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26 papers · ranked by Valyu relevance

01
PPubMed24 Nov 2014
A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network A Novel Hybrid Classification Model
Nader Salari, Shamarina Shohaimi, Farid Najafi, Meenakshii Nallappan + 2 more
'Isthrinayagy Karishnarajah' 'Sergio Gómez'] Among numerous artificial intelligence approaches, k-Nearest Neighbor algorithms, genetic algorithms, and artificial neural networks are considered as the most common and effective methods in classification problems in numerous studies. In the present study, the results of…
84relevance
02
PPubMed29 Aug 2025
Enhancing Alzheimer’s Diagnosis with Machine Learning on EEG: A Spectral Feature-Based Comparative Analysis
Yeliz Senkaya, Cetin Kurnaz, Ferdi Ozbilgin, Yong-An Chung
Background/Objectives: Alzheimer’s disease (AD) is a devastating neurodegenerative disorder that progressively impairs cognitive, neurological, and behavioral functions, severely affecting quality of life. The current diagnostic process relies on expert interpretation of extensive clinical assessments, often leading to…
84relevance
03
BbioRxiv22 Aug 2019
Beyond accuracy: Measures for assessing machine learning models, pitfalls and guidelines
Richard Dinga, Brenda W.J.H. Penninx, Dick J. Veltman, Lianne Schmaal + 1 more
Pattern recognition predictive models have become an important tool for analysis of neuroimaging data and answering important questions from clinical and cognitive neuroscience. Regardless of the application, the most commonly used method to quantify model performance is to calculate prediction accuracy, i.e. the…
83relevance
04
BbioRxiv25 Aug 2026
Artificial intelligence model: optimizing cancer risk level predictions using machine learning and deep learning approaches
Areen Arabiat, Hamza Abu Owida, Suhaila Abuowaida, Nawaf Alshdaifat + 2 more
This study emphasizes the potential of computational techniques in cancer risk assessment, highlighting opportunities for specific and data-driven healthcare solutions. It examines the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) approaches to improve cancer risk assessment using a…
83relevance
05
BbioRxiv19 Jun 2019
ShinyLearner: A containerized benchmarking tool for machine-learning classification of tabular data
Stephen R. Piccolo, Terry J. Lee, Erica Suh, Kimball Hill
Classification algorithms assign observations to groups based on patterns in data. The machine-learning community have developed myriad classification algorithms, which are employed in diverse life-science research domains. When applying such algorithms, researchers face the challenge of deciding which algorithm(s) to…
82relevance
06
CchemRxiv23 Dec 2025
Predicting Sequence Dependent Fluorescence with Classic Machine Learning Models
Authors not listed
Terminally labeled DNA oligonucleotides have wide applications in modern biology and biotechnological applications. It has been observed that the fluorescent intensity of light released from these fluorescent labels is heavily influenced by the terminal sequence of nucleotides. Recent studies have assayed and published…
82relevance
07
PPubMed10 Apr 2022
Preference-Driven Classification Measure
Jan Kozak, Barbara Probierz, Krzysztof Kania, Przemysław Juszczuk + 1 more
Classification is one of the main problems of machine learning, and assessing the quality of classification is one of the most topical tasks, all the more difficult as it depends on many factors. Many different measures have been proposed to assess the quality of the classification, often depending on the application…
82relevance
08
PPubMed18 Sep 2019
The Pragmatic Classification of Upper Extremity Motion in Neurological Patients: A Primer
Avinash Parnandi, Jasim Uddin, Dawn M. Nilsen, Heidi M. Schambra
Recent advances in wearable sensor technology and machine learning (ML) have allowed for the seamless and objective study of human motion in clinical applications, including Parkinson's disease, and stroke. Using ML to identify salient patterns in sensor data has the potential for widespread application in neurological…
81relevance
09
AarXiv2020
Classification Performance Metric for Imbalance Data Based on Recall and Selectivity Normalized in Class Labels
Robert Burduk
In the classification of a class imbalance dataset, the performance measure used for the model selection and comparison to competing methods is a major issue. In order to overcome this problem several performance measures are defined and analyzed in several perspectives regarding in particular the imbalance ratio.…
81relevance
10
AarXiv17 Jan 2020
Appropriateness of performance indices for imbalanced data classification: An analysis
Sankha Subhra Mullick, Shounak Datta, Sourish Gunesh Dhekane, Swagatam Das
'Swagatam Das'] Indices quantifying the performance of classifiers under class-imbalance, often suffer from distortions depending on the constitution of the test set or the class-specific classification accuracy, creating difficulties in assessing the merit of the classifier. We identify two fundamental conditions that…
81relevance
11
AarXiv1 Oct 2015
A generalized flow for multi-class and binary classification tasks: An Azure ML approach
Matthew Bihis, Sohini Roychowdhury
The constant growth in the present day real-world databases pose computational challenges for a single computer. Cloud-based platforms, on the other hand, are capable of handling large volumes of information manipulation tasks, thereby necessitating their use for large real-world data set computations. This work…
79relevance
12
AarXiv28 Jun 2016
Performance Based Evaluation of Various Machine Learning Classification Techniques for Chronic Kidney Disease Diagnosis
Sahil Sharma, Vinod Sharma, Atul Sharma
Areas where Artificial Intelligence (AI) & related fields are finding their applications are increasing day by day, moving from core areas of computer science they are finding their applications in various other domains. In recent times Machine Learning i.e. a sub-domain of AI has been widely used in order to assist…
79relevance
13
AarXiv12 Nov 2020
Predicting Autism Spectrum Disorder Using Machine Learning Classifiers
Koushik Chowdhury, Mir Ahmad Iraj
— Autism Spectrum Disorder (ASD) is on the rise and constantly growing. Earlier identify of ASD with the best outcome will allow someone to be safe and healthy by proper nursing. Humans can hardly estimate the present condition and stage of ASD by measuring primary symptoms. Therefore, it is being necessary to develop…
78relevance
14
BbioRxiv20 Jul 2022
Class imbalance should not throw you off balance: Choosing classifiers and performance metrics for brain decoding with imbalanced data
Philipp Thölke, Yorguin Jose Mantilla Ramos, Hamza Abdelhedi, Charlotte Maschke + 10 more
Machine learning (ML) is becoming a standard tool in neuroscience and neuroimaging research. Yet, because it is such a powerful tool, the appropriate application of ML requires a sound understanding of its subtleties and limitations. In particular, applying ML to datasets with imbalanced classes, which are very common…
77relevance
15
PPubMed19 Aug 2021
The Fermi–Dirac distribution provides a calibrated probabilistic output for binary classifiers
Sung-Cheol Kim, Adith S. Arun, Mehmet Eren Ahsen, Robert Vogel + 1 more
'Gustavo Stolovitzky'] Title: Significance While it would be desirable that the output of binary classification algorithms be the probability that the classification is correct, most algorithms do not provide a method to calculate such a probability. We propose a probabilistic output for binary classifiers based on an…
77relevance
16
AarXiv2020
Capacity of Remote Classification Over Wireless Channels
Qiao Lan, Yuqing Du, Petar Popovski, Kaibin Huang
Wireless connectivity creates a computing paradigm that merges communication and inference. A basic operation in this paradigm is the one where a device offloads classification tasks, such as object recognition, to the edge serves. We term this remote classification, with a potential to enable many intelligent…
76relevance
17
CchemRxiv17 Dec 2025
Multi-Reactor Batch Scheduling Strategies for Pharmaceutical Production Under Uncertainty: A Comparative Statistical and Machine Learning Analysis
Authors not listed
Background: Pharmaceutical batch scheduling in multi-reactor configurations presents complex optimization challenges under operational uncertainty, yet limited research addresses how parallel processing capacity affects heuristic performance and predictive modeling. Objectives: This study investigated scheduling…
75relevance
18
CchemRxiv20 Jul 2023
Parameter-Free Molecular Classification and Regression with Gzip
Daniel Probst
Last year, a preprint gained notoriety, proposing that a k-nearest neighbour classifier is able to outperform large-language models using compressed text as input and normalised compression distance (NCD) as a metric. In chemistry and biochemistry, molecules are often represented as strings, such as SMILES for small…
75relevance
19
BbioRxiv12 Mar 2021
A Proposal of New Feature Selection Method Sensitive to Outliers and Correlation
Mert Demirarslan, Aslı Suner
In disease diagnosis classification, ensemble learning algorithms enable strong and successful models by training more than one learning function simultaneously. This study aimed to eliminate the irrelevant variable problem with the proposed new feature selection method and compare the ensemble learning algorithms’…
75relevance
20
PPubMed20 Mar 2023
Early detection of student degree-level academic performance using educational data mining
Areej Fatemah Meghji, Naeem Ahmed Mahoto, Yousef Asiri, Hani Alshahrani + 3 more
'Hani Alshahrani' 'Adel Sulaiman' 'Asadullah Shaikh' 'Shadi Aljawarneh'] Higher educational institutes generate massive amounts of student data. This data needs to be explored in depth to better understand various facets of student learning behavior. The educational data mining approach has given provisions to extract…
75relevance
21
BbioRxiv13 Aug 2022
Optimizing InterProScan representation generates a surprisingly good protein function prediction method
Henri Tiittanen, Liisa Holm, Petri Törönen
Automated protein Function Prediction (AFP) is an intensively studied topic. Most of this research focuses on methods that combine multiple data sources, while fewer articles look for the most efficient ways to use a single data source. Therefore, we wanted to test how different preprocessing methods and classifiers…
74relevance
22
AarXiv22 Aug 2019
ClassyTune: A Performance Auto-Tuner for Systems in the Cloud
Yuqing Zhu, Jianxun Liu
—Performance tuning can improve the system performance and thus enable the reduction of cloud computing resources needed to support an application. Due to the ever increasing number of parameters and complexity of systems, there is a necessity to automate performance tuning for the complicated systems in the cloud. The…
72relevance
23
CchemRxiv29 Sep 2025
TabPFN Opens New Avenues for Small-Data Tabular Learning in Drug Discovery
Authors not listed
Early-stage drug discovery often suffers from data scarcity and out-of-distribution (OOD) shifts, which constrain the reliability of predictive models. While deep learning has advanced representation learning from molecular and biological data, tabular modeling remains indispensable, particularly in small-sample and…
70relevance
24
CchemRxiv5 Jun 2023
Critical Assessment of pH-Dependent Lipophilic Profiles of Small Molecules for Drug Design: Which One Should We Use and In Which Cases?
Esteban Bertsch Aguilar, Sebastián Suñer Sánchez, Silvana Pinheiro, William J. Zamora Ramírez
- 1. 1. CBio3 Laboratory, School of Chemistry, University of Costa Rica, San Pedro, San José, Costa Rica - 2. 2. Laboratory of Computational Toxicology and Artificial Intelligence (LaToxCIA), Biological Testing Laboratory (LEBi), University of Costa Rica, San Pedro, San José, Costa Rica - 3. 3. Advanced Computing Lab…
68relevance
25
CchemRxiv24 Jan 2020
Spectral Deep Learning for Prediction and Prospective Validation of Functional Groups
Jonathan Fine, Anand Rasjashekar, Krupal P. Jethava, Gaurav Chopra
State-of-the-art identification of the functional groups present in an unknown chemical entity requires expertise of a skilled spectroscopist to analyse and interpret Fourier Transform Infra-Red (FTIR), Mass Spectroscopy (MS) and/or Nuclear Magnetic Resonance (NMR) data. This process can be time-consuming and…
68relevance
26
BbioRxiv6 Sep 2021
CluSem: Accurate Clustering-based Ensemble Method to Predict Motor Imagery Tasks from Multi-channel EEG Data
Md. Ochiuddin Miah, Rafsanjani Muhammod, Khondaker Abdullah Al Mamun, Dewan Md. Farid + 3 more
The classification of motor imagery electroencephalogram (MI-EEG) is a pivotal task in the biosignal classification process in the brain-computer interface (BCI) applications. Currently, this bio-engineering-based technology is being employed by researchers in various fields to develop cuttingedge applications. The…
68relevance

Results for classification performance

A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network A Novel Hybrid Classification Model

Enhancing Alzheimer’s Diagnosis with Machine Learning on EEG: A Spectral Feature-Based Comparative Analysis

Beyond accuracy: Measures for assessing machine learning models, pitfalls and guidelines

Artificial intelligence model: optimizing cancer risk level predictions using machine learning and deep learning approaches

ShinyLearner: A containerized benchmarking tool for machine-learning classification of tabular data

Predicting Sequence Dependent Fluorescence with Classic Machine Learning Models

Preference-Driven Classification Measure

The Pragmatic Classification of Upper Extremity Motion in Neurological Patients: A Primer

Classification Performance Metric for Imbalance Data Based on Recall and Selectivity Normalized in Class Labels

Appropriateness of performance indices for imbalanced data classification: An analysis

A generalized flow for multi-class and binary classification tasks: An Azure ML approach

Performance Based Evaluation of Various Machine Learning Classification Techniques for Chronic Kidney Disease Diagnosis

Predicting Autism Spectrum Disorder Using Machine Learning Classifiers

Class imbalance should not throw you off balance: Choosing classifiers and performance metrics for brain decoding with imbalanced data

The Fermi–Dirac distribution provides a calibrated probabilistic output for binary classifiers

Capacity of Remote Classification Over Wireless Channels

Multi-Reactor Batch Scheduling Strategies for Pharmaceutical Production Under Uncertainty: A Comparative Statistical and Machine Learning Analysis

Parameter-Free Molecular Classification and Regression with Gzip

A Proposal of New Feature Selection Method Sensitive to Outliers and Correlation

Early detection of student degree-level academic performance using educational data mining

Optimizing InterProScan representation generates a surprisingly good protein function prediction method

ClassyTune: A Performance Auto-Tuner for Systems in the Cloud

TabPFN Opens New Avenues for Small-Data Tabular Learning in Drug Discovery

Critical Assessment of pH-Dependent Lipophilic Profiles of Small Molecules for Drug Design: Which One Should We Use and In Which Cases?

Spectral Deep Learning for Prediction and Prospective Validation of Functional Groups

CluSem: Accurate Clustering-based Ensemble Method to Predict Motor Imagery Tasks from Multi-channel EEG Data

A Novel Hybrid Classification Model of Genetic Algorithms, Modified k-Nearest Neighbor and Developed Backpropagation Neural Network A Novel Hybrid Classification Model

Enhancing Alzheimer’s Diagnosis with Machine Learning on EEG: A Spectral Feature-Based Comparative Analysis

Beyond accuracy: Measures for assessing machine learning models, pitfalls and guidelines

Artificial intelligence model: optimizing cancer risk level predictions using machine learning and deep learning approaches

ShinyLearner: A containerized benchmarking tool for machine-learning classification of tabular data

Predicting Sequence Dependent Fluorescence with Classic Machine Learning Models

Preference-Driven Classification Measure

The Pragmatic Classification of Upper Extremity Motion in Neurological Patients: A Primer

Classification Performance Metric for Imbalance Data Based on Recall and Selectivity Normalized in Class Labels

Appropriateness of performance indices for imbalanced data classification: An analysis

A generalized flow for multi-class and binary classification tasks: An Azure ML approach

Performance Based Evaluation of Various Machine Learning Classification Techniques for Chronic Kidney Disease Diagnosis

Predicting Autism Spectrum Disorder Using Machine Learning Classifiers

Class imbalance should not throw you off balance: Choosing classifiers and performance metrics for brain decoding with imbalanced data

The Fermi–Dirac distribution provides a calibrated probabilistic output for binary classifiers

Capacity of Remote Classification Over Wireless Channels

Multi-Reactor Batch Scheduling Strategies for Pharmaceutical Production Under Uncertainty: A Comparative Statistical and Machine Learning Analysis

Parameter-Free Molecular Classification and Regression with Gzip

A Proposal of New Feature Selection Method Sensitive to Outliers and Correlation

Early detection of student degree-level academic performance using educational data mining

Optimizing InterProScan representation generates a surprisingly good protein function prediction method

ClassyTune: A Performance Auto-Tuner for Systems in the Cloud

TabPFN Opens New Avenues for Small-Data Tabular Learning in Drug Discovery

Critical Assessment of pH-Dependent Lipophilic Profiles of Small Molecules for Drug Design: Which One Should We Use and In Which Cases?

Spectral Deep Learning for Prediction and Prospective Validation of Functional Groups

CluSem: Accurate Clustering-based Ensemble Method to Predict Motor Imagery Tasks from Multi-channel EEG Data