservation of both the aspects at the same time is a challenging task. However, most of the existing methods utilize the manual extraction of features; and manual complicated designing of fusion rules resulted in a blurry artifact in the fused image. Therefore, this study has proposed a hybrid algorithm for the integration of multi-features among two heterogeneous images.… More >

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Innovations in Artificial Intelligence using Data Mining and Big Data

Guest Editors

Summary

Keywords

Published Papers

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2714

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1167

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1741

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955

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2283

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2454

View

2841

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1556

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2

Cited by

1

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2951

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1543

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1

Cited by

11

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