Machine learning (ML) has emerged as a powerful tool in transformer condition assessment, enabling more accurate diagnostics by leveraging historical test data. However, imbalanced datasets, often characterized by limited samples in poor transformer...
Presently, security is a hot research topic due to the impact in daily information infrastructure. Machine-learning solutions have been improving classical detection practices, but detection tasks employ irregular amounts of data since the number of...
Effective detection of electricity theft is essential to maintain power system reliability. With the development of smart grids, traditional electricity theft detection technologies have become ineffective to deal with the increasingly complex data o...
This study was focused on deriving the MTSA-related accident reduction rate (ARR) required to calculate the safety benefits before and after expanding the scope of the system. By performing spatial analysis using geographic information system technol...
Background and Objectives: Determining the presence or absence of cochlear dead regions (DRs) is essential in clinical practice. This study proposes a machine learning (ML)-based model that applies oversampling techniques for predicting DRs in patien...
In recent years, weakened by the fall of economic growth, many enterprises fell into the crisis caused by financial difficulties. Bankruptcy prediction, a machine learning model, is a great utility for financial institutions, fund managers, lenders,...
In this study, we propose an approach to address the pressing issue of false negative errors by enhancing minority class recall within imbalanced data sets commonly encountered in machine learning applications. Through the utilization of a cluster-ba...
Insider threats are malicious acts that can be carried out by an authorized employee within an organization. Insider threats represent a major cybersecurity challenge for private and public organizations, as an insider attack can cause extensive dama...
Classification problems due to data imbalance occur in many fields and have long been studied in the machine learning field. Many real-world datasets suffer from the issue of class imbalance, which occurs when the sizes of classes are not uniform; th...
Malware detection using hybrid features, combining binary and hexadecimal analysis with DLL calls, is crucial for leveraging the strengths of both static and dynamic analysis methods. Artificial intelligence (AI) enhances this process by enabling aut...
In view of the data of fault diagnosis and good product testing in the industrial field, high-noise unbalanced data samples exist widely, and such samples are very difficult to analyze in the field of data analysis. The oversampling technique has pro...
Adaptive machine learning has increasing importance due to its ability to classify a data stream and handle the changes in the data distribution. Various resources, such as wearable sensors and medical devices, can generate a data stream with an imba...
Cybersecurity is one of the important considerations when adopting IoT devices in smart applications. Even though a huge volume of data is available, data related to attacks are generally in a significantly smaller proportion. Although machine learni...
Electricity theft has caused massive losses and damage to electricity utilities. The damage affects the electricity supply’s quality and increases the generation load. The losses happen not only for the electricity utilities but also affect the...
Jerk monitoring, defined as the first derivative of acceleration, has become a major issue in computerized numeric controlled (CNC) machines. Several works highlight the necessity of measuring jerk in a reliable way for improving production processes...
Image recognition models often struggle with class imbalance, which can impede their performance. To overcome this issue, researchers have extensively used resampling methods, traditionally focused on tabular datasets. In contrast to the original met...
A model framework for the prediction of defects in strip steel is proposed with the objective of enhancing the accuracy of defect detection. Initially, the data are balanced through the utilisation of the Improved Synthetic Minority Oversampling Tech...
Alzheimer’s Disease (AD) is a neurological brain disorder that causes dementia and neurological dysfunction, affecting memory, behavior, and cognition. Deep Learning (DL), a kind of Artificial Intelligence (AI), has paved the way for new AD det...
Class imbalance is one of the significant challenges in classification problems. The uneven distribution of data samples in different classes may occur due to human error, improper/unguided collection of data samples, etc. The uneven distribution of...
Southern blight significantly impacts peanut yield, and its severity is exacerbated by high-temperature and high-humidity conditions. The mycelium attached to the plant’s interior quickly proliferates, contributing to the challenges of early de...
Deep learning models, known as convolutional neural networks (CNNs), have paved the way for reliable automated image recognition. These models are increasingly being applied in research on freshwater biodiversity, aiming to enhance efficiency and tax...
Quick and accurate prediction of crop yields is beneficial for guiding crop field management and genetic breeding. This paper utilizes the fast and non-destructive advantages of an unmanned aerial vehicle equipped with a multispectral camera to acqui...
The COVID-19 virus has made a huge impact on people’s lives ever since the outbreak happened in December 2019. Unfortunately, the COVID-19 virus has not completely vanished from the world yet, and thus, global agitation is still increasing with...
This study aims to develop a better Financial Statement Fraud (FSF) detection model by utilizing data from publicly available financial statements of firms in the MENA region. We develop an FSF model using a powerful ensemble technique, the XGBoost (...
In the cybersecurity industry, where legitimate transactions far outnumber fraudulent ones, detecting fraud is of paramount significance. In order to evaluate the accuracy of detecting fraudulent transactions in imbalanced real datasets, this study c...
Intrusion prevention and classification are common in the research field of cyber security. Models built from training data may fail to prevent or classify intrusions accurately if the dataset is imbalanced. Most researchers employ SMOTE to balance t...
Deep learning-based fault diagnosis usually requires a rich supply of data, but fault samples are scarce in practice, posing a considerable challenge for existing diagnosis approaches to achieve highly accurate fault detection in real applications. T...
Melanoma, a very severe form of skin cancer, spreads quickly and has a high mortality rate if not treated early. Recently, machine learning, deep learning, and other related technologies have been successfully applied to computer-aided diagnostic tas...
Extracellular matrix (ECM) proteins play an important role in a series of biological processes of cells. The study of ECM proteins is helpful to further comprehend their biological functions. We propose ECMP-RF (extracellular matrix proteins predicti...
Background: Early diagnosis of depression is crucial for effective treatment and suicide prevention. Traditional methods rely on self-report questionnaires and clinical assessments, lacking objective biomarkers. Combining functional magnetic resonanc...
Deep learning is crucial in marine logistics and container crane error detection, diagnosis, and prediction. A novel deep learning technique using Long Short-Term Memory (LSTM) detected and anticipated errors in a system with imbalanced data. The LST...
The detection of Android malware is of paramount importance for safeguarding users’ personal and financial data from theft and misuse. It plays a critical role in ensuring the security and privacy of sensitive information on mobile devices, the...
This study presents a novel feature-engineered–natural gradient descent ensemble-boosting (NGBoost) machine-learning framework for detecting fraud in power consumption data. The proposed framework was sequentially executed in three stages: data...
The conventional evaluation of maize seed vigor is a time-consuming and labor-intensive process. By contrast, this study introduces an automated, nondestructive framework for classifying maize seed vigor with different seed DNA repair capabilities us...
Unmanned aerial vehicles (UAVs) are vulnerable to global positioning system (GPS) spoofing attacks, which can mislead their navigation systems and result in unpredictable catastrophic consequences. To address this issue, we propose a detection method...
Detecting the crystal system of lithium-ion batteries is crucial for optimizing their performance and safety. Understanding the arrangement of atoms or ions within the battery’s electrodes and electrolyte allows for improvements in energy densi...
This study aimed to develop a risk-prediction model for second primary skin cancer (SPSC) survivors. We identified the clinical characteristics of SPSC and created awareness for physicians screening high-risk patients among skin cancer survivors. Usi...
Machine predictive maintenance plays a critical role in reducing unplanned downtime, lowering maintenance costs, and improving operational reliability by enabling the early detection and classification of potential failures. Artificial intelligence (...
This research paper presents novel approaches for detecting credit card risk through the utilization of Long Short-Term Memory (LSTM) networks and XGBoost algorithms. Facing the challenge of securing credit card transactions, this study explores the...
The Golgi Apparatus (GA) is a major collection and dispatch station for numerous proteins destined for secretion, plasma membranes and lysosomes. The dysfunction of GA proteins can result in neurodegenerative diseases. Therefore, accurate identificat...
Recent advancements in intelligent diagnosis rely heavily on data-driven methods. However, these methods often encounter challenges in adequately addressing class imbalances in the context of the fault diagnosis of mechanical systems. This paper prop...
Achieving cyber-security has grown increasingly tricky because of the rising concern for internet connectivity and the significant growth in software-related applications. It also needs a robust defense system to defend itself from multiple cyberatta...
This paper presents the design and validation of a novel adaptive islanding detection method (AIDM) for a hybrid AC/DC microgrid network using a combination of Artificial Intelligence (AI) and Signal Processing (SP) approaches. The proposed AIDM is a...
Breast cancer prediction datasets are usually class imbalanced, where the number of data samples in the malignant and benign patient classes are significantly different. Over-sampling techniques can be used to re-balance the datasets to construct mor...
Recent advances in artificial intelligence (AI) technology have led to increasing interest in the development of AI-based tool wear condition monitoring methods, heavily relying on large training samples. However, the high cost of tool wear experimen...
The early diagnosis of dementia, a progressive condition impairing memory, cognition, and functional ability in older adults, is essential for timely intervention and improved patient outcomes. This study proposes a novel multiclass classification th...
Harmful algal blooms (HABs) caused by harmful cyanobacteria adversely impact the water quality in aquatic ecosystems and burden socioecological systems that are based on water utilization. Currently, Korea uses the Environmental Fluid Dynamics Code-N...
The objective of this research was to classify the geographical origin of durians (cv. Monthong) based on geographical identification (GI) and regions (R) using near infrared (NIR). The samples were scanned with an FT-NIR spectrometer (12,500 to 4000...
The early detection of dementia, a condition affecting both individuals and society, is essential for its effective management. However, reliance on advanced laboratory tests and specialized expertise limits accessibility, hindering timely diagnosis....
Software-defined networking (SDN) is becoming a predominant architecture for managing diverse networks. However, recent research has exhibited the susceptibility of SDN architectures to cyberattacks, which increases its security challenges. Many rese...
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