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Jayashree Piri, Puspanjali Mohapatra, Raghunath Dey, Biswaranjan Acharya, Vassilis C. Gerogiannis and Andreas Kanavos
The efficiency and the effectiveness of a machine learning (ML) model are greatly influenced by feature selection (FS), a crucial preprocessing step in machine learning that seeks out the ideal set of characteristics with the maximum accuracy possible. D...
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Weijian Huang, Qi Song and Yuan Huang
Short-term power load forecasting is of great significance for the reliable and safe operation of power systems. In order to improve the accuracy of short-term load forecasting, for the problems of random fluctuation in load and the complexity of load-in...
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Ibomoiye Domor Mienye and Yanxia Sun
With the rapid developments in electronic commerce and digital payment technologies, credit card transactions have increased significantly. Machine learning (ML) has been vital in analyzing customer data to detect and prevent fraud. However, the presence...
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Xuebo Liu, Yingying Wu and Hongyu Wu
The 3D body scan technology has recently innovated the way of measuring human bodies and generated a large volume of body measurements. However, one inherent issue that plagues the use of the resultant database is the missing data usually caused by using...
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Maya Hilda Lestari Louk and Bayu Adhi Tama
As a system capable of monitoring and evaluating illegitimate network access, an intrusion detection system (IDS) profoundly impacts information security research. Since machine learning techniques constitute the backbone of IDS, it has been challenging ...
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Chen Qiao, Lujia Lu, Lan Yang and Paul J. Kennedy
The hybrid feature selection method, which combines both machine learning and traditional statistical methods, is proposed to identify the brain abnormalities of schizophrenia. The results suggest that the brain regions and connectivity in SZs are destro...
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Md Rashedul Islam, Young-Hun Kim, Jae-Young Kim and Jong-Myon Kim
The proposed model of this paper is for the bearing fault diagnosis of industrial rotating machinery. Specifically, the general fault diagnosis model only can predict the bearing fault based on the predefined number of stored fault information. The propo...
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Sergey Voronin and Jarmo Partanen
A forecasting methodology for prediction of both normal prices and price spikes in the day-ahead energy market is proposed. The method is based on an iterative strategy implemented as a combination of two modules separately applied for normal price and p...
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Soojeong Lee, Gyanendra Prasad Joshi, Anish Prasad Shrestha, Chang-Hwan Son and Gangseong Lee
Cuffless blood pressure (BP) monitoring is crucial for patients with cardiovascular disease and hypertension. However, conventional BP monitors provide only single-point estimates without confidence intervals. Therefore, the statistical variability in th...
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Fatma Yaprakdal and Merve Varol Arisoy
In the smart grid paradigm, precise electrical load forecasting (ELF) offers significant advantages for enhancing grid reliability and informing energy planning decisions. Specifically, mid-term ELF is a key priority for power system planning and operati...
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Nejood Faisal Abdulsattar, Firas Abedi, Hayder M. A. Ghanimi, Sachin Kumar, Ali Hashim Abbas, Ali S. Abosinnee, Ahmed Alkhayyat, Mustafa Hamid Hassan and Fatima Hashim Abbas
Flying ad hoc networks (FANETs) or drone technologies have attracted great focus recently because of their crucial implementations. Hence, diverse research has been performed on establishing FANET implementations in disparate disciplines. Indeed, civil a...
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Barbara Pes
Class imbalance and high dimensionality are two major issues in several real-life applications, e.g., in the fields of bioinformatics, text mining and image classification. However, while both issues have been extensively studied in the machine learning ...
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Ehsan Taheri, Saeid Esgandarzadeh Fard, Yousef Zandi and Bijan Samali
This study evaluates an innovative reinforcement method for cold-formed steel (CFS) upright sections through finite element assessment as well as prediction of the normalized ultimate load and deflection of the profiles by artificial intelligence (AI) an...
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Wen Cao, Jiaqi Xu, Yong Zhang, Siqi Zhao, Chu Xu and Xiaofeng Wu
The artificial bee colony algorithm (ABC) is a promising metaheuristic algorithm for continuous optimization problems, but it performs poorly in solving discrete problems. To address this issue, this paper proposes a hybrid discrete artificial bee colony...
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Yixuan Li, Charalampos Stasinakis and Wee Meng Yeo
Supply Chain Finance (SCF) has gradually taken on digital characteristics with the rapid development of electronic information technology. Business audit information has become more abundant and complex, which has increased the efficiency and increased t...
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Lei Fu, Tiantian Zhu, Guobing Pan, Sihan Chen, Qi Zhong and Yanding Wei
Power quality disturbances (PQDs) have a large negative impact on electric power systems with the increasing use of sensitive electrical loads. This paper presents a novel hybrid algorithm for PQD detection and classification. The proposed method is cons...
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Muhammad Mateen Yaqoob, Muhammad Nazir, Muhammad Amir Khan, Sajida Qureshi and Amal Al-Rasheed
One of the deadliest diseases, heart disease, claims millions of lives every year worldwide. The biomedical data collected by health service providers (HSPs) contain private information about the patient and are subject to general privacy concerns, and t...
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Annwesha Banerjee Majumder, Somsubhra Gupta, Dharmpal Singh, Biswaranjan Acharya, Vassilis C. Gerogiannis, Andreas Kanavos and Panagiotis Pintelas
Heart disease is a leading global cause of mortality, demanding early detection for effective and timely medical intervention. In this study, we propose a machine learning-based model for early heart disease prediction. This model is trained on a dataset...
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Chun-Liang Lu, Tsun-Chen Lin
Pág. 53 - 57
In recent years, support vector machine (SVM) based on empirical risk minimization is supervised learning model which has been successfully used in the classification and regression. The standard soft-margin SVM trains a classifier by solving an optimiza...
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Borislav Slavchev, Evelina Masliankova and Steven Kelk
We present an algorithm selection framework based on machine learning for the exact computation of treewidth, an intensively studied graph parameter that is NP-hard to compute. Specifically, we analyse the comparative performance of three state-of-the-ar...
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