Classification Algorithms - Science topic

Tidal stream turbines (TSTs) are crucial for renewable energy generation but face challenges from marine biofouling, significantly impacting their efficiency. Traditional methods for predicting performance and detecting biofouling rely on empirical models and manual inspections, which are often time-consuming and less accurate. This study introduce...

Diabetes mellitus, a chronic metabolic disorder characterised by elevated blood glucose levels, is a global health concern. Non-invasive techniques for predicting Type 2 diabetes are less burdensome than invasive methods, yet developing a machine learning model on non-invasive data remains underexplored. This study evaluated six classification algo...

This study aimed to predict suicidal ideation among youth with autism spectrum disorder (ASD) by applying machine learning techniques. A cross-sectional sample of 368 ASD-diagnosed young people (aged 18–24years) was recruited, and 34 candidate predictors—including sociodemographic characteristics, psychiatric symptoms (e.g., anxiety problems and de...

One of the principal challenges in developing robust machine learning (ML) classification algorithms for human activity recognition (HAR) from real-time smart home sensor data is how to account for variations in (1) the activity sequence length, (2) the contribution each sensor has to a specific activity, and (3) the amount of activity class imbala...

Detecting changes or shifts in data over time is known as change-point detection. This phenomenon is crucial for identifying the deterioration of industrial components and preventing costly breakdowns or failures. There are several supervised and unsupervised approaches used in change-point detection, which involve evaluating the difference between...

Machine learning has increasingly gained prominence in the healthcare sector due to its ability to address various challenges. However, a significant issue remains unresolved in this field: the handling of imbalanced data. This process is crucial for ensuring the efficiency of algorithms that utilize classification techniques, which are commonly ap...

Although coffee is a crucial commodity for the economies of developing nations, defects in processing can significantly impact the safety, commercial value, and quality of coffee beans. Traditional coffee bean grading methods are labor-intensive and prone to error, necessitating automated and accurate classification of bean diseases. This study pro...

Multi-label classification is a significant challenge in machine learning, especially as the dimensionality of the problem increases. As the number of dimensions grows, the performance of traditional classification algorithms often degrades substantially. Feature selection is a key technique for reducing dimensionality in multi-label scenarios, ope...

Walking is a fundamental human activity, and a deep understanding of its complexities is essential for accurately diagnosing and treating gait abnormalities and musculoskeletal disorders. This study investigates the application of machine learning (ML) methods for categorizing gait phases into their individual subphases, through two training method...

The alteration of land use/land cover change (LULCC) is an environmental issue that impacts affects ecosystems by increasing the land surface temperature (LST). This study aimed to investigate the influence of human activities on LST in the Sekota watershed northern Ethiopia. This study used Landsat images and a supervised support vector machine (S...

Background Detailed knowledge of the spatial distribution of vegetation fuels is essential for assessing wildfire hazard and behavior, as well as for planning effective management. In southern Europe, the Prometheus project has proposed the differentiation of seven fuel types, but their characterization using remote sensing techniques remains chall...

Introduction: Employee performance is a critical factor in achieving organizational goals. Human Resource (HR) departments often struggle with timely and objective performance assessments, which are essential for decisions like promotions, terminations, and training. This paper investigates the application of ML techniques to predict and categorize...

Access to safe drinking water remains a global priority, necessitating robust and accurate methods for potability assessment. This research implements advanced machine learning approaches to develop a predictive framework for water quality classification. The study utilizes a comprehensive dataset containing critical physicochemical parameters incl...

We propose a novel classification algorithm, the Boltzmann Classifier, inspired by the thermodynamic principles underlying the Boltzmann distribution. Our method computes a probabilistic estimate for each class based on an energy function derived from feature-wise deviations between input samples and class-specific centroids. The resulting probabil...

Heart disease is one of the complex diseases and globally many people suffered from this disease. On time and efficient identification of heart disease plays a key role in healthcare, particularly in the field of cardiology. In this article, we proposed an efficient and accurate system to diagnosis heart disease and the system is based on machine l...

Spatial proteomics measures multiple proteins in situ, capturing tissue complexity. However, cell classification in densely packed tissues remains challenging due to the lack of efficient classification algorithms, annotation tools, and high-quality labeled datasets to benchmark computational methods. We introduce CellTune, an integrated software f...

Malicious URLs and websites continue to undermine online security, with search engines inadvertently becoming platforms for fraudulent sites. Traditional phishing detection methods often based on white lists, blacklists, or single-model approaches fail to address the evolving sophistication of phishing attacks. This persistent threat highlights the...

The advent of the COVID-19 pandemic has undoubtedly affected the political scene worldwide and the introduction of new terminology and public opinions regarding the virus has further polarized partisan stances. Using a collection of tweets gathered from leading American political figures online (Republican and Democratic), we explored the partisan...

This paper aims to examine the effectiveness of machine learning classification algorithms as a strategy to overcome the limitations associated with traditional methods for developing computerized adaptive versions of the Minnesota Multiphasic Personality Inventory-2 (MMPI-2). The focus is on the three scales in the neurotic area of the instrument,...

In today's increasingly digital world, database security is a critical concern for government agencies handling classified information. This paper proposes a comprehensive security solution that integrates artificial intelligence (AI), machine learning (ML), and blockchain technologies to offer multiple layers of defense against sophisticated cyber...

Many neurological conditions negatively affect a person’s walking quality, which is a vital aspect of their quality of life. Gait quality, through the collection of spatiotemporal variables, can also help infer disease status; however, in-clinic access to these metrics is limited or cannot be assessed frequently enough to proactively monitor diseas...

This work is aimed at developing intelligent systems capable of automatically classifying types of educational materials. This will allow students to find the resources they need faster, and it will make it easier for teachers to manage content in educational platforms. The solution of the problem of recognition of information objects using fuzzy o...

This article examines the ethical foundations of cloud-native bankruptcy risk detection systems, exploring the tension between institutional efficiency and social responsibility in financial distress contexts. The article presents a comprehensive framework for designing automated systems that reduce wrongful collections while ensuring appropriate l...

Mental health in adolescents, especially students, is an important concern in the world of education. Early detection of symptoms of depression in students can help preventive efforts in handling them. This study aims to compare the performance of two classification algorithms, namely Decision Tree and Support Vector Machine (SVM) in detecting the...

Quantum information theory, and in particular, the theory of quantum state discrimination, has enabled the development of a supervised multi-class classification algorithm. Inspired by the Pretty Good Measurement (PGM), a quantum-inspired classifier named the PGM Classifier has been designed, capable of classifying among multiple classes without re...

This paper proposes an enhanced DenseNet-based strategy to improve mural classification performance. Briefly, a new independent component layer structure separates the larger 7 × 7 convolution kernels in the input layer into the trunk of three series of 3 × 3 small convolution kernels. The original ReLU activation is replaced with Mish activation....

Hyperspectral imaging (HSI) has shown significant diagnostic potential for both intra- and postoperative perfusion assessment. The purpose of this study was to combine machine learning and neural networks with HSI to develop a method for detecting flap malperfusion after microsurgical tissue reconstruction. Data records were analysed to assess the...

Purpose Schizophrenia (SCZ) is a severe psychiatric disorder marked by abnormal dopamine synthesis, measurable through [ ¹⁸ F]FDOPA PET imaging. This imaging technique has been proposed as a biomarker for treatment stratification in SCZ, where one-third of patients respond poorly to standard antipsychotics. This study explores the use of radiomics...

This paper introduces the detailed description of the submitted model by the team NAYEL to Fake News Detection in Dravidian Languages shared task. The proposed model uses a simple character n-gram TF-IDF as a feature extraction approach integrated with an ensemble of various classical machine learning classification algorithms. While the simplicity...

This paper proposes a combined framework of CNN+RFC to brain tumor categorization/classification using MRI (Magnetic-Resonance Imaging) images, which combines both CNN (Convolution Neural Networks) and RFC (Random Forest Classification). Preprocessing, Feature bring-out, and Categorization are the three phases of the proposed framework. In the firs...

The rapid advancement of computer networks offers substantial benefits but also introduces growing cybersecurity risks. Cyber-attacks are increasing in both frequency and sophistication, demanding continuous improvements in security systems. Consequently, intrusion detection technologies are being actively researched and optimized to enhance threat...

Road traffic accidents are a significant global concern, with developing countries accounting for 85% of annual fatalities and 90% of disability-adjusted life years lost. This study investigates the severity of road accidents in Jordan using a machine learning-based predictive approach. A dataset of 73,000+ accident reports from 2018 was analyzed,...

Hyperspectral remote sensing images are widely used in resource exploration, urban planning, natural disaster assessment, and feature classification. Aiming at the problems of poor interpretability of feature classification algorithms for hyperspectral images, multiple feature dimensions, and difficulty in effectively improving classification accur...

lncRNAs are transcripts larger than 200 nucleotides that cannot carry system modules. Various plant species have identified a slew of lncRNAs Using computational techniques. According to current studies, plant lncRNAs are engaged in multiple biological processes, including flower cycle regulation of animation development and biotic and abiotic stre...

The current high mortality rate associated with infectious diseases is an alarming issue worldwide. The emergence of multidrug-resistant bacterial strains has led to the development of new ailments and the resurgence of old pathogenic infections. To address this issue, metal and metal oxide nanoparticles have emerged as potent nanoantimicrobial age...

This study presents a machine learning-based approach to predicting the outcosmes of NBA games, with the aim of enhancing decision-making in sports betting and performance analysis. Using a dataset spanning 20 NBA seasons (2003–2023), we incorporated key features such as team statistics, player performance metrics, and external factors like team fa...

Support vector machine (SVM) is a pivotal classification algorithm, and its evolutionary counterpart, the twin SVM (TWSVM), has gained acclaim for its advanced generalization capabilities, particularly in handling imbalanced data. TWSVMs achieve swift training by explicitly exploring a pair of non-parallel hyperplanes, yet selecting numerical value...

The study focused on analyzing shoreline changes along the western beaches of Mersin Province, located on Turkey’s Mediterranean coast. Landsat satellite imagery from 1985 to 2022 was used to detect long-term coastal alterations. The Google Earth Engine (GEE) platform facilitated data acquisition, classification, and edge detection. A Support Vecto...

Effective cyber threat monitoring relies on deploying robust Security Information and Event Management (SIEM) systems. SIEM applications receive security events generated by different devices, systems, and applications. They should properly correlate them to identify potential cyber threats based on tactics, techniques, and procedures (TTP), bypass...

Effective sleep stage classification requires identifying discriminative EEG features that remain consistent across different subjects. This study proposes an ensemble feature selection framework for robust sleep stage classification using the Physionet EEG dataset. We extract 40+ features from time and frequency domains, then employ multiple selec...

For sentiment analysis of user opinions on online platforms such as X (formerly known as Twitter), dictionary-based approaches and machine learning methods are generally used. Recent studies emphasize that hybridizing these approaches improves model performance. In this study, we propose a hybrid classification model for sentiment analysis of texts...

Microarray technology has transformed the biotechnological research to next level in the recent years. It provides the expression levels of various genes involved in a particular disease. Prostate cancer disease turned into life threatening cancer. The genes causing this disease are identified through the classification methods. These gene expressi...

Information and communication technology (ICT) is essential in rapidly disseminating information. This research discusses the influence of ICT use in marketing promotions through TV, radio, and social media and compares the performance of several classification algorithms in processing the promotion data. The dataset is from Kaggle, with promotiona...

In the context of the digital era, our lives have become more dynamic, and the development of advanced technologies is transforming the way civil law relationships are handled. Today, legal professionals commonly use software that accelerates daily processes through the application of artificial intelligence technologies. Legal Information Retrieva...

The study aims to improve the diagnosis of arrhythmia in cardiovascular disease management. A novel approach using a deep convolutional network combined with a selective attention mechanism is proposed for electrocardiogram signal classification. The deep convolutional network extracts relevant features directly from raw electrocardiogram signals,...

Data science (DS) methods and Artificial intelligence (AI) are critical in today's healthcare services operations. This study focuses on evaluating the effectiveness of AI and DS in biomedical diagnostics, including automatic detection and counting of white blood cells (WBCs) and types, which provide valuable information for diagnosing and treating...

Transformers, serving as critical components in power systems, are predominantly affected by winding faults that compromise their operational safety and reliability. Frequency Response Analysis (FRA) has emerged as the prevailing methodology for the status assessment of transformer windings in contemporary power engineering practice. To mitigate th...

To efficiently compress the sign information of images, we address a sign retrieval problem for the block-wise discrete cosine transformation~(DCT): reconstruction of the signs of DCT coefficients from their amplitudes. To this end, we propose a fast sign retrieval method on the basis of binary classification machine learning. We first introduce 3D...

With the continuous development of machine learning technology, classification has become increasingly important in various fields, such as disease detection, user analysis, etc. However, traditional classification algorithms frequently encounter challenges such as class imbalances, noise and outliers, and large-scale dynamic data processing, which...

Research interest in the application of electroencephalogram (EEG) as a non-invasive diagnostic tool for the automated detection of neurodegenerative diseases is growing. Open-access datasets have become crucial for researchers developing such methodologies. Our previously published open-access dataset of resting-state (eyes-closed) EEG recordings...

Terraces have long transformed steep slopes into gradual steps, reducing erosion and enabling agriculture on marginal land. In France's Roya Valley, these dry stone structures, neglected for decades, demonstrated remarkable resilience during storm Alex in October 2020. This prompted civil society and researchers to identify terraces that could supp...

To expose the Performance of classification algorithms on endometrial cancer data. The best algorithms are listed based on the result of various test options and ranked based on their accuracies.

With the increasing use of machine learning (ML) algorithms in scientific research comes the need for reliable uncertainty quantification. When taking a measurement it is not enough to provide the result, we also have to declare how confident we are in the measurement. This is also true when the results are obtained from a ML algorithm, and arguabl...

Railway systems are critical components of transportation networks requiring consistent maintenance. This paper proposes a novel data-driven approach to detect various maintenance needs of railway track systems using acceleration data obtained from a passenger train in operation. The framework contains four modules. Firstly, data pre-processing and...

Automated machine-learning algorithms that analyze biomedical signals have been used to identify sleep patterns and health issues. However, their performance is often suboptimal, especially when dealing with imbalanced datasets. In this paper, we present a robust sleep state (SlS) classification algorithm utilizing electroencephalogram (EEG) signal...

The hippocampus, including the cornu ammonis (CA) and dentate gyrus (DG) subregions, is a brain area highly susceptible to seizure-like activity (SLA). Most studies conducted in vivo have been performed in a single hippocampal subregion. In our study, we used the high [K+] (HK+) model of SLA to investigate the role of oscillatory activity in predic...

This paper proposes an improved Embedded Discrete Fracture Model (EDFM) that integrates fluid flow-fracture mechanics mechanisms with microseismic event triggering criteria to achieve high-precision dynamic simulation of hydraulic fracture propagation and prediction of Stimulated Rock Volume (SRV). The model innovatively introduces critical pore pr...

Intrusion Detection Systems (IDSs) play a pivotal role in maintaining cybersecurity within increasingly complex and dynamic network environments. This study introduces a novel algorithmic framework that integrates graph-theoretic modeling with advanced machine learning techniques to enhance the performance and adaptability of IDSs. Central to the p...

In recent years, the volume and variety of biological data being acquired have increased significantly. Among these data types, the diagnosis of Parkinson's disease holds a critical place in medical research. For this study, speech signals were recorded from patients and healthy controls in a controlled environment at the Neurology Department of Fı...

Breast cancer is the most common type of cancer and a significant contributor to the high death rates among women. The death rate increases when this condition is manually diagnosed causing delay of cancer detection since it takes several hours and requires the availability of specialists. Therefore, an automated breast cancer diagnosis has been su...

This paper studies the important problem of quantum classification of Boolean functions from a entirely novel perspective. Typically, quantum classification algorithms allow us to classify functions with a probability of $1.0$, if we are promised that they meet specific unique properties. The primary objective of this study is to explore whether it...

Predicting student academic outcomes through automated systems has become increasingly crucial due to the growing volume of data maintained by educational institutions This challenge is being tackled by the field of educational data mining (EDM), which focuses on developing techniques for extracting valuable insights from educational data. These te...

Melanoma Detection, Skin Cancer Classification , XceptionNet , DenseNet121 ,Convolutional Neural Networks (CNN) , Image Classification Melanoma, the most lethal variant of skin cancer, is a considerable public health challenge globally. Timely identification is essential for enhancing patient outcomes; yet, conventional techniques frequently depend...

The early diagnosis of Alzheimer’s disease remains an unmet medical need due to the cost and invasiveness of current methods. Early detection would ensure a higher quality of life for patients, enabling timely and suitable treatment. We investigate microwave sensing for low-cost, non-intrusive early detection and assessment of Alzheimer’s disease....

Image processing is an advanced technology that significantly supports production, identification, and quality control for fruits. This paper uses image processing techniques to develop a mango classification system based on size and ripeness. The system integrates hardware, including an Arduino microcontroller, camera, sensors, actuators, and a us...

Viral diseases are widespread and their impact is expressed in millions of cases of infection and mortality around the world, chronic viral diseases include COVID-19, HIV, and hepatitis. To prevent and treat these viral infections, novel agents, and antiviral peptides (AVPs) have been developed. Thus, identifying AVPs is crucial because these piece...

The study utilizes text classification (TC) to observe ''interpretese'' in simultaneous interpreting (SI) at United Nations Security Council conferences. ''Interpretese'' is a term coined to describe the distinctive linguistic patterns interpreters employ. A text vectorization method known as TF-IDF is improved with Shannon's entropy and used to co...

A hyperspectral reflectance database was acquired for Baltic Sea submerged aquatic vegetation (SAV) and bare substrates by using Ramses (TriOS GmbH, Germany) radiometers capturing spectral data within the visible (VIS) and near-infrared (NIR) spectral ranges. The target samples included the most dominant and characteristic SAV species in the Baltic...

We propose a novel alternative to generate and classify electroencephalography images, involving the spatial correlation structure among the functional brain-signals. We combine spatial functional methods with image classification techniques to carry out supervised classification of a new silent speech thought, in one specific option that determine...

White blood cell (WBC) classification plays a crucial role in hematopathology and clinical diagnostics. However, traditional methods are constrained by limited receptive fields and insufficient utilization of contextual information, which hinders classification performance. To address these limitations, this paper proposes an enhanced WBC classific...

The water level-area-storage volume (Z-A-V) relationship serves as the cornerstone of reservoir operations, governing water allocation, flood mitigation, and power generation. Sedimentation-induced capacity alterations can progressively degrade Z-A-V accuracy, yet systematic curve updates remain inadequately implemented across developing nations. T...

The Soil Moisture and Ocean Salinity (SMOS) satellite is a valuable tool for monitoring global soil freeze-thaw dynamics, particularly in high-latitude environments where these processes are important for understanding ecosystem and carbon cycle dynamics. This paper introduces the updated SMOS Level-3 (L3) Soil Freeze-Thaw (FT) product and details...

In order to accurately identify and evaluate the interference caused to vegetation environment during the construction process of current power transmission and transformation projects in hilly areas, especially in response to the obvious shortcomings of traditional unmanned aerial vehicles and manual detection methods, this study analyzes the comp...

Method that is use to optimize the criterion efficiency that depend on the previous experience is known as machine learning. By using the statistics theory it creates the mathematical model, and its major work is to surmise from the examples gave. To take the data straightforwardly from the information the approach uses computational methods. For r...

Icing on transmission lines is an inevitable natural phenomenon, and different types of icing bring different degrees of harm to transmission lines, especially soft rime, hard rime, and glaze, the three types of icing need more concerning. In order to know the ice types well and further to evaluate the icing state, a classification algorithm of ici...

In this paper, we introduce a novel data transformation framework based on Opposition-Based Learning (OBL) to boost the performance of traditional classification algorithms. Originally developed to accelerate convergence in optimization tasks, OBL is leveraged here to generate synthetic opposite samples that replace the acutely training data and im...

Background Childhood brain tumours, even though rare, present with significant diagnostic and treatment challenges. Radiomics involves feature extraction that is data-driven from standard imaging modalities, such as magnetic resonance imaging (MRI). In paediatric brain tumour imaging, MRI is often preferred because it is non-invasive and avoids exp...

As e-commerce live streaming becomes increasingly popular, the textual analysis of bullet comments is becoming more and more important. Bullet comments is characterized by its brevity, diverse content, and vast quantity. Faced with these challenges, this study proposes an improved BERT model based on a hierarchical structure for classifying e-comme...

Compared to manual and automated testing, AI-driven testing provides a more intelligent approach by enabling earlier prediction of software defects and improving testing efficiency. This research focuses on predicting software defects by analizing CK software metrics using classification algorithms. A total of 8924 data points were collected from f...

Brain‐Computer Interface (BCI) based on motor imagery (MI) has attracted great interest as a new rehabilitation method for stroke. Riemannian geometry‐based classification algorithms are widely used in MI‐BCI due to their strong robustness and generalization capabilities. However, the clustering performance of current algorithms needs to be improve...

El análisis de datos médicos es una herramienta fundamental en la toma de decisiones clínicas y en el diagnóstico de enfermedades. La presente investigación se enfoca en la implementación de una interfaz gráfica que permite la representación visual de datos de química sanguínea y la aplicación de un algoritmo de clasificación basado en K-Nearest Ne...

Coronary heart disease is a disease in which the occurrence of blockages in the blood vessels in the heart. Coronary heart disease is a fatal disease, it is better to get as much information about this disease as possible. Data Mining can classify whether a person has heart disease or not based on symptoms. Data mining builds a model that can predi...